Computation offloading strategy in mobile edge computing can help users decide how to execute tasks, which is related to user experience, and has become a research hotspot in mobile edge computing. At present, most computation offloading strategies are carried out under the condition of overall task offloading, and only consider a single indicator of delay or energy consumption, and do not combine the two for optimization. To solve this problem, this paper takes the weighted sum of task processing delay and energy consumption as the optimization goal, and proposes a partial offloading algorithm based on reinforcement learning. We divide the processing of a single task into local computing and partial offloading computing, and introduce a variable to determine the offloading weight in partial offloading. Finally, we use reinforcement learning Q-learning to complete the computation offloading and resource allocation of all tasks. Experimental results show that the proposed algorithm can effectively reduce the delay and energy consumption of task processing.

In the field of indoor applications of UWB(Ultra Wide Band) positioning technology, it is important to establish an efficient and accurate 3D coordinate positioning system to overcome signal interference. Machine learning methods are used to investigate the problem of accurate positioning of indoor UWB signals under interference. Firstly, various statistical analysis models are used to clean up invalid or error measurements, then the a priori knowledge of TOF ( Time Of Flight) algorithm is combined with neural network and XGBoost algorithm to build a neural XGB(Exterme Gradient Boosting) 3D oriented system. The system can accurately predict the coordinate value of the target point by “ normal data冶 and “ abnormal data冶 ( disturbed), the coordinates of four anchor points, and the final error is as low as 5. 08 cm in two鄄dimensional plane and 8. 03 cm in three鄄dimensional space. A neural network classification system is established to determine whether the data is disturbed or not, with an accuracy of 0. 88. Finally, by combining the above systems, continuous and regular motion trajectories are obtained, which proves the effectiveness and robustness of the systems.

 The design of the loop filter in noise-shaping SAR ADC(Successive Approximation Register Analogto Digital Converter) is the key to the effect of noise shaping and is also an important module to achieve high accuracy performance. Compared with the active lossless integral loop filter, the traditional passive lossy integral loop filter has the characteristics of low power consumption and simple circuit design, but its NTF(Noise Transfer Function) is smooth and the noise shaping effect is weak. To solve this problem, a passive lossless second-order integral loop filter is proposed, which retains the advantages of the passive lossy integral loop filter and has a good noise shaping effect. A hybrid architecture noise-shaping SAR ADC with a resolution of 16 bits and a sampling rate of 2 Ms/ s is also designed. The simulation results show that high SNDR( Signal to Noise and Distortion Ratio) (91. 1 dB), high accuracy ( 14. 84 bits), and low power consumption ( 285 uW) are achieved when the bandwidth is 125 kHz and the oversampling ratio is 8.

To solve the problem that Q function overestimation may cause SAC ( Soft Actor Critic) algorithm trapped in local optimal solution, SCSAC ( Stable Constrained Soft Actor Critic) algorithm is proposed for perfectly resolving the above weakness hidden in maximum entropy objective function improving the stability of Stable Constrained Soft Actor Critic algorithm in trailing process. The result of evaluating Stable Constrained Soft Actor Critic algorithm on the suite of OpenAI Gym Mujoco environments shows less Q value overestimation appearance and more stable results in trailing process comparing with SAC algorithm.

The connection between mobile internet devices based on the Android platform and people’s lives is becoming increasingly close, and the security issues of mobile devices have become a major research hotspot. Currently, many visual Android malware detection methods based on convolutional neural networks have been proposed and have shown good performance. In order to better utilize deep learning frameworks to prevent malicious software attacks on the Android platform, a new application visualization method is proposed, which to some extent compensates for the information loss problem caused by traditional sampling methods. In order to obtain more accurate software representation vectors, this study uses the Swin Transformer architecture instead of the traditional CNN(Convolutional Neural Network) architecture as the backbone network for feature extraction. The samples used in the research experiment are from the Drebin and CICCalDroid 2020 datasets. The research experimental results show that the proposed visualization method is superior to traditional visualization methods, and the detection system can achieve an accuracy of 97. 39% , with a high ability to identify malicious software.

In order to improve the recognition performance and accuracy of traditional object detection and to accelerate the computation speed, a CNN( Convolutional Neural Network) model with more powerful feature learning and representation capabilities and with related deep learning training algorithms is adopted and applied to large-scale recognition tasks in the field of computer vision. The characteristics of traditional object detection algorithms, such as the V-J(Viola-Jones) detector, HOG(Histogram of Oriented Gradients) features combined with SVM( Support Vector Machine) classifier, and DPM ( Deformable Parts Model) detector are analyzed. Subsequently, the deep learning algorithms that emerged after 2013, such as the RCNN ( Region-based Convolutional Neural Networks) algorithm and YOLO(You Only Look Once) algorithm are introduced, and their application status in object detection tasks is analyzed. To detect fallen individuals, the YOLOv5(You Only Look Once version 5) model is used to train the behavior of individuals with different heights and body types. By using evaluation metrics such as IoU(Intersection over Union), Precision, Recall, and PR curves, the YOLOv5 model is analyzed and evaluated for its performance in detecting both standing and fallen activities. In addition, by pre- training and data augmentation, the number of training samples is increased, and the recognition accuracy of the network is improved. The experimental results show that the recognition rate of fallen individuals reaches 86% . The achievements of this study will be applied to the design of disaster detection and rescue robots, assisting in the identification and classification of injured individuals who have fallen, and improving the efficiency of disaster area rescue.

To address the issue of frequent faults in direct current electric vehicle charging piles and the difficulty of precise diagnosis, a fault diagnosis method based on an improved BP(Back Propagation) neural network is proposed. Firstly, the operation data set of the charging pile is preprocessed, such as normalization and filling in missing values, and the processed data set is input into the BP model for training. Secondly, an optimization method based on the BOA-SSA ( Butterfly Optimization Algorithm improved Sparrow Search Algorithm) is introduced to optimize the weights and thresholds of the BP model to obtain the optimal model. Finally, the fault status of the charging pile is diagnosed based on the optimized BP model. The simulation results show that the proposed BP method has good computational advantages in terms of MAE(Mean Absolute Error), MAPE(Mean Absolute Percentage Error), and RMSE(Root Mean Square Error). Compared to the traditional BP algorithm, the diagnostic accuracy of the improved BP method has increased by 14. 85% , which can diagnose the state of the charging pile accurately, providing a strong guarantee for the fault diagnosis of electric vehicles.

The obstacle avoidance control problem of UAVs( Unmanned Aerial Vehicles) and UGVs ( Unmanned Ground Vehicles) named heterogeneous unmanned swarm system is studied using a game-theoretic approach combined with the artificial potential field method. Unlike most multi-agent formation control schemes that only consider the group formation objective, we allow each agent to have individual objectives, such as individual tracking and obstacle avoidance. Specifically, when the heterogeneous unmanned swarm system completes the cooperative formation, each agent needs to track the target point and avoid obstacles in real-time according to its own interests. The heterogeneous unmanned swarm system formation problem is transformed into a non- cooperative game problem between agents because there may be conflicts between the individual and group objectives of the agents. Real-time obstacle avoidance is realized by adding an obstacle avoidance term based on the artificial potential field function into the cost function. And the controller is designed based on the Nash equilibrium seeking strategy to achieve a balanced formation mode of individual and group objectives. Finally, the correctness of the theoretical results is verified through simulation experiments. The proposed method can enable heterogeneous unmanned swarm system to achieve formation motion and real-time obstacle avoidance.

In order to solve the problem that traditional density peak clustering method without considering similarity of bands in information theory and number of bands in band selection, an adaptive density peak band selection method based on spectral angle mapping and spectral information divergence (SSDPC: Spectral angle mapping and Spectral information divergence Density Peaks Cluster)is proposed. SSDPC combines spectral angle mapping and spectral information divergence for density peak clustering band selection in hyperspectral images, replacing the traditional Euclidean distance to construct a band similarity matrix. By constructing a band scoring strategy, an important subset of spectral bands can be selected automatically and effectively. Using RX(Reed- Xiaoli) algorithm for anomaly detection on three sets of hyper-spectral datasets, the accuracy of anomaly detection is 1. 16% ,1. 18% and 0. 07% higher than that of Euclidean distance measurement under the similarity measure of SSDPC. Under the adaptive SSDPC band selection method, the accuracy of anomaly detection is 6. 49% ,2. 71% and 0. 05% higher than that of the original RX algorithm, respectively. The experimental results show that the SSDPC is robust, can improve the performance of hyper-spectral image anomaly detection and reduce its false alarm rate.

Aiming at the problem of information loss, such as details of remote sensing images reconstructed by a super-resolution algorithm, in order to ensure that remote sensor reconstruction images contain more texture and high-frequency information, a remote-sensitive image super resolution network is proposed based on a pyramid- based attention mechanism and the generation of confrontational networks. Firstly, a new pyramidal dual attention module is designed, including channel attention network and spatial attention network. Pyramid pooling is used instead of average pooling and maximum pooling in the channel attention network structure to enhance the feature representation capability from the perspective of global and local information. The spatial attention network structure adopts large scale convolution to expand the integration capability of local information, which can effectively extract texture, high frequency and other information. Secondly, the dense multi-scale feature module is designed to extract feature information at different scales using asymmetric convolution, and the extraction accuracy of texture, high frequency and other information is enhanced by fusing multi-level scale features through dense connection. Experimental validation is performed on the publicly available NWPU- RESISC45 dataset, and the experimental analysis shows that the algorithm outperforms the comparison methods in both subjective visual effect and objective evaluation metrics, and the reconstruction performance is relatively good. 

Aiming at the privacy protection requirements of existing image datasets, a privacy-preserving scenario of image datasets and a privacy-preserving image alternative data generation method is proposed. The scenario is to replace the original image dataset with an alternative image dataset processed by a privacy-preserving method, where the substitute image is in one-to-one correspondence with the original image. And humans can not identify the category of the substitute image, the substitute image can be used to train existing deep learning images classification algorithm, having a good classification effect. For this scenario, the data privacy protection method based on the PGD ( Project Gradient Descent) attack is improved, and the attack target of the original PGD attack is changed from the label to the image, that is the image-to-image attack. A robust model for image-to- image attacks as a method for generating alternative data. On the standard testset, the replaced CIFAR(Canadian Institute For Advanced Research 10)dataset and CINIC dataset achieved 87. 15% and 74. 04% test accuracy on the image classification task. Experimental results show that the method is able to generate an alternative dataset to the original dataset while guaranteeing the privacy of the alternative dataset to humans, and guarantees the classification performance of existing methods on this dataset. 

To improve the efficiency of path planning and avoid ant colony algorithm outputting non optimal paths, a multi-agent path planning model is proposed. The grid method is used to establish the environment awareness model of agents, improving the local and global pheromone update rules in the ant colony algorithm, and constraining the ants to travel by adjusting the number of turns and pheromone concentration. The algorithm can intelligently enlarge or reduce the pheromone concentration in the path. When the number of iterations reaches the set maximum, the output value is the optimal path planning result. Experimental results have shown that the improved algorithm achieves shorter planning paths and faster iterative convergence speed.

Aiming at the problem that it is difficult to extract and recognize the dynamic features of facial expression due to local occlusion, a dynamic recognition algorithm of facial expression with local occlusion based on deep learning is proposed, a deep belief network model is established, taking the output value of the previous layer as the input value of the next layer, a feature stacking unit is designed, the distribution of state variables of neurons in the visible layer, and the state variables of hidden neurons are calculated by taking the state value of the visible layer as the input value of the hidden layer according to the dynamic correlation of facial features. The recognition process is divided into two steps: training and forward propagation. The feature change rule is output. In the forward propagation process, the pixel point that conforms to the rule change is found, and the weight of the pixel point is solved. And as a loss function standard, the recognition weight of multiple positions on the face is used to constrain the recognition rate, and the dynamic recognition of facial partial occlusion expression is completed. Experimental data show that the proposed method can reduce image distortion and detail loss, improve image resolution, and achieve high recognition rate. It can complete efficient recognition for different local occlusion situations.

In view of the problem of urban traffic congestion and traffic accidents caused by cabs stopping at will, it is very necessary to reasonably delineate the fixed parking areas for cabs. Using the cab GPS(Global Position System) data and crawled POI ( Point of Interest) data in the actual area of Chengdu, DBSCAN (Density-Based Spatial Clustering of Application with Noise) clustering algorithm is used to cluster the pick-up and drop-off points to get the hotspots of cabs, the types of hotspots are delineated according to the types of POIs, and the travel demand of cabs at different times is analyzed, so as to delineate the fixed parking area of cabs. The results of the study show that the setting of the fixed parking area of cabs is related to the travel demand of travelers, so that the fixed parking area is set in the area where the travel demand of travelers is high, which can satisfy the different travel demands of travelers. The method of combining cab passenger hotspots and crawling POI data to delineate fixed parking areas is highly practical and can provide theoretical and practical significance in urban transportation safety. 

The ATMADDPG ( Attention Mechanism based Multi-Agent Deep Deterministic Policy Gradient) algorithm is proposed to improve the navigation ability of a multi-unmanned ship formation system. In the training phase, the algorithm trains the best strategy through a large number of experiments, and directly uses the trained best strategy to obtain the best formation path in the experimental phase. The simulation experiment uses four ' Baichuan' unmanned ships as experimental objects. The experimental results show that the formation maintenance strategy based on the ATMADDPG algorithm can achieve stable navigation of multiple unmanned ship formations and meet the requirements of formation maintenance to some extent. Compared to the MADDPG (Multi-Agent Depth Deterministic Policy Gradient ) algorithm, the developed ATMADDPG algorithm shows superior performance in terms of convergence speed, formation maintenance ability, and adaptability to environmental changes. The comprehensive navigation efficiency can be improved by about 80% , which has great application potential.

The optical crossbar chip is the core device used to realize optical routing in the field of optical communication. A control and driver system is designed based on a multi-channel DAC ( Digital to Analog Converter) array to achieve optical routing through the optical crossbar chip. The system consists of a control system module, a multi-channel drive circuit module, and a host computer control module. This system has several advantages, including simple adjustment, bipolar output, more output channels, and higher power accuracy. It solves the problems of the current driving circuit, such as complex operation, single power polarity, fewer output channels, and poor accuracy. The host computer control module can control the driving circuit to apply the control voltage and receive the optical power signal collected from the data acquisition device as the feedback signal of the control driving system. By analyzing the relationship between the control voltage and the received optical power, the best control driving voltage of the optical crossbar chip can be obtained. The system test results show that the system can provide high-precision bipolar driving voltage to effectively drive the optical crossbar chip and can calibrate the control voltage of the optical switch in a short time, fully meeting the requirements of the driving voltage in the active optical crossbar chip control. We believe that this system could be useful for optical crossbar chip control.

In order to enhance the performance of FSS(Frequency Selective Surfaces) and precisely control the propagation characteristics of electromagnetic waves in the microwave frequency range to achieve reflection, transmission, or absorption of electromagnetic waves, a miniaturized FSS for the microwave frequency band is proposed. The unit cell size of the FSS is 0.024λ x 0.024λ, demonstrating excellent miniaturization performance. Within the range of 1 ~10 GHz, the FSS exhibits three passbands with exceptional polarization stability and angle stability, maintaining consistent operating frequencies and bandwidth, while exhibiting good transmission performance. This study on the miniaturized FSS serves as a basis for FSS analysis and provides insights for the design of miniaturized frequency selective surfaces.

Existing industrial time series data anomaly detection algorithms do not fully consider the temporal data on time dependence. An improved HTM(Hierarchical Temporal Memory)-Attention algorithm is proposed to address this problem. The algorithm combines the HTM algorithm with the attention mechanism to learn the temporal dependencies between data. It is validated on both univariate and multivariate time series data. By introducing the attention mechanism, the algorithm can focus on the important parts of the input data, further improving the efficiency and accuracy of anomaly detection. Experimental results show that the proposed algorithm can effectively detect various types of time series anomalies and has higher accuracy and lower running time than other commonly used unsupervised anomaly detection algorithms. This algorithm has great potential in the application of industrial time series data anomaly detection.

As one of the important components of the detection system, the performance of photoelectric detector is directly related to the quality of system data acquisition. In order not to affect the final detection result, it is essential to ensure the detector performance. The performance of high performance PtS₂ / MoTe₂ heterojunction infrared photodetector is studied. First, the materials, reagents and equipment are prepared to make PtS₂ / MoTe₂ heterojunction infrared photodetectors. The detector performance test environment, the four indicators of light response, detection rate, response time and photoconductivity gain are set up, and the detector performance is analyzed. The results show that the optical responsivity of PtS₂ / MoTe₂ heterojunction infrared photodetector is always above the 5 A/ W limit with the passage of test time. The detection rate of the detector is greater than 10 cm·Hz1 / 2 W ^-1 regardless of the infrared light reflected from any material. Whether the photocurrent is in the rising time or the falling time, its response time is always below the limit of 150 μs; The photoconductivity gain value has been kept above 80% . 

 With the advancement of information technology, the digital transformation of libraries has become a key avenue for enhancing service efficiency. During this transformation process, information security issues have become increasingly prominent, posing threats to the protection of library resources and the security of user data. This study is to address the information security challenges in the digital transformation of libraries by proposing a comprehensive information security protection system. By analyzing the main information security risks faced by libraries currently, including cyber attacks, copyright disputes, insufficient security awareness among management personnel, and low levels of resource sharing, a four-tier information security protection system is constructed consisting of the user application layer, service platform layer, data center layer, and infrastructure layer. This system can effectively enhance the security and access control of library information resources, and strengthen the security of digital resource access. In the process of digital transformation, libraries must consider information security as a core factor, and build a comprehensive information security protection system through the collaborative work of technology, management, and organization to ensure the security and efficient use of digital resources.

The wide application of the Internet is likely to lead to various kinds of privacy data leakage. In order to solve the problems, a cloud computing down-centric dual differential privacy data protection algorithm is proposed. First, the purpose of accurate collection of private data is achieved by learning the network model of private data transmission channel, and then the method of reconstructing the spatial characteristics of private data is used to obtain the ontological characteristics of private data. Finally, the collected private data is accurately noised through the characteristics of private data to achieve the purpose of accurate protection of private data, and the decentralized dual differential privacy data protection is completed. The experimental results show that the proposed algorithm has high real-time and good security for privacy data protection, and can accurately protect privacy data in different noise environments.

A novel approach is proposed to address the issues of high accuracy but slow speed or low accuracy but appropriate training speed in traditional image recognition methods for fault diagnosis of oil pumps. The proposed method is based on an enhanced residual network model, with several improvement strategies. Firstly, the first-layer convolution kernel of the model is replaced with a smaller one. Secondly, the order of residual modules is changed. Thirdly, the fully connected layer of ResNet50( a Residual Network model) is replaced with an RBF( Radial Basis Function) network as an additional classifier. Finally, data augmentation techniques are used to expand the dataset, and transfer learning is utilized to obtain pre-trained weight parameters on ImageNet for the improved ResNet50-RBF model. Experimental results demonstrate that the proposed model achieves 98. 86% accuracy in pump curve recognition, exhibiting stronger robustness and improved speed compared to other networks. This provides some reference for fault diagnosis of oil pumps. The proposed method can significantly enhance the efficiency and accuracy of image recognition in fault diagnosis for oil pumps, which is of great significance for practical applications in the industry.

To address the problem that RGB ( Red Green Blue ) modal and thermal modal information representations are inconsistent in form and feature information can not be effectively mined and fused, a new joint attention reinforcement network-FCNet ( Feature Sharpening and Cross-modal Feature Fusion Net ) is proposed. Firstly, the image feature mapping capability is enhanced by a two-dimensional attention mechanism. Then, a cross-modal feature fusion mechanism is used to capture the target region. Finally, a layer-by-layer decoding structure is used to eliminate background interference and optimize the detection target. The experimental results demonstrate that the improved algorithm has fewer parameters and shorter operation times, and the overall detection performance of the model is better than that of existing multimodal detection models.

A node deployment and scheduling algorithm based on fitness function and zero tolerance coverage is proposed to solve the problems of sensor blind area and poor connectivity between sensor nodes in wireless sensor network coverage. The network coverage is considered as a two-dimensional plane, the relationship between the maximum coverage range of node sensing and the distance value is analyzed to obtain the attribute values of the target points with hot spot distribution and overlapping coverage. Then, according to the deployment indicators such as wireless sensor target point coverage, connectivity and candidate locations, the fitness function is used to calculate the optimal deployment relationship of indicators, and to obtain the redundant parameters of nodes. The redundant complementary nodes are found within the same sensing range to achieve replacement scheduling. The experimental results show that the algorithm performs well in terms of network coverage and scheduling effectiveness, and has strong comprehensive performance.

To ensure timely distribution of emergency power supplies, enable them to quickly restore power supply, and reduce economic losses, a planning method for the distribution path of emergency power supplies is proposed based on geographic information systems. Firstly, based on the Map X component in the GIS (Geographic Information System) geographic information system, a preprocessing model for geospatial data is constructed. Then, based on the processed data, a mathematical model and constraint conditions for distribution path planning are established. Finally, genetic algorithm, mountain climbing algorithm, and ant colony algorithm are integrated, and the mathematical model is iteratively operated to obtain the optimal distribution path. The experiment is based on a power equipment emergency. When the material demand is met, the delivery time of the planned path under normal and abnormal road conditions is reduced by 14 minutes and 30 minutes respectively, and the cost is reduced by 10. 9 yuan and 5. 09 yuan respectively. This proves that the designed planning method has significant superiority. 

 In order to improve the invulnerability and working ability of the satellite constellation network after being attacked, a simulated annealing wolf pack algorithm is proposed. We use the subjective and objective weight method combined with the TOPSIS( Technique for Order Preference by Similarity) to Ideal Solution to evaluate the importance of nodes in the network, and attack the network according to the order of node importance. The network connection efficiency is the optimization goal, and the satellite constellation network communication limitation is the constraint condition. The idea of motion operator is adopted to realize the walking, summoning and sieging of wolves with adaptive step size. The network structure is optimized using the edge-adding scheme obtained through optimization. Experiments show that compared with other optimization algorithms, this algorithm has superiority. It solves the problem that the satellite constellation network爷s working ability declines after being attacked, and improves its invulnerability after being attacked. Key words: satellite network; invulnerability optimization; simulated annealing algorithm; improved wolf colony algorithm

In order to solve the problems of low lip contour detection accuracy and poor visual speech synthesis effect, a Tibetan-driven visual speech synthesis algorithm based on audio matching is proposed. This algorithm extracts short-term energy and short-term zero-crossing rate from Tibetan-language-driven visual speech signal, establishes short-term autocorrelation function of speech signal, and extracts feature information in speech signal, so as to obtain the pitch track of Tibetan speech signal. Secondly, the temporal and spatial analysis model of lip is established to analyze the changing trend of lip contour in the pronunciation process, and the feature of lip contour is extracted by principal component analysis. Finally, the correlation between audio features and lip contour features is obtained through the input-output hidden Markov model, and Tibetan-driven visual speech is synthesized on the basis of audio matching. Experimental results show that the proposed method has high lip contour detection accuracy and good visual speech synthesis effect. 

Medical image classification is an important method to determine the illness of patients and give corresponding treatment advice. As medical image labeling requires relevant professional knowledge, it is difficult to obtain large-scale medical image classification labels. And the development of medical image classification based on deep learning method is limited to some extent. To solve this problem, self-supervised contrast learning is applied to medical image classification tasks in this paper. Contrast learning method is used in pre-training of medical image classification. The features are learned from unlabeled medical images in the pre-training stage to provide prior knowledge for subsequent medical image classification. Experimental results show that the proposed improved method of medical image classification based on self-supervised contrast learning enhances the classification performance of the ResNet. 

 Most of the existing detection methods for concept evolution are essentially based on supervised learning and are often used to solve the problem that only one novel class appears in a period of time. However, they can not handle the task of a class disappearing and recurring in streaming data. To address the above problems, an adaptive detection method for concept evolution based on weakly supervised ensemble (AD_WE) is proposed. The weakly supervised ensemble strategy is used to construct an ensemble learner to make local predictions on the training samples in the data block. Similar data with strong cohesion in the feature space are detected and clustered using local density and relative distance. The similarity of the clustering results is then compared to detect novel class instances and distinguish between different novel classes. And a dynamic decay model is established according to the characteristics of data change over time. The vanished class is eliminated in time, and the recurring class is detected through similarity comparison. Experiments show that the proposed method can respond to concept evolution in a timely manner, effectively identify vanished classes and recurring classes, and improve the generalization performance of the learner.

BNNs( Binarized Neural Networks) are popular due to their extremely low memory requirements. While BNNs can be further compressed through pruning techniques, existing BNN pruning methods suffer from low pruning ratios, significant accuracy degradation, and reliance depending on fine-tuning after training. To overcome these limitations, a filter-level BNN pruning method is proposed based on evolution from ternary to binary, named ETB ( Evolution from Terry to Binary). ETB is learning-based, and by introducing trainable quantization thresholds into the quantization function of BNNs, it makes the weights and activation values gradually evolve from ternary to binary or zero, aiming to enable the network to automatically identify unimportant structures during training. And a pruning ratio adjustment algorithm is also designed to regulate the pruning rate of the network. After training, all zero filters and corresponding output channels can be directly pruned to obtain a simplified BNN without fine-tuning. To demonstrate the feasibility of the proposed method and the potential for improving BNN inference efficiency without sacrificing accuracy, experiments are conducted on CIFAR-10. ETB is pruned the VGG-Small model by 46. 3% , compressing the model size to 0. 34 MB, with an accuracy of 89. 97% . The ResNet-18 model is also pruned by 30. 01% , compressing the model size to 1. 33 MB, with an accuracy of 90. 79% . Compared with some existing BNN pruning methods in terms of accuracy and parameter quantity, ETB has certain advantages.

In order to solve the problem of droplet transmission risk of breath alcohol detector, a portable and pollution-free blood alcohol concentration detection scheme based on near-infrared spectroscopy is proposed. we successively completed the signal acquisition, amplification, filtering and re-amplification with hardware, and obtained the standard pulse wave signal with software algorithms such as Kalman filtering and three-sample interpolation method to remove the base. Later, the mathematical model is established in the upper computer by the measurement results of the expiratory type and the amplitude of the pulse wave signal, and the mathematical model is written into the microcontroller to realize the offline measurement. The accuracy of the mathematical model is tested through validation experiments. The results show that it meets the accuracy requirement, the relative error is less than 10% and meets the practical requirements.

With the gradual development of oil and gas exploration and development into deep and complex formations, the risks and rewards faced in the drilling process are increasing. The market increasingly needs software that can integrate monitoring and control to ensure safe and efficient drilling. Using managed pressure drilling technology can effectively control the pressure in the gas production process and make it more convenient to deal with equipment failure. It can realize real-time monitoring of fluid pressure and density changes in the production process, effectively reducing potential safety hazards, preventing the occurrence of downhole accidents, and providing a guarantee for the safe and stable production of oil and gas wells. The Managed Pressure Drilling Simulation and Control Software aims to obtain drilling-related information from logging, PWD ( Pressure While Drilling ), MWD ( Measure While Drilling ), pressure control, and other equipment. It establishes hydraulic models to calculate wellbore pressure, flow, and other parameters. This software adopts client / server architecture, which allows multiple clients to connect to a server simultaneously and synchronize data. The client data synchronization effect has been verified on-site, meeting the needs of single machine use and facilitating network connection. The results indicate that this software can accurately simulate and calculate various drilling parameters, ensuring safe and efficient drilling. The centralized analysis, processing, and remote control have created a good foundation for pressure control drilling, transitioning from on-site engineer processing mode to a data platform-based approach. This lays the foundation for interconnectivity between multiple on-site pressure control drilling equipment on one platform, effectively promoting the development of intelligent pressure control drilling.

 Aiming at the characteristic that the vehicle is more prone to instability in the snow and ice environment, the stable tracking problem of the vehicle to the reference trajectory under the low adhesion and uneven distribution condition of the road surface is studied. To address this, a fuzzy PID(Proportional-Integral- Differential) controller model based on neural network regulation and MPC ( Model Predictive Control ) a linearized vehicle model are designed. The controller takes the road adhesion coefficient and vehicle speed as input to construct a BP(Back-Propagation)neural network and outputs the adjustment coefficient to optimize the control performance of the PID controller. A ten-degree-of-freedom model is designed to characterize the dynamic characteristics of the vehicle in snow and ice-covered environments, and the lateral stability control of the vehicle is realized by using MPC. CarSim / Simulink is used for co-simulation experiments. Results show that the controller can significantly improve the performance of vehicle trajectory tracking. The dynamic characteristics of the vehicle under snow and ice are analyzed, and good simulation results are obtained.

 In order to solve the problem that Yolov3-Tiny algorithm has insufficient feature extraction in gas station monitoring scene detection, which results in low detection accuracy, a new target detection algorithm based on gas station scene is proposed. This method first introduces Mosaic data enhancement algorithm to make the picture contain more feature information. Secondly, InceptionV2 and PSConv ( Poly-Scale Convolution) multiscale feature extraction methods are used to improve the network multiscale prediction ability. Finally, combined with the scSE(Concurrent Spatial and Channel ‘ Squeeze & Excitation’) attention mechanism, the output characteristics of the backbone network are reconstructed. The experimental results show that the algorithm has high detection accuracy and the detection speed meets the actual needs. The performance of the optimized algorithm is greatly improved and can it be applied to other target detection. 

To help readers quickly find the books they need and avoid overloading digital information, an intelligent recommendation algorithm for digital book resources based on tag similarity is proposed. Firstly, based on the entered user information in the digital library system, the user feature similarity and user interest similarity are obtained and regarded as comprehensive similarity indicators. Then, combined with the tag similarity index, the similarity nearest neighbors of the target user’s book resources are obtained. Finally, the tags of the book resources browsed by the user are put into a tag set, and the digital book resources that the target user likes are formed into a recommendation list through a hybrid recommendation method of user implicit behavior scoring and linear weighted fusion, and recommended to the target user. Experimental results show that the proposed algorithm performs better than traditional recommendation algorithms.

 Due to the dense traffic flow and stochastic uncertainty of vehicle behaviors, the scenario of unsignalized intersection poses significant challenges for autonomous driving. An innovative approach for autonomous driving decision-making at unsignalized intersections is proposed based on the MAPPO(Multi-Agent Proximal Policy Optimization) algorithm. Applying the MetaDrive simulation platform to construct a multi-agent simulation environment, we design a reward function that comprehensively considers traffic regulations, safety including arriving safely and occurring collisions, and traffic efficiency considering the maximum and minimum speeds of vehicles at intersections, aiming to achieve safe and efficient autonomous driving decisions. Simulation experiments demonstrate that the proposed decision-making approach exhibits superior stability and convergence during training compared to other algorithms, showcasing higher success rates and safety levels across varying traffic densities. These findings underscore the significant potential of the autonomous driving decision-making solution for addressing challenges in unsignalized intersection environments, thereby advancing research in autonomous driving decision-making under complex road conditions.自动驾驶;智能决策;无信号灯交叉口;MAPPO算法 

Big data clustering processing has the problem of poor clustering effect and long clustering time for different types of data. Therefore, a big data clustering processing method based on the improved PSO-Means (Particle Swarm Optimization Means) clustering algorithm is proposed. The particle swarm optimization algorithm is used to determine the flight time and direction of unit particles during a cluster, preset the selection range of the initial cluster center, and appropriately adjust the inertia weight of unit particles. It eliminates the clustering defects caused by particle oscillation and successfully obtains the clustering center based on large-scale data. Combined with the spanning tree algorithm, the PSO algorithm is optimized from two aspects: sample skewness and centroid skewness. The optimized clustering center is then input into the k-means clustering algorithm to realize the clustering processing of big data. The experimental results show that the proposed method can effectively cluster different types of data, and the clustering time is only 0. 3 s, which verifies that the method has good clustering performance and clustering efficiency.

 Due to its low background noise, solar-blind ultraviolet photodetection technology is widely used in fields such as fire monitoring, missile detection, and military communication. Compared to other solar-blind ultraviolet sensitive materials, amorphous gallium oxide offers several advantages, including a bandgap that matches the solar-blind ultraviolet region, structural stability, and good mechanical strength. The horizontal metal-semiconductor-metal structured photodetectors are known for their simple production processes, ease of integration, and suitability for industrialization. Given the non-uniform distribution of the internal electric field and the photo-generated carriers along the thickness direction in horizontal devices, the thickness of the photoreceptive layer plays a crucial role in the performance of the photodetectors. In order to fabricate high- performance solar-blind ultraviolet photodetectors, amorphous gallium oxide thin films were prepared using low- temperature metal organic chemical vapor deposition method. Structural characterization of the films confirmed their amorphous nature, and the film surfaces were found to be relatively flat, with the optical absorption edge located within the deep ultraviolet spectral range. Solar-blind ultraviolet photodetectors were subsequently developed. As the thickness of the photoreceptive layer increased from 33. 2 nm to 133. 6 nm, the dark-current of the photodetector rose from 2. 33*10^-10 A to 2. 12*10^-8 A, and the photo-current under 254 nm illumination increased from 1. 66 * 10^-7 A to 3. 2 * 10^-5 A. Additionally, both the responsivity and the external quantum efficiency of the photodetectors increased by orders of magnitude with the increase in the photoreceptive layer thickness, reaching maximum values of 2. 91 A/ W and 1 419. 12% , respectively. The thickness-dependent characteristics of the photodetectors can be attributed to the interfacial high-defect layers, light absorption intensity, and the geometric parameters of the photodetectors. The photodetectors exhibited excellent wavelength selectivity, the current of each photo-detector under 365 nm illumination and the photo-current under 254 nm illumination differ by more than two orders of magnitude. Moreover, over the tested 5 cycles, the response / recovery behavior of each photodetector consistently demonstrates good repeatability and stability.

In order to avoid more duplicate data in the encryption process of IoT privacy data, which leads to higher computational complexity and reduces computational efficiency and security, an encryption method of IoT privacy data that combines DES(Data Encryption Standard) and ECC(Ellipse Curve Ctyptography) algorithms is proposed. Firstly, the TF-IDF(Tem Frequency-Inverse Document Frequency) algorithm is used to extract feature vectors from the privacy data of the Internet of Things. They are input into the BP(Back Proragation) neural network and are trained. The IQPSO( Improved Quantum Particle Swarm Optimization) algorithm is used to optimize the neural network and complete the removal of duplicate data from the privacy data of the Internet of Things. Secondly, the Data Encryption Standard and ECC algorithm are used to implement the primary and secondary encryption of the privacy data of the Internet of Things. Finally, a fusion of DES and ECC algorithms is adopted for digital signature encryption to achieve complete encryption of IoT privacy data. The experimental results show that the proposed algorithm has high computational efficiency, security, and reliability.

A dual-mode address hopping method is proposed to address security issues faced by the IoT(Internet of Things), such as resource scarcity and low obfuscation of traffic data. Address hopping diversity and unpredictability are enhanced through a dual-mode address selection algorithm, thereby solving the problem of limited address pool resources. Additionally, a dual-virtual address hopping method is introduced to enhance the obfuscation of data packets and reduce the correlation of network data. This method is demonstrated to be effective in reducing network data correlation, conserving IoT resources, increasing network address pool capacity, preventing data theft by attackers, and ensuring IoT security through simulation experiments conducted in an SD-IoT(Software Defined Internet of Things) environment.

In order to maintain the consistency of parameters such as current, voltage, frequency, and phase during wind power grid connection, and to improve the safety and stability of wind power grid connection, a method for identifying control parameters of wind power grid connected converters based on optimization algorithms is proposed. The control model is established for the wind power grid connected converter, and the power control command of the PI(Proportional Integral) regulator is changed based on the power voltage support. Using differential function equations, to set the control conditions of the PI regulator, to calculate the functional relationship in the complex frequency domain, and to clarify the logical relationship between the adjustment integral coefficients. The identifiability of the control parameters is obtained through the control transfer function, and the parameter control output value and characteristic data are analyzed. Finally, the optimization and identification results of the control parameters are completed. The experimental results show that the proposed method can complete the identification of control parameters in various environments, with small identification error and high accuracy.

To provide more efficient solutions for handling high-dimensional matrices and applying SVD(Singular Value Decomposition) in the context of big data, with the aim of accelerating data analysis and processing, how to quickly calculate the eigenvalues and eigenvectors ( singular value singular vectors) of high-dimensional matrices is studied. By studying random projection and Krylov subspace projection theory, six efficient calculation methods are summarized, making comparative analysis and related application research. Then, the six algorithms are applied, and the algorithms in related fields are improved. In the application of spectral clustering, the algorithm reduces the complexity of the core step SVD( Singular Value Decomposition), so that the optimized algorithm has similar accuracy to the original spectral clustering algorithm, but significantly shortens the running time. The calculation speed is more than 10 times faster than the original algorithm. When this work is applied in the field of image compression, it effectively improves the operation efficiency of the original algorithm. Under the condition of constant accuracy, the operation efficiency is improved by 1 ~ 5 times.

This study aims to improve the intelligence and accuracy of fault diagnosis in oilfield wastewater systems. A composite neural network is constructed using convolutional neural networks and long short-term memory networks, and the structure is optimized using Adam and random gradient descent method to improve the convergence speed and fault diagnosis accuracy of the model. The study is validated through relevant experiments, and the experimental results show that the optimization algorithm used in the study improves the accuracy of the model to around 0. 87 and reduces the diagnostic loss rate of the model to around 0. 032. The average detection accuracy of the composite neural network structure reaches 0. 888, with an accuracy value of 0. 883 and a recall rate of 0. 789. The composite neural networks is applied to fault diagnosis of oilfield wastewater systems, can achieve intelligent fault detection, reduce economic costs, and build smart oilfield.

Microseismic monitoring technology has been widely used in unconventional oil and gas development. The microseismic signal has weak energy and strong noise, which makes the follow-up work difficult and requires high-precision and accurate data. To solve the problem of extracting weak microseismic signals, an EM-KF (Expectation Maximization Kalman Filter)-based method is proposed for denoising microseismic signals. By establishing a state space model that conforms to the laws of microseismic signals and using the EM(Expectation Maximization) algorithm to obtain the optimal solution of the parameters for the Kalman filter, the signal-to-noise ratio of microseismic signals can be effectively improved while retaining the effective signals. The experimental results of synthetic data and real data show that this method has higher efficiency and better accuracy than traditional wavelet filtering and Kalman filtering.

 Due to the influence of changes in lighting conditions and pedestrian height differences, there are large cross modal differences in surveillance video images at different times. In order to accurately identify pedestrians in cross modal images, a pedestrian recognition algorithm based on generalized transfer depth learning is proposed. The cross modal image is formed through Cyele GAN(Cycle Generative Adversarial Network), and the reference map is segmented using single object image processing to obtain candidate human body regions. The matching regions are searched in the matching map to obtain the disparity of human body regions, and the depth and perspective features of human body regions are extracted through the disparity. The attention mechanism and cross modal pedestrian recognition are combined to analyze the differences between the two types of images. The two subspaces are mapped to the same feature space. And the generalized migration depth learning algorithm is introduced to learn the loss function measurement, automatically screen the pedestrian features of the cross modal images, and finally complete pedestrian recognition through the modal fusion module to fuse the filtered features. The experimental results show that the proposed algorithm can quickly and accurately extract pedestrians from different modal images, and the recognition effect is good. 

Traditional ROV(Remote Operated Vehicle) attitude control methods, operated underwater by ROVs, have suffered from chattering and poor stability. A new cooperative control law is proposed and ROV attitude controlleris designed based on thrust vector distribution. Firstly, the ROV kinematics model and dynamics model are established, and the thrust vector distribution model and decoupling dynamics model are carried out. Then, a new cooperative control law is proposed. By constructing appropriate macro variables, the macro variables converge exponentially to provide continuous control rate for the ROV attitude control system and eliminate chattering. Finally, a new cooperative control law is used to design the ROV attitude controller based on thrust vector allocation. The results of Matlab / Simulink simulation show that the proposed new cooperative control law can improve the control accuracy and stability of the ROV attitude control system. The control strategy provides a new feasible scheme for ROV attitude control. 

Aiming at the many causes and difficult features of internal network security threat nodes, an internal network security threat detection method based on XGBoost algorithm is proposed. Using the state differences between the internal network communities as an indicator, the edge weights of the nodes within different community types are calculated to find the nodes associated with the target values. Eigenvalues extracted through multiple assignments are taken as the initial input value XGBoost decision tree to construct the threat feature objective function, solve the corresponding Taylor coefficient of each node, and realize internal network security threat detection. The experimental data show that the proposed method has high feature extraction accuracy and can achieve accurate detection under various network attack conditions.

In the context of rapid advancements in information technology, the application of digital technology in higher education has become a crucial means of driving educational reform. Addressing issues such as unequal resource distribution and low teaching efficiency in traditional teaching models, the paper proposes the “6i” digital education application and support service system, encompassing teaching (iLearn), learning (iSocial), evaluation (iSense), management (iEdu), services (iHelp), and environment (iMeta). Through specific measures such as the construction of three types of classrooms, the development of digital teaching resources, teaching model reform, digital teaching quality management, and the exploration of new digital technology applications in teaching, Jilin University has achieved a digital transformation of teaching resources and innovation in teaching models. The results indicate that the application of digital technology significantly enhances teaching quality and learning outcomes, providing students with more opportunities for autonomous and personalized learning. The findings of this study offer valuable insights for other universities in promoting digital transformation in education, helping to build a more open, efficient, and intelligent educational system, and advancing high-quality educational development.

In order to solve the problem of communication mismatch between autopilot system using CAN (Controller Area Network)bus and navigation equipment using serial port communication, a communication conversion interface module based on Cortex-M3 is designed and the function of data conversion between serial port and CAN bus is realized. Aiming at the problems of poor signal stability and low baud rate accuracy of traditional CAN transceiver circuit CTM1050, an alternative hardware scheme is proposed and implemented to improve the timeliness and stability of data communication. Based on the CAN2. 0B extension frame, the internal CAN bus protocol of the autopilot system is designed to ensure the scalability and stability of the bus. The protocol can assign identity frames according to the priority of message information to ensure the orderly transmission of bus data. The actual test results indicate that the communication module is normal and the communication effect is good. The communication module has a certain universality and can be used in a variety of equipment systems. 

To improve the employment matching and human resource utilization efficiency of college students, many researchers are dedicated to developing effective job recommendation algorithms. However, existing recommendation algorithms often rely solely on a single information source or simple user classification, which can not fully capture the multidimensional features and personalized needs of college students, resulting in poor recommendation performance. Therefore, a job recommendation algorithm for college students based on user profiles and bipartite graphs is proposed. With the aid of the conditional random field model based on the integration of long and short-term memory neural networks, the basic user information is extracted from the archives management system of the university library, based on which the user portrait of university students is generated. The distance between different user profile features is calculated, and the k-means clustering algorithm is used to complete the user profile clustering. The bipartite graph network is used to build the basic job recommendation structure for college students and a preliminary recommendation scheme is designed based on energy distribution. Finally, based on the weighted random forest model, the classification of college students’ employment positions is realized by considering users’ preferences for project features, and the score of the initial recommendation list is revised to obtain accurate recommendation results for college students’ full employment positions. The experimental results show that after the proposed method is applied, a recommendation list of 120 full employment positions for college students is given, and the hit rate of the recommendation result reaches 0. 94. This shows that the research method can accurately obtain the results of college students’ employment position recommendation, so as to improve the employment matching degree and human resource utilization efficiency. 

To detect transactional fraud in financial services industry and maintain financial security, an end-to- end modeling framework, methodology, and model architecture are proposed for financial transactional data with imbalanced and discrete classes. The framework covers data preprocessing, model training, and model prediction. The performance and efficiency of different models with different numbers of features are compared and validated on a real-world dataset. The results demonstrate that the proposed approach can effectively improve the accuracy and efficiency of financial transactional fraud detection, providing a reference for financial institutions to select models with different types and numbers of features according to their own optimization goals and resource constraints. Tree-based models excel with over 200 features in resource-rich settings, while neural networks are optimal for medium-sized feature sets (100 ~200). Decision trees or logistic regression are suitable for small feature sets in resource-constrained, long-tail scenarios. 

In order to improve the approximate performance of CVI(Copula Variational Inference), the CHVI (Copula Hierarchical Variational Inference) method is proposed. The main idea of this method is to combine the Copula function in the CVI method with the special hierarchical variational structure of the HVM(Hierarchical Variational Model), so that the variational prior of the HVM obeys the Copula function in the CVI method. CHVI not only inherits the strong ability of the Copula function in CVI to capture the correlation of variables, but also inherits the advantage of the variational prior structure of HVM to obtain the dependencies of the hidden variables of the model, so that CHVI can better capture the relationship between hidden variables. correlation to improve the approximation accuracy. The author validates the CHVI method based on the classical Gaussian mixture model. The experimental results on synthetic datasets and practical application datasets show that the approximate accuracy of the CHVI method is greatly improved compared to the CVI method. 

Rapidity and accuracy are most important in medical treatment. Traditional blood tests rely on experienced physicians, which leads to low efficiency and accuracy. For the first time, a comprehensive determination method based on image recognition technology, named P-HSV ( Perimeter-Hue, Saturation, Value), is proposed for microfluidic blood sample chips. Size and color are used for integrated interpretation of blood types. Size interpretation is based on the contour perimeter and number of agglutination clusters within the reaction chamber, while color interpretation is based on categorization of the color saturation (S: Saturation) to brightness ( V: Value ) ratio of agglutination clusters within the reaction chamber. The grade of blood agglutination reaction is synthetically determined by size and color results. In this method, machine vision is used to determine the grade of blood agglutination reaction, resulting in accurate and rapid blood type determination. This reduces the subjective judgment of artificial judgment, improving the detection speed and accuracy greatly.

Fault identification plays an important role in geological exploration, reservoir description, structural trap and well placement. Aiming at the problem that traditional coherence attribute and machine learning are poor in complex fault recognition, a fault recognition method based on UNet++ convolutional neural network is proposed. The weighted cross entropy loss function is used as the objective function to avoid the problem of data sample imbalance in the training process of the network model. Attention mechanism and dense convolution blocks are introduced, and more jump connections are introduced to better realize the feature fusion between the semantic information of deep faults and the spatial information of shallow faults. Furthermore, the UNet ++ network model can realize fault identification better. The experimental results show that the F1 value increased to 92. 38% and the loss decreased to 0. 012 0, which can better learn fault characteristic information. The model is applied to the identification of the XiNanZhuang fault. The results show that this method can accurately predict the fault location and improve the fault continuity. It is proved that the UNet ++ network model has certain research value in fault identification. 

fMRI ( functional Magnetic Resonance imaging) is an efficient research method for brain imaging technique. In order to reduce the redundancy of the fMRI data and transform the fMRI data to the constructed features with more classification potential, a feature construction method based on the siamese network named as SANet(Siamese Network) is proposed. It engineered the brain regions features under multiple scanning points of an fMRI image. The improved AlexNet is used for feature engineering, and the incremental feature selection strategy is used to find the best feature subset for the encephalopathy prediction task. The effects of three different network structures and four classifiers on the SANet model are evaluated for their prediction efficiencies, and the ablation experiment is conducted to verify the classification effect of the incremental feature selection algorithm on the SANet features. The experimental data shows that the SANet model can construct features from the fMRI data effectively, and improve the classification performance of original features.

To improve the efficiency of manual detection of illegal and irregular collection of personal information, an APP(Application) personal information evaluation system for illegal and irregular collection is developed based on techniques such as regular expression semantic analysis and machine learning. We conducted illegal and irregular detection on online apps, generated detection algorithms and rules, and focused on solving technical difficulties such as semi automated access to privacy policies, app detection engines, and dynamic sandboxes for custom ROM(Read Only Memory) . The developed prototype system is used to conduct regular technical testing on the apps listed on major application platforms. The testing results show that the system significantly improves the efficiency of comprehensive governance and judgment of illegal and irregular collection of personal information apps, and effectively supports the relevant work of higher-level management departments.

Existing Chinese named entity recognition algorithms inadequately consider the data features of entity recognition tasks, leading to imbalance in the categories of Chinese sample data, excessive noise in the training data, and significant differences in the distribution of generated data. An improved Chinese named entity recognition model based on BERT-BiLSTM-CRF ( Bidirectional Encoder Representations from Transformers-Bidirectional Long Short-Term Memory-Conditional Random Field) is proposed. The first improvement involves combining the P-Tuning v2 technology with BERT-BiLSTM-CRF to accurately extract data features. And three
loss functions, including Focal Loss, Label Smoothing, and KL Loss(Kullback-Leibler divergence loss), are utilized as regularization terms in the loss calculation to address the problems. The improved model achieves F1 scores of 71. 13% ,96. 31% , and 95. 90% on the Weibo, Resume, and MSRA( Microsoft Research Asia)datasets, respectively. The results validate that the proposed algorithm outperforms previous research achievements in terms of performance and is easy to combine and extend with other neural networks for various downstream tasks.

Considering that the randomness of the new power load is enhanced, the traditional accurate forecasting methods can not meet the requirements, an EMD-BiLSTM-ANFIS (Empirical Mode Decomposition Bi-directional Long Short Term Memory Adaptive Network is proposed based Fuzzy Inference System) quantile method to predict the load probability density. It replaces the accurate value of point prediction with the load prediction interval, which can provide more data for power System analysis and decision-making, The reliability of prediction is enhanced. First, the original load sequence is decomposed into several components by EMD, and then divided into three types of components by calculating the sample entropy. Then, the reconstructed three types of components and the characteristics of external factors screened by correlation. And they are used together with the Bilstm and ANFIS models for prediction training and QR(Quantile Regression), and accumulate the results of the prediction interval of the components to obtain the prediction interval of the final load. Finally, the kernel density estimation is used to output the user load probability density prediction results at any time. The validity of this method is proved by comparing the point prediction and interval prediction results with CNN- BiLSTM(Convolutional Neural Network-Bidirectional Long Short-Term Memory) and LSTM ( Long Short-Term Memory)models. 

 FBG(Fiber Bragg Grating) sensing technology has achieved rapid development in scientific research and engineering applications, but it is rarely used in undergraduate experimental teaching. Currently, there are few devices available in the market that can be directly used for FBG sensing experimental teaching, and cutting- edge scientific research technology is disconnected from undergraduate experimental teaching. To address this situation, a teaching experiment device for temperature stress sensing based on FBG has been designed. The device consists of three parts: a fiber laser, a spectrometer, and upper computer control software. The fiber laser enables laser output of about 1 550 nm. The spectrometer measures the change of FBG center wavelength and collects data into the computer. The upper computer control software is used for graphic display and data storage. The experimental device has the advantages of simple operation, flexible assembly, good repeatability, and stability, and can be used for undergraduate experimental teaching. We introduce cutting-edge science and technology into undergraduate experimental teaching, promote the integration of scientific research and experimental teaching, and realize the synchronous improvement of scientific research and teaching levels. 

In order to improve the problem of large trajectory tracking control error and low control accuracy of ball and plate system, a differential flatness-based backstepping sliding mode control method is proposed for enhancing the tracking accuracy in ball and plate system. Firstly, based on the Euler-Lagrange equation, the kinematic model of ball and plate system is established, the decoupled linear state-space model is obtained by reasonable simplification. Taking the X-direction controller design as an example, the target state quantity and feedforward control quantity of the system are obtained by using differential flatness technology, and an error system is constructed. Then, the backstep method is used to realize the sliding mode control of the error system, and the stability of the closed-loop system is proved by Lyapunov stability theory. The hyperbolic tangent function is used in the algorithm to suppress the jitter of the sliding mode, and the trajectory tracking control of ball and plate system is realized with high accuracy. Simulation results show that the proposed control strategy has high control accuracy and better control performance.

Aiming at the problem that the current D2D ( Device-to-Device) communication applied in the Internet of Vehicles, is only considering the resource utilization rate or system traversal capacity of the D2D multiplexing mode, but does not include other D2D modes into the system, an algorithm that takes into account resource allocation and mode selection is proposed. The communication resource utilization rate is improved by the priority multiplexing mode, the cellular mode is adopted for the D2D vehicle pair (D-UE:Device-to-device UsErs) that does not meet the multiplexing mode, the C-UE (Cellular UsErs) is adopted to meet the basic requirements of D-UE, and different resource access modes are allocated to D-UE considering factors such as vehicle and cellular users, BS ( Base Station) distance and signal-to-noise ratio between D-UE. Theoretical calculation and simulation results show that the proposed resource allocation and mode selection scheme can effectively improve the system capacity and D-UE QoS (Quality of Service).

A football competition risk intelligent warning method based on an improved Copula model is proposed to address the issues of large errors between warning values and actual values, and multiple false alarms in football matches. Based on the fuzzy comprehensive evaluation matrix, the evaluation system for football competition risk indicators is determined. The indicator level status is classified, the Copula function is selected, and an improved Copula football competition risk intelligent warning method is constructed to accurately judge football competition risks and reduce risk losses. The experimental results show that the interference suppression of this method is high, maintained above 20 dB, and have high anti-interference ability. It can effectively suppress interference. This method also reduces the error between the warning value and the actual value, reduces the number of false alarms in the warning, and verifies the practicality and feasibility of this method.

The training actions of skiing athletes can directly reflect their level, but traditional methods for identifying and evaluating actions have shortcomings such as subjectivity and low accuracy. To achieve accurate analysis of skiing posture, a motion analysis algorithm based on improved OpenPose and YOLOv5(You Only Look Once version 5) is proposed to analyze athletes爷 movements. There are two main improvements. First, CSP-Darknet53(Cross Stage Paritial-Network 53) is used as the external network for OpenPose to reduce the dimension of the input image and extract the feature map. Then, the YOLOv5 algorithm is fused to optimize it. The key points of the human skeleton are extracted to form the human skeleton and compared with the standard action. According to the angle information, the loss function is added to the model to quantify the error between the actual detected action and the standard action. This model achieves accurate and real-time monitoring of athlete action evaluation in training scenarios and can complete preliminary action evaluation. The experimental results show that the detection and recognition accuracy reaches 95%, which can meet the needs of daily skiing training. 

A control method based on the combination of the CSA ( Cuckoo Search Algorithm) and the conductivity incremental method INC( Incremental Conductivity method) is proposed to improve the speed and accuracy of the maximum power point tracking as well as to reduce the loss and harmonic content of the output power of the PV power generation system when the PV array is locally shaded. To prevent the algorithm from settling on the local optimal solution in the early stages, the cuckoo algorithm is used for global search. Later, a thorough search within a limited range is carried out using the incremental conductivity method in order to lock the maximum power point. And to see if it satisfies the criteria for grid connected harmonic content, this algorithm applied to grid connected control. Then a different strategy suggested. The results of a simulation model created in Matlab / Simulink demonstrated that the composite algorithm based on CSA paired with the conductivity increment approach has a faster tracking speed, less error, and satisfies the grid connected harmonic content requirements.

Weil pairing and Tate pairing are widely used in encryption, signature, password exchange and cryptosystem security analysis. It has been suggested that the computational efficiency of Tate pairing is better than that of Weil pairing, but this problem is still doubtful and needs to be further verified. The parameter selection algorithm of binary group structure proposed by Miller belongs to probabilistic algorithm, and the algorithm efficiency is not high. To solve the above problems, the analysis models of Tate pairing and Weil pairing on execution efficiency are established, and a new method is proposed to find the parameters of the distortion value by using the quadratic relation of the order of the elliptic curve. The research shows that when the distortion value is small, the computational efficiency of Tate pairing is better than that of Weil pairing, which is consistent with previous studies. However, when the distortion value is large, the computational efficiency of Weil pairing is better than that of Tate pairing, and the time complexity of the new method to find the distortion value parameter is less than that of Miller method O(M). Compared with Miller’s probabilistic

method, the new method is deterministic. The correctness of the analysis model is verified by analysis and example, and the new method greatly improves the efficiency and accuracy of parameter selection.

 In order to improve the quality of denoised images, an algorithm based on 2D-VMD ( Two Dimensional Variational Mode Decomposition ) and BD ( Bhattacharyya Distance ) is proposed for image denoising. Firstly, the algorithm uses 2D-VMD algorithm to decompose the image into several IMFs ( Intrinsic Mode Functions), and then BD is used to measure the geometric distance between the PDF (Probability Density Function) of each IMF and the original image to distinguish the signal-dominated IMF and the noise-dominated IMF. Finally, the denoising noise-dominated IMF through wavelet threshold denoising and the signal-dominated IMF are reconstructed to obtain the denoised image. The proposed algorithm is applied to medical images. The theoretical analysis and simulation result show that, compared with ROF ( Rudin Osher Fatemi) algorithm, median filter and wavelet threshold algorithm, the algorithm of combining 2D-VMD and BD has better denoising effect in both subjective and objective evaluation, and it effectively improves the quality of denoised images. 

Unlike single noise, mixed noise has inconsistent characteristics and is difficult to suppress. In order to improve the noise suppression effect and image clarity, a digital image mixed noise suppression algorithm based on lifting wavelet is proposed. By using probabilistic neural networks, digital image noise is divided into pulse noise and Gaussian noise. The median filtering method is used to remove pulse noise from the digital image, and the lifting wavelet method is used to remove Gaussian noise from the digital image, achieving mixed noise suppression. The experimental results show that the proposed algorithm achieves higher image clarity and signal-to-noise ratio, and significantly improves the ENOB( Effective Number Of Bits) value of the digital image after denoising, indicating that the hybrid noise suppression effect of the algorithm is better.

To address issues such as excessive turning points and suboptimal paths in traditional JPS(Jump Point Search) algorithms, an improved jump point search algorithm is proposed. First, based on the feasibility of the map, the obstacles are adaptively expanded to ensure a safe distance. Then, an improved heuristic function based on directional factor is integrated. And a key point extraction strategy is proposed to optimize the initial planned path, significantly reducing the number of expanded nodes and turning points while ensuring the shortest path. The experimental results show that compared to traditional JPS algorithms, the proposed ensures a shorter path length and fewer corners, while reducing the number of extended nodes by an average of 19% and improving search speed by an average of 21. 8%. 

Aiming at the problems of difficult modeling and low calculation efficiency of equivalent harness method, the effect of electromagnetic pulse radiation on the vehicle harness is studied using CST ( Computer Simulation Technology). The influence of the number of vehicle cables on the electromagnetic coupling effect of the harness is analyzed. By controlling the variables, we changed the number of cables in the harness and observed the maximum value of the coupling voltage in the harness. We also studied the maximum coupling voltage and current in the harness by varying the cable size and load resistance. The simulation results show that the peak value of the coupling voltage decreases linearly with an increase in the number of cables and increases linearly with an increase in cable size. The peak value of the coupling current decreases with an increase in load resistance, which follows a power series relationship. Finally, we combined the simulation results and fitted the maximum coupling voltage and current under different parameters, drawing a conclusion about the relationship between them, which provides a reference for the electromagnetic protection of vehicle wiring harnesses. 

In order to improve the quality and accuracy of residual oil edge detection, a method of residual oil edge detection with improved multi-direction Sobel operator is proposed. The improved bilateral filtering method is used to remove the image noise of the microscopic residual oil distribution, so as to achieve the purpose of edge preserving and denoising. Combined with Otsu algorithm, the optimal threshold of the residual oil image can be obtained adaptively. The amplitude and direction of the gradient of the remaining oil image are calculated by using the Sobel operator with the amplitude of 3×3 in the four improved directions. Non-maximum suppression algorithm is used to filter out the pseudo-edge pixels to obtain the final remaining oil edge detection image. The experimental results show that the proposed method can accurately detect the information of residual oil edge in the microscopic residual oil distribution image while removing the noise of the image.

Bridge sensor anomaly detection is a method based on sensor technology to monitor the status of bridge structure in real time. Its purpose is to discover the anomalies of the bridge structure in time and recognize them to prevent and avoid accidents. The author proposes an abnormal signal detection and identification method for bridge sensors based on deep learning technology, and by designing an abnormal data detection algorithm for bridge sensors based on the LSTM (Long Short-Term Memoy) network model, it can realize the effective detection of the abnormal data location of the bridge cable sensor, and the precision rate and recall rate of the abnormal data detection can reach 99. 8% and 95. 3%, respectively. By combining the deep learning network and the actual working situation of bridge sensors, we design the abnormal classification algorithm of bridge cable-stayed force sensor based on CNN(Convolution Neural Networks) network model to realize the intelligent identification of 7 types of signals of bridge cable-stayed force sensor data, and the precision rate of identification of multiple abnormal data types and the recall rate can reach more than 90%. Compared with the current bridge sensor anomaly data detection and classification methods, the author's proposed method can realize the accurate detection of bridge sensor anomaly data and intelligent identification of anomaly types, which can provide a guarantee for the accuracy of bridge sensor monitoring data and the effectiveness of later performance index identification. 

The current intelligent prediction methods for traffic flow have not analyzed and considered the spatiotemporal correlation of the road network. We conduct research and improvement to address this issue by adding spatiotemporal correlation information to the intelligent prediction methods to solve the problem of reduced prediction accuracy caused by the lack of spatiotemporal information. The spatiotemporal correlation of the urban road network is analyzed by combining the map connection of the traffic network and the vehicle traffic delay. Considering the spatiotemporal correlation of urban traffic, based on the GCN( Graph Convolutional Neural)

network and LSTM ( Long Short-Term Memory) network methods, the urban traffic flow prediction method considering spatiotemporal information based on GCN and LSTM is studied. Urban traffic flow prediction network is optimized and trained by using the open source urban traffic flow dataset. The performance of LSTM, BiLSTM (Bidirectional Long Short-Term Memory) network and different number of nodes in solving the traffic flow prediction problem is compared. The results of this research show that the proposed method can effectively predict urban traffic flow, and the accuracy of the proposed method is improved compared with the prediction method without considering spatiotemporal information. This research can provide a theoretical reference for traffic prediction in intelligent transportation systems.

At the traditional image processing algorithms for the detection of steel surface defects, there are problems such as low recognition efficiency and a high false detection rate of leakage. The YOLOv8-DSG (Deformable Convolution Network Squeeze and Excitation Network Generalized Intersection over Union) steel surface defect detection algorithm is proposed. Based on the traditional YOLOv8 algorithm, several improvements are made. Firstly, the DCN ( Deformable Convolutional Network) is embedded in the C2f ( Convolution to Feature) module of the Backbone network, which enhances the feature extraction ability of the model under

complex background conditions. Secondly, the SE ( Squeeze and Excitation network ) attention module is introduced into the Neck network, which highlights the important feature information of the steel surface and enhances the richness of the feature fusion. Lastly, the GIOU ( Generalized Intersection Over Union) loss function is used instead of the original CIOU(Complete Intersection Over Union). Compared with CIOU, GIOU introduces the minimum enclosing frame area ratio, which can more accurately measure the overlapping area of the frames. The experimental results show that the YOLOv8-DSG algorithm achieves an average accuracy mAP of

80% on the NEU-DET dataset, which is 3. 3% higher compared to the original YOLOv8 algorithm. And it has a low rate of misdetection and omission, demonstrating higher detection accuracy and arithmetic efficiency. This algorithm can play an important role in quality inspection.

 Under local shading conditions, the power-voltage characteristic curves of photovoltaic arrays show multiple peaks, and traditional population intelligence optimization suffers from slow convergence, large oscillation amplitude and the tendency to fall into local optimality. To address the above problems, an MPPT (Maximum Power Point Tracking)control method based on the PSO-GWO(Particle Swarm Optimization-Grey Wolf Optimization) algorithm is proposed. The algorithm introduces a convergence factor that varies with the cosine law to balance the global search and local search ability of the GWO algorithm; the PSO algorithm is introduced to improve the information exchange between individual grey wolves and their own experience. Simulation results show that the proposed PSO-GWO algorithm not only converges quickly under local shading conditions, but also has a smaller power output oscillation amplitude, effectively improving the maximum power tracking efficiency and accuracy of the PV(Photovoltaic) array under local shading conditions. 

To address the issue of source code plagiarism detection and the limitations of existing methods that require a large amount of training data and are restricted to specific languages, we propose a source code plagiarism detection method based on pre-trained Transformer language models, in combination with word embedding, similarity and classification models. The proposed method supports multiple programming languages and does not require any training samples labeled as plagiarism to achieve good detection performance. Experimental results show that the proposed method achieves state-of-the-art detection performance on multiple public datasets. In addition, for scenarios where only a few labeled plagiarism training samples can be obtained, this paper also proposes a method that combines supervised learning classification models to further improve detection performance. The method can be widely used in source code plagiarism detection scenarios where training data is scarce, computational resources are limited, and the programming languages are diverse.

A modified receiving coil derived from the BP( Bipolar Pad) coil structure is proposed to address the problem of mutual inductance dips and increased mutual inductance fluctuations in the process of wireless charging of dynamic electric vehicles using magnetically coupled resonant radio energy transmission technology during straight-line driving and turning. The modified coil solves the problem of reduced effective area of positive pair coupling caused by decoupling of conventional BP coils by means of double coils fitted to the inner and outer diameter of the track respectively, and Ansys / Maxwell software is used to carry out simulation to find out the reasonable design position and relative size of the compensation coil. The experimental data shows that the new receiver coil can suppress the mutual inductance fluctuation and enhance the mutual inductance value to a certain extent, in the process of turning and driving in a straight line. The peak mutual inductance fluctuation rate is 5. 1% , and the maximum mutual inductance reception is 28. 7% higher than that of the traditional BP coil.

The use of distributed deployment of sensors will lead to the edge of the server data distribution single imbalance phenomenon, the model training under edge computing can also result in serious privacy leakage problem due to the data set pollution caused by gradient anomaly. RDSEC(Strongly Robust Data Security Algorithms for Edge Computing) is proposed, encryption algorithm is used to encrypt the parameters of the edge server to protect privacy. If an anomaly is found in the gradient anomaly detection of the edge node, the edge node uploads the gradient with a signal to tell the cloud center if the current parameters uploaded by the edge node are available. The experimental results on CIFAR10 and Fashion data sets show that the algorithm can efficiently aggregate the parameters of edge servers and improve the computing power and accuracy of edge nodes. Under the condition of ensuring data privacy, the robustness, accuracy and training speed of the model are greatly improved, and the high accuracy of edge node is achieved. 

To effectively reuse URLLC(Ultra-Reliable Low-Delay Communications) and eMBB(enhanced Mobile Broad Band) on the same carrier band and improve the performance of the hybrid traffic system, a dynamic bandwidth allocation strategy based on scalable transmission time interval is proposed. The system bandwidth is dynamically divided according to the traffic type. URLLC scheduling priority is promoted in time domain. And in the frequency domain, different lengths of TTI ( Transmission Time Interval) are adopted to carry out user- centered wireless resource allocation. The dynamic system-level simulation shows that, compared to the traditional wireless resource allocation algorithm, the proposed scheme can effectively meet the delay requirements of URLLC users and optimize the throughput consumption of eMBB users under different load levels. The maximum delay gain of URLLC users is 83. 8% . The quality of service for different types of traffic in the 5G hybrid traffic system is satisfied.

Aiming to the problem that the original manifold learning algorithm only utilizes spectral characteristics without incorporating spatial information, a locality preserving projections algorithm based on spatial-spectral (SS-LPP: Spatial-Spectral Locality Preserving Projections) union is proposed. Firstly, the weighted mean filtering algorithm is used to filter the dataset, fuse the spatial information with the spectral information, and eliminate the interference of noise, to increase the smoothness of similar data. Then, the label set is used to construct intra-graph and inter-graph. Through the intra-graph and inter-graph, identification features can be effectively extracted, and the classification performance can be improved. The effectiveness of the algorithm is verified on the Salinas dataset and the PaviaU dataset. Experimental results show that the algorithm can effectively extract data features and improve the accuracy of classification.

To improve the recognition accuracy of occluded faces, an intelligent recognition method for occluded faces based on the improved Gabor algorithm is proposed. Firstly, the dynamic range of facial images is compressed and the anti sharpening mask filtering algorithm is selected for image enhancement processing. Secondly, Gabor filters are used to extract features from half faces with relatively complete information preservation and high brightness. Finally, the extracted Gabor features are input into an extreme learning machine to achieve intelligent recognition of occluded faces. The experimental results show that the proposed method has good processing performance for occluded facial images, and the processed facial image recognition has high accuracy and short recognition time.

A single embedded processor is difficult to efficiently complete the massive computing tasks such as image processing. Therefore, a set of SoC(System on Chip) with image processing function is designed based on FPGA(Field-Programmable Gate Array) and Coretx-M3 processor kernel. Based on Xilinx’s Kintex-7 FPGA and Arm’s Cortex-M3 kernel, the processor architecture is implemented on FPGA. The memory, bus system and basic peripherals are designed using IP(Internet Protocol) core and Verilog, and are connected to the processor through the bus. The image processing unit is designed, and the commonly used digital image processing

algorithm is mapped to the hardware description language. And the bus interface is designed to connect to the processor, providing the image processing capability for SoC. Based on Keil MDK tool and C language, the drivers for the peripheral and image processing unit of SoC are written, and the system function is simulated. And the digital image processing based on Matlab and the image processing unit in SoC are fully compared and tested by taking the binarization algorithm as an example. This image processing SoC has excellent performance and all the advantages of FPGA and SoC. The author has successfully developed a SoC with image processing function based on FPGA platform. The system is board-validated on Xilinx’s Kintex-7 family, model XC7K325TFFG676-2 FPGAs. This design reflects the high flexibility and efficiency of the system designed on FPGA platform, and provides a solution to solve the disadvantages of a single embedded processor that is difficult to efficiently complete the massive computing tasks such as image processing. The system is designed based on a reconfigurable platform, which can realize the customization of peripheral functions according to requirements, and has the advantage of higher flexibility.

Under the current situation of normalized management of epidemic situation, in order to ensure the normal development of computer experiment courses in colleges and universities, a virtual laboratory for computer basic experiments is established based on Docker technology. Students can access the server through a browser to obtain an independent experimental environment. The Docker-Compose tool is used to create, open, stop, delete and other multi-dimensional management of students' experimental environments, and to ensure their performance, which is equivalent to moving the offline laboratories online. This scheme can meet the needs of online computer basic experiments and provide high-quality experimental services for corresponding theoretical teaching.

 In response to the chattering phenomenon that traditional SMO(Sliding Mode Observer) faces during switching functions, novel sliding mode observer using saturation function instead of switching function is proposed to weaken chattering. In the process of extracting position information, a phase-locked loop is selected to replace the traditional arctangent method, there by improving the observation accuracy of PMSM(Permanent Magnet Synchronous Motor) rotor position. In the Matlab environment, by comparing traditional SMO and new SMO, it can be observed that the speed error of the rotor has increased by about 14 r/ min, and the position error of the rotor has increased by about 0. 03 rad.

Incomplete big data, due to its irregular structure, has a large amount of computation and low interpolation accuracy when interpolation misses values. Therefore, a missing value interpolation algorithm for incomplete big data based on spatial autoregressive model is proposed. Using a migration learning algorithm to filter out redundant data from the original data under dynamic weights, to distinguish abnormal data from normal data, and to extract incomplete data. Using least square regression to repair the incomplete data. The missing value interpolation is divided into three types, namely, first order spatial autoregressive model interpolation, spatial autoregressive model interpolation, and multiple interpolation. The repaired data is interpolated to the appropriate location according to the actual situation, implementing incomplete big data missing value interpolation. Experimental results show that the proposed method has good interpolation ability for missing values. 

To address the shortcomings of traditional model robustness evaluation methods, such as low description consistency and difficulty in obtaining accurate scene matching data, a new power artificial intelligence index algorithm model of multi scenario robustness evaluation method is proposed. The multi scene data is extracted, the disturbance range interval of multi scene data in local space is set, the interval movement distance of spatial range is controlled, and the data acquisition results of sample points within the interval range are predicted. The basic feature parameters of the algorithm model are input, the multiple scene data is selected to obtain distance range values while increasing the input parameter dimension, and the initial data evaluation operations are performed based on the selected values. Based on the characteristics of uncertain control objectives, conduct data foundation analysis to ensure that the system is in a stable state and maintains its dynamic characteristics. Effectively analyze the differences between different system parameters, construct a range of deviation values, judge the multi scenario characteristics of the algorithm model, and achieve data evaluation. The experimental results show that the multi scenario robustness evaluation method of the electric power artificial intelligence index algorithm model can effectively transform the coordinates of sampling points, ensure the invariance of multi scenario sampling point data images, overcome the problem of scene data rotation sensitivity, and improve response speed. Compared with traditional evaluation methods, the proposed evaluation method has strong advantages in interference robustness and affine deformation robustness. 

To achieve optimal efficiency and constant voltage output when the electric vehicle is charged wirelessly even after the load resistance value is changed, a synchronous Sepic converter is connected on the load side to identify different load resistance values, and impedance matching is performed by changing the duty cycle to achieve optimal transmission efficiency. The phase shift angle of therectifier is closed-loop controlled using a phase shift full bridge to achieve constant voltage output. Finally, simulation experiments using Matlab / Simulink software demonstrates the feasibility of this impedance-matching method and closed-loop control scheme.

To ensure network security and timely control the security situation, a security threat quantification assessment algorithm is proposed for wireless networks based on susceptible, infected, and susceptible infected recovered models. Asset value, system vulnerability, and threat are selected as quantitative evaluation indicators. Value and vulnerability quantification values are obtained based on the security attributes of information assets and the agent detection values of host weaknesses, respectively. Based on the propagation characteristics of the virus, the SIR ( Susceptible Infected Recovered) model is improved, the propagation characteristics of the virus are analyzed. A quantitative evaluation algorithm for wireless network security threat situation is established based on the quantification of three indicators, and the obtained situation values is used to evaluate the network security situation. The test results show that the security threat situation values of the host and the entire wireless network evaluated by this method are highly fitted with the expected values, and the evaluation time is shorter. It can be seen that the proposed algorithm has good evaluation accuracy and real-time performance, which can provide effective data basis for network security analysis and provide reliable decision- making support to administrators in a timely manner.

Due to the complex structure and features of current massive data, big data exhibits unstructured and small sample characteristics, making it difficult to ensure high accuracy and efficiency in its classification. Therefore, a big data classification optimization method is proposed to improve the decision tree algorithm. A fuzzy decision function is constructed to detect sequence features of big data, and these features inputted into a decision tree model to mine and train rules. The decision tree model is improved using grey wolf optimization algorithm. The big data is classified using the improved model, and then a classifier accuracy objective function is established to achieve accurate classification of big data. The experimental results show that the proposed method achieves the highest accuracy in classification results and the lowest false positive case rate, ensuring the overall high throughput of the algorithm and improving its classification efficiency.

With the development of Internet technology, the application of AI(Artificial Intelligence) technology in education has become a key trend of educational innovation. The research focuses on the role of AI in key areas such as classroom teaching optimization, student evaluation, exam assessment, and teacher training. By analyzing existing AI education products, summarizing the current application status of AI in the field of education, and looking forward to future development trends. The results indicate that AI technology can significantly improve teaching efficiency, achieve intelligent analysis of students’ learning situations, and provide personalized teaching support. In addition, AI has shown great potential in the allocation of educational resources and the analysis of learner characteristics, which can help achieve personalized and precise education. Although there are challenges in terms of application scope, research and development efforts, and application modes, the in-depth application of AI technology is expected to promote the development and progress of the education industry.

Due to the complexity of digital information, massive and multi-angle unstructured big data, and external interference, data structure damage and other factors cause its information loss, a missing value interpolation algorithm for unstructured big data based on transfer learning is proposed. Through the migration learning algorithm, the missing parts of unstructured big data are predicted, and the naive Bayesian algorithm is used to classify data features, to measure the weight value between attributes, to clarify the feature difference vector of data categories, and to identify the degree of feature difference. The kernel regression model is used to implement nonlinear mapping for the missing part of the data, and the polynomial change coding is used to describe the cross-space complementary condition of the data, completing the interpolation of the missing value of unstructured big data. The experimental results show that the proposed algorithm can effectively complete the interpolation of missing values of unstructured large data, has good interpolation effect and can improve the interpolation accuracy.

In response to the demand for simulation effects consistent with real hardware, a smooth transition method is proposed to enhance the imaging effects of the entire simulation system. By analyzing visual persistence effects and system imaging delays, a two-point third-order Hermite interpolation is used to handle smooth transition time and imaging color respectively. Through comparative experiments, the results demonstrate that this method can adaptively smooth the imaging of the entire simulation system, thereby having solved issues such as real-time dynamic imaging flicker and instability. The significance of this method lies in enhancing the imaging quality of the virtual 3D simulation experimental system for embedded microcontrollers, improving the visual effects of embedded microcontroller 3D simulation, and mitigating the impact of problems such as abrupt changes and artifacts. The significant application value of a virtual 3D simulation experimental system for embedded microcontrollers in the fields of education and design is presented.

Efficient and accurate water injection flow prediction can help oilfield departments formulate reasonable production plans, reduce the waste of resources, and improve the injection-production rate of the oilfield. RNN( Recurrent Neural Networks) in deep learning is often used for time series prediction, but it is difficult to extract features from historical series and can not highlight the impact of key information. Early information is also easy to lose when the time series is too long. A method of oilfield water injection flow prediction based on dual attention mechanism CNN ( Convolutional Neural Networks)-BiLSTM ( Bi-directional Long Short-Term Memory) is proposed. Taking the historical water injection data of the oilfield as the input, the CNN layer extracts the characteristics of the historical water injection data, and then enters the feature attention mechanism layer. The corresponding weights are given to the features by calculating the weight value. The key features are easier to get large weights, and then have an impact on the prediction results. The BiLSTM layer models the time series of data and introduces the time step attention mechanism. By selecting the key time step and highlighting the hidden state expression of the time step, the early hidden state will not disappear with time,

which can improve the prediction effect of the model for long time series, and finally complete the flow prediction. Taking public datasets and oilfield water injection data from a certain region in southern China as examples, and comparing them with MLP ( Multilayer Perceptron), GRU ( Gate Recurrent Unit), LSTM ( Long Short Term Memory), BiLSTM, CNN, it is proven that this method has higher accuracy in oilfield water injection flow prediction, can help oilfield formulate production plans, reduce resource waste, and improve injection recovery rate, and has certain practical engineering application value.

To improve the stability and accuracy of satellite electromagnetic detection target tracking, a twin network based satellite electromagnetic detection target tracking algorithm is proposed to avoid the tedious target acquisition process. Firstly, a multi-satellite scheduling model is established for electromagnetic detection satellites, matching suitable satellites and working modes for electromagnetic detection targets, in order to complete the collection of target electromagnetic signals. Secondly, a twin network is used to train the target signal, obtaining the electromagnetic feature information and true position information of the target by eliminating interfering clutter in the target signal. Finally, a particle filter algorithm is used to achieve stable tracking of satellite electromagnetic detection targets. The test results show that the proposed algorithm can effectively improve the efficiency of target tracking, and has high stability and accuracy.

Traditional manual repair of fishing nets is inefficient and costly. To solve the problem of traditional manual repair of fishing nets, a robotic arm that can automatically sew fishing nets is designed. The robotic arm is mainly controlled by STM32F103C8T6, and its functions are achieved through infrared sensor module, motor actuator module, and communication module. The infrared sensor module is responsible for detecting the position of damaged fishing nets. The motor actuator module drives the motors in six axis to flexibly send the sewing device to the preset node, and the communication module is responsible for transmitting the detected information of damaged fishing nets to the controller for processing. After practical testing, the designed robotic arm can achieve high-precision, fast, and effective automatic sewing of fishing nets. Compared with traditional manual repair methods, this automatic repair technology improves the efficiency and quality of fishing net repair, reduces labor costs, and has obvious advantages.

To address the problem of significantly degraded image quality in hazy weather, a new multi-task learning method is proposed based on the classical atmospheric scattering model. This method aims to jointly learn the transmission map and dehazed image in an end-to-end manner. The network framework is built upon a new biconditional generative adversarial network, which consists of two improved CGANs( Conditional Generative Adversarial Network). The hazy image is inputted into the first stage CGAN to estimate the transmission map. Then, the predicted transmission map and the hazy image are passed into the second stage CGAN, which generates the corresponding dehazed image. To improve the color distortion and edge blurring in the output image, a joint loss function is designed to enhance the quality of image transformation. By conducting qualitative and quantitative experiments on synthetic and real datasets, and comparing with various dehazing methods, the results demonstrate that the dehazed images produced by this method exhibit better visual effects. The structural similarity index is measured at 0. 985, and the peak signal-to-noise ratio value is 32. 880 dB.

Aiming at the poor effect of D2D ( Device-to-Device) communication channel allocation, an optimization mathematical model of D2D communication channel allocation based on KM (Kuhn Munkras) algorithm is proposed. Based on the model of D2D communication system, the transmission rate of D2D communication channel is calculated, and the variables in the system are expressed in a two-dimensional coordinate system. A linear planning diagram is constructed, according to which the optimal transmission power of D2D users is solved. Based on KM ( Kuhn Munkras) algorithm, the mathematical model of D2D communication channel allocation optimization is established to realize D2D communication channel allocation. The experimental results show that the practical application effect of D2D communication channel allocation optimization mathematical model is better, and the throughput of communication system is greater.

To increase the coverage of MIoTs(Maritime Internet-of-Things) devices, a coverage Optimization algorithm based on Deployment of MEC-UAV(UMCO: MEC-UAV-based Coverage Optimization algorithm) is proposed. In UMCO, MEC(Mobile Edge Computing) empowered UAVs(Unmanned Aerial Vehicles) is used to meet the network coverage demand for MIoT, and to maximize the network profit. We formulate a problem of joint MEC-UAVs deployment and their association with MIoT devices as an ILP(Integer Linear Programming) to maximize the network profit. An iterative algorithm is developed based on the Bender decomposition to solve the ILP. Finally, numerical results demonstrate that the proposed UMCO algorithm achieves a near-optimal solution.

In the process of 3D reconstruction of digital images, problems such as noise and distortion in the original data lead to low efficiency and accuracy of feature matching. To address this issue, a 3D digital image reconstruction method based on SIFT(Scale-Invariant Feature Transform) feature point extraction algorithm is proposed. The Bilateral filter algorithm is used to eliminate the environmental noise in the digital image, retain the edge information of the digital image, and improve the accuracy of feature point extraction. The SIFT algorithm is used to obtain feature point pairs. Using this feature point pair as the initial patch, a dense matching method for spatial object multi view images is used to achieve 3D reconstruction of digital images. The experimental results show that the proposed method has high feature matching efficiency and accuracy and strong noise reduction ability. The average time required for generating 3D reconstructed images is 26. 74 ms. 

Fundus images often contain a large number of small blood vessels with significant noise interference and blurred boundaries, making segmentation challenging. To address these characteristics, a fundus image segmentation method called MDF _Net&CD ( Multi-Directional Features neural Network and Connectivity Detection) is proposed, based on multidirectional features and connectivity detection. A deep neural network model, MDF_Net( Multi-Directional Features neural Network), is designed to take different directional feature vectors of pixels as input. MDF_Net is used for the initial segmentation of the fundus images. A connectivity detection algorithm is proposed to revise the preliminary segmentation results of MDF _ Net, according to the geometric characteristics of blood vessels. In the public fundus image dataset, MDF_Net&CD is compared with recent representative segmentation methods. The experimental results show that MDF_Net&CD can effectively capture the detailed characteristics of pixels, and has a good segmentation effect on irregular, severely noisy, and blurred boundaries of small blood vessels. The evaluation indices are balanced, and the sensitivity, F1 score, and accuracy are better than other methods participating in the comparison.

Traditional methods for identifying table structures are difficult to fully learn complex table structures such as merge cells with multiple rows and columns, blank cells, nested cells, and are lack of information in the process of extracting features. An OCR(Optical Character Recognition) table structure identification method based on improved SLANet (Structure Location Alignment Network) is proposed. Firstly, the lightweight CPU (Central Processing Unit) convolutional neural network is used and attention mechanism is introduced to enhance the generalization ability and explanation ability of the network. The information vector obtained by training is

inputed into the lightweight high-low level feature fusion module to extract features, and then the outputted features are aligned with the structure and position information through the feature decoded module to obtain the prediction label. Experiments show that compared to EDD ( Encoder-Dual-Decoder), TableMaster and other models, the accuracy of the proposed method has been significantly improved, reaching 76. 95% , and the TEDS (Tree-Edit-Distance-based Similarity) has reached 95. 57% , which significantly enhances the model’s ability to identify complex table structures and provides an optimization strategy for identifying table structures.

Dynamic load balancing is an indispensable link to ensure the normal operation of digital trunking system, but it is easy to be disturbed by communication failures and other problems in the control process. Therefore, a dynamic load balancing algorithm for digital trunking based on Kent mapping is proposed. The virtual machine system based on cloud platform collects data information such as the number of node connections, response time, dynamic load of the digital cluster, and analyzes the load of the digital cluster system. Secondly, a resource utilization model of digital trunking is constructed, and the resource utilization of digital trunking is obtained by solving the model with the Grey Wolf algorithm based on Kent map. Finally, the resource utilization rate is input into the LQR( Linear Quadratic Regulator) control loop, and the dynamic load balancing of the digital cluster is realized by controlling the migration of the server. The experimental results show that the digital trunking processed by the proposed algorithm has short response time, large fitness value, and strong fault tolerance ability.

In traditional mobile edge computing systems, users face issues such as communication channel interruptions caused by dense obstacles and terrain structures, and under-utilization of idle system resources.These issues make it challenging to complete intensive computing tasks with low delay and power consumption.To address this, a UAV(Unmanned Aerial Vehicle)-assisted terminal pass-through collaborative mobile edge computing system is established. In this system, the computing user offloads part of the task to the idle user by establishing a direct connection link on the ground. The idle user uses their computing resources to assist with the calculation while offloading the remaining tasks to the UAV configured with a mobile edge computing server.A mathematical model of this new system is established, and a computational offloading strategy based on the deep deterministic policy gradient algorithm is proposed. This strategy optimizes the dual offloading rate and the maneuverability of the UAV to minimize the processing delay of computing tasks, under the constraints of UAV power and user movement range. Simulation tests in a simulated continuous state space environment show that the proposed offloading computing strategy optimization scheme can efficiently use resources in the collaborative network and effectively reduce task processing delay compared to other baseline algorithms.

AI(Artificial Intelligence) plays an important role in medical imaging education, driving innovation in teaching methods and medical education. With the continuous development of AI technology, especially breakthroughs in deep learning, image recognition, and natural language processing, AI is gradually demonstrating its unique advantages in the field of medical imaging education. AI helps students and healthcare professionals quickly identify disease features, and provides automated image analysis results, allowing learners to intuitively understand the imaging manifestations of different diseases. It enhances the interactivity and practicality of learning. AI can offer personalized learning paths recommending relevant educational content or exercises based on the student’s progress and understanding, ensuring that learners receive tailored educational services. The efficiency and accuracy of AI assist students in better comprehending complex medical imaging content improving learning outcomes. However, AI in medical imaging education also faces certain challenges.As technology continues to advance, AI will play a more significant role in medical imaging education. Future educational systems are likely to become more intelligent, integrating technologies such as virtual reality and augmented reality to provide students with a more immersive learning experience.

 In order to ensure data security and alleviate data storage, a distributed data fault-tolerant storage algorithm based on density partitioning is proposed. High-density data areas of distributed data are filtered, highly similar targets are divided into different areas, the density distribution of data is described through data source sample points, the data elasticity is set, probability and data granularity is used to calculate the corresponding storage gradient and intensity index, and data storage gradient and data elasticity is introduced into information storage to complete distributed data fault-tolerant storage. Experiments show that the proposed algorithm has high fault tolerance, stable bandwidth throughput, small average path length, and can improve the security of network data. 

Due to the large scale and variety of information data in the process of internet information security warning, the warning accuracy is low and the time is long. To improve the efficiency of early warning, a security warning for unknown access sources in mobile network privacy information databases is proposed. Principal component analysis method is used to reduce the dimensionality of information base data to reduce the difficulty of detection. The IMAP( Iterative Multivariate AutoRegressive Modelling and Prediction) algorithm is used to carry out data clustering processing, to extract discrete isolated data points, and complete the screening of unknown access source data in the information base. Unknown access source data is inputted into a support vector machine, a time window is used to transform the construction problem of the information base security warning model into a convex optimization problem of support vector machine learning. Security warning results are outputted, and globally optimize the construction parameters of the warning model are optimized to improve the warning output ability of the security warning model. The experimental results show that the proposed method has high security detection efficiency for information databases, and can achieve stable and accurate warning output in the face of multiple types of information database intrusion attacks.

 Camera is utilized to capture target LED (Light Emitting Diode) image in OCC(Optical Camera Communication) system, the performance degradation of OCC system occurs due to outdoor ambient light interference. The strong sunlight causes great difficulty in decoding at the receiving end of OCC system, in order to solve the problem a Gradient-Harris decoding algorithm based on piecewise linear gray transformation is proposed. A set of OCC experimental system is built, original images are captured by camera at the receiving end of OCC experimental system, and the target LED array region is extracted by standard correlation coefficient matching method. The image of target LED array region is enhanced by segmented linear gray transformation, a Gradient-Harris decoding algorithm is used for shape extraction and state recognition of target LED array. The experimental results show that the proposed Gradient-Harris decoding algorithm based on piecewise linear gray transform is effective for OCC experimental system in strong sunlight environment, the average decoding rate is 128. 08 bit/ s, the average bit error rate is 4. 38 x 10-4, and the maximum communication distance is 55 m. 

In order to solve the problem that using traditional technologies such as Java and .NET to develop and deploy data services are complex and difficult to integrate with advanced cloud and container technologies, a lightweight 3D visualization data service solution for drilling based on Web is proposed, providing data interface support for front-end visualization applications. Based on NodeJS、 Angular、 TypeScript and other open source lightweight technologies, a lightweight drilling database system is designed, which can be used as an auxiliary tool for front-line technical managers and providing the most concerned data items in the fastest way with high efficiency and practicability. With the data loading tool, drilling technicians can easily load data into the database, including surface and seismic slices, measurements, events and well logs of blocks. And the system provides a comprehensive data security mechanism, including JWT ( JSON Web Token ) based identity authentication and JWE ( JSON Web Encripytion ) based data encryption, to ensure data security. The application results show that this solution can provide efficient data transmission services for drilling 3D visualization systems. 

The low-light level digital sighting telescope encounters strong stray light interference, which causes imaging overexposure and submerges useful information in the image. To address this issue, a set of strong stray light suppression technology solutions is proposed. First, absorbance flannelette is pasted to the inner surface of the objective lens, and then several algorithms such as cumulative integration of adjacent images, histogram statistics, and wide dynamic gray enhancement are used in software image processing to suppress strong stray light. In outdoor environments with night sky illumination below 1 伊10 -3 lx, the experiment is conducted with added strong stray light interference. The results show that the technical solution can effectively suppress strong stray light and enhance image details, thereby improving image quality. The software runs based on FPGA(Field Programmable Gate Array), with a maximum processing time of 2 ms, meeting the real-time requirements of the system.

In the process of assembly and production, due to certain errors in geometric parameters, the linkage and joints will inevitably have slight differences, resulting in some errors when the robot operates. In order to reduce the influence of environment on robot motion accuracy, a design scheme of robot motion error compensation algorithm based on improved weight function distance is proposed. The twist angle is added before positioning the robot position to obtain the transformation matrix between the two coordinate systems of the robot. The absolute error of the robot motion positioning is calculated according to the linear calibration. The mathematical model of the robot distance error is established using the improved weight function, and preliminarily compensate the motion error. The deviation of the center point position and attitude of the robot end effector is calculated. The compensation problem is transformed into the robot motion optimization problem, and the objective function of the motion deviation optimization problem is obtained. The final compensation result is obtained through multiple iterations. The experimental results show that the error compensation effect of the proposed method is good, and the motion stability of the robot after center of gravity compensation is good. 

So far, the international community has only proposed an assessment metric for the extension conciseness of knowledge graph, but has not provided a standardized assessment method and process. To address this issue, the assessment method of the extension conciseness of knowledge graph is studied and a new method to assess the extension conciseness of the Chinese English mixed knowledge graph is proposed. The formulas for grouping at the overall level and assessing the head entities, relations, and tail entities are proposed and defined. To enhance the accuracy of the evaluation, the sentence level assessment formula is also defined. Finally, the four formulas are combined to create an algorithm for assessing the extension conciseness of the knowledge graph. To verify the accuracy and performance of the proposed algorithm, the open data set OPEN KG( Knowledge Graph) is used to assess and compare the proposed algorithm with related algorithms. The results confirm that the proposed algorithm provides a certain guarantee for the accuracy and time efficiency of the conciseness assessment of the Chinese English mixed knowledge graph, and the overall performance of the proposed algorithm is better than that of the related algorithm. 

Plaintext transmission is easily tampered with in distributed databases. To address the security risk, a multi-level control algorithm for secure access is proposed to distributed databases based on searchable encryption technology. The algorithm groups the authorized users according to the security level, and uses TF-IDF( Tem Frequency-Inverse Document Frequency) algorithm to calculate the weight of plaintext keywords, then uses AES (Advanced Encryption Standard) algorithm and round function to generate the key of the ciphertext, uses matrix function and inverse matrix function to encrypt the plaintext, and uploads the encryption results to the main server. And the Build Index algorithm is used to generate an index of ciphertext, and whether the user has access to ciphertext is reviewed based on the relevant attribute information of the user’s security level. After the review is passed, the user can issue a request for the number of ciphertext and keyword search. The server sends the ciphertext back to the user and decrypts it using a symmetric key method, achieving multi-level access control. The experimental results show that this method takes a short time in the encryption and decryption processes, and has good security access control performance.

As the most widely adopted backbone network in classification, object detection and instance segmentation tasks, the representation capability of ResNet ( Residual Neural Network) has gained extensive recognition. However, there are still certain limitations that hinder the representation ability of ResNet, including feature redundancy and inadequate effective receptive field. To address these problems, a feature fusion block is proposed, which can fuse features of different scales to construct multi-scale features with richer information and improve channel utilization, when the model channel is increased. The block employs a small number of large kernel convolutions, which is benefit to the expansion of the effective receptive field of the model and the trade-off between performance and computational efficiency. And a lightweight downsampling block and a shuffle compression block are also proposed, which can effectively reduce the parameters of the model and make the entire method more efficient. The feature fusion block, downsampling block and shuffling compression block are introduced to the ResNet can build a FFNet(Feature Fusion Network), which will have faster convergence speed and a larger effective receptive field and better performance. Extensive experimental results on CIFAR (Canadian
Institute for Advanced Research ), ImageNet and COCO ( Microsoft Common Objects in Context ) datasets demonstrate that the feature fusion network can bring significant performance improvements in classification, object detection and instance segmentation tasks while only adding a small number of parameters and FLOPs(Floating Point Operations).

In order to reduce the total overhead of the MEC (Mobile Edge Computing) system, the weighted sum of latency and energy consumption of all devices are considered as the optimization objective, and the problem of task offloading is solved in a multi-user multi-channel mobile edge computing system. Specifically, multiple user devices are able to offload computationally-heavy tasks to the MEC server over a wireless channel. Considering the difference in residual energy among multiple smart devices, an energy factor is introduced to measure the bias of smart devices between energy consumption and latency. A reinforcement learning scheme based on the Q-learning algorithm is applied to co-optimize the offloading decision, the allocation of computational resources, and the selection of wireless channels. Simulation results show that the algorithm can effectively reduce the delay and energy consumption of task processing and accommodate more users.

A fusion model based on material balance equation and Informer is proposed to solve the prediction problem of oilfield development indicators. Firstly, the mechanism model before and after the decline of oil field development production is established through the knowledge of the material balance equation field. Secondly, the established mechanism model is fused with the loss function of the Informer model as a constraint to establish an indicator prediction model that conforms to the physical laws of oil field development. Finally, the actual production data of the oil field is used for experimental analysis. The results indicate that compared to several purely data -driven cyclic structure prediction models, this fusion model has better prediction performance under the same data conditions. The mechanism constraints of this model can guide the training process of the model, so that its rate of convergence is faster, and the prediction at the peak and trough is more accurate. This fusion model has better predictive and generalization abilities, and has a certain degree of physical interpretability. 

In order to address the problem that the number of connected devices in the OGIoT(Oil and Gas IoT) has increased dramatically, resulting in insufficient computing power of the edge nodes in the EC ( Edge Computing) system, and it is difficult to effectively identify the service collapse caused by malicious attacks from other edge nodes, an EMLDI(Efficient Machine Learning method for Improved Data Contamination Detection of Oil and Gas IoT algorithm) is proposed, which solves the problem of fluctuating and inaccurate results of edge nodes due to their poor robustness, data distortion or mild qualitative changes. The problem of large and inaccurate edge node results due to robustness of edge nodes and data distortion or mild qualitative changes is solved. The network is trained by adding GN(Gaussian Noise) to the expanded data set through randomly selected batch samples, which enables the network to have broader data fitting and prediction capabilities, and solves the problem of systemic collapse due to the difficulty of implementing correct operations at the edge nodes when the data is severely corrupted. The algorithm is able to identify noise contaminated and random label contaminated samples more effectively and the algorithm achieves the best results within the specified training batches.

In order to reduce the access risk of medical information shared by multiple end users, the compulsory access control system for medical information sharing is optimized and designed from two aspects of database and software functions. The database tables of medical shared information and users are built, connecting the database tables according to the logical relationship, and completing the design of the system database. The process of medical information sharing is simulated and the sensitivity level of medical information sharing is determined. Multi-terminal user roles and permissions are allocated, abnormal access behaviors of access users are detected in real time, and authorization and behavior detection results are combined to realize the mandatory access control function of medical information sharing of the system. The test results show that the access control error rate of the design system is reduced by about 24. 4% , and the access risk of medical shared information is significantly reduced under the control of the design system.

Aiming at the problems of difficult detection and low recognition accuracy of brain cognitive diseases, a multi-head attention-guided convolutional neural network ( MAGINet: Multi-Head Attention-Guided Convolutional Network) is proposed. This integrates the local dependent modeling ability of the convolutional neural network with the global dependent modeling ability of the attention mechanism. It is used to identify NC (Normal), EMCI(Early Mild Cognitive Impairment), LMCI(Late Mild Cognitive Impairment), and AD (Alzheimer’s Disease), and to explore the complete evolution from NC through MCI(EMCI and LMCI) to AD. First, the complementary information of three FCN(Functional Connectivity Network) variants is integrated to form a multi-view learning framework. Secondly, a new multi-head attention module is designed in the convolutional neural network module in each view. By completing self-attention, channel attention, and spatial attention successively, it helps to model the global dependence relationship, compensates for the local mechanism of the convolutional neural network, optimizes the performance of the model, and proves its effective information extraction ability. Finally, the model is applied to several encephalopathy classification experiments to prove the strong universality and repeatability of the model. 

The energy consumption of a UAV(Unmanned Aerial Vehicle) determines the length of its operational cycle. To address the issue of low communication-to-energy consumption ratio, a reinforcement learning-based UAV path planning solution is proposed to reduce energy consumption while maintaining high communication quality. The continuous flight space is divided into multi-layer two-dimensional grids to facilitate the generation of UAV state points, and a reward function based on communication quality parameters and energy consumption parameters is established. The Q-Learning algorithm is employed to learn and obtain the path with the optimal communication-to-energy consumption ratio. Experimental results show that the path planned by this learning model can achieve a higher communication-to-energy consumption ratio, demonstrating its practical value.

With advancements in modern science and technology across structural design, materials, and sensor manufacturing processes, pipeline leak detection sensors are becoming increasingly miniaturized and intelligent, and technologies for measuring physical changes caused by pipeline leaks continue to mature. For the convenience of technology selection and optimzation in pipeline leak detection, a systematic review of widely used piezoelectric, optical fiber, and laser sensors in pipeline leak detection is provided, with a focus on analyzing their characteristics and differences in materials, structures, and working principles. It further explores their performance in practical applications and the current state of research both domestically and internationally, offering theoretical support and technical references for the selection, optimization, and future development of pipeline leak detection technologies.

The dung beetle optimization algorithm suffers from the problems of easily falling into local optimum, imbalance between global exploration and local exploitation ability. In order to improve the searching ability of the dung beetle optimization algorithm, a mixed-strategy dung beetle optimization algorithm is proposed. The Sobol sequence is used to initialize the population in order to make the dung beetle population better traverse the whole solution space. The golden sine algorithm is added to the ball-rolling dung beetle position updating stage to improve the convergence speed and searching accuracy. And the hybrid variation operator is introduced for perturbation to improve the algorithm’s ability to jump out of the local optimum. The improved algorithms are tested on eight benchmark functions and compared with the gray wolf optimization algorithm, the whale optimization algorithm and the dung beetle optimization algorithm to verify the effectiveness of the three improved strategies. The results show that the dung beetle optimization algorithm with mixed strategies has significant enhancement in convergence speed, robustness and optimization search accuracy.

We present an improved target detection algorithm based on Yolov3-Tiny for gas station scene because of the low accuracy of target detection algorithm in gas station scenes. This algorithm takes Yolov3-Tiny model as the basic network, innovates Mosaic image enhancement method proposed in Yolov4 algorithm for data preprocessing, uses dense connection modules to reconstruct the feature extraction network, and adds CBAM (Convolutional Block Attention Module) attention mechanism and Pyramid Pooling Module into the network, finally target detection in the gas station scene is realized. The experimental results show that the improved algorithm improves the overall mAP by 8. 2% compared with the original algorithm, and can be more effectively applied to gas station target detection.

The core of the cadre information management system lies in enhancing the leadership level of the Party, strengthening the governance capacity of the Party, and reinforcing the Party’s self-construction, thereby promoting the sustained and healthy development of the cause of socialism with Chinese characteristics. By introducing information technology, the system improves the scientificity and standardization of cadre selection and appointment through modeling, visualization, and intelligence. It comprehensively supervises the daily ideology, work, and conduct of cadres, achieving comprehensive assessment and standardized management. The enhanced systemimproves the accuracy and efficiency of cadre selection and appointment, it enhances daily cadre management and supervision,it achieves the scientific and standardized performance assessment of cadres. In conclusion, through the cadre information management system, the Party’s level of development will be further elevated, and organizational work will progress towards scientification, standardization, and refinement, thereby achieving a new leap forward in the construction of Party member and cadre teams. 

In order to enrich the experimental content of the university experimental teaching platform, a programmable brushless motor control experimental system is developed. To address the problems of inflexible control mode and poor versatility in existing brushless controllers, the experimental system provides a repeatable development scheme, which can directly drive a Hall brushless motor by establishing a free mapping between control signals and motor outputs using its accompanying upper computer programming software. The hardware is based on ARM(Advanced RISC Machine)-Cortex M3 MCU(Multipoint Control Unit), which can support digital level, analog signal and PWM(Pulse Width Modulation) signal input at the same time. The upper computer software is developed based on PyQt5, including the guide window interface and the main window. After testing, the motor can communicate with external devices according to the customized control signal and can reach the expected design speed of the program, and the system can provide a new experimental solution for teaching of motor control.

In order to solve the problem of heterogeneous data from multiple sources encountered by electric power construction companies in the process of transitioning to whole-process consulting business, as well as the challenge of design managers needing to work together remotely due to unforeseen circumstances, it is proposed to construct a knowledge graph by using the enterprise’s private cloud as the basic environment and combining the technology of fusion of heterogeneous data from multiple sources. The system optimises the data management process and ensures data consistency and availability by integrating diverse data sources from various provinces and cities across the country. It ultimately realises distributed collaborative management of the whole process of consulting business and significantly improves the core competitiveness of the enterprise. At the same time, it effectively solves the problems of data variety, wide range of sources and diverse and inconsistent protocols, improves data quality and accuracy, and unifies the storage architecture to enhance the overall data management efficiency and decision-making support capabilities.

 In view of the low accuracy and efficiency of Internet of Things authentication due to the influence of noise, a new optimization method of Internet of Things authentication based on improved RSA(Rivest-Shamir- Adleman) algorithm was proposed. In this method, a transmission channel model is constructed to obtain user identity information and a noise reduction model is constructed to preprocess user identity data. Based on the processed data, the user identity characteristic information is extracted to build the Internet of Things identity authentication algorithm. On this basis, RSA algorithm is introduced to encrypt and process user identity information data to realize the optimization of Internet of Things identity authentication. In addition, the proposed method is not easily affected by noise environment. Under the condition of noise, the maximum error between the authentication rate and the ideal authentication rate is only 3.7%. Therefore, the proposed method is feasible and effective. 

Approximate matching is an indispensable link in the normal use of multimodal data technology, but the process of approximate matching is vulnerable to data redundancy, heterogeneous components and other issues. Firstly, parallel wavelet algorithm is used to eliminate the noise in multimodal data to avoid the impact of noise on the matching process. Secondly, tensor decomposition clustering algorithm is used to divide the data with different similarity into different clusters to eliminate the data difference of different clusters. Finally, the preprocessed data is input into the data matching model based on spatial direction approximation, The approximate matching of multimodal data is completed by calculating the spatial direction approximation and editing the distance between the reference data and the data to be matched. The experimental results show that the proposed method has high matching precision, high recall and short matching time. 

The L_OOA(An Improved Osprey Optimization Algorithm) is proposed to address the issues of the original OOA (Osprey Optimization Algorithm), which is prone to local optima and slow optimization speed. Firstly, to maintain population diversity, the Tent chaotic mapping strategy is adopted to initialize the individual positions of the population. Secondly, by introducing the Levy strategy to update the position of the Osprey, the Osprey Optimization Algorithm can improve its ability to jump out of local optima. The spiral curve strategy is introduced into the Osprey optimization algorithm to improve its computational accuracy. Finally, comparative

experiments are conducted with other intelligent algorithms on the CEC2021 ( Computational Experimental Competition 2021)testfunction set. Experiments prove that L_OOA has better accuracy and faster speed.

To address the deficiency in the local search ability of the artificial bee colony algorithm, a multi-strategy self-optimizing artificial bee colony algorithm based on reinforcement learning is proposed. This algorithm combines the Q-learning method in reinforcement learning with the artificial bee colony algorithm. The distance between the best value of the population and the individual fitness value, along with the diversity of the population are used as the basis for dividing the state. The algorithm creates an action set that contains multiple search strategies, adopts the ε-greedy strategy for selecting the best, produces high-quality offspring, and achieves intelligent selection of the ABC (Artificial Bee Colony) algorithm update strategy. Through 20 test functions and application in stock prediction, the results show that the proposed algorithm has better performance, a better balance between exploration and exploitation, faster convergence speed, and better self- optimizing ability.

In order to better utilize solar energy, it is very important to accurately predict photovoltaic power generation. To improve the accuracy of photovoltaic power prediction,a photovoltaic power prediction method based on the combination of factor-related complementary ensemble empirical mode decomposition and optimized long short-term memory network is proposed. Firstly, the CEEMD(Complementary Ensemble Empirical Mode Decomposition) algorithm is used to decompose the photovoltaic power sequence, and the Pearson correlation coefficient matrix of the decomposed power components and environmental factors is established. Three key factors are selected as the input of the subsequent prediction for each decomposed power component.

In order to improve the safety and efficiency of tunnel operation, a multi-sensor based tunnel operation risk perception method is proposed. Firstly, an object detection method based on YOLOv7(You Only Look Once v7) is used to effectively detect information such as traffic flow and speed from video sensors, and a deep temporal convolutional network algorithm is used to dynamically evaluate tunnel operation risks. Video sensors are integrated with multiple sensing devices such as smoke and temperature sensors to form a fire risk monitoring system, and multiple sensing devices are integrated to form a fire monitoring system. By numerically modeling
the fire monitoring status under different combustion conditions inside the tunnel, the distribution and development patterns of temperature, toxic gas concentration, and smoke inside the tunnel are analyzed, providing a risk assessment of tunnel fires. This method is applied in some tunnels of Shandong Jiwei Expressway to ensure the safety of the tunnels.

In order to solve the problem of network traffic management in the development of campus network informatization, associated with the practical problems of campus network management of a university in Xinjiang, a network traffic monitoring platform based on CactiEz is proposed and implemented. Based on the actual environment of the campus network, the current situation of the campus network is analyzed, and the traffic is monitored with specific hardware and software equipment. The application results show that the monitoring system can monitor the changes of network traffic in real time, reflect the network status in time, and carry out statistics and analysis of network traffic, providing data support for network performance and security. Therefore, the network traffic monitoring platform based on CactiEz plays a significant role in improving the efficiency of campus network management and helps to optimize network management.

Digital media deep interactive images are affected by noise, resulting in poor edge detection performance and affecting segmentation accuracy. Therefore, a digital media deep interactive image segmentation algorithm based on edge detection is proposed. Firstly, the wavelet transform method is used to denoise images in digital media to improve the accuracy of image segmentation. Secondly, Gaussian function and low-pass filter are used to enhance the denoised image, improve the image definition, and facilitate image segmentation. Finally, based on the adaptive threshold algorithm, edge detection is performed on digital media images. There are two thresholds in the pixel collection, the upper threshold and the lower threshold. The high and low thresholds in the pixel set are calculated based on the calculation of their upper and lower thresholds, and edge connections between the two thresholds are implemented to achieve digital media image segmentation. The experimental results show that the proposed method has good denoising effect, high segmentation accuracy, and high segmentation efficiency for segmented digital media images. 

In order to overcome the oversimplified treatment of the defect discovery rate in the traditional G-O (Goel-Okumoto) software reliability model, an improved model that more accurately describes the actual change of the defect discovery rate over time is proposed. Unlike the conventional assumption that it is treated as a constant or monotonic function, the improved model considers the progress of testers’ learning and debugging capabilities and the inherent tendency of the software’s defect discovery rate to decrease over time. Therefore, it assumes that the defect discovery rate first increases before showing a dynamic trend of decline. The model’s effectiveness is verified by applying it to two sets of public software defect detectionda tasets and comparing it with a variety of classic models. Experimental results confirm that the improved G-O model demonstrates excellent performance in both fitting and prediction capabilities, proving its applicability and superiority in software reliability assessment.

To extend the lifetime of oil and gas IoT( Internet of Things), a dual cluster head based oil and gas IoT routing algorithm is proposed. The algorithm fully considers the current residual energy of sensor nodes, historical average energy, distance between nodes and base stations, density of neighboring nodes and distance between nodes and energy harvesting sources in the cluster head election process, and elects dual cluster heads in the same cluster, while proposes a novel routing method to balance the energy consumption of cluster heads in the data transmission phase. A novel node working mode switching strategy is adopted along with the introduction of energy harvesting techniques. Simulation experiments show that the algorithm can balance the network energy consumption more effectively and extend the network lifetime compared with the traditional algorithm.

 There is a phenomenon of domain drift in webpage search results, where the returned webpage is not related to the search keyword domain, resulting in that users are unable to search for demand information. Therefore, a web search sorting algorithm based on Softmax regression classification model is proposed. Through the Feature selection of web search text, the corresponding feature items are obtained. Using the vector representation model, the selected web search text feature items are converted into formatted data, and the web search text data is balanced to obtain the web search text data set. Using the Softmax regression classification model, the web search text dataset is classified and processed, the types of web search texts is predicted. And the OkapiBM25 algorithm is used to sort web search texts, achieving web search sorting. The experimental results show that the proposed algorithm performs well in web search sorting, effectively improving the accuracy of web search sorting and avoiding domain drift during the process of web search sorting.

When photovoltaic panels are under uneven solar irradiation, the problem of low power generation efficiency arises. In order to solve the problem a chaotic Harris hawk algorithm combined with the conductivity increment method is proposed. The Harris Hawk algorithm introduces Levy flight function and Henon chaotic mapping to expand the search range of the algorithm in the early stage of tracking. Then the optimal individual strategy is introduced, which can further reduce the number of iterations of the algorithm. The algorithm makes it easier for the system to jump out of the local maximum power point, while in the late stage of tracking, the algorithm runs precisely in a small range, improving the local exploitation capability. The use of the conductance increment method alleviates the power oscillations when the system is located near the GMPP (Global Maximum Power Point) and stabilizes the output. 

In the process of virtual network layout, there are a lot of repetitive features and features with less correlation, which affect the efficiency of its layout. Therefore, a hierarchical layout algorithm of virtual network clustering features under big data redundancy technology is proposed. A weighted undirected graph is used to establish a virtual network graph, and the community structure of the virtual network is divided by communities, so that the clustering characteristics of the virtual network are eliminated to the maximum extent under the premise of keeping the original characteristics unchanged, and the characteristics with high correlation are obtained. According to the repulsion of Coulomb force, the distance between communities is increased, and the distance between network nodes and central points is reduced by the gravity of Hooke's law. Combined with FR (Flecher-Reeves) algorithm, the relationship between repulsion and gravity of virtual network clustering feature layer nodes is adjusted, and the hierarchical layout algorithm is realized. The experimental results show that the proposed algorithm can more clearly show the internal structure characteristics of each community, and the layout time is the shortest. 

In order to show the virtual image more intuitively, a visual communication algorithm for small and weak target images in virtual reality environment is studied. An image model is constructed based on the imaging characteristics and influencing factors of the target image in the virtual environment, the image target is adjusted based on the actual situation and image model, time domain and spatial domain are combined, and the spatial background is constrained to suppress the background image. The filtered image and residual background are used to complete image denoising. Based on the above preprocessing results, and other control factors such as the motion speed of the target image sequence, characteristic window area, and so on, image sequences are sampled, and feature tracking is converted into optical flow calculations, accurately tracking target images, obtaining optical flow results, and achieving visual communication of small and weak target images. Experimental results show that this algorithm has a higher success rate in visual communication, a shorter communication time, and a higher visual communication integrity.

The era of big data has arrived, and the digitization of archive information is the future development trend. How to protect the privacy of digital archive information is a key research topic in the computer field. At present, the archive privacy protection algorithm based on blockchain technology has problems such as poor protection effect and long operation time. In order to solve the problems existing in traditional methods, a digital archive information privacy protection algorithm based on blockchain technology is proposed. Firstly, apply blockchain technology to the privacy protection process of digital archive information. The specific protection

process is as follows: the data owner uses symmetric encryption algorithms to encrypt the digital archive information and upload it to the private chain; At the same time, generate a secure index of digital archive information and upload it to the alliance chain; The data user generates a query threshold for the keywords to be queried, sends it to the private chain, obtains the query results on the private chain, and sends them to the alliance chain. The alliance chain cooperates with the private chain to verify the correctness of the query results. If it is correct, the alliance chain will send the converted encrypted data to the data user. The experimental results show that the privacy protection algorithm for digital archive information of the proposed method has good privacy protection effect and practical application effect.

For the uncertainty decision-making problem of privacy risk, based on the theories of intuitionistic fuzzy and set pair analysis, a set pair relationship of information weights is established for privacy certainty & uncertainty. The intuitionistic fuzzy set pair operator is provided, and the relevant concepts, operations, properties, expected values, size ranking, and several intuitionistic fuzzy set pair information aggregation operators are defined, including Intuitionistic fuzzy set pair analysis operators, intuitionistic fuzzy set pair analysis weighted average operators, intuitionistic fuzzy set pair analysis weighted geometric operators, intuitionistic fuzzy set pair analysis ordered weighted average operators, intuitionistic fuzzy set pair analysis ordered weighted geometric operators, intuitionistic fuzzy set pair analysis hybrid aggregation operators, intuitionistic fuzzy set pair analysis hybrid geometric operators and their related properties. On this basis, the intuitionistic fuzzy set pair information aggregation method for privacy risk multi-attribute decision-making is analyzed, and it shows that the proposed method has feasibility and rationality. 

The phenomenon of oil theft at well sites is an important issue that affects the safe production and stable operation of oil fields. The current behavior recognition methods pay less attention to the need for detecting oil theft in well pads, and there are often limitations in the application of the oil theft target feature recognition process. An algorithm for identifying oil theft behavior at well sites is proposed based on 3D attention residuals. This network consists of multiple three-dimensional attention residual blocks, which embed channels and spatiotemporal attention modules in each residual block to obtain more feature discrimination information and enhance the model’s attention to important features. The effectiveness of the algorithm is varified on the dataset of oil theft behavior at the well site. The experimental results indicate that, compared to similar algorithms, this method has higher recognition accuracy. It can provide a reference for the practical application of automatic detection of oil theft behavior in oilfield well sites.

 The current learner model lacks exploration of this dimension of cognitive load, which, as a load generated by the cognitive system during the learning process, has a significant impact on the learning state of learners. Based on the CELTS-11(China E-Learning Technology Standardization-11) proposed by the China E-Learning Technology Standardization Committee, cognitive load is integrated into the learner model as a dimension, and an LMICL ( Learner Model Incorporating Cognitive Load) combining static and dynamic information is constructed. Afterwards, relying on an adaptive learning system, the data of the unmixed cognitive load learner model and the LMICL data were used as the basis for recommending learning resources, resulting in two different learning resource recommendation results. Two classes of learners were randomly selected to learn system, and then their academic performance. The results of cognitive load and satisfaction were used to validate the effectiveness of LMICL , and it was found that the recommendation learning effect based on LMICL was better than that of the learner model without integrating cognitive load.

To solve the problem of difficulty in segmentation of weak edge ultrasound images, an improved CNN (Convolutional Neural Networks) based weak edge ultrasound image segmentation method is proposed. The method first uses stationary wavelet transform to remove the noise in the image, and then uses weighted least square filter to enhance the image edge details. Then, an improved convolutional attention module is added to the residual network model to extract image features. Finally, the image segmentation accuracy is improved by optimizing the loss function. The experimental results show that the proposed method has good performance in processing weak edge details of ultrasound images and can improve the segmentation accuracy of medical ultrasound images. 

When using data from a single data source to complete tasks, there may be significant errors in the data, and there may even be data missing, which can affect the progress of the task. A dual channel data association and fusion algorithm based on fuzzy mathematics theory is proposed for this purpose. The correlation of dual channel data is measured and the missing data in the dual channel data is predicted according to the missing data prediction process. The missing data in the dual channel dataset is filled in to obtain complete dual channel data. The dual channel data is standardized, and the principal component analysis is used to calculate

the similarity between the dual channel data and the principal components, obtaining the comprehensive support level of the dataset, and obtain effective data. By using fuzzy mathematics theory, effective data is fuzzified, and the closeness between the fuzzification results and real data is calculated to determine the data fusion weight, in order to achieve dual channel data association and fusion. The experimental results show that using the proposed algorithm for dual channel data association fusion, when the total number of data reaches 1 500, the value of the comprehensive evaluation index exceeds 9, indicating that the proposed algorithm can improve the accuracy of dual channel data association fusion and has good dual channel data association fusion results.

The channel resources of RFID ( Radio Frequency Identification) systems are limited, and when multiple tags compete for the same frequency or time slot, it can lead to collisions and conflicts. In order to optimize the communication efficiency of broadcast channels, a collision prevention algorithm for RFID broadcast channels in the Internet of Things based on frame time slot ALOHA is proposed. This method introduces the concept of frame time slots and divides the communication time into time slots; By analyzing the probability of occurrence of idle, successful identification, and collision states within the time slot, the cause of collision in the

broadcast channel is obtained. By combining Bayesian algorithm and Poisson distribution rules, the probability distribution of the number of tags is calculated to estimate the number of tags within the range of the reader and writer, and the next frame length is adjusted based on the calculation result of the number of tags. If there is still label collision problem within the adjusted frame time slot range, FastICA( Indcpendent Component Analysis) independent principal component analysis is used to transform the label recognition problem within the frame time slot into an EPC(Electronic Product Code) encoding generation problem, thereby achieving parallel recognition of multiple labels within a unified time slot and avoiding collision situations. The experiment shows that the estimation of the number of labels proposed is accurate, which can improve the label recognition rate within the time slot and effectively improve the propagation efficiency of the broadcast channel while ensuring the stability of the communication channel.

Although traditional recommendation systems use graph structure information, most of them only consider the basic attributes of users and items, ignoring the important factor of contextual interaction information between users and items. Even if contextual interaction information is taken into account, there is a lack of attention in the layer combination stage. force mechanism to assign weight. To solve this problem, a CIAGCN (Context Information Attention Graph Convolutional Networks) recommendation model that integrates contextual interactive information and attention mechanism is proposed. This model utilizes the contextual interaction

information of users and items while applying the high-order connectivity theory of graphs to obtain deeper collaborative signals. An attention mechanism is introduced in the layer combination stage to improve the interpretability of this stage. The model was experimentally compared on the Yelp-OH, Yelp-NC and Amazon- Book data sets. The results showed that the model had a certain effect compared with other algorithms, indicating that the recommendation effect was better than some traditional recommendation models.

In response to the issues of inaccurate neighborhood selection and deficiencies in the metric used in the LLE(Locally Linear Embedding) algorithm, which hinder its ability to extract the true manifold structure, an algorithm called AN-RWLLE ( Locally Linear Embedding Algorithm Based on Adaptive Neighborhood and Reconstruction Weight) is proposed. Firstly, the local neighborhoods of each sample point are identified by calculating the cosine similarity of high-dimensional sample points, followed by an adaptive selection of the optimal neighborhood within those neighborhoods. Secondly, the distance features and structural features of the sample points within the optimal neighborhood are combined to thoroughly explore the manifold structure of high- dimensional data and achieve weight reconstruction. Lastly, support vector machines are employed for feature recognition, preserving the intrinsic characteristics of high-dimensional data in a lower-dimensional space. Experimental results demonstrate that the AN-RWLLE algorithm exhibits excellent visualization, clustering performance, and effective feature extraction capabilities on two sets of bearing fault datasets.

To ensure the security of cross domain data flow in the Internet of Things, a security detection algorithm for cross domain data flow sharing the same frequency in the Internet of Things is proposed. This method calculates the information entropy of the data set based on the data outlier characteristics, takes the data points with larger information entropy calculation results as the cluster center, and analyzes the data distribution characteristics through the calculation of the cluster center distance. The data distribution features are inputted into the BP( Back Propagation) neural network and combined with genetic learning algorithms to achieve deep mining of shared cross domain data on the same frequency. Wavelet analysis is used to segment effective signals and noise signals in the same frequency shared data, and introduce the wrcoef function achieving the reconstruction output of noise free signals. Based on the Markov chain state transition probability matrix, a detection model of Markov cross domain data flow security is established. By calculating the relative entropy difference value between the test sample and the standard sample, the security detection of cross domain data flow for the same frequency sharing of the Internet of Things is completed. The simulation results show that this method can effectively improve the efficiency of data flow security detection and achieve accurate perception of data flow trends across domains.

The MCU(Microcontroller Unit) programming language is mainly C language implemented in soft logic, which implements specific functions by sequentially executing instructions, and can not avoid the shortcomings of low speed. The MCU based on FPGA(Field-Programmable Gate Array) and ARM(Advanced RISC Machines) Cortex-M0 is designed to obtain a high-speed system while still retaining the advantages of MCU. FPGA-based MCUs execute in parallel because the logic is directly implemented by the hardware, which greatly improves the speed and can be widely used in complex logic control and data operations and processing. Based on the analysis of the ARM Cortex-M0 core and the AMBA(Advanced Microcontroller Bus Architecture) bus system, each unit is designed for the MCU system. The Verilog code of each module is designed according to the characteristics of each module. The simulation results verifys that the module functions well. The design of special peripherals based on the FPGA platform and the verification of hardware algorithms are explored, which reflects the high flexibility and efficiency of the FPGA platform for design of MCUs. Taking the timer interrupt system as an example, combined with software and hardware, a comprehensive simulation of the entire MCU system is carried out, the working state of the ARM Cortex-M0 core in actual operation is analyzed, and the data communication and scheduling between each module of the bus system are analyzed, verify the feasibility and efficiency of the FPGA platform to develop MCU. The MCU is designed based on a reconfigurable platform, which can customize peripheral functions according to needs, and has the advantage of higher flexibility than traditional MCUs. 

Air combat is an intense, complex, and continuous process, filled with many influencing factors,complex interactions, and uncertainties. Aiming at the problem of low accuracy of UAV ( Unmanned Aerial Vehicle) cluster air combat effectiveness assessment, a method based on System Dynamics is proposed. The interactions between various factors is analyzed in the UAV air combat system during a reconnaissance and strike mission carried out by the red formation. A causal relationship model and a stock flow model are established.The evaluation indicators of combat effectiveness are constructed based on three aspects: combat effect, combat
efficiency, and combat cost. These indicators include mission completion, combat efficiency, and combat loss rate. Then, the influencing factors of the air combat system and the combat effectiveness under different equipment schemes are studied. finally the feasibility and effectiveness of the proposed method are verified through simulation, which solves the complexity and uncertainty problems arising in the assessment process.

During the retrieval process of imbalanced stream data, the performance of data retrieval decreases due to the presence of imbalance in the data stream and the susceptibility to differential and edge data. In order to reduce the impact of the above factors, an imbalanced stream data retrieval method based on multi similarity fuzzy C-means clustering is proposed. This method calculates the multiple similarities between imbalanced flow data, and uses fuzzy C-means algorithm to cluster data with different similarities. By constructing a octree retrieval model, the data after clustering is stored, encoded and judged to complete the retrieval of unbalanced stream data. The experimental results show that the retrieval time of the proposed method is less than 20 seconds, and the recall and precision rates remain above 80% , with high NDCG( Normalized Discounted Cumulative Gain) values.

In the D2D(Device-to-Device) communication system, when the distance between the source node and the destination node is too long, the communication delay is large and the communication process is interrupted. A relay selection algorithm for D2D communication based on social relationship and energy cache is proposed. Under the premise of limiting the location of the relay node, the algorithm considers the conventional conditions such as transmission rate, residual energy of the relay and residual buffer space, and considers the social relationship between nodes and the outage probability of the link. The weight of each link is calculated comprehensively, and the link with the highest weight is selected for data transmission. The simulation results show that the proposed algorithm effectively reduces the transmission delay of D2D communication link, improves the successful transmission probability of the system, and improves the stability of the relay system.

In order to solve the problems of large proportion of small targets in optical remote sensing data, the aspect ratio is large, and multiple targets are closely arranged and difficult to detect,we present an optical remote sensing small ship target detection algorithm based on the YOLO V5(You Only Look Once V5), UPCBAM- RYOLO V5 (Upsampling Convolutional Attention Block Module-RYOLO V5) algorithm. An up-sampling attention mechanism module is designed to enhance the feature extraction ability of small size targets. The rotation angle loss is introduced into the frame regression formula to improve the algorithm’s perception ability of the ship’s appearance and direction. Based on the experiment of small ship target datasets composed of GF1 and GF2, the results show that the UPCBAM-RYOLO V5 algorithm model improves the positioning accuracy and classification accuracy of small ship target detection. The P value, R value, and MAP(Mean Average Precision) value reach 93%, 94%, and 95% respectively, which are 3%, 7%, and 7% higher than the original YOLO V5 model. In the upsampled attention-mechanism module added location ablation experiment to the network, the results show that compared with the addition of UPCBAM in Backbone and Prediction, the addition of UPCBAM in Neck has the greatest influence on the detection of small target ships in remote sensing images. The performance is the best, with P value, R value, and MAP value increased by 5%, 4%, and 2%, respectively. The UPCBAM-RYOLO V5 model is proved to have a certain research significance in optical remote sensing ship small target detection.

In order to have a clear understanding of the exclusive access principle and mechanism of mutex on shared resources, based on the brief analysis of the meaning, application occasions, scheduling mechanism and key elements of mutex in real-time operating systems, the mbedOS mutex scheduling mechanism are theoretically analyzed. Takes the KL36 chip as an example the mbedOS mutex is realized and the scheduling process information of thread response mutex is output spontaneously based on the sequence diagram and printf method. And the real-time performance of mutex scheduling mechanism is analyzed. The analysis of mutex scheduling mechanism is helpful to further analyze other synchronization and communication methods of mbedOS, and can also provide reference for in-depth understanding of other real-time operating system synchronization and communication methods. 

A portable nitrogen concentration detector based on the STC89C52 single chip microcomputer and an oxygen sensor is designed for the purpose of real-time detection of gas concentration in the gas regulating storage room. According to the principle that the concentrations of nitrogen and oxygen in the atmosphere is almost complementary when the rare gases are ignored, the monitoring of nitrogen concentration in grain storage rooms can be converted into monitoring oxygen concentration, which can ultimately be converted back to calculate the percentage of nitrogen concentration in the environment. When the nitrogen concentration percentage in the grain storage room is below the preset value of 97% , an audible and visual alarm function can be activated, while the nitrogen concentration percentage, temperature, and humidity information inside the grain storage room can be displayed in real-time. The hardware system includes signal acquisition circuits such as an oxygen sensor and a temperature and humidity sensor, signal conditioning circuits, analog-to-digital conversion circuits, an STC89C52 single chip microcomputer, and an audible and visual alarm unit. For software design, the C language and Keil development environment are selected. The experimental test results indicate that the detector can display environmental information in real-time and immediately trigger an alarm when the nitrogen concentration in the environment falls below the preset value of 97% , fulfilling its expected functions.

Pilots need to simultaneously process multiple information sources and tasks while performing tasks, which increases the workload of mental labor. In order to improve flight safety and pilot work efficiency, a multidimensional comprehensive evaluation model for pilot mental workload based on random forest algorithm is studied. A linear finite pulse response bandpass filter is used to process EEG(Electroencephalogram) signals, removing high-frequency and low-frequency noise, calculating mismatched negative waves, obtaining linearly interpolated EEG signal sampling points, and extracting power spectral density and energy features of each rhythm based on overlapping sampling points in the EEG signal neighborhood. A multi-dimensional comprehensive evaluation model of the random forest algorithm is constructed, determine the output points of each signal fluctuation frequency, and combine the voting mode to obtain the optimal classification results of multi-dimensional mental load, achieving comprehensive evaluation of pilot mental load. The experimental results demonstrate that the proposed method has high classification accuracy and can accurately evaluate the mental workload status of pilots.

Rolling bearing is one of the key components in rotating machinery, and long-term mechanical operation leads to wear easily. Traditional fault diagnosis relies on feature extraction, but due to loud noise during mechanical operation, effective signals are drowned. And the fault diagnosis network structure is complicated and there are too many parameters. Therefore, a bearing fault diagnosis model based on variational mode decomposition and deep learning is proposed for bearing wear detection. Firstly, the bearing signal is decomposed by VMD( Variational Mode Decomposition) and denoised by Hausdorff distance. Secondly, the

selected effective signals are inputted into the network structure of one-dimensional convolutional neural network and gate recurrent unit to complete the classification of data and realize the fault diagnosis of bearings. Compared to common bearing fault diagnosis methods, the proposed VMD-1DCNN-GRU(Variational Mode Decomposition- 1D Convolutional Neural Networks-Gate Recurrent Unit) model has the highest accuracy. The experimental results verify the feasibility of the proposed model for the effective classification of bearing faults, which has certain research significance.

To address the efficiency and quality issues in current molecular diagnosis talent cultivation, the application status and future development trends of AI(Artificial Intelligence) technology in molecular diagnosis talent cultivation is explored. The research content covers the current application of AI technology in molecular diagnostics, its advantages and challenges, and focuses on analyzing how AI can enhance the efficiency and quality of talent cultivation through automated experimental processes, precise data analysis, and
interdisciplinary knowledge integration. The study summarizes practical experiences from domestic and international universities in integrating AI with molecular diagnostic talent cultivation and outlines future development trends, including the integration of VR ( Virtual Reality ) and AR ( Augmented Reality )technologies, the precision of intelligent diagnostic systems, and the intelligence of personalized learning platforms. The conclusion of the study indicates that AI technology holds great potential in the cultivation of
molecular diagnostic talents, significantly enhancing their comprehensive competitiveness and promoting the further development of molecular diagnostic technologies to provide robust talent support for precision medicine. However, the application of AI technology still faces multiple challenges, including the integration of interdisciplinary knowledge, data quality, and ethical and privacy issues, which need to be addressed through the joint efforts of educational institutions, industries, and governments.

 To solve the problem of low accuracy in identifying abnormal data in existing methods. An abnormal data recognition algorithm for multi-dimensional feature-based communication network is proposed. The current speed and position of particles in particle swarm optimization algorithm is adjusted to obtain multi-dimensional data samples of communication network. Data features are extracted through clustering analysis in data mining, determining density indicators, and obtaining multidimensional features of the data. The extracted multidimensional features are Introduced into the deep belief network for recognition, and anomaly recognition of communication network data is achieved based on changes in feature spectrum amplitude. The experimental results show that the algorithm can effectively identify abnormal data features in communication networks and has high recognition accuracy. 

In order to clearly understand the working principle and mechanism of events, to analyze the role, response principle, and response process of events in real-time operating systems, based on the KL36 microcontroller, a PC-like printf output method is adopted to analyze the event response mechanism of mbedOS from the aspects of scheduling process timing, response time performance, etc. The experimental results show that the printf function can intuitively output information such as thread address, queue address, queue content, thread in and out queue status, and event bits during the event response process. This provides convenience for readers to understand the event response principle and process of mbedOS from the bottom layer, and also provides a method reference for analyzing the context structure of other synchronization and communication methods of mbedOS.

Considering that the traditional trial-and-error method of parameter setting for rotary inverted pendulum PD( Proportion Differentiation) controller has strong subjectivity and poor response ability, genetic algorithm is used to set parameters of PD controller so as to conduct model simulation and to ensure its operation on QUBE-Servo2 rotary inverted pendulum experiment system. The experiment shows that compared to the trial and error method, the PD controller parameters obtained by genetic algorithm further optimize the response performance of the system, and are not limited by subjective experience. The steady-state errors of the swing rod and swing arm are both within 0. 01 rad.

To accurately obtain the longitudinal velocity of the vehicle, a longitudinal velocity estimation method applicable to four-wheel drive vehicles is proposed. Firstly, a finite state machine is utilized to identify the vehicle state at the current moment and the vehicle state in the time-domain window, which effectively switches between the adaptive Kalman filtering method and the integration method. For the four-wheel non-total skidding state, an adaptive Kalman filter method that updates the measurement noise in real time is designed. This method introduces the measurement value and estimation error in the time-domain window to improve the estimation accuracy. For the four-wheel total skidding state, the last longitudinal velocity estimate from adaptive Kalman filtering is used as the initial value, and the longitudinal velocity is calculated by integrating the longitudinal acceleration of the vehicle. The effectiveness of the algorithm is verified by Carsim and Simulink joint simulation experiments and real vehicle data experiments. The experimental results show that the estimation accuracy of the proposed estimation method is improved by at least 65% and 75% on low-adhesion road surfaces such as snow and ice, respectively, compared with the integral method and the method of estimating longitudinal velocity using wheel speeds.

To address the issues of motion distortion and error accumulation in SLAM(Simultaneous Localization and Mapping) systems during prolonged operations, a mapping method called IRIS-LOAM ( Lidar-Iris Based Lidar Odometry and Mapping in Real-Time ) is proposed, which leverages Lidar-Iris to construct global descriptors for loop closure detection. This algorithm has two major  innovations based on the LOAM algorithm.First, in the data processing stage, it integrates lidar data with IMU(Inertial Measurement Unit) data and uses the IMU data to correct the point cloud data. Second, in the mapping optimization stage, it employs an information matrix-based graph optimization algorithm and utilizes Lidar-Iris global descriptors for loop closure detection of key frames. And it preprocesses the input point cloud to improve the time efficiency of optimization. Comparing the improved algorithm with A _ LOAM through experiments, the results show that IRIS-LOAM achieves better mapping performance in various real-world scenarios, demonstrating its feasibility and practicality.

 To effectively solve the problem of uneven brightness in 3D virtual images, a dynamic feature enhancement algorithm for 3D virtual images is proposed. Median filtering algorithm and wavelet soft thresholding algorithm are combined effectively for denoising of 3D virtual images. By setting new structural elements based on visual selection characteristics, connected particle attributes are constructed, and using hierarchical statistical models to perform color conversion and structural element matching on the image, corresponding mapping subgraphs are obtained, and dynamic features are extracted. The 3D virtual image is inputted into an improved U-net++network, dense connections are used at different layers to enhance the correlation of image features at different levels, and all dynamic features are fused for detail reconstruction to achieve dynamic feature enhancement of the 3D virtual image. According to the experimental results, the proposed algorithm can achieve satisfactory dynamic feature enhancement effects in 3D virtual images. 

 The traditional desktop application library integration method has some limitations in practical applications, such as the expansion of the standard OS directory, the complexity of distribution package making, the need to modify the middle layer library when multiple level library calls are included, and the inconsistency between development and deployment environments. To solve the problems, an integration framework is proposed. The cores of it are managing libraries in the way of managing resources such as images and implement the dynamic deployment of libraries at runtime based on the detection results of constraints and dependencies between libraries. Through the design of the four components, Library resource management, runtime dynamic deployment, runtime dynamic loading and resource manager, and their collaboration, the integration framework for the first time implements the combination of the above two cores. The practical application of CIFLog Integrated Logging Platform method module integration shows that the integration framework can solve the problems existing in the traditional library integration. The applicability of this framework can be applied to the library integration of all desktop applications, providing a new idea for the library integration of desktop applications. 

To maintain the security of network operation and ensure the secure storage of network information, a network security situation assessment system based on multi-source data mining is proposed. This study first establishes a three-layer network security situation system architecture with application layer, control layer, and data forwarding layer as the core. To ensure effective information transmission between the application layer and network devices, the OSGi (Open Service Gateway Initiative) design pattern is used to construct a five layer parallel architecture for the ONOS(Oper Network Operating System) controller of the control layer to ensure the decision-making response of the network security situation. Utilize the deployment of multiple detectors within the traffic detection module to achieve deep mining of network multi-source data; Introduce the LEACH(Low Energy Adaptive Clustering Hierarchy) algorithm to achieve multi-source data fusion at the network cluster head. After analyzing the threat level of network intrusion factors through the security situation assessment module, combined with the weight coefficient theory, the threat level of the network situation threat factors is assigned. Combined with the network hierarchical division method, the security situation of the operational network service layer, host layer, and network layer is evaluated in layers. The experiment shows that the proposed method has a high ability to analyze the operational status of network data, and can accurately identify attacks from multiple types of network threat factors, providing important guarantees for network security operation.

UAV(Unmanned Aerial Vehicle) aerial photography, characterized by multi-angle, large field of view, and large-scale scenes, often results in images with numerous small objects, complex backgrounds, and difficult feature extraction. To address these issues, a new model, CA-NWD-YOLOV5 ( Coordinate Attention- Normalized Wasserstein Distance-You Only Look Once v5) is proposed. Based on the YOLOv5 model, a multi- scale detection layer is added to the head network to extract the features of small targets. It also incorporates a CA attention mechanism into the backbone network to prevent the model from overlooking target location

information. Lastly, the normalized Wasserstein distance loss function replaces the loss function based on intersection ratio, enhancing the model’s sensitivity to small targets. Experiments on the VisDrone2019 dataset demonstrate that, compared to the improved YOLOv5 model, the CA-NWD-YOLOv5 model can effectively enhance the detection accuracy of small and medium-sized targets in UAV aerial photography images. The mAP_ 0. 5 of the improved algorithm reaches 50% , proving its effective application to the detection of small targets in aerial photography.

In order to solve the problems of uncertainty, discontinuity, randomness and nonlinearity in stock price forecasting, a TRSSA-ELM ( Tent Random Walk Sparrow Optimization Algorithm-Extreme Learning Machine) stock price forecasting model is proposed. Firstly, adaptive Tent chaotic mapping and random walk strategy are used to improve the algorithm, which enhances the diversity and randomness of the population and improves the local and global optimization ability of the algorithm. Secondly, the performance of TRSSA( Tent Random Walk Sparrow Optimization Algorithm) is verified by using single peak, multi-peak and fixed multi-peak

test functions. Compared to SSA( Sparrow Optimization Algorithm), AO( Aquila Optimizer), POA( Pelican Optimization Algorithm) and GWO(Grey Wolf Optimizer), TRSSA algorithm has better convergence speed, accuracy and statistical properties. Finally, because the ELM ( Extreme Learning Machine) model randomly generates weights and thresholds, which reduces the prediction accuracy and generalization ability, TRSSA algorithm is applied to optimize the weights and thresholds of the ELM model, and the TRSSA-ELM model is tested in Sanan Optoelectronic stock data set. The experimental results show that TRSSA-ELM model has better prediction accuracy and stability than SSA-ELM, ELM, SVR(Support Vector Regression) and GBDT(Gradient Boosting Decision Tree).

During the classification process of unstructured big data, due to the large amount of redundant data in the data, if the redundant data cannot be cleaned in a timely manner, it will reduce the classification accuracy of the data. In order to effectively improve the effectiveness of data classification, a non structured big data classification optimization method based on the improved ID3(Iterative Dichotomiser 3) algorithm is proposed. This method addresses the problem of excessive redundant data and complex data dimensions in unstructured big data sets. It cleans the data and combines supervised identification matrices to achieve data dimensionality reduction; Based on the results of data dimensionality reduction, an improved ID3 algorithm is used to establish a decision tree classification model for data classification. Through this model, unstructured big data is classified and processed to achieve accurate data classification. The experimental results show that when using this method to classify unstructured big data, the classification effect is good and the accuracy is high. 

Big data usually comes from different data sources with diverse formats, structures, and qualities. Big data often contains a large number of redundant features, which can affect the accuracy of data classification during feature integration. To address these issues, a deep learning-based algorithm is proposed for feature integration classification in hospital big data. A feature extraction model is established based on deep learning to extract relevant features from the data. However, since the training process of the model introduces a significant amount of noise, the extracted features may contain irrelevant information, which can impact the results of feature integration classification. Therefore, a stacked sparse denoising autoencoder is employed to suppress irrelevant features. The best training parameters are determined using divergence functions and greedy algorithms, and a loss function is utilized to sparsify the irrelevant features in the feature space, resulting in practical data features.A feature integration classification model is constructed using an autoencoder network, and with the assistance of type-constrained functions and objective functions, the optimal integration centers for each class are obtained to achieve data feature integration classification. Experimental results demonstrate that the proposed method exhibits excellent classification performance, with macro-averaged values above 0. 95, and it also shows fast classification speed, indicating its effectiveness in classification.

Currently, the docking of single-column steel pipe towers in power systems mainly relies on manual implementation, which has a high risk factor and is time-consuming and laborious. Aiming to research the autonomous docking technology of single-column steel pipe tower for this specific technical condition and environment, a vision-based navigation-based hydraulically driven autonomous flexible docking system for single- column steel pipe tower is proposed. The single-post steel pipe tower autonomous flexible docking system uses a microcontroller that enables autonomous positioning and docking through image tracking control, and the

computational performance of the microcontroller makes it easier for technicians to operate. Numerical simulations and hardware tests are carried out for the docking of a single column steel pipe tower in the two- dimensional plane. The results show that the effectiveness of the proposed method is verified by controlling the motion of the steel pipe tower assembly at the docking interface using a small steel pipe tower model with air bearings in the two-dimensional plane with a minimum propellant thruster and a small control moment gyroscope.

In order to fulfill the observation requirements for the total magnetic field intensity of geomagnetic stations, a specialized magnetometer architecture is independently designed for the stations, and the Overhauser magnetometer is developed for station monitoring. Sensitivity evaluations are conducted using both single-station direct measurement and dual-station synchronized methods under field conditions with low noise and in environments with high electromagnetic interference. The experimental results from both direct measurement and synchronized methods indicate that the sensitivity of the JOM-5J magnetometer can reach 0. 02 nT at a 1s period. It is capable of replacing the GSM-90F for applications in earthquake precursor observations and long-term

volcano monitoring.

The scale of multimedia digital image data is enormous, including ordinary RGB(Red Green Blue)images and various types of data such as depth maps, texture information, and normal maps. The difficulty of depth estimation leads to low accuracy in 3D scene reconstruction. To effectively address this issue, an interactive 3D virtual scene algorithm for multimedia digital images is proposed. Corners from multimedia digital images are extracted and they are used as initial values to perform a checkerboard edge search to determine the true corners. All corners are used as feature points to perform camera calibration and obtain the corresponding
camera pose for each multimedia digital image. By using the improved PatchMatchNet to perform depth estimation on the reference image, the output depth map is obtained through multiple iterations. By using the method of reprojection to filter the outer points of the depth map and projecting it into the world coordinate system, an interactive 3D virtual scene is finally obtained. The experimental results show that the proposed algorithm can obtain high-precision interactive 3D virtual scene reconstruction results, and the reconstruction time is less than 50 ms.

In order to suppress the influence of low-frequency magnetic field interference, such as geomagnetic diurnal variation, on the measurement results, multiple magnetometers are usually used for synchronous measurement. The time synchronization error has an obvious influence on the suppression effect. The influence of different time synchronization errors is studied on the JOM-5SF Overhauser magnetometer in geomagnetic detection and instrument sensitivity evaluation, based on the magnetometer developed in the laboratory. Two Overhauser magnetometers are used to conduct experiments on the campus of Jilin University. After comparing

the experimental results with the evaluation results of professional institutions, it is found that the smaller the time synchronization error, the smaller the difference between the magnetic field values of the two instruments, and the more accurate the sensitivity of the evaluation instruments by the synchronous method.

In order to effectively improve the visual effect and matching degree of text matching generated images, a text matching generated image model based on improved GAN( Generating Adversarial Networks) algorithm is proposed. Initial matching of text and images are unfolded through a mixed index tree. On the basis of GAN, they are improved to form an adversarial generation network based on cross attention mechanism encoding, and the improved GAN is used to establish a text matching image generation model. The cross attention encoder in the bidirectional LSTM( Long Short-Term Memory) network optimization model is used to translate and align text and visual information, obtaining cross modal mapping relationships between text and images, completing fine matching between text and images, and ultimately generating images that meet the requirements of the text. The experimental results show that the proposed model can generate images with higher quality that match image details with text.

In order to distinguish the effective component and noise component after VMD(Variational Mode Decomposition), and improve the denoising effect of VMD. A denoising algorithm (VMD-HD-VMD) combining VMD and HD(Hausdorff Distance) is proposed. Firstly, the original signal is decomposed into K IMF(Intrinsic Mode Functions) by VMD, the HD value of the probability density function of IMF component is calculated respectively, and the effective component and noise component are distinguished according to the HD value. Then the noise component is decomposed by VMD again, the effective component is selected by correlation coefficient, and reconstructed with the effective component decomposed for the first time. This method is applied to the denoising of pipeline leakage signal. The simulation experiment and pipeline leakage signal processing show that this method has better effect than EEMD(Ensemble Empirical Mode Decomposition), VMD and VMD combined wavelet denoising.

In order to ensure the security management of the hospital information network and avoid medical information leakage, an intrusion detection algorithm for abnormal information in the hospital network based on deep generative model was proposed. Using binary wavelet transform method, multi-scale decomposition of hospital network operation data, combined with adaptive soft threshold denoising coefficient to extract effective data. The Wasserstein distance algorithm and MMD(Maximun Mean Discrepancy) distance algorithm in the optimal transportation theory are used to reduce the dimension of the hospital network data in the depth generative model, input the reduced dimension network normal operation data samples into the anomaly detection model, and extract the sample characteristics. Using the Adam algorithm in deep learning strategy, generate an anomaly information discrimination function, and compare the characteristics of the tested network operation data with the normal network operation data to achieve hospital network anomaly information intrusion detection. The experimental results show that the algorithm can achieve efficient detection of abnormal information intrusion in hospital networks, accurately detect multiple types of network intrusion behaviors, and provide security guarantees for the network operation of medical institutions. 

In response to the difficulty in determining the characteristics of users and data for mobile terminal access, which leads to high difficulty in access control, an access control technology based on EVM(Error Vector Magnitud) measurement algorithm is proposed to effectively solve the problem. Considering the impact of noise and other interference factors in the environment, the Qos(Quality of Srvice) condition is used as the initial access condition for users. The characteristics of users or data that meet this condition are calculated, and the feature values are converted into weight factors as reference for access control. The EVM measurement algorithm is used to calculate the difference between the internal and external signals of the terminal channel, and the user weight factor is used to derive the access threshold of the mobile terminal. The increasing and decreasing functions between different user threshold values and control values are solved, and precise control of mobile terminal access is achieved according to the priority order of the functions. The experimental data shows that the proposed method has high access control accuracy, and after control, the terminal transmission delay and blocking rate have been significantly improved, and the data arrival rate has also been significantly improved. 

Aiming at the limitations of existing named entity recognition methods in identifying entities involving multiple elements and nested entities in oil and gas exploration texts, a novel approach is proposed. This approach integrates multiple features using a BERT-CNN-BiGRU-Attention-CRF(Bidirectional Encoder Representations from Transformers-Convolutional Neural Network-Bidirectional Gated Recurrent Unit-Attention-Conditional Random Field) architecture for named entity recognition. The model leverages BERT's semantic extraction capability to obtain character Vectors with global features for the entire sentence. Additionally, it utilizes CNN's ability to capture local features, overcoming limitations of BERT character Vectors, and obtains character-level Vectors for words. By incorporating a custom oil and gas exploration domain dictionary and employing a bidirectional maximum matching method, dictionary feature Vectors are obtained. These three types of Vectors are concatenated and used as input for the BiGRU-Attention-CRF model. Experimental results on a self-constructed small-scale oil and gas exploration dataset demonstrate an F₁ score of 91.10% . Compared to other mainstream NER ( Named Entity Recognition) methods, this model exhibits superior recognition performance. Furthermore, it provides valuable assistance in constructing knowledge graphs for the oil and gas exploration domain.

As the environment of geophysical exploration becomes more complex, and it is limited by acquisition and processing technology which results in low resolution and signal-to-noise ratio of post-stack seismic data.Therefore, how to enhance its resolution while realizing noise attenuation is a non-negligible problem. A design called DESRGAN(Depth-Enhanced Super-Resolution Generative Adversarial Network) is proposed, which is intended to be applied to the task of super-resolution reconstruction of seismic data. DESRGAN uses a LRDB(Lightweight Residual Dense Block) as the base unit to improve the efficiency and stability of the training
process, passes through channel attention in the deep feature extraction phase to increase the focus on important features and performs an up-sampling operation using pixel reorganization instead of interpolation to take into account the spatial relationship between pixels. Experimental results on synthetic and field data show that the network can reconstruct the synthetic data as the test set and it is well generalized to the field data. Compared with classic GAN(Generative Adversarial Network) and CNN(Convolutional Neural Network), the reconstructed results are visually clearer, and have higher peak signal-to-noise ratio and structural similarity in quantitative analysis.

To address the challenge of efficiently mining spatiotemporal information for traffic prediction, a novel vehicle travel time prediction method is proposed based on bidirectional multi-attention spatiotemporal graph convolution. To extract the spatial dependencies within the road network, a traffic transfer matrix is constructed using a Markov chain approach, which captures the bidirectional traffic flow transfer relationships. Graph convolution is employed to learn the spatial dependencies within the graph network. Subsequently, an attention mechanism is utilized to capture both local and global temporal features within the traffic flow map. Finally, a MLP ( Multi-Layer Perceptron) is used to forecast travel times, producing the final prediction results. The Xuancheng road network traffic data is selected for model validation. The results demonstrate that the proposed model reduces the RMSE (Root Mean Square Error) by 7. 6% , 3. 7% , and 9% , respectively, compared to baseline models such as STGCN( Spatio-Temporal Graph Convolutional Networks), ASTGCN(Attention Based Spatial-Temporal Graph Convolutional Networks), and A3T-GCN ( Attention Temporal Graph Convolutional Network). This significant reduction in RMSE indicates that this model substantially improves prediction accuracy, highlighting its effectiveness in capturing and utilizing spatiotemporal information for more precise traffic predictions.

Magnus rotor is a new type of auxiliary propulsion device for ships. Addressing the disparity between the lift model of Magnus rotors and traditional lift formulae, a combined approach of theory and numerical simulation is employed for investigation. Initially, the geometric and flow domain models of the Magnus rotor are established. Subsequently, computational fluid dynamics software is utilized for grid independence verification and numerical validation. A numerical simulation method is then employed to analyze the aerodynamic characteristics of the Magnus rotor model, considering the impact of various wind speeds and rotor rotation rates on the generated thrust. Finally, based on the obtained data, adjustments are made to the traditional lift model, and the reliability of the modified model is verified by comparing simulation data under different conditions with literature data. The results indicate that the propulsion force of the Magnus rotor increases with the increase of rotation ratio, and the optimized lift model exhibits a high degree of fit with simulated values, demonstrating higher accuracy. 

When solving high-dimensional equations, the CS (Cuckoo Search) has the drawback of falling into local optima. To address this deficiency, an improved hybrid cuckoo search is proposed. Firstly, the population is initialized using chaotic mapping and reversed learning mechanisms. Then the search mechanisms of TLBO (Teaching Learning Based Optimization) and CS are performed alternately. Finally, the discovery probability and embeds DE (Differential Evolution) are dynamically adjusted to comprehensively improve the algorithm's performance. The comparative results of simulation experiments with 6 benchmark functions and 1 optimized grating coupler design show that this algorithm is better for solving high-dimensional equations and effectively avoids the CS algorithm getting stuck in local optima.

In view of the defects that the projection matrix learned from LatLRR ( Latent Low Rank Representation) can not explain the importance of the extracted features and preserve the local geometry of data, a novel method named LLRSP (Latent Low-Rank and Sparse Projection) with dual neighborhood preserving and feature selection is proposed. The algorithm first combines low-rank constraint and orthogonal reconstruction to hold the main energy of the original data, and then applies a row sparse constraint to the projection matrix for feature selection, which makes the features to be more compact and interpretable. Furthermore, a l^2,1 norm is introduced to regularize the error component to make the model more robust to noise. Finally, neighborhood preserving regularization is applied on the low dimensional data and low-rank representation matrix to preserve the local manifold geometrical structure of data. Datasets results of extensive experimental on various benchmark show that this method can obtain better performance than other state-of-the-art methods.

During the process of digital video image acquisition, due to external environmental noise interference and low resolution of the original image, there may be significant distortion and artifacts during the compression process. Each compression and decompression introduces a certain amount of error, which gradually accumulates, resulting in poor compression performance. A research on digital video image compression method based on sparse encoding is proposed. Using multi threshold iterative methods to remove noise from digital video images is beneficial for subsequent image compression processing. The orthogonal basis coefficients of the denoised digital video image are obtained through sparse encoding method, redundant dictionary sparse encoding and compression transmission are performed on this coefficient, a multi frame decompressing artifact network is established, and the motion compensation module is used in the network to perform motion offset estimation and pixel compensation on the digital video image. The motion compensated frames are inputted into the decompressing artifact module to eliminate compressed artifacts and achieve digital video image compression. The experimental results verify that this method can effectively remove artifacts in compressed digital video images, and has high compression efficiency and signal-to-noise ratio.

In order to ensure the normal operation of the ventilator, an intelligent monitoring method for the operating status of the ventilator based on the HHT(Hilbert-Huang Transform) algorithm is proposed. Firstly,wavelet neural network is used to denoise the running signal of the ventilator; Secondly, combined with the HHT algorithm, the denoised ventilator operation signal is decomposed by EMD(Empirical Mode Decomposition), and the decomposed IMF( Intrinsic Mode Functions) component is transformed by Hilbert spectrum to obtain the signal spectrum as the signal feature. Finally, the obtained signal spectrum is placed in the MLP neural network
classifier, and the backpropagation algorithm is used to train the MLP neural network to achieve recognition of the operating status of the ventilator. The experimental results show that the proposed method has a good denoising effect, and the monitored results are consistent with the actual spectrum. At the same time, the sensitivity of monitoring is above 96% , and the accuracy of operating status recognition is above 95% . This indicates that the proposed method can effectively monitor the operating status of the ventilator and has good monitoring performance.

To ensure stable transmission of big data, a device big data transmission load optimization algorithm based on 5G network is proposed. The factors that affect the performance of big data transmission are analyzed,including data latency, average stream bandwidth utilization, and throughput. Morphological filtering algorithms are used to perform low-pass filtering on big data, eliminating noise in the data and reducing data transmission delay. Dynamically big data transmission channels are selected to avoid data congestion in the network and improve network throughput. On the basis of information transmission matrix mapping, data transmission accuracy is improved, and a capacity expansion mechanism is designed to improve network bandwidth utilization
and complete load optimization. The experimental results show that after optimization using the proposed algorithm, the bandwidth utilization rate is improved, and the network energy consumption and data transmission delay are reduced.

The EEMD(Ensemble Empirical Mode Decomposition) algorithm faces challenges in aligning the generated IMF(Intrinsic Mode Function) components during the decomposition process. To address this issue, a novel denoising method that combines EEMD with the PRT(Phase Randomization Technique) is proposed, enhancing the denoising performance of the improved EEMD algorithm. By incorporating PRT, the method effectively handles nonlinear and nonstationary signals, significantly improving the stability and reliability of the IMFs, and enhances the performance of the EEMD algorithm in noisy environments. The experimental results strongly demonstrate the innovation’s value, as the EEMD-PRT algorithm shows superior performance compared to traditional methods by improving the signal-to-noise ratio and correlation coefficient of noisy signals, reducing the mean square error and mean absolute error. Furthermore, its effectiveness has been thoroughly validated in pipeline leak detection for pipes with varying diameters.

Deep learning methods have improved the efficiency and accuracy of fault identification, but current research often relies on extracting fault features from single attributes such as seismic amplitude, which leads to issues like poor fault continuity and missed detections. These problems limit the exploration and development of oil and gas reservoirs in complex areas. An intelligent fault identification method based on deep learning technology is proposed, which adopts a multi-level fusion strategy to construct a dual-attribute parallel fusion network PE-Net(Parallel Elements Network). Firstly, the ant body attributes and amplitude attributes are input
into the ant body feature extraction network and the amplitude feature extraction network respectively, capturing the fault features of different angles from both paths using the AIFM ( Attribute Intensive Feature Module). Secondly, two attribute feature modules are used to integrate cross-layer features of the output of each branch, mining multi-scale information and mitigating scale changes. Finally, the FFM(Feature Fusion Module) is used to integrate the two parallel branches, reducing the limitation of a single attribute. Synthetic data experiments demonstrate that the PE-Net model achieves an accuracy of 97. 95% , with a 1. 33% improvement compared to the U-Net model. The fault identification results on the Kerry3D dataset and ablation experiments confirm that the proposed method is capable of capturing more contextual fault features, reducing missed and false detections,thereby improving the accuracy of complex fault identification and enhancing the detection of small faults.

Aiming at the problem of low spectrum efficiency in MIMO(Multiple Input Multiple Output) C-IoT (Cognitive Internet of Things) systems, a block coordinate descent algorithm based on alternating iterative assisted by IRS(Intelligent Reflecting Surface) is proposed. System weighted sum rate is maximized by jointly optimizing active beamforming at secondary transmitter and passive beamforming at IRS, and is constrained by the interference power at the primary receiver, the transmit power at the secondary transmitter, and the unit mode at the IRS. After decomposing the complex non-convex optimization problem into subproblems, the subproblems are processed using the Lagrange Dual method and the Successive Convex Approximation method, respectively. The simulation results show that the proposed algorithm can converge quickly in a multi-antenna user scenario, and the spectrum efficiency of the C-IoT system can be effectively improved by increasing the number of IRS reflective elements or correctly deploying the location of the IRS. 

In order to improve the contrast enhancement effect of remote sensing blurred images and increase clarity, a method based on mean filtering for remote sensing blurred image contrast enhancement is proposed. Firstly, a fast median adaptive mean filtering algorithm is used to denoise the entire remote sensing blurred image. Secondly, combining the fractal self-similarity feature of remote sensing image edge and the change of gray scale gradient, the edge points of the image are extracted. On this basis, the whole area of the image is divided into bright areas and dim areas. Finally, the detail preserving mapping algorithm and perceptual contrast mapping method are used to enhance the contrast of the two regions, respectively, and the overall contrast of the remote sensing blurred image achieving color restoration of the image. The experimental results show that the proposed method can effectively denoise images, with an absolute mean difference of less than 0. 85, and exhibits good performance in enhancing image contrast and clarity. 

 In order to solve the problems of low brightness, serious noise, and color distortion of color polarization images in low-illumination scenes, an unsupervised learning algorithm for color enhancement of low- illumination color polarization images is proposed, which is named LPEGAN(Low-Light Polarization Enhance Generative Adversarial Network). Firstly, a double-branch feature extraction module is designed and used different branches to extract features from Stokes parameters S0 and S1 ,S2 , respectively. Secondly, the residual void convolution module is constructed. And the different expansion rates can expand the receptive field to improve the model extraction ability and reduce the image color distortion. The edge texture loss function is constructed to ensure the structural similarity between the enhanced image and the input image. Experimental verification is carried out on the public datasets LLCP(Low-Light Chromatic Intensity-Polarization Imaging), IPLNet(Intensity-Polarization Imaging in Low Light Network), and self-built datasets. The experimental results show that the proposed algorithm has better visual effects, and all evaluation indicators are significantly improved. Polarized image brightness is enhanced, noise is significantly suppressed, and image colors are more realistic and natural.

In order to explore the international research hotspots and international cooperation trends of scientific and technological resources, this paper uses the method of scientific bibliometrics and visual analysis to analyze the scientific and technological resources literature included in Web of Science. The results show that scientific and technological resources are in a period of rapid growth. The United States, Spain, Brazil and China are the main publishing countries. The core research institutions are the University of Sao Paulo and the Chinese Academy of Sciences. The main journals are “Sustainability” and “ Strategic Management Journal”. The research hotspots are technical resources, communication technology and data collection.

 The low efficiency of video surveillance in identifying emergencies results in that the recognition system is unable to detect and respond to emergencies in a timely manner, increasing the risk of potential hazards. Therefore, a recognition algorithms of monitoring image abnormal behavior based on computer vision is proposed. Based on the initial background of the monitoring image, a differential operation is used to obtain the differential image between the background image and the monitoring image, and the background subtraction method is used to perform binary processing on the combined sorted new monitoring image to complete target area recognition. Then, a rectangle is used to traverse the target area, collect effective motion blocks from the target area, extract the feature vectors of the motion blocks, and complete the extraction of abnormal behavior features in the monitoring image. And the identification of abnormal behavior in monitoring images through Kuhntak conditions is completed. The experimental results show that the proposed method has an abnormal behavior recognition time of less than 1. 0 s, and the recognition accuracy remains above 94%. It can accurately identify abnormal behavior in monitoring images, effectively improving recognition efficiency and recognition rate.

Currently, the lack of standardized support for ontology quality assessment models in the field of data governance is a significant issue. Building a standardized ontology quality assessment model is of utmost importance in addressing this challenge. By studying the dimensions under ISO/ IEC 25012 data quality standards, the GQM (Goal-Question-Metric) methodology is used as a guide to define metrics under the dimensions and realize the mapping from metrics to dimensions. Finally, based on the DQV(Data Quality Vocabulary)data quality model proposed by W3C(World Wide Web Consortium), a scalable and robust ontology quality model is constructed. The proposed quality assessment model provides a complete, unified, and standardized terminology system to describe the various elements of ontology quality, and provides a standardized quality knowledge representation model for ontology quality assessment. Finally, taking the completeness dimension as an example,the corresponding quality assessment model is constructed, and the feasibility of the model is verified by using the downhole operation data set. It effectively solves the problem of the lack of standardization of ontology quality assessment model in data governance field, and provides a unified and standardized term system to describe each element of ontology quality in data governance field. 

The specific high complexity and nonlinear characteristics of large-scale road network traffic flow in a short period of time affect the accuracy of short-term traffic flow prediction. A short-term prediction method for large-scale road network traffic flow is studied based on improved neural network algorithms. Large-scale road network functions are constructed, road network functions are optimized by treating road sections as the core of the network and treating road nodes as corresponding connecting elements. Based on the optimized road network function, traffic flow features are extracted by combining the K-means algorithm with the EM ( Expectation-Maximization) algorithm. By combining genetic algorithm with Elman neural network algorithm, a short-term prediction of the traffic flow of the road network is carried out, and relevant prediction results are obtained.Experimental results have shown that the improved method's single point average speed prediction results are closer to the actual values, and the short-term prediction error of large-scale road network traffic flow is lower,resulting in higher reliability of the prediction results.

In order to explore the analysis of research points and theme evolution of science and technology resources in China. Bibliometrics and knowledge mapping analysis tools are used to statistically and visually analyse the research papers on S&T resources in China in the past 10 years. The results show that China's technological resources resources are in the growth period, with China Institute of Science and Technology Information, National Science and Technology Basic Condition Platform Centre and Guilin University of Science
and Technology as the main issuing institutions, Research on Science and Technology Management, China Science and Technology Resource Guide, and Science and Technology Progress and Countermeasures as the main journals carrying the papers, and the hotspots of research are the allocation of S&T resources and the influencing factors, the sharing and integration of S&T resources, and the construction of the resource-sharing platform and the The research hotspots are S&T resource allocation and influencing factors, S&T resource sharing and integration, resource sharing platform and service platform construction, collaborative innovation is the research hotspot after 2014, and S&T resource structure, innovation chain and S&T resource pool are the cutting-edge hotspots in the recent three years.

In order to solve the problem that most of the existing microelectronics major courses are based on theory and lack simulation experiments, a set of FPGA(Field Programmable Gate Array) microelectronics and integrated circuit design experiment system are designed based on RISC-V(Reduced Instruction Set Computer) CPU(Central Processing Unit). The ModelSim software compiler is used to simulate and verify, and FPGA is used as development platform to realize CPU system functions. Taking RISC-V reduced instruction set as the instruction set of the CPU and modularization as the design idea, the five-level pipeline CPU is designed from the local microprocessor to the whole. The five-level pipeline includes value, decoding, execution, memory access and write back. The system integrates software and hardware development to stimulate students' interest in learning. The experimental platform built gradually realizes the configuration and instruction set of CPU to the architecture, programming, simulation, writing and debugging of the whole CPU, enabling students to have a deep understanding of the design of integrated circuit system with FPGA, which is conducive to the study of professional theoretical courses. The design simulation content comes from the application of OBE(Outcomes- Based Education) teaching theory to integrated circuit EDA(Electronic Design Automation) course. This design method and content can also be applied to the combination of industry, university and research to improve innovation and entrepreneurship ability of students. 

A proton magnetometer is a type of geomagnetic field measuring instrument based on the Larmor precession effect. However, a proton magnetometer with a single sensor is easily affected by geomagnetic diurnal variation and environmental interference. To improve the measurement accuracy of the proton magnetometer, sensor design and system performance evaluation are studied. Firstly, based on MAXWELL electromagnetic simulation software, simulation models of four kinds of sensors are established, including solenoid, cylindrical coil, ring coil, and "8" coil, and the directivity and anti-interference ability of the four coils are analyzed. Finally, it is determined that the "8" coil is used as the sensor to build a proton magnetic gradiometer. Secondly, the single and differential measurement results based on the fourth-order difference and mean square error algorithms are evaluated to analyze the performance of the magnetic gradiometer. The experimental results show that the initial signal-to-noise ratio of the "8"coil sensor can reach 40 / 1, the sensitivity of the single-channel mode can reach 0. 054 nT, and the sensitivity of the dual-channel differential mode can reach 0. 071 nT even under strong interference conditions, which is √2 times that of the single-channel mode.

In order to avoid interference of language meaning on English oral translation results and improve the accuracy of English oral translation, an accurate English oral translation system based on attribute features is proposed. The system extracts oral semantic feature parameters by analyzing input data variables. Semantic feature parameters can capture differences between vocabulary and expression methods, improving translation accuracy. Variational autoencoders is used to capture effective information of features and obtain English spoken semantic matching results. Oral semantic matching is encoded and decoded, and translation rules are set based on parameters to identify interpreting ambiguous word parameters, and CBOW(Continuous Bag-of-Words) model is used to identify and evaluate parameters. Translation rules for complex sentence structures are established and they are connected through semantic translation based on these rules, forming accurate English oral translation results. The experimental results show that the designed English oral translation system has high translation accuracy and can achieve accurate English oral translation. Therefore, it indicates that the studied translation system can meet the requirements of high-precision English oral translation.

In order to encrypt and store communication network faster and more effectively, a communication network data encryption and the data of storage method based on improved RSA(Rivest-Shamir-Adleman) algorithm is proposed. Firstly, wavelet transform method is combined with empirical mode decomposition method of complementary set to denoise communication network data and improve the accuracy of communication network data. Then, the fuzzy C-means clustering algorithm is used to cluster communication network data, and similar data is uniformly encrypted to improve the efficiency of data encryption storage. Finally, the conventional distributed access management mode is replaced in communication networks with hash access data algorithms to enhance the security of data storage and prevent data loss. By improving the encryption process of RSA encryption algorithm, encrypted storage of communication network data is achieved. The experimental results show that the proposed method has good denoising effect, high security, and high encryption storage efficiency, making it suitable for encrypted storage of communication network data. 

With the emergence of advanced communication technologies such as high-throughput satellites, the integration of power satellite and smart grid has become an inevitable trend, and its related security certification has become the focus of research. However, the existing authentication schemes are mainly studied under the isomorphic architecture focusing on ensuring the authenticity verification of entities. Therefore, a heterogeneous authentication scheme for smart grid based on power satellite is introduced. The proposed scheme realizes the authenticity verification of each communication entity in a heterogeneous environment, and provides a validity verification method for terminals. Security analysis confirms the correctness and security of the scheme, and performance analysis shows that the proposed scheme effectively reduce the time cost of authentication phase than the existing schemes. 

In order to reduce the error between the target drilling trajectory and the actual drilling trajectory measurement results, a drilling trajectory measurement error correction method based on particle swarm optimization algorithm is proposed. A drilling trajectory calculation model is established, the values of drilling inclination angle, drilling orientation angle, and drilling azimuth angle are determined, and drilling trajectory data is collected. The sources of errors in drilling trajectory measurement is analyzed, an error transfer state space model is constructed, and the historical errors are merged to complete the calculation of drilling trajectory

measurement errors. The error correction objective function is constructed with the goal of minimizing the measurement error of the borehole trajectory. The velocity and position of particles are updated, and the fitness function is constructed. The objective function is solved by continuously updating and calculating the fitness function to complete the correction of the measurement error of the borehole trajectory. The experimental results show that the roll angle error in each direction of the proposed method is only between 0. 3° and 1. 8 °, and the drilling trajectory is highly fitted with the actual value curve, which can effectively correct the trajectory

measurement error and provide valuable reference for the actual exploration work of underground engineering.

A modified detection algorithm of electric bicycle helmet based on YOLOv5s(You Only Look Once version 5 small) is proposed to address the issues of small target missed detection and low accuracy in electric bicycle helmet wearing detection. CBAM ( Convolutional Block Attention Module) is introduced into the backbone network enhancing attention to clustered targets and effectively solving the problem of poor detection performance caused by occlusion. The PANet structure in the neck network is changed to a feature fusion structure that combines the idea of cross-scale feature fusion network (BiFPN: Bidirectional Feature Pyramid Network) enhances the multi-scale fusion ability of the model in different directions and effectively fuses multi- scale features of the target. Using SIoU(Structured Intersection over Union)localization loss function instead of CIoU(Complete Intersection over Union)loss function improves the accuracy of bounding box regression. The experimental results show that the accuracy P and recall R of the improved YOLOv5s model are 94. 7% and 91. 2%, respectively, and the average accuracy value mAP is 95. 6%, which is 6%,7%, and 6. 5% higher than that of the original YOLOv5s model, respectively. The method has significantly improved the accuracy of electric bicycle helmet wearing detection.

 In order to solve the problem of constructing the overall impedance model of electric vehicle cascade- type charging module, a cascade-type charging module impedance modeling method based on VIENNA rectifier and full-bridge LLC resonant converter is proposed. Firstly, a typical topology of the charging module is determined, and the small signal model of the front VIENNA rectifier based on the state space method and the rear full-bridge LLC resonant converter based on the equivalent circuit method are constructed respectively. Secondly, the closed-loop output impedance of VIENNA rectifier and the closed-loop output impedance and input impedance of full-bridge LLC resonant converter are obtained by combining the control strategy. The small Signal circuit model of the charging module can be obtained by integrating the front and rear small signal models and control strategies, and then the overall impedance model of the charging module can be derived. According to Nyquist stability criterion, the influence of system parameters on the stability of charging module is analyzed. The charging module simulation system is built based module. The proposed modeling method realizes the overall impedance modeling of single-stage to two-stage charging modules, and provides a theoretical basis for analyzing the parallel stability of charging modules in the future.

 In order to solve the problems of low efficiency of manual laboratory management, slow transmission speed of monitoring data and insufficient level of informatized management, an intelligent laboratory management and control system based on all optical access network is designed and implemented. It consists of laboratory front-end monitoring module, all optical access network data transmission module and laboratory management and control center module. The ESP 8266 microcontroller is used as main control chip in the laboratory front-end monitoring module. The acquisition unit of laboratory operation status data is designed. It consists of fingerprint recognition module, temperature and humidity sensor, smoke sensor and network camera. Real-time data acquisition of personnel management and control, video surveillance and environmental monitoring is thus realized. Optical network units, optical fiber distribution network and optical line terminal are utilized to construct all optical access network data transmission module, realizing remote high-speed transmission of each laboratory monitoring data. The web page of laboratory management and control platform is developed, real-time operation status data of each laboratory is displayed in terminal management and control server. The joint debugging result shows that real-time personnel management and control, video surveillance and environmental monitoring are realized, and the laboratory management and control system provides reliable stability in a long time. 

In order to effectively improve the efficiency and performance of intelligent retrieval of book information resource services, a machine learning algorithm based intelligent retrieval method is proposed. Machine learning algorithms are used to classify the keyword frequency of books, providing the corresponding index for each class, calculating the weight of feature words corresponding to each document in the classification,and obtaining the score of feature words through repeated iterations. A contextual feature matrix is established,the corresponding score for each class is calculated, and the feature with the highest score is selected for classification processing. Based on the classification results of book information resources, grid computing technology is introduced to construct an intelligent retrieval model for book information resource services, and the model is used to achieve intelligent retrieval of book information resource services. The experimental results show that when using the proposed method for intelligent retrieval of book information resource services, the precision obtained is above 96. 0% , the recall rate remains above 90% , and the retrieval time under different datasets is around 450 ms, indicating that the proposed method has good performance in intelligent retrieval of book information resource services.

The anti-disturbance ability and dynamic performance are poor in the inverter stage of PET(Power Electronic Transformer)based on traditional VSG(Virtual Synchronous Generator)control. In order to address the problem, a VSG control combined with linear active disturbance rejection control is proposed. A second-order active disturbance rejection controller is added to the active part of the traditional VSG control. State observation and feedback are introduced to the controller to estimate the system error in real time, so that the frequency and power of the inverter can be stably tracked under different operating conditions, while the oscillation phenomenon is weakened. With the improved control strategy, the maximum overshoot is reduced by 54. 5 percent and the frequency fluctuation time is reduced by 0. 22 s, which make the PET inverter stage have stronger dynamic response and steady-state performance. The simulation demonstrates that the control strategy is feasible and effective. It also shows that the improved VSG control strategy accelerates the recovery speed of the PET inverter stage after being disturbed, and has better dynamic performance.

 Due to the Irreversible process of scrambling and extraction process, the hidden information of digital media video image information can not be completely recovered in the extraction process, resulting in information loss or error, and reducing the effectiveness of the hiding algorithm. To solve this problem, a digital media video image information hiding algorithm based on reference frames is proposed. Firstly, the NLEMD(Neighborhood Limited Empirical Mode Decomposition) algorithm is used to enhance digital media video images and improve video image quality. Secondly, the Arnold transform scrambling method is used to perform scrambling transformation on the enhanced image, completing the preprocessing of information hiding. Finally, the scrambled digital media video image information is hidden through an information hiding algorithm based on reference frames. The experimental results show that the proposed method can improve the peak signal-to-noise ratio of digital media video images, reduce the time required for embedding and extracting hidden information,and achieve high accuracy in information extraction.

Due to the limitation of environmental and economic factors, the collected seismic records usually have a lot of noise interference, which may cause some obstacles to the subsequent seismic data processing. Effectively attenuating noise is a key issue in seismology. In recent years, CNNs ( Convolutional Neural Networks) have achieved some success in the field of seismic data denoising. However, weak signal recovery in the presence of strong background noise is insufficient for existing convolutional neural networks. To address the above issues, a denoising network called MAUnet(Multi-Scale U-Net and An Attention Fusion Mechanism) is proposed. based on a multi-scale U-Net and an attention fusion mechanism. MAUnet innovatively introduces a dual-mechanism architecture, where a multi-scale module enables the network to learn features at different scales. And an attention-based feature fusion mechanism allows the network to combine shallow high-frequency details with deep semantic information, enhancing its learning capability and achieving feature complementarity. Experimental results demonstrate that our method has better noise attenuation and recovery capability for weak signals than competitive methods. 

There are many types of information features in the supply chain privacy network, and it is difficult to determine data ciphertext, which leads to high encryption difficulty. Therefore, a chaotic sequence based information encryption algorithm is proposed. Considering the risk of privacy information being leaked or tampered with, the risk variation is used as a reference feature for encryption algorithms. The risk index is obtained by solving the same, different, and opposite risk variation characteristics of privacy information through ternary and quinary functions. A chaotic sequence is established based on the risk index of the information to be
encrypted, a key sequence is generated through the chaotic sequence, the dimensions corresponding to different types of information are calculated, and finally the information encryption is achieved through chaotic mapping between the information and the key sequence. Experimental data shows that the proposed algorithm has high accuracy in encrypting supply chain privacy network information, low packet loss rate, and can effectively improve the phenomenon of privacy information leakage and loss during transmission.

 Based on the background that colleges and universities focus on talent cultivation work, 31 universities in Jilin Province were analyzed in order to provide reference significance for the Ministry of Education to explore the potential of universities and allocate college resources rationally. Taking 31 universities in Jilin province as samples in 2017, 24 indicators were selected from the number of students, faculty level and school conditions of college profiles, and descriptive statistical analysis, factor analysis, cluster analysis and typical correlation analysis were used. The study showed that Jilin University outperformed other colleges and universities in terms of the number of students, teachers’ resources, and schooling conditions, followed by Northeast Normal University directly under the Ministry of Education and 12 universities under the province, and they all outperformed 17 universities directly under the local government. Moreover, the titles of teachers with high education are also generally high, and the titles of teachers with low education are also generally low.

Due to the lack of practical teaching resource of optical communication network, an all-optical access network practical teaching system based on virtual-real combination is designed. The communication service opening is regarded as main task, a real practical teaching system is built based on all-optical access network commercial equipment. The application layer server and communication user terminal are simulated by software and hardware combination. Configuration information from application layer server to communication user terminal through all-optical access network is planned and the success of various communication services opening is verified. The network troubleshooting is regarded as main task, a virtual simulation experiment teaching system of all-optical access network is developed by utilizing virtual reality technology. The virtual campus application scenarios including central office, fiber distribution terminal and university-enterprise joint laboratory are constructed. Concentrated presentation of optical distribution network, multi-service gateway, optical line terminal and application layer server related network troubles is realized. With the design and application of this practical teaching system, the teachers’ construction ability of optical network practical teaching resources is improved, and the students’ practice and innovation ability to solve complex problems in the application of optical network is cultivated. 

In order to make up for the lack of selectivity of a single gas sensor in the face of a variety of gases and to identify a variety of gases more accurately, an improved convolutional neural network based on structural reparameterization technology and depth-separable convolution technology is proposed. It integrates the multi- branch convolution structure during model training into the single branch simple convolution layer during inference. In addition to simplifying the complexity of the inference model, the feature extraction ability of the model for gas response data is greatly enhanced. When this method is applied to a common data set of gas sensor array containing 10 common VOCs, the recognition accuracy reaches 96. 46%, and the accuracy reaches 97. 44% after adjusting the complexity of the model and adding the convolutional layer.

The warship’s common computing environment integrates computing and storage resources through virtualization technology to build a public infrastructure platform for warships. However, it is limited by the space and energy consumption requirements of the maritime combat platform. Optimizing the virtual machine deployment strategy is an important development to reduce the energy consumption level of the warship’s commoncomputing environment and improve basic resource support capabilities. Several commonly used virtual machine optimization deployment methods are compared and a virtual machine deployment strategy for warship’s common computing environments is proposed based on an improved flower pollination algorithm. An improved maximum

and minimum distance method is designed and applied to the initial population generation process to enhance the initial solution. To improve the quality, a local search strategy with an information exchange mechanism is proposed by introducing the hybrid frog leaping algorithm. And an adaptive switching probability strategy is proposed to balance global pollination and local pollination, and generate an optimized deployment plan for mapping virtual machines to servers. It is verified through simulation experiments that the proposed virtual machine deployment optimization strategy can significantly reduce the energy consumption level of the warship’s common computing environment.

 In order to enhance the overall efficiency of the laser pump source system, a design for a load- adaptive semiconductor laser array constant current driver is presented. This driver can adaptively adjust the supply voltage of the load based on the number of series loads in the laser array and the changes in the drive current, thereby optimizing work efficiency. The maximum output voltage of the driver is 22 V, and the maximum output current is 1 200 mA. When driving eight series loads of the laser array, the efficiency can exceed 91. 6%. This load-adaptive technology provides a new approach for designing efficient semiconductor laser drivers. 

 In order to improve the accuracy and efficiency of face recognition results of smart access control system, and thus enhance the intelligent service of smart door security, a smart access control face recognition algorithm based on twin neural network is proposed. The wavelet coefficients of the face image signal are obtained by wavelet transform, the appropriate threshold is selected to process the wavelet coefficients, and the inverse transform of the wavelet coefficients is carried out again to obtain the de-noised face image. After the face image is de-noised, the output value of the face image is mapped and processed in the twin neural network to form a feature vector with a dimension of 128. The contrast loss function is introduced to determine the similarity of the face image by comparing the Euclidean distance between the output feature vectors of the sample network, and finally realize intelligent access control face recognition. The experimental results show that the intelligent access control face recognition results and recognition efficiency of the proposed algorithm are significantly better than other algorithms. 

To address the problem of determining the optimal strategy for air combat maneuver decision making with incomplete information, an evaluation method based on interval projection pursuit is proposed. Firstly, the interval number is introduced to represent the incomplete air combat data, the Euclidean distance in the TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) is replaced by the grey relational degree, the positive and negative ideal solutions are given, and the RIGRA-TOPSIS(Referential Interval Grey Relation Analysis- Technique for Order Preference by Similarity to Ideal Solution) method is proposed to quantitatively obtain the objective weight of air combat indicators. Then, an improved interval projection pursuit method is introduced, which takes the obtained objective weights as the initial projection vector of the interval projection pursuit method, uses Gini coefficient instead of standard deviation to calculate the projection density, uses the particle swarm optimization algorithm to calculate the projection value, and determines the optimal strategy corresponding to the maximum projection value in the air combat game. The simulation results show that the proposed method has a good effect in the case of small sample air combat strategy set.

Integrated passenger transport hubs usually involve a large number of vehicles and routes, and the traffic flow, passenger demand, and traffic conditions of passenger transport hubs are dynamically changing, which can easily lead to schedule conflicts and make multi route vehicle scheduling difficult. Therefore, a comprehensive passenger transportation hub vehicle multi line scheduling method based on improved ant colony algorithm is proposed. Considering the reduction of operating costs, waiting time, and overall travel time, with the goal of minimizing the operating costs and passenger travel time of the integrated passenger transport hub system, a scheduling optimization model is constructed. Ant colony algorithm is used to the model, introducing search hotspots, optimizing pheromone update strategies and heuristic factors to improve the ant colony algorithm, and the multi line scheduling of comprehensive passenger transportation hub is completed. The experimental results show that the proposed method can more comprehensively carry out multi line scheduling of vehicles, with a waiting rate of less than 5% and an average scheduling time of only 5. 8 s, effectively improving convergence rate, accuracy, and efficiency.

Aiming at the problem of limited communication conditions in the control of severe slug flow in the horizontal pipe-downward inclined pipe-riser system of submarine mixed transportation, a MPC (Model Predictive Control) strategy based on DET (Dynamic Event Triggering) is proposed. Firstly, the causes and processes of slug flow are described, and the simplified model for control is analyzed. Secondly, a dynamic event triggering mechanism based on the deviation between predicted value and actual value is designed to monitor the running state of the system in real time and adjust the event triggering conditions to improve the steady-state performance of the system and reduce the triggering frequency. Finally, the comparative experiment analysis is carried out. The results show that the proposed method can effectively reduce the triggering frequency of the system on the basis of ensuring the control performance. Compared to the small opening valve, the oil production increases by about 2. 8%.

For the classification of networked data, most relational network classifiers are based on the homophily hypothesis, and the simplified processing based on the first-order Markov assumption has certain limitations. The local graph ranking algorithm ( activation spreading) is introduced into the network Bayes classifier instead of the original direct neighborhood acquisition method. The neighborhood range of nodes to be classified is appropriately expanded by setting the initial energy and the minimum energy threshold, increasing the homophily of nodes. Combined to the collective inference method of relaxation labeling, the classification
accuracy of network data is improved to a certain extent. Compared to 4 network classifiers, the experimental results show that the classification performance of the proposed method on 6 networked datasets is improved in different degrees.

Because there are many factors affecting the network link state, it is difficult to accurately judge the network link state and the location where congestion is about to occur, resulting in the decline of the quality for network link congestion control. A priority based mathematical modeling method for network link congestion automatic control is proposed. A heterogeneous network model combined with the current occupation and change of network interface queue buffer space is built to detect the network link congestion, and judge the network link status and the location where congestion is about to occur. Through the data request sending mechanism, the
detection results are fed back to other network nodes in time. The high priority burst packets in the network nodes are set to have absolute priority. The burst packets of the network link are divided and deflected to realize the automatic control of network link congestion. The experimental results show that the model can effectively detect the current network link congestion and control the congestion degree of the network. It has better network communication data transmission effect, blocking rate, better throughput and higher control quality.

For a class of uncertain networked stochastic T-S(Takagi-Sugeno) fuzzy systems with time-varying delay, quantization error, the problem of hybrid-triggered robust H∞ control system with two-terminal quantization is studied. Firstly, in order to alleviate the burden of networked communication, a hybrid-triggered scheme is adopted to reduce data transmission. The construction of quantizers at the sensor side and the actuator side is stadied to quantify the sampling and control signal respectively, and the quantization error is considered to improve the control accuracy of the system. By taking the effect of network-induced delay, uncertainty and quantization error are taken into consideration, the T-S fuzzy systems based on the hybrid-triggered mechanism is remodeled. Secondly, by selecting the delay-dependent and fuzzy basis-dependent Lyapunov function, and introducing the free weight matrix, the sufficient conditions for the double-terminal quantized fuzzy system are satisfied, and the influence of energy bounded noise signals on the output is suppressed under the H∞ performance index γ. Finally, the simulation proves that the proposed scheme can reduce data transmission effectively, improve the system control accuracy, and reduce the conservatism of design. 

Texture feature extraction is the key to texture classification, and there are various factors such as rotation, illumination, and scale variations in texture images. To enhance the robustness of the texture feature extraction algorithm for rotation, illumination, and scale variations, the FWLMP ( Fractal Weighted Local Morphological Pattern) is proposed. First, a scale-invariant descriptor is constructed by using the relative invariance of fractal dimensionality to scale variation. Then, it is sampled and analyzed using the expansion, erosion, and opening-closing operations in mathematical morphology, and its weights are calculated by using the fractal dimension image. This algorithm is scale-invariant and robust to rotation and illumination changes. To achieve the classification of Qing Dynasty costume images, the Qing Dynasty Buzi image dataset is constructed. The FWLMP and similar algorithms are tested on four public texture datasets and a private dataset constructed by ourselves. The experimental results show that the FWLMP algorithm performs well in texture image classification and in Buzi image classification for Qing Dynasty civil and military officials. 

To protect the valuable field geological resources that have become increasingly vulnerable to natural or human damage in recent years, and to facilitate teaching reform in online virtual geological practice in the post-pandemic era, an automatic field geological section reconstruction technology based on 3D laser scanning is proposed. Using the FARO 3D laser scanner, 3D high-density point cloud data is collected from field geological outcrops of various scales. By processing, optimizing, and visually modeling the high-quality data collected from the field, this technology can protect precious field geological resources and enrich students’ virtual geological practice resources. Taking some field geological outcrops from the Longhuitou Scenic Area in Xingcheng City, Liaoning Province, as examples, virtual geological modeling and visualization applications are carried out, enhancing the quality of geological practice teaching. This approach serves the preservation of field geological legacies and significantly contributes to the advancement of geological education by providing a more interactive and enriched virtual learning environment.

A crowd counting model based on background suppression and noise monitoring is proposed to solve the problems of large-scale change of crowd, complex background, and label noise. In the coding stage, the first 13 layers of VGG16_bn are used as the backbone, and the initially extracted features are sent to the two-branch feature extraction module and the background information aggregation module respectively, to mitigate the large- scale changes of the population and improve the discriminability of the background. Finally, the information processed by the two modules is fused, and the predictive density map is generated by decoder regression, which is supervised with the ground truth density map to achieve noise suppression. Compared with other algorithms, the counting accuracy of this model has been improved. MAE(Mean Absolute Error) and MSE(Mean Squared Error) on ShanghaiTech PartA are 58. 1 and 95. 9 respectively. Ablation experiments conducted on ShanghaiTech PartA also verified the effectiveness of the modules. Experimental results show that the algorithm can effectively improve the accuracy of crowd counting. 

In order to solve the problem of poor obstacle avoidance and low efficiency of robots in obstacle environments, a wheeled industrial robot obstacle avoidance control algorithm based on neighborhood rough sets is proposed. Firstly, the environment perception method based on 2D Laser rangefinder is used to analyze the local environment of the robot and the obstacle environment, so as to provide accurate environmental information for subsequent obstacle avoidance control; Secondly, based on the initial obstacle avoidance decision rules of robots, the knowledge reduction effect of neighborhood rough sets is utilized to reduce them to the minimum obstacle avoidance decision rules, and a feasible path set is obtained; Finally, based on the feasible path set, the ND +(Nondimensional) algorithm is used to determine the direction of the robot’s obstacle avoidance decision, thereby achieving obstacle avoidance control for wheeled industrial robots. The experimental results show that this method can achieve high accuracy and control efficiency of machine obstacle avoidance control while ensuring the stability of obstacle avoidance control. 

The recognition rate of gesture recognition is low because of the poor segmentation effect. Therefore, a dynamic multi-point gesture recognition method based on improved support vector machine is proposed. The depth threshold method is used to segment the dynamic multi-point gesture image, extract the largest circular fine hand area in the palm, obtain 7-dimensional HOG(Histogram of Oriented Gradients) feature vector of the hand, complete the gesture action image preprocessing, introduce support vector machine, and improve the algorithm by error term, and adopt the optimized linear classification feature vector of the improved support vector machine. The dynamic multi-point gesture recognition is realized by using the gesture feature vector after input classification by support vector machine. The experimental results show that the recognition rate reaches more than 92. 5% under the condition of illumination, while the recognition rate is still higher than 90. 0% under the condition of no illumination. The proposed method has little fluctuation under the influence of illumination, and the image segmentation information is complete and the recognition accuracy is high.