In order to improve the magnetic flux leakage caused by the traditional Halbach magnetizing structure in the non-air gap side of the linear generator, which leads to the reduction of the main flux content, power density and load capacity, the traditional Halbach magnetizing structure and the magnetic field distribution of the linear generator is improved. Firstly, the structural dimensions of traditional rectangular permanent magnets are optimized, and the variants of trapezoidal, T-shaped and U-shaped Halbach magnetizing structures are proposed. Then, the fundamental wave content of magnetic flux density under different sizes is analyzed, so as to find the optimal design size of each variant under the condition of maximum magnetic flux density content. Through the simulation results, it is found that the U-shaped Halbach magnetizing structure is more obvious than other structures in improving the magnetic flux leakage of the non-air gap side, and the output power and efficiency are improved.

 To monitor changes of indoor carbon dioxide concentrations in real time, a carbon dioxide alarm based on a gas sensor with a STC89C52 microcontroller as the core is designed. When the CO2 concentration in the air exceeds the preset value, the sound and light alarm function can be activated, and the indoor CO2 concentration value can be displayed in real-time. The hardware system includes a carbon dioxide sensor, signal conditioning circuit, analog-to-digital conversion circuit, STC89C52 microcontroller, and acousto-optic alarm unit. The software system includes data acquisition, data processing, alarm logic, and other functional units. The alarm can be activated in time when indoor CO2 concentration exceeds 1. 5% . 

The traditional mobile robot based on DWA ( Dynamic window Approach ) algorithm exists the following deficiencies: longer obstacle avoidance time and the inability to optimize the local path planning in obstacle-intensive dynamic zone. Aimed at the problems mentioned above, a hybrid path algorithm combined A * algorithm with VO(Velocity Obstacle) is proposed to optimize the velocity of obstacle avoidance for mobile robots. Via the comparative experiment combined the DWA algorithm with the VO hybrid path algorithm in the case of three obstacles, the ROS (Robot Operating System) adopted the modular software design is put into practice to test the obstacle avoidance effect of the hybrid path planning algorithm. The results of simulation experiment in multiple environments clearly indicate that the obstacle avoidance effect will be significantly improved via the VO hybrid path algorithm in the scenarios scattered with multiple dynamic obstacles, and it has high speed of the obstacle movement and low frequency of radar scanning.

Due to the influence of instrument and equipment work, outdoor environment and other factors, there will be some random noise in the collected pipeline signal, which will make the original signal lose its characteristics, leading to the failure to accurately identify the pipeline signal. Therefore, a feature extraction method based on VMD (Variational Mode Decomposition) algorithm-entropy method is proposed. First VMD algorithm based on working condition of gathering pipeline deals with the noise signal, then from energy, impact properties, three angles, complexity of time series extracts signal characteristics under different working conditions of three kinds of signal reconstruction after the signal are calculated separately, and the energy entropy, kurtosis entropy and fuzzy entropy, and finally establishs characteristic vector input to the extreme learning machine to identify the condition. The experimental results show that the method proposed can classify and recognize pipeline working condition signals more accurately than other feature parameters, and the recognition rate is up to 98. 33% , which proves the feasibility of this method to classify and recognize pipeline leakage signals.

A laboratory calibration and a downhole online calibration method for the JLU-FG03A downhole fluxgate magnetometer are designed for the application needs of geomagnetic field observation under high pressure and humid environment, and the construction of the experimental test system and the related test methods are presented. The laboratory calibration method tests the range, bandwidth, noise and sensitivity of temperature drift of the magnetometer, while the downhole online calibration method tests the reliability of the magnetometer data and noise levels when working in the well. Laboratory calibration results show that the instrument has a range of +- 100 000 nT, a bandwidth of DC-10 Hz, RMS ( Root Mean Square ) noise less than 0. 01 nT (DC-0. 3 Hz) and a temperature drift of 23. 39 ppm when the adaptive feedback function is on, which meets the application requirements for downhole geomagnetic observation. The downhole online calibration experiments show that the instrument is in normal working condition downhole and the noise measurement results indicate a good downhole magnetic environment, which is conducive to the best performance of the magnetometer.

Because of the high cost of artificial fruit and vegetable picking and slow development of domestic fruit and vegetable picking robots, an intelligent robot is designed to reduce labor costs and time costs. According to the College Students’ Innovative Entrepreneurial Training Program, the design process of the fruit and vegetable piking robot is studied, using NVIDIA Jetson Nano development board to improve VI( Visual Identity) computing efficiency. The VI algorithm adopts the YOLO V5s algorithm and the motion control part to analyze the forward kinematics solution of the 5-DOF( Degree-Of-Freedom) mechanical arm, the forward kinematics formula of the 5-DOF mechanical arm is derived, and thus finding out about its activity space. In the End-Effector section, the gripper is used to facilitate the picking of various fruits and vegetables. A clearer intelligent structure for fruit and vegetable picking is established, the speed of VI is increased by more than 10 times by using NVIDIA Jetson Nano. Through the analysis of the forward kinematics formula, the End-Effector Action is more accurate, and also the fruit and vegetable picking aiming is completely realized within an acceptable deviation.

There is a deficiency of information available on waste classification, and many municipalities and educational institutions struggle with this issue. We address this challenge by utilizing the efficiency and accuracy of the neural networks to classify items and implement waste image classification with a deep residual shrinkage network built on the ResNet network and SENet network. By filtering the Garbage dataset to obtain the data set necessary for the experiment, and by enhancing ResNet, SENet and soft threshold processes are incorporated into the ResNet structure. And by training the network and optimizing its hyperparameters, a greater recognition rate and recognition effect are achieved for the classification of campus waste. The experimental findings indicate that the proposed approach is feasible to a certain extent.

In order to improve the accuracy and robustness of SAR ( Synthetic Aperture Radar) image change detection, an unsupervised SAR image change detection method based on image enhancement and fusion is proposed. In order to obtain better effects of background noise suppression, change region enhancement and edge preservation, the log-ratio and mean-ratio differential image are constructed based on the adaptive image enhancement of the original SAR image. The differential image is fused by the fusion strategy of weighted average of low-frequency wavelet coefficients and selecting high-frequency wavelet coefficients according to the minimum local energy. The experimental results show that the fused differential image combined with fuzzy local information C-means clustering has achieved high detection accuracy and kappa coefficient on different data sets, and has strong robustness. It can be widely used in the field of SAR image change detection. 

In the short-term power load forecasting, when the power load data is sufficient, the accuracy of load forecasting is usually high, but when the data is missing or the data quantity is small, the accuracy of load forecasting is often poor. Therefore, when the power load data in a certain region is small, the load prediction accuracy is difficult to meet the prediction accuracy requirements. A short-term load prediction method based on 1DCNN-LSTM ( 1D Convolutional Neural-Long Short-Term Memory Networks ) and parameter transfer is proposed. 1DCNN-LSTM combined with transfer learning is used to solve the problem of low prediction accuracy. The actual load data of a certain area in the United States are used for simulation analysis. Experimental results show that this method can effectively improve the accuracy of load prediction when regional power load data is missing.

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.

Because the existing methods can not reduce the dimension of vehicle license plate, which leads to inaccurate recognition results, an intelligent vehicle license plate fuzzy recognition method based on Relief algorithm is proposed. The Relief algorithm is used to calculate the weight coefficients of different license plate image features and reduce the dimension of the feature set. The smart license plate is enhanced by sequence video images, the full convolution network is used to detect the significant areas of the license plate, roughly extract the significant areas in the image, the sliding window method is used to accurately detect the license plate in the candidate area, locate the exact position of the license plate, add the context information of the characters, accurately detect and recognize the characters, and finally achieve the fuzzy recognition of the smart license plate. The simulation results show that the proposed method can obtain high precision license plate recognition results.

The introduction of deep neural network technology greatly improves the extraction accuracy of text semantic features of relation classification. The common sense knowledge graph is used to construct the contextual semantics other than the text′s own semantics, and the pre-trained model is used to obtain the contextual semantic features. Aiming at the semantic features of text, context and marked entity, a multiple semantic fusion mechanism is established to realize the relation classification model, which is named MSF-RC. The model is tested on two different datasets, SemEval-2010 task and TARCED. The experimental results show that the introduction of contextual information helps to strengthen the semantic understanding of labeled entities, and the hierarchical fusion of multiple semantics can further improve the performance of relation classification model.

 In the existing researches on closed frequent item set mining algorithms, pruning strategies are relatively single, most of which are for 1item set pruning, and there are relatively few pruning strategies for 2item set and nitem set (n逸3). However, effective pruning strategies can find and cut off a large number of hopeless item sets in advance. Therefore, improving the pruning strategy of closed frequent item set is of great help to improve the efficiency of this kind of algorithm. On the basis of ESCS(Estimated Support Cooccurrence Structure) structure, an ESCS pruning strategy for 2itemsets is proposed, and the classical closed frequent itemset mining algorithm DCI_Closed(Direct Count Intersect Closed) is improved to DCI_ESCS(Direct Count Intersect Estimated Support Cooccurrence Structure) algorithm, and the effect of ESCS pruning strategy is verified. On multiple public datasets and under different minimum support thresholds, experiments are conducted to compare the time performance of the algorithm before and after the improvement. The experimental results show that the improved DCI_ESCS algorithm performs well on long and dense data sets with long transaction and itemsets, and the time efficiency is improved to a certain extent.

In order to help undergraduates understand the technology of satellite remote sensing and master machine learning algorithms, combined with college student innovation training program in Jilin University, a project named “Research on Change Detection of Buildings around Campus Based on Remote Sensing Images" is designed. GF-2 satellite images and JL1-3B night time glimmer images are used as experimental data, and the area for experiment is around a primary school in China. Various machine learning algorithms are used to extract the message of buildings in the area for experiment in different periods, and the precision of the results is analyzed. The results of building extraction are compared with ground truth data. Finally, the changes of buildings in different periods are gained. The JL1-3B night time glimmer images are used to analyze the buildings around the school and the activities of residents. The experimental results show that buildings in the remote sensing images can be effectively discerned by random forest algorithm and VGG(Visual Geometry Group) neural network algorithm. The number of buildings in different periods and the results of change detection of lamp light show the influence of campus on the development of surrounding area and provide reference information for city planning.

In order to explore the status quo and development trend of electronic payment research, an analysis of electronic payment research hotspots is performed. We use bibliometric method to further grasp the development status of electronic payment field from aspects of research overview, research strength and discipline distribution in the field of electronic payment, and uses the method of scientific knowledge mapping to visualize the research hotspots. The results show that China and the United States produce the most papers in the electronic field. The main output institutions are Beijing University of Posts and Telecommunications and Central South University. The research focuses on the theory and practical application of the security of electronic payment system.

Traditional segmentation algorithms have problems such as low accuracy, low precision and time- consuming. Therefore, an adaptive segmentation algorithm for 3D breast ultrasound images using improved deep learning is proposed. First, the images are pre-processed, and the deep multiple example learning method is used to detect the lesion image blocks and remove the normal image blocks. Second, the breast ultrasound image data set is augmented and processed for neural network training. Then, a residual convolutional neural network model is constructed, a residual learning unit is designed, a feature mapping is formed by combining the augmented dataset. A softmax function is used to train the network and perform feature block judgment. Finally, combined with threshold settings, the achieves adaptive segmentation of 3D breast ultrasound images is realized. The results show that the proposed algorithm can complete image segmentation more carefully, and the average running time of the algorithm is 52. 3 s, the image segmentation accuracy is 95. 5% , the F1 score value is high, and the overall performance is good, which provides a reference for the application of convolutional neural network segmentation.

During the evolution of public opinion in group social networks, it is difficult for the current methods to obtain the data in key nodes, resulting in the inability to accurately obtain parameters such as the number of public opinion propagation, search index, time to reach the peak of public opinion, and the problem of low evolution accuracy. A simulation method of public opinion evolution in group social network based on clustering algorithm and SIS(Susceptible Infected Susceptible Model) model is proposed. The PageRank algorithm is used to obtain the key nodes, and the clustering algorithm is used to cluster the data in the key nodes. The SIS model is constructed, and the public opinion evolution simulation of the group social network is completed through the SIS model. The experimental results show that the proposed method can accurately obtain the parameters such as the number of public opinion propagation, search index and the time to reach the peak of public opinion, and the evolutionary simulation accuracy is high.

In order to realize eye movement interaction with high frame rate, a deep-learning based single-camera dual-light source identification method is proposed. This method uses correlation between the reflected spot and the gaze landing point to obtain the mapping law from the eye image to the gaze. Human-computer eye movement interaction device is constructed, which results in a high-quality dataset, obtains the gaze estimating model with high precision and speed by training, solves the problems of complex mathematical model and large amount of calculation of gaze estimation. It realizes the function of real-time recognition and interaction of users蒺 gaze, supports the development and application of psychological experimental research and virtual reality application technology.

To inherit and protect the traditional blue calico patterns, it is necessary and challenging to protect blue calico in a digital manner. Due to the lack of blue calico pattern datasets with original manual characteristics, which limits the application of powerful deep learning technology on pattern recognition field like the recognition of Blue Calico patters, therefore a large-scale dataset called Blue-Calico pattern dataset is provided, which is the first publicly available benchmark for blue calico pattern recognition. This dataset contains about 50 216 blue calico patterns, covering 85 pattern classes, such as animals, plants, myths and legends. The construction of this dataset will concern the digital construction of blue calico, such as calico image retrieval and caption, and enable researchers to design and validate data-driven algorithms. On the basis of the new dataset, the experimental results of four state-of-the-art networks are provided as a baseline for future work.

In order to enable users to understand the integrity of cloud documents, a new retrievability proof scheme M-POR ( Proof of Retrievability-Based Message Authentication Codes) is proposed based on MACs (Message Authentication Codes). The MACs are generated by data blocks as authenticators, which can verify data integrity and locate the error data block. An original document is uploaded to the cloud server after encoding. Users can verify data integrity by using challenge-response-verification algorithms. The error-correcting code is introduced in encoding. If the error data is less than the threshold, the data can be recovered. Performance analysis shows that M-POR scheme can provide data integrity proof, and has low storage cost and calculation cost.

The traditional methods of manually identifying electrocardiogram signals have problems such as high workload and recognition errors. The existing electrocardiogram monitoring equipment still faces drawbacks such as limited recognition types of electrocardiogram signals, low diagnostic accuracy, and excessive reliance on network services. In order to improve the performance of electrocardiogram monitoring systems, ECG (Electrocardiogram Signals) analysis and detection system is designed based on deep learning technology. SENet-LSTM ( Squeeze-and-Excitation Networks-Long Short Term Memory) network model is built to realize automatic diagnosis of seven categories of ECG signals. The model is deployed on an intelligent hardware platform which uses ADS1292R as the ECG acquisition module, STM32F103 as the data processing module, and Raspberry PI as the central processing module. The system uses the integrated high-performance microcomputer Raspberry PI for calculation and analysis, and provides users with offline AI(Artificial Intelligence) services. The preciseness of the model can reach 98. 44% , and the accuracy can reach 90. 00% , realizing the real-time monitoring and accurate classification of ECG, and providing accurate disease diagnosis for patients.

Aiming at the problem of inconsistencies between the voltages of the two capacitors on the DC(Direct Current) side of the midpoint clamping (Neutral-Point-Clamped) three-level inverter and the traditional SVPWM (Space Vector Pulse Width Modulation) can solve the problem of inconsistency of the two capacitor voltages within a certain modulation system, but the midpoint potential cannot be balanced under a large modulation ratio, the control method is optimized on the basis of the traditional SVPWM and VSVPWM ( Virtual Space Vector Pulse Width Modulation). The method is based on the flow direction of current, different positive and negative small vectors using different size of balance factor, is in accordance with the midpoint potential difference value, and introduces voltage adjustment coefficient. On this basis, it is combined with the beat-free control. This closed-loop control strategy adjusts the output waveform by using the midpoint potential difference and the inverter three-phase current output value as the feedback to suppress the midpoint potential. Simulation results show that the proposed method can still maintain the balance of the midpoint potential on the DC side when the three-level inverter modulation system is high, which proves the correctness and effectiveness of the control strategy.

Aiming at the problem that the traditional named entity recognition method can not fully learn the complex sentence structure information of ancient Chinese and it is easy to cause information loss in the process of long sequence feature extraction, an ancient Chinese fusion of SikuBERT ( Siku Bidirectional Encoder Representation from Transformers) model and MHA (Multi-Head Attention) is proposed. First, the SikuBERT model is used to pre-train the ancient Chinese corpus, the information vector obtained from the training into the BiLSTM (Bidirectional Long Short-Term Memory) network is input to extract features, and then the output features of the BiLSTM layer are assigned different weights through MHA to reduce the information loss problem of long sequences. And finally the predicted sequence labels are obtained through CRF (Conditional Random Field) decoding. Experiments show that compared with commonly used BiLSTM-CRF, BERT-BiLSTM-CRF and other models, the F1 value of this method has been significantly improved, which verifies that this method can effectively improve the effect of ancient Chinese named entity recognition.

Aiming at the problems of poor prediction effect in the existing power system short-term load forecasting, a power system short-term load forecasting algorithm based on deep data mining is proposed. Taking the normalized historical power system load data, fuzzy temperature data, weather conditions, precipitation probability and other data as the input of the prediction model, a power system short-term load prediction model based on fuzzy gbdt is constructed, and the boosting algorithm is introduced to solve the problems of slow training speed and large memory occupation in the prediction model. The experimental results show that the short-term load forecasting results of the proposed method are close to the actual load at different times on weekdays and weekends. The MAPE and rmspe values of power system short-term load forecasting in the next week are lower than 0. 2% .

In order to reduce the execution time of visual image target tracking and improve the accuracy of tracking track, a visual image target tracking method based on improved convolutional neural network algorithm is proposed. In order to obtain shorter target tracking execution time and better target tracking track, video image processing technology is used to extract the foreground of visual image, improved convolutional neural network algorithm is used to extract the features of visual image, MeanShift target tracking algorithm is used to track visual image targets on the basis of visual image features. And the tracking results of MeanShift target tracking algorithm are further optimized through Kalman filtering, realizing visual image target tracking. Experimental results show that the proposed method has short execution time and high tracking accuracy.

At present, the integration models in various fields mainly focus on data integration and information integration, but these integration models lack the integration of standards. Ontology and linked data technology can fundamentally solve the “knowledge island冶 phenomenon, and link semantics at the pattern level, reducing data redundancy, and providing an integrated knowledge model for knowledge elements. Firstly, the traditional knowledge organization methods are compared and analyzed, and then a standard driven knowledge integration model is proposed and constructed according to the knowledge organization methods of ontology and linked data. The model realizes the representation of domain knowledge ontology by re-using the domain thesaurus and the vocabularies in the relevant international fact standard ontology. Finally, the RDF ( Resource Description Framework) is serialized through the linked data technology, and linked with entities in the external knowledge base to publish unified and integrated linked data to provide knowledge services for the field. In order to verify the feasibility of the proposed model, taking the petroleum field as the background, the standard vocabularies such as
petroleum subject words is re-used, and the standardized petroleum field ontology is constructed, which integrates the petroleum knowledge in the petroleum field with the knowledge of GeoNames and DBpedia encyclopedia. The rationality and availability of the proposed model are verified. The proposed standardized integration model is also suitable for the integration of knowledge in other fields.

In the previous alarm correlation analysis algorithms, the alarm importance is regarded as the same. In order to distinguish the difference in importance of different alarms and the difference in the amount of information contained in the alarms, an alarm correlation analysis algorithm with improved weighting method is proposed. First, the attributes related to alarm importance in the alarm information are quantified, and the XGBoost(eXtreme Gradient Boosting) integrated learning model is used to train them to obtain the weight value of the alarm attribute, and the weight assigned to the alarm data. Then, the network topology data is added to the sliding window to improve the problems in the division of transactions by the sliding window. The improved transaction set divided by the sliding window is more realistic and reliable. Finally, the weighted alarm transaction set is used to mine frequent alarms and association rules by using the weighted FP-Growth(Frequent
Pattern Growth ) algorithm. Experiments show that the alarm correlation analysis algorithm with improved weighting method has good performance in mining frequent alarms, important association rules and time.

The aim is to complete the master data management requirements of colleges and universities, comprehensively analyze the current situation and management requirements of business data, and build a master data management platform based on the micro service distributed architecture. A platform is developed and deployed based on spring boot microservice architecture and devops mode to realize master data related microservices such as basic microservices, business microservices and interface microservices, so as to meet the requirements of master data life cycle management. The actual operation shows that the platform can operate stably and efficiently in the daily management of colleges and universities, and meet the effectiveness and integrity management objectives of all kinds of data in university.

For the online learning problem of dynamic data flow, the traditional online BLS ( Broad Learning System) algorithm can not accurately capture the latest change trend of the data. Therefore, two online BLS algorithms with forgetting mechanism, one is based on forgetting factor ( FF-OBLS: Online Broad Learning System based on Forgetting Factor) and other is based on sliding window ( SW-OBLS: Online Broad Learning System based on Sliding Window), are proposed. FF-OBLS reflects the different contributions of old and new samples to the learning model by adding forgetting factors to old samples in the online learning process, SW-OBLS eliminates the impact of old samples on the learning model by deleting old samples in the online learning process, so as to enable the learning model to accurately analyze and predict the subsequent trend of dynamic data flow. In order to verify the effectiveness of the proposed two algorithms, dynamic regression data sets are used in the experiment. The experimental results show that the online BLS models with forgetting mechanism are better than the traditional online BLS model in the perspective of prediction accuracy and time cost, therefore they are more suitable to deal with dynamic data flow problems.

Low-field NMR(Nuclear Magnetic Resonance) technology has been widely used in the detection of physical properties of substances due to its fast and non-destructive characteristics. To solve the problem that the imaging accuracy of T₂ spectral is low, which affects the accuracy of detection results. Therefore the imaging method of NMR T₂ spectral based on Bayesian is studied. Firstly, the basic characteristics of NMR signals are showen. Based on the Bayesian principle, the likelihood function of NMR signals is deduced, and the T₂ spectral imaging framework is constructed. Secondly, the T₂ spectrum and its uncertainty are obtained by using an improved Markov chain Monte Carlo strategy. Finally, the effectiveness of the Bayesian-based NMR T₂  spectral imaging method is verified by randomly constructing a T₂ spectral model that obeys a multimodal mixture Gaussian probability density function. This method can be used as a comprehensive experimental content of communication principle, and can also be used as an innovative training experiment.

Feature extraction is the basic work for data mining. The quality will largely affect the results of the excavation. The algorithm for LLE ( Locally Linear Embedding) does not consider the correlation between different characteristics of the same data, and can not well retain the main form trend of timing signals. We proposed CC-LLE ( Local Linear Embedding Algorithm Based on Characteristic Correlation) which is used to diagnosis of bearings. In response to the periodic characteristics of the bearing fault signal, the algorithm combines the data segmentation during the feature extraction stage. The standard deviations on each segment are selected as a characteristic to construct the characteristic sample set of the original data to effectively extract the identification characteristics. The experiments on the bearing data set proved the effectiveness of the algorithm in the feature extraction.

To solve the problems that the difference in crowd image background and the change in crowd scale caused by perspective effect have a serious impact on the accuracy of crowd counting, a multi-level fusion and attention mechanism based crowd counting algorithm is proposed, which includes two sub networks: scale attention extraction and multi-level fusion. The scale attention extraction network adopts coder-decoder structure, which is responsible for scale extraction to combat the problems of crowd scale change and crowd occlusion in complex crowd scenes; the multi-level fusion network adds a feature fusion operation before each convolution block to fuse the attention map with the input of each convolution block to remove the redundant image information, and then generate a high-quality crowd density map. Compared to other excellent crowd counting algorithms, the MAE(Mean Absolute Error) and MSE(Mean Squared Error) of the proposed algorithm on the ShangHaitech dataset Part _ B are increased by 17% and 25% , respectively, and the MAE on Part _ A is increased by 1. 7% . The MAE is increased by 7% on the UCF_CC_50 dataset. The experimental results show that the proposed algorithm has high accuracy and robustness in dealing with complex crowd scenes.

 In vehicle recognition, due to different shooting angles and distances, the size of the imaged vehicle is smaller and the vehicle has different degrees of occlusion, resulting in detection error and missed detection. In order to solve this problem, based on the single stage target detection network YOLOv4(You Only Look Once version 4) algorithm, a recursive YOLOv4 target detection algorithm is proposed based on attention mechanism, namely RC-YOLOv4 algorithm. In order to improve the detection capability of the algorithm for small size vehicles after imaging, the CBAM ( Convolutional Block Attention Module ) module is added to YOLOv4 algorithm. This module combines the channel and spatial attention mechanism, which can help the network model pay more attention to the key information and small target information in the detected image. For the detection of partial occlusion of vehicles, a RFP(Recursive Feature Pyramid) structure is adopted to enhance the model’s ability to extract deep feature information. The RFP structure is similar to the human visual perception that selectively enhances or inhibits the activation of neurons. The features extracted from the backbone network are recursively fused and then fed back to the backbone network. Multiple feature fusion improves the network’s ability to extract and integrate contextual semantic information. It improves the detection accuracy of occluded vehicles. The experimental results show that the average precision of RC-YOLOv4(Recursive and CBAM You Only Look Once version 4 ) algorithm is 12. 69% higher than YOLOv4 algorithm on the self-made vehicle detection data set, and the detection speed can also meet the real-time requirements.

Information enables the iterative upgrading of traditional logistics industry. In order to solve the transportation problems caused by the unique logistics characteristics of automobile manufacturing industry, it considers the improvement of the speed for milk-run, reducing the cost and alleviation the traffic pressure caused by logistics vehicles in the city, based on the actual transportation demands of milk-run of automotive equipment manufacturer A in city Q. An intelligent path planning method for automobile parts transportation based on improved GA(Genetic Algorithm) algorithm is designed. The genetic algorithm is improved by using the coupling factors such as the demand of parts and components in the current month, the details of supplier orders, the capacity rate of optional transportation vehicles, the volume proportion of single vehicle appliances, and the demand of time window in the process of milk-run. In this way, the optimal path using Solomon data example is solved and compared with genetic algorithm, and the optimal distribution scheme for solving the actual transportation demands between A and the suppliers. The experimental results show that the method has some advantages in performance. The numerical simulation results illustrate the applicability of the method and the convergence in the optimization process.

At present, Face sketch synthesis has a series of problems, such as generateing fuzzy outline, lacking of detail texture and so on. Therefore, using CycleGAN(Cycle-Generative Adversarial Networks) as a solution to build multi-scale cyclegan is proposed. Method innovation is mainly reflected in: The generator adopts the deep supervised U-net++ structure as the basis, and performs down sampling dense jump connection at its decoder; The encoder end of the generator designs the channel attention and spatial attention mechanism to form a feature enhancement module; a pixel attention module is added to the generator. Compared with some existing classical algorithms, from the subjective visual evaluation and using the existing four image quality evaluation algorithms for quality evaluation, the experimental results show that this algorithm can better synthesize the geometric edge and facial detail information of sketch image, and improve the quality of sketch image.

In order to optimize the magnetic coupling mechanism of underground radio energy transmission system in oil field, an improved solenoid type magnetic coupling mechanism is proposed for the magnetic coupling mechanism of the underground wireless power transmission system in the oil field. And an optimization design method is proposed by using the Maxwell electromagnetic field simulation tool. The characteristics of the LCC-S high-order compensation topology are studied. The compensation design formula and main circuit characteristics of LCC-S are obtained. Finally, the feasibility and correctness of the proposed coupling structure and compensation topology are verified by closed-loop PI ( Proportion Integration) control through PSIM ( Power Simulation) simulation software.

 The electromagnetic method has a good response to harmful unexploded ordnance and harmless metal targets, so the false positive detection rate is high, resulting in the subsequent cleaning work is extremely time- consuming. To solve this problem, portable and towed transient electromagnetic detection systems is used to detect multiple unexploded bombs and harmless targets, and estimate the characteristic response of underground targets. According to the estimated electromagnetic characteristics, an accurate classification of unexploded ordnance and harmless targets is achieved based on the SVM( Support Vector Machine) algorithm, and the influence of noise on the classification results is discussed. The results show that the classification model trained by the characteristic responses at different times and the fitting parameters of target response can recognize and classify the targets effectively, the accuracy of target classification has reached 100% , and 59 targets have been recognized successfully in the actual verification. In contrast, the classification method based on characteristic response has fast calculation and simple way of processing, while the classification method based on fitting parameters has strong anti-interference ability and higher accuracy. 

The improvement of consensus mechanism is a key research content in the development process of blockchain technology. In the traditional consensus mechanism, all endorsement nodes participate in endorsement, which consumes a lot of time, and has the possibility of forging and manipulating the consensus process with low security. Based on the verifiable random function, the endorsement nodes in the candidate set of endorsement nodes for trading and any endorsement node for endorsement operation are randomly selected which can effectively improve the processing efficiency and reduce the processing time of the consensus mechanism. Based on theoretical analysis and experimental verification of Hyperledger fabric model, the results show that the optimized consensus mechanism has faster transaction processing speed, lower delay time and higher security.

The rock image recognition algorithm model based on traditional deep learning is cumbersome and requires certain computing power when it is applied to mobile terminals, so it is difficult to realize real-time and accurate identification of rock types. Based on the ShuffleNetV2 network, we insert the ECA (Efficient Channel Attention) module of the channel connection attention mechanism, use the Mish activation function to replace the ReLU activation function, and introduce the depthwise separable convolution in the lightweight network components. Experiments are performed on rock images with this method. Experiments show that the recognition accuracy of the algorithm reaches 94. 74% . The improved algorithm structure is not complex and maintains the characteristics of lightweight, which lays a foundation for its application in limited resource environments such as mobile terminals.

In order to solve the impact of excessive charging load on the power grid, a two-layer optimization model of distribution network considering energy storage operators and electric vehicles is proposed. The upper layer of the model considers the economic benefits of energy storage operators participating in distribution network peak shaving and valley filling and electric vehicle charging, and the lower layer considers electric vehicle owners actively responds to the economic benefits of energy storage operators dispatching charging and discharging based on the time-of-use electricity price. The improved simulated annealing algorithm is used to solve the two-layer model, and the IEEE 33 node is used to verify that the model has considerable benefits in ensuring the safe operation of the distribution network. 

Seismic denoising and signal recovery is a key step to improve the quality and accuracy of seismic exploration. By combining the sparsity of the convolution framelet transform and the flexibility of the low dimensional manifold learning, a CFR-LDMM ( Convolutional Framelet Regularization based Low Dimensional Manifold Model) is proposed for seismic signal recovery by using the convolution framelet coefficient energy as the low dimensional constrains. The seismic signals are then jointly represented on the low dimensional manifold in a certain embedded space by the data-driven local and nonlocal basis function, avoiding explicitly defining the manifold coordinate function. Therefore, the significant improvement is made on the denoising ability and signal recovery accuracy. The results of the synthetic and field seismic data tests show that the CFR-LDMM can concentrate the energy of the framelet coefficients for seismic data into a certain block in the coefficient matrix, and the seismic random noise can be removed and the missing traces can be reconstructed well at low signal-to-noise ratio.

In order to solve the problem that the existing scheduling algorithm can not adapt to the high retransmission probability caused by the rapid decline of the air port signal quality in the handover scenario, a new handover command transmission scheme optimized for the handover scenario applied to the B-TrunC is proposed to reduce the bit error rate of handover command. In the new transmission scheme, the PDCP( Packet Data Convergence Protoco1) module identifies the handover command sent to the terminal, and distinguishes the handover command from the ordinary signaling and data transmission processing, using the specified low-key mode and coding rate transmission. The simulation results of switching scene show that the new transmission scheme can effectively reduce the re-transmission and failure probability of handover command, and significantly improve the handover delay performance of B-TrunC.

Aiming at the problems of unreasonable route selection and low distribution efficiency of distribution vehicles, a distribution vehicle route optimization method based on improved cuckoo algorithm is proposed. According to the principle of shortest route distribution, the objective function is built, the relevant constraints of route optimization is set in order to simplify the model structure, ensure that each demand point can only be distributed once, and the vehicle must drive within the maximum distance load range, and establish the route optimization model. The nest position update process of classical cuckoo algorithm is analyzed, adjustment factor is added and the dynamic inertia weight is introduced. The optimization model is solved by cuckoo search algorithm, and the global optimal solution is continuously found through the process of population initialization and nest location update. When the iteration stops condition is met, the optimal optimization scheme is output. Experimental results show that this method has strong searching ability, uniform distribution of solution set, and can ensure the shortest distribution path and improve distribution efficiency.

In the two-phase hybrid stepping motor position control system, the sliding mode algorithm is adopted. However, the problem of large chattering exists. The sliding mode control algorithm based on the concept of boundary layer is improved. And the quasi-sliding mode sliding control algorithm is optimized. A two-phase hybrid stepping motor model is built in SIMULINK, and simulation verification is carried out. The results show that the use of quasi-sliding mode control reduces the litter amplitude of the system by 50% compared to the general sliding mode control. Dynamic performance is greatly improved.

Since various unpredictable disturbances are found in the actual system, it is difficult for the system to accurately achieve the expected performance. Therefore, the probability constraint H_∞ performance is used to make the system more suitable for practical engineering applications. In order to avoid network congestion and resource occupation, RR protocol is introduced to schedule data transmission between network nodes. The filter of probability-guaranteed and non-fragile is constructed. The uncertain parameters of the system are controlled by random variables, which are uniformly distributed and independent of each other. The filter that can guarantee performance requirements under probability constraints is designed by seeking a parameter box. The probability-guaranteed H∞ filtering problem is solved by recursive matrix inequality. Finally, the effectiveness of the filtering scheme is verified by a simulation example. 

In the research of distributed multidimensional data stream frequent pattern mining algorithm, the non frequent items in multidimensional data stream are not deleted, and there is a problem of long average processing time. A distributed multidimensional data stream frequent pattern mining algorithm based on artificial neural network is proposed. According to the characteristics of artificial neural network, this method establishes an artificial neural network model and trains multi-dimensional data flow, so as to improve the mining efficiency; Based on the training results, a frequent pattern information tree, FR-tree ( Frequent Pattern tree ), is constructed. Because there are many expired multidimensional data streams in fr tree, it is necessary to prune fr tree and delete non frequent itemsets, so as to speed up the calculation of frequent patterns. Then, the distributed mining algorithm is used to mine the global fr tree to obtain the complete set of frequent itemsets of multidimensional data streams, so as to realize the mining of frequent patterns of distributed multidimensional data streams. The experimental results show that the average processing time of the method is tested to verify the practicability of the method.

 In order to explore the ability of imaging to diagnose benign and malignant breast lesions, and compare the value of MR(Magnetic Resonance) radiomics and traditional MRI(Magnetic Resonance Imaging) in diagnosing breast diseases, a total of 190 cases with benign or malignant breast lesions confirmed by pathological findings are collected from patients who underwent MR Plain and enhanced examination in the Department of Radiology in First Hospital of Jilin University from January 2019 to January 2022. MR radiomics is performed by building logistic regression model. The traditional MR Diagnosis is performed by a radiologist with an associate senior title. The results show that the sensitivity, specificity and AUC (Area Under Curve) of the MR radiomics test set are 0. 92, 0. 83 and 0. 92 respectively. The above values are higher than the corresponding values of traditional MR diagnosis, and the differences are statistically significant ( P = 0. 00 ). The method of MR radiomics can assist in the diagnosis of benign and malignant breast lesions, and the diagnostic ability is better than the traditional MR diagnostic mode.

Aiming at the problem that the development of fissures in the rock mass of grotto temple is slow and the influencing factors are diverse, it is difficult to predict the development of fissures. A new prediction network for the development of fissures in rock mass based on deep learning is proposed. It is a hybrid network with parallel self-attention mechanism. It models temporal correlations through local convolution modules and global recurrent modules to capture temporal patterns at different time scales accurately. Self-attention mechanism is introduced to model the complex dependencies between different sequences in multivariate time series data. To further improve the robustness of the model, traditional autoregressive processing is followed. We constructed the first dataset in this field based on the monitoring data of fissure development-related factors in Cave No. 32 of North Grotto Temple in Q City. Comparative experiments on this dataset show that the proposed model has a better performance in fracture development prediction of grotto rock mass.

 In order to solve the problems of high-performance cluster monitoring and management, such as system monitoring is restricted by time and place, which causes cluster administrators to be unable to find cluster abnormal situations in time and affects the running of the cluster system, the open function and message transmission mechanism of WeChat are used in combination with the cluster monitoring and management method of Linux (GNU/ Linux) operating system, a kind of simple and easy-to-use cluster monitoring and management system is developed. It is suitable for small and medium-sized clusters with the ability to expand easily. We mainly expound the system requirements, system framework and function design, technical framework and data flow, as well as the specific process of system deployment and development. At present, the system has been developed and applied in the cluster monitoring management of the institute and molecular physics of Jilin University, and has achieved good application results. The cluster administrator and users can monitor the cluster performance and job completion status through APP(Application) on the mobile phone without login system, so as to facilitate the follow-up work in time. Especially during the COVID-19 period, when the cluster access is not convenient, the implementation of this function has assisted the efficient scientific research work of the institute. 

In order to solve the problem of computer aided diagnosis of novel coronavirus pneumonia (Covid-19: Corona virus disease 2019), a bilinear convolutional neural network model is created and a feature extraction subnetwork with VGG(Visual Geometry Group network) 16 and VGG19 is employed. The algorithm is applied to COVID-19 image classification and compared with the basic image classification algorithm. The results and lesion visualization analyses demonstrate that the bilinear convolutional neural network model outperforms other deep learning network models in terms of accuracy, with an accuracy of 95. 19% . By replacing softmaxlayer and using SVM(Support Vector Machines) classifier, the model classification accuracy is improved to 96. 78% . The study provides a trustworthy tool for the quick and accurate diagnosis and treatment of neonatal pneumonia and a confirmation of the viability of fine-grained imaging algorithms for the categorization of COVID-19 CT images. 

 With the advent of big data era, the development of recommendation systems has become more and more vigorous. It has become a research hotspot to push information that may be of interest to users in a timely manner among massive amounts of information. Traditional recommendation algorithm lack implicit information and contextual information about graph structures. In response to this, a recommendation model is proposed based on graph neural network. The main innovations are: 1) Based on the higher-order connectivity theory of graphs, the graph neural network is used to mine the hidden information in the user-item bipartite graph, and a the order is extended to multiple orders, so as to obtain more accurate embedded representation and recommendation effect; 2 ) Consider context information in the update process, which is conducive to understanding the interaction between contexts. The model is tested on the Yelp-OS, Yelp-NC and Amazon-book datasets, and the results show that it is better than the related comparison algorithms in both HR(Hit Ratio)and NDCG(Normalized Discounted Cumulative Gain) indicators, which proves that the algorithm can optimize the recommendation effect and improve the recommendation quality. 

 Aiming at the difficulty of detecting subtle defects on O-ring surface, we present a method of detecting defects on O-ring surface based on six photometric stereoscopic method and image comprehensive feature analysis. First, the images of six different light source angles are collected, and the surface gradient map and reflectance map are reconstructed by photometric stereoscopic method. The surface gradient image is first converted into the average curvature and Gaussian curvature image, and then converted into the gray-scale image. The defect region is segmented using a fixed threshold. After the reflectivity map is filtered by Gauss, the local mean and variance thresholds are used to segment the defect area. Finally, the defects are accurately selected by analyzing the connected domain characteristics of the obtained defect regions. The experimental test results show that it has a good effect on the subtle defects such as weld marks, concave-convex and flow marks on the surface of the seal ring. In the application of the designed seal ring quality detection system, the detection accuracy is more than 98. 4% , which can solve the problem of low recognition rate of the current industrial sealing ring defect detection.

Taget Detection of Photovoltatic Remote Sensing Based on Improved Yolov5 Model TONG Xifeng, DU Xin, WANG Zhibao (School of Computer and Information Technology, Northeast Petroleum University, Daqing 163318, China) Abstract: Aiming at high-sensing photovoltaic image resolution, high environmental noise, and complex background, an improved Yolov5 model is proposed to achieve positioning of photovoltaic power plants. First of all, the CA(Coordinate Attention) mechanism is added to the compassionate layer of the main feature extraction network to improve the learning ability of the network characteristics; second, the Ghostconv network structure is added to Backbone, useing the Ghostconv network module to replace the Conv network module, designing a new GhostC3 network network instead of the original C3 network module to improve the learning efficiency of the model; finally, the GIoU_Loss function is changed to the SIoU_Loss function. Compared with the original Yolov5 method, the average accuracy of the improved algorithm mAP, accuracy, and recall rate reached 97. 5% , 98. 9% , and 94. 9% , respectively, which have increased by 1. 8% , 1. 7% , and 5. 8% , respectively. The algorithm has a good effect on photovoltaic detection.

In order to accurately detect the fault signal of the system, and to ensure safe and stable operation, the method of building fault detection filter, the H_∞ fault detection problem of neutral systems with time-varying delays is studied. The delay-dependent Lyapunov function is selected to make the fault detection system is asymptotically stable and satisfies H_∞ performance criterion. And the LMI(Linear Matrix Inequality) technique is applied to converted the design problem of fault detection filter into a convex optimization problem of LMI. Finally, the proposed method is applied to two-stage dissolution tank system. Simulation results show that the proposed method can detect system faults quickly and accurately, and the proposed method can reduce the conservatism of system design compared with delay-independent Lyapunov function.

Aiming at the problems of insufficient data and unbalanced sample distribution of oil pumping unit failures, a CDCGAN(Conditional Deep Convolutional Generative Adversarial Networks) model based on self-attention mechanism is proposed. The model adds a regular term to the loss function that constrains the distribution of generated images, improves the quality and diversity of generated images and effectively prevents the occurrence of mode collapse. Using Alexnet, VGG16 and other networks to classify and test the generated pumping unit fault samples, the experimental results show that the improved network generates higher quality data, can effectively balance the pumping unit fault data, and further improves the accuracy of the pumping unit fault diagnosis rate.

A CoMaCOA(Co-evolution Multi-Objective Constrained) optimization algorithm is proposed to deal with the problem that it cannot be combined convergence and diversity effectively in multi-objective COA (Constrained Optimization Algorithms). First, a COA is transformed into the multi-objective evolutionary algorithm with dynamic constraint processing. Then, DE(Differential Evolution) is used to generate the first population. The second population is generated by the known feasible solution in the first population and coevolved with the first. The first population accelerates convergence by global search that does not deal with constraints. The second population evolves through local search to maintain and obtain more feasible solutions. Finally, the standard constrained multi-objective test function is used for experiments in order to test the performance of the proposed algorithm. The experiment result shows that the proposed algorithm achieves good results on both IGD( Inverted Generational Distance) and HV( Hypervolume), comparing with PF ( Penalty Function) method and dynamic boundary processing to constrain problem DCMaOP(Dynamic Constrained Many Objective optimization Problem). It shows that the algorithm is both effective in convergence and diversity.

At present, the tunnel lighting control often adopts the method of sectional control, and the lamps are always on, which leads to excessive lighting and waste of power. In order to reduce the energy consumption of tunnel lighting, using LED ( Light Emitting Diode) lamps and stepless control, the tunnel lighting design is carried out according to the human visual characteristics and traffic flow state parameters. Based on vehicle arrival and departure data, the control strategy of “ vehicle entry light on, vehicle exit light off" is adopted to design the lamp on and off control method from the two aspects of driving safety and energy saving. The experimental results show that the tunnel lighting changes continuously and gradually, which is more suitable for the visual characteristics of human eyes, and the method can reasonably control the on-off of tunnel lamps according to the traffic flow data, so as to ensure the safety of tunnel driving and reduce energy consumption.

In order to overcome the shortcomings of the traditional experiment box of the communication principle in signal processing and detection, it is necessary to use simulation to show the experimental phenomena in a more comprehensive way. However, Simulink, LabVIEW, and other software can only simulate the digital communication system. To solve this problem, Multisim is used to perform component-level circuit simulation of analog communication systems, including PAM ( Pulse Amplitude Modulation) encoding and decoding, FSK (Frequency Shift Keying ) modulation and demodulation, and PSK ( Phase Shift Keying ) modulation and demodulation. Through simulation, the inherent working mechanisms of sampling theorem, phase-locked loop, and Costas loop are intuitively demonstrated, and the principles of two possible distortions that can occur during FSK demodulation are thoroughly analyzed. 

Based on a portable transient electromagnetic system, we use a dipole model with a differential evolutionary algorithm to investigate the effects of target size, depth and inclination angle on the accuracy of its characterization, using the electromagnetic characteristics of a calibrated unexploded bomb as a reference. The experimental results show that for a target with an inclination angle of 0毅, the characteristic response is more concentrated in the head of the target and the result is smaller than the calibration value, when for an inclination angle of 90°, the characteristic response is the sum of the characteristic responses of all parts of the target, so the characteristic response increases with the increase of the inclination angle. The larger the depth, the higher the applicability of the dipole model, and the closer the characteristic response is to the calibration value. The larger the depth, the higher the applicability of the dipole model, the closer the characteristic response to the calibration value, and the better the consistency of the inversion results under different inclination angles. The larger the size of the target, the more the target is affected by the change of inclination angle, the better the consistency of the characteristic response from all angles of the small-size target, and the better it matches the calibration results. In summary, factors such as size, depth and inclination angle lead to errors in the results of the feature response, but in general do not affect the classification of targets based on the dipole model. 

In order to improve the experimental teaching of relevant courses in automatic control, the design of a four-tank system is presented. The hardware is composed of general industrial components, while a Matlab programming environment is used for the software to directly control the system. A graphical user interface is also adopted to provide an easy way to run the system. The four-tank system model is built, and its parameters are identified based on a step response experiment. Furthermore, a distributed internal mode controller is designed for the liquid tracking control of the four-tank system. The results show that the four-tank system, using the internal mode controller, has little effect on the water level of the other tank when the water level of one tank changes, and the system has a satisfactory control effect. This experiment could play an important role in the teaching of relevant courses in automatic control.

Aiming at the problems of low luminous efficiency and large working bias of AlGaN-based deep-UV (Ultraviolet) LEDs (Light Emitting Diodes), a nitrogen-polar AlGaN-based deep-UV LED device structure with n ⁺ -GaN/ Al _{0. 4}Ga_{0. 6}N/ p ⁺ -GaN tunnel junction is designed. The LED structure is consist of an electron supplying layer n-Al _{0. 65}Ga_{0. 35}N, a multiple quantum wells of Al _{0. 65} Ga_{0. 35} N/ Al _{0. 5} Ga_{0. 5} N, a compositionally graded p-Al _xGa1_-xN and n ⁺ -GaN/ Al _{0. 4}Ga_{0. 6}N/ p ⁺ -GaN tunnel junction. The simulation results show that the tunnel junction LED has higher internal quantum efficiency and light output power, and it has a lower turn-on voltage than the reference LED without tunnel junction. The improvement of the optoelectronic characteristics of the tunnel junction LED is attributed to the introduction of the tunnel junction improving the hole injection efficiency of the LED, and improving the current spreading capability of the LED device. The results of this work show that the simulation of carrier transport and optoelectronic characteristics of semiconductor devices through simulation software is helpful deepening the understanding of the physical characteristics of semiconductor devices. If the study of semiconductor device simulation software is added to the learning process of “ semiconductor device physics冶, it will help the cultivation of talents in the semiconductor field. 

 In order to explore the driving method of LED ( Light Emitting Diode), to improve the stability of LED output brightness, a LLC resonant converter controlled by frequency conversion to control the constant current output is proposed. LLC half-bridge resonant converter has the advantages of high switching frequency, power-off retention, wide allowable input voltage range, high efficiency, light weight, small size, low EMI (Electromagnetic Interference) noise, low switching stress, etc. Therefore, LLC half-bridge resonant converter is a good topology choice for LED constant current output. And the frequency conversion control method is adopted and the simulation analysis is carried out with PSIM(Power Simulation) software. The simulation results show that the LLC resonant converter controlled by frequency conversion has good steady-state performance and transient performance, which also has far-reaching significance for engineering applications. 

At present, State Grid Jilin Electric Power Co. , Ltd. relies on confidential personnel to manually mark the confidentiality level of documents, and its accuracy depends on the professional quality of relevant personnel, which is easy to cause the problem of inaccurate labeling. Therefore, we establish an enterprise document security classification system based on the transformer model, which can automatically extract the feature expression of text security information and intelligently assist the decision-making of enterprise secret documents. The proposed model is trained and tested on the data set constructed by the internal core commercial secret files, ordinary commercial secret files and non secret files of State Grid Jilin Electric Power Company Limited. The accuracy rate is 97. 37% and the recall rate is 98. 67% . The results show that the model achieves high recognition effect and can effectively prevent the disclosure of secret files.

 In the field of electromagnetic detection, skin depth is an important parameter for electromagnetic data analysis and imaging. In practice, the induced field and the polarization field coexist. If the polarization effect of the medium is not considered, there will be obvious errors in the imaging results. In order to solve the above problems, the generalized skin depth formula of the GEMTIP(Generalized Effective-Medium Theory of Induced Polarization) model in the frequency domain is deduced based on the plane wave theory and the GEMTIP model. The accuracy of the generalized skin depth of the GEMTIP model is verified by comparison with the classical skin depth. The generalized skin depth calculation of the GEMTIP model is mainly related to the resistivity and volume fraction. The BP(Back Propagation) neural network inversion method is used to extract parameters. And by constructing a reasonable data sample set, the training error can meet the accuracy requirements, and the mapping relationship between the input and output data is obtained. Several typical three-layer geological model structures are discussed. When the polarization effect is considered, the generalized skin depth formula of the GEMTIP model is verified to improve the identification accuracy of the underground polarized medium. 

In the deep Reinforcement Learning, the PPO ( Proximal Policy Optimization) performs very well in many experimental tasks. However, KL(Kullback-Leibler) -PPO with adaptive KL divergence affects the update efficiency of KL-PPO strategy because of its asymmetry. In order to solve the negative impact of this asymmetry, Proximal Policy Optimization algorithm based on CIM( Correntropy Induced Metric) is proposed characterize the difference between the old and new strategies, update the policies more accurately, and then the experimental test of OpenAI gym shows that compared with the mainstream near end strategy optimization algorithms clip PPO and KL PPO, the proposed algorithm can obtain more than 50% reward, and the convergence speed is accelerated by about 500 ~ 1 100 episodes in different environments. And it also has good robustness.

Aiming at the problems of poor edge security and limited storage effect of unstructured big data, a dynamic secure storage algorithm of unstructured big data based on edge computing is proposed. The unstructured big data is effectively analyzed and identified. The constructed data sensitivity level recognition model is used to establish and encrypt the sensitivity of unstructured big data. Based on edge computing and cloud computing, a cloud edge collaboration architecture is established, and the DCS-SOMP ( Distributed Compressed Sensing Simultaneous Orthogonal Matching Pursuit) algorithm written by the architecture is used to compress and collect encrypted data, so as to reduce data storage. Finally, the unstructured encrypted data is uploaded to each edge of the cloud side collaboration framework to realize the dynamic and secure storage of unstructured big data. Through experimental comparison, it is found that the robustness of storage test, metadata proportion test, encryption time-consuming test and bandwidth consumption is high, which ensures the practical application.

To meet the need of integrating scientific research into teaching of Emerging Engineering Education, a multi-dimensional and hierarchical integrated experimental platform based on Python is designed. Guided by the talent-training plan, hierarchical modules involving image recognition, machine learning and data analysis is designed from scientific research hotspots. Image recognition module starts from character recognition, then face and license plate recognition are realized by several algorithms. In the machine learning module, commonly used machine learning algorithms are studied and corn disease is identified by various methods based on Python. In the data processing and analysis module, Excel data processing experiment based on Python is designed to analyze the data of workload and bioinformatics data. The platform enables students to learn the application of Python in the experiments, and choose different experimental projects according to professional needs and research directions to realize the goal of teaching students in accordance with their aptitude. By applying the experimental platform to teaching practice, it is demonstrated that students have a deeper understanding of Python’s programming implementation in image recognition, machine learning, and data analysis and enhanced research interest. And the goal of integrating scientific research into teaching and improving the quality of undergraduate teaching could be achieved.

Federated learning has become increasingly important for modern machine learning, especially for data privacy sensitive scenarios. It is difficult to carry out secure machine learning between heterogeneous data islands. A federated learning communication mode between heterogeneous data islands is proposed, which realizes the hybrid federated learning communication between horizontal and vertical, and breaks the communication barrier of the disunity of model structure between horizontal and vertical participants in traditional federated learning. Based on the special privacy requirements of the government, banks and other institutions, the third party aggregator is further removed on the basis of the hybrid federated learning model, and the calculation is carried out only among the participants, which greatly improves the privacy security of local data. In view of the computational speed bottleneck caused by vertical homomorphic encryption in the communication process in the above model, by increasing the local iteration round q, the encryption time of vertical federation learning is shortened by more than 10 times, and the computational bottleneck between horizontal and vertical participants is reduced, and the accuracy loss is less than 5% .

At present, the distribution room mainly relies on the traditional manual inspection method, which has the problems of high inspection cost and high false alarm rate of hidden trouble. Based on the knowledge map and intelligent cognitive technology, the basic theory and key technology of the knowledge base and advanced application of the operation inspection business of the distribution station building are expounded. The knowledge sharing management, knowledge intelligent search, knowledge intelligent recommendation, knowledge data label and fault intelligent reasoning are studied. The distribution station building knowledge base is established and applied in Shanxi power grid. The research results build a knowledge base for intelligent operation and inspection of power distribution station buildings, and carry out intelligent application research, realize knowledge empowerment such as intelligent analysis of fault hidden dangers, reduce the inspection cost, and improve the intelligent operation and inspection capability.

The pressure and blocking effect of the tunnel side wall on the traffic flow causes the tunnel traffic flow to deviate from the lane, reduce the operating speed or collide. The relationship between the traffic flow operating characteristics under the tunnel side wall effect and the tunnel section optimization needs to be studied. Therefor we firstly analyze the influence of tunnel sidewall effect on traffic flow. Secondly, combined with the knowledge of fluid mechanics, the theoretical modeling of the viscous characteristics of tunnel traffic flow is carried out. Finally, the relationship between lane width, traffic flow density and viscous coefficient, the relationship between traffic flow speed, sidewall spacing and viscous force are analyzed respectively. And the conventional viscous force calibration of traffic flow is carried out by taking a one-way multi-lane tunnel as an example. The relevant conclusions of the experiment will provide a theoretical guidance for tunnel speed management and section optimization.

In order to solve the problem that the model fitting effect is not good due to the complex coupling relationship between drilling controllable factors, a prediction model of mechanical drilling speed based on fuzzy neural network is proposed. The fuzzy control idea is used to solve the parameter coupling problem and to predict. Clustering algorithm is used to divide the data with high similarity into a fuzzy set as the initialization parameter of the second layer of fuzzy neural network. Taking an oilfield as the background, the simulation results show that the empirical knowledge extracted by fuzzy neural network conforms to the coupling relationship between controllable parameters of drilling in the oilfield, and it is suitable for most drilling operations in the region. It proves that the model has good prediction ability, and verifies the feasibility and applicability of the model, which is of great significance to improve drilling efficiency and save cost.

Because the traditional single image super-resolution reconstruction algorithm fails to make full use of the shallow feature information, ignores the spatial structure information in the visual target, is difficult to capture the dependence between the feature channel and the high-frequency feature information, and there are artifacts and edge blur in the reconstructed image, an image super-resolution reconstruction algorithm based on residual network and attention mechanism is proposed. The feature extraction part of the model combines the WDSR-B (Wider Activation Super-Resolution B) residual network to enhance the flow of feature information in the network, weights the feature parameters through the coordinate attention mechanism, and guides the network to better reconstruct high-frequency features and restore image details. The experimental results show that under quadruple image reconstruction, the PSNR(Peak Signal to Noise Ratio) on Set5 and Set14 test sets is 31. 00 dB and 28. 96 dB, and the SSIM( Structural Similarity) is 0. 893 and 0. 854. The reconstructed image performs better in detail and contour, which is better than other mainstream super resolution reconstruction algorithms.

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.

To reduce the transportation cost in the VRP (Vehicle Routing Problem) of milk-run, a discrete seagull algorithm is proposed. Firstly, in the process of seagull migration, insert and reverse operations are used to update the seagull position to improve the algorithm's search speed. Secondly, swap and 3-opt operations are used to update the seagull position to improve the algorithm's local search capability. Finally, combined with simulated annealing algorithm, the phenomenon of landing on local optimum is prevented during the operation of the algorithm, the update strategy is redefined under the discrete vehicle routing problem. With the lowest total cost as the objective function, the corresponding mathematical model is constructed. Experiment results show that the algorithm is able to efficaciously deal with the vehicle routing problem of milk-run, the finding effect and solution quality are better than the standard seagull optimization algorithm, particle swarm algorithm, simulated annealing, gray wolf optimization, whale optimization algorithm, and moth-flame optimization.

Feature extraction is a troublesome problem in the pipe signal degrading the classification accuracy. To address this problem, a pipe signal diagnosis method that combines the signal processing method with the intelligence algorithm is proposed. Firstly, CEEMDAN(Complete Ensemble Empirical Mode Decomposition with Adaptive Noise) algorithm is used to decompose the signal to obtain several IMFs (Intrinsic Mode Functions) and the correlation coefficient method is used to select the useful mode function components and recombine them. Then the Lempel-Ziv complexity and Margin of the reconstructed signal are calculated as feature vector. Finally, the feature vector are inputted into the ELM ( Extreme Learning Machine ) optimized by SOA ( Seagull Optimization Algorithm) for classification. And validation is performed with laboratory data. Experimental results show that comparing with conventional ELM and GA-ELM(Extreme Learning Machine Optimized by Genetic Algorithm). SOA-ELM model can identify the pipe signals effectively, and has higher recognition rate and faster diagnosis speed. 

The coverage of radar communication network gradually increases, the number of access users increases sharply, and the risk of terminal access expands. Radar communication terminal is one of the key equipment to block the access of bad users. Therefore, a security access control algorithm of radar communication terminal based on electronic signature technology is proposed. The security access control framework of radar communication terminal is build, applying CA authentication technology to authenticate the identity information of the user to enter the radar communication terminal, the user authentication information in the radar communication is stored terminal database, the user's personal electronic signature is made through electronic signature technology, and the electronic signature is matched through SIFT matching algorithm, It determines whether the electronic signature authentication information of the user of the radar communication terminal to be accessed is safe, to realize the control of the security access of the radar communication terminal. The experimental data shows that the security rate and security access efficiency of radar communication terminal obtained by the proposed algorithm are larger, and the security access control effect of radar communication terminal is better.

Convolutional neural network model has the disadvantages of large volume, high computation and poor performance in small and resource limited embedded platform. The existing lightweight model can not take into account the detection speed and accuracy. The mainstream target detection algorithm Yolo v4 is selected to lighten the model, and the mobilenet network and depthwise deparable convolution are used in Yolo v4 model. The results show that compared with the original Yolo v4 model, the improved Yolo v4 model of different mobilenet networks can process an image about 19 ms faster on average, and the accuracy rate can reach more than 92% . The accuracy rate of the improved Yolo v4 model with mobilenet v3 as the backbone feature extraction network is 95. 13% , which is 2. 99% higher than of the original Yolo v4 model. The parameter of this model is about 1 / 6 of Yolo v4 model, and the model can process a patrol image 20 ms faster than the original Yolo v4 model. Insulator is an important part of transmission line, The identification of insulators in many images can help to analyze the operation of transmission lines.

The complex anatomical structure of the human body and significant individual differences, the limited two-dimensional anatomical images in textbooks often make it difficult for students to understand, and there are problems such as unclear learning objectives, unscientific learning methods, low learning efficiency, and poor learning outcomes during the learning process. To address a clinical anatomy teaching model based on image 3D visualization technology is proposed. The clinical teaching achievements based on 3D visualization technology in recent years are summarized, and the feasibility and superiority of 3D visualization technology in clinical teaching mode pointed out. Finally, the expansion content and development ideas of the visualization clinical teaching mode are discussed, and the possibility of applying the construction of clinical anatomy case library based on image visualization technology to the visualization teaching mode is prospected. 

OBC(Ocean Bottom Cable) records are often limited by the limitations of data collection techniques and the environment, leading to a large amount of random noise that negatively impacts the identification of effective reflection information. Suppressing random noise requires accurate analysis of noise characteristics, therefore the multichannel method and a stability test method based on the energy distribution of the sequence are used to analyze the power spectral characteristics and stability of OBC random noise. Real noise data collected in a certain location in the South China Sea is used as the analysis dataset. The results show that OBC random noise is a colored noise sequence with noise energy mainly concentrated in the low-frequency part. The OBC noise is a weak non-stationary time sequence, and the conclusion is reasonable by combining the characteristics of the ocean exploration environment. This study has a certain practical significance and application prospects for the basic research on OBC random noise characteristics. 

With the continuous development of distributed power supply, the load in distribution network presents a trend of diversification. The reactive power regulation strategy and traditional algorithm of traditional distribution network can not meet the demand of reactive power compensation of modern distribution network. Therefore, a dynamic reactive power optimization strategy of active distribution network based on improved krill swarm algorithm is proposed to solve the above problems. First of all, the reactive power optimization process is divided into two parts, the dynamic compensation regulation and days before recently considered discrete reactive compensation devices of reactive power compensation capacity, days after fully considering scenery output to compensate the system and other continuous adjusting equipment, active power distribution network based-days before dynamic reactive power optimization model of multiple time scales. Secondly, an improved krill colony algorithm based on cosine control factor and Cauchy factor is proposed to solve the model. Finally, the feasibility and effectiveness of this strategy are verified by the modified IEEE33 node system experiment, which can ensure the smooth operation of active distribution network and realize the maximum economic benefit.

When the current method is used to predict the load of the power system of the distribution network, because the missing value interpolation processing of the power data is not performed before the power load prediction, the method has poor prediction accuracy, long prediction time. For the problem of poor forecasting performance, a research on the load forecasting of the distribution network power system based on the DE-ELM (Differential Evolution-Extreme Learning Machine) algorithm is proposed. This method first denoises the power data according to the wavelet transform method, completes the interpolation of the missing values of the power data according to the denoising results, and obtains a complete power data set; then divides the data set into two parts: a training set and a test set. The optimization method introduces the extreme learning machine, uses the DE-ELM algorithm to calculate the training set, builds a network model based on the results. Finally puts the test set into the constructed model for training, and realizes the load forecast of the distribution network power system based on the output results. The experimental results show that when the method is used to forecast the load of the distribution network power system, the forecasting accuracy is high, the forecasting time is short, and the forecasting performance is good.

In order to solve the problem of strong forward security in digital signature schemes, a class of forward security digital signature scheme based on elliptic curve is analyzed. It is proved that the scheme does not satisfy the backward security. An elliptic curve digital signature scheme with strong forward security is proposed by introducing the double private key evolution method. After security analysis, the improved scheme has strong forward security and anti-forgery. It is also a backward-secure digital signature scheme.

A multi-scale full-convolution timing model is proposed in order to detect the partial discharge phenomenon of high-voltage power lines in a timely manner. This method uses a multi-scale fully convolutional timing model to train the power signal data collected in high-voltage power lines. The trained model can be used to monitor the future continuous signal to detect whether it has a partial discharge phenomenon. The experimental results show that the model proposed has good accuracy on the used data set.

In order to ensure economic and reliable operation of micro power grid, reduce the pollution to the environment, an optimal dispatching model for microgrid is proposed, which considered the operating cost and environmental pollution of microgrid, through the adaptive weighting method weights for different fitness function are distributed, the multi-objective problem is changed to single objective problem. The improved gull algorithm is used to find the optimal configuration scheme. Gulls experiment results show that the improved algorithm has better ability for global optimization, the solving precision and convergence speed compared with the standard algorithm. The micro grid economic operation has certain advantages in solving the problem, and reducing the micro grid operation cost and the environmental pollution, improving the reliability of the micro grid operation.

Aiming at the problems of poor security and slow encryption efficiency of color image, a spatial color image composite encryption algorithm based on chaotic mapping is designed. The depth residual network is constructed, the jump connection is added in the residual unit block, the nonlinear mapping of noisy image is created, and the activation function is moved to the convolution layer to accelerate the convergence rate of the network achieving the goal of image denoising. The wavelet transform operation is adopted for the image to analyze the spatial characteristics of the image, the Doppler wave is used to scramble the high and low frequency coefficients of the image, the diffusion operation is used to calculate the adjacent area of the symmetrical points of the image, and the pixels to be diffused are arranged and combined according to the zero mean normalized cross-correlation score, so as to make the chaotic sequence have sufficient coupling correlation with the image and strengthen the image security. The mapping parameters are introduced into Logistic chaotic mapping, the random values of mapping variables are adjusted, the probability distribution function of chaotic system sequence is calculated, and the composite encryption of color image is completed. Simulation results show that the proposed method can effectively eliminate the pixel feature information of color image, and the encryption effect is good and feasible.

In order to realize the high-precision application of digital images and reduce the influence of external light on visual imaging, a fast blind restoration algorithm for visual defocused light images based on variational Bayesian is proposed. Through gradient and convolution processing, the posterior probability expectation of the visual defocused light image is calculated, and the optimal initial image and the prior probability of the defocused light blur function are extracted by using the Sobolev space function distribution method. The actual posterior probability is reached infinitely, using relative entropy to calculate the distance between multiple distributions, to approximate the true value of the greatest extent, and input the minimum loss cost function into the bilateral filter, that is, take the approximate clear image as the guide map, to remove the remaining high-frequency noise. The optimal image blind restoration results are obtained. The experimental results show that the proposed algorithm has high image contrast, clear edge details and fast restoration speed after blind restoration, which has extremely high application value.

In order to solve the problems of management, coordination and association of cross regional scientific and technological resource sharing and collaborative innovation, practical application and theoretical research of scientific and technological resource platform are carried out to build an ecological digital display system of scientific and technological innovation. First, the definition, composition and index system of scientific and technological innovation elements are constructed, and innovation map, innovation ability, innovation subject, innovation carrier are proposed. The digital display system with innovation resources and innovation environment is the core. Taking Jilin Province as an example, the index method is used to monitor the level of regional scientific and technological innovation, the text mining method is used to analyze the hot spots of scientific and technological policies, and the system architecture and function design are carried out based on the Vue + Django + MySQL technology architecture. Finally, the ecological digital display of scientific and technological data innovation in the region is realized, so that the system has data sharing, data association and intelligent service functions such as decision support. The application practice shows that the system enriches the visualization of scientific and technological data resources, improves the visualization of scientific and technological innovation ecology, and improves the expansibility, efficiency and performance of the system.

Aiming at the defect that the original LPP ( Local Preserving Projection) algorithm is difficult to accurately obtain the local manifold structure of non-uniform high-dimensional data and can not use the sample category information, a MIF-LPP (Multi-Information Fusion Local Preserving Projection) algorithm is proposed. MIF-LPP algorithm uses the improved standard Euclidean distance to obtain the nearest neighbor information and mutual neighbor information of samples, reducing the impact of uneven distribution of sample points and the difference of data dimensions of a single sample. The weight matrix is constructed by fusing the class information of the samples, and then the low dimensional essential manifold of the data is obtained. The validity of the algorithm is verified on CWRU(Case Western Reserve University) data set and our laboratory bearing data set respectively. The experimental results show that the feature extraction performance of MIF-LPP algorithm is obviously superior to other algorithms, and it is robust to neighborhood values. 

 To address the issue of the lack of interoperability and data sharing among different information and application systems on campus, we aim to leverage data integration technology to merge diverse educational data sources. We intend to establish a multi-source, heterogeneous education big data mining and application platform. The platform system will utilize the output of artificial intelligence models and the input from a multi- source, heterogeneous education big data mining engine. It will be based on big data mining techniques to analyze and process multiple data sources, including student records, teaching resources, and social behavior information. This will enable functionalities such as educational sign diagnosis, intelligent learning state comparison, analysis of teaching impact factors, identification of potential issues, and prediction of teaching quality trends. Our goal is to scientifically enhance the quality of personalized campus teaching services, objectively assess the teaching proficiency of individuals and teaching teams, assist in analyzing the strengths and weaknesses of teaching individuals and teams, and provide robust support to decision-makers in managing the education system. 

Demand side response is an important means of active distribution network optimization scheduling. Aiming at the problem of poor energy scheduling in power grid operation, firstly, based on the uncertainty characteristics of demand side response, introducing non-economic factors and characteristics of consumer psychology, the active distribution network optimization is modeled with the minimum power grid operation cost and environmental cost as the objective function; secondly, aiming at the premature problem of sparrow algorithm, latin hypercube sampling is used to improve the initial population quality, sine factor is introduced to improve the local search ability of the algorithm, and mutation operation is implemented to optimize the global search accuracy of the algorithm; finally, the improved sparrow search algorithm is applied to the solution of the active power grid optimization model. The simulation results verify the accuracy of the proposed model and the efficiency of the algorithm, and effectively solve the problem of poor energy scheduling. 

It is difficult to select the preset scale K for VMD(Variational Mode Decomposition) and to distinguish the effective mode from the noise mode after decomposition. In order to solve the problem, a joint criterion method (VMD-HD-KT) is proposed to determine the presetscale K by HD(Hausdorff Distance) and the effective mode by Kendall correlation coefficient KT(Kendall ’s Tau). And it is used to denoise the leakage signal of natural gas pipeline. First, the HD of the last mode and the original signal when K = 2 to 8 is calculated, K is determined by evaluating HD, and then K value is input for VMD decomposition. The original signal is decomposed into K IMF(Intrinsic Mode Functions) with different characteristic time scales. IMF with KKT greater than 0. 1 is selected as the effective mode for signal reconstruction. The experimental results show that the VMD- HD-KT algorithm can accurately select the preset scale K and effective modes, and has a good denoising effect on the simulation signals and pipeline leakage signals. 

 Aiming at the problem that the classical deep learning method has low accuracy in multi-step load forecasting, a short-term load forecasting model based on double attention sequence to sequence is proposed. Through the self-attention mechanism, the hidden related factors affecting the load data are effectively extracted, so that the model can better find the laws between the load data, adaptively learn the related characteristics between the load data, and the temporal-attention mechanism captures the time-related time-series characteristics. Through two actual load data experiments, the simulation results show that under the condition of (t+12) prediction, the model evaluation index MAPE(Mean Absolute Percentage Error) is 2. 09% , which is 56. 69% lower than that of LSTM(Long Short-Term Memory) model. The validity and feasibility of the model are verified. The prediction effect of the model is better than that of linear regression, LSTM model and Seq2Seq (Sequence to Sequence) model.

In order to solve the problem of the unreasonable weight distribution of the evaluation index system and the uncertainty of air combat data in the evaluation process, game theory and interval cloud models are proposed for effectiveness evaluation of incomplete air combat decision-making. Aiming at the attack and defense decision of air combat, an evaluation index system is constructed, and the subjective and objective weights are reasonably adjusted based on game theory to obtain the comprehensive weight value of the index. Then, for the randomness and ambiguity in the evaluation, the interval cloud model method is studied, and the effectiveness of incomplete air combat decision-making is determined by the interval cloud generator. Finally, the feasibility and effectiveness of the proposed method are verified by simulation. It provides technical support for solving the problem of evaluating the effectiveness of air combat decision-making under incomplete information.

In order to solve the problem of “information island" of science and technology resources, the deep integration and application analysis of heterogeneous data is performed. Guided by the application demand of multi perspective users for science and technology resources, the metadata characteristics of multi-source and heterogeneous science and technology resources are analyzed, and the integration design of heterogeneous data is carried out by using association aggregation method and knowledge organization tool to establish the relationship between various types of science and technology resources. Based on the application of science and technology resources metadata of information service platform of Jilin Province science and technology literature (referred to as “the platform"), the metadata storage and sharing service of multi-source heterogeneous data of the platform is realized, and the degree and effect of science and technology resources sharing service are enhanced.

Currently, SnO2(Tin Dioxide) has become the most commonly used material for the electron transport layer in high-performance PSCs(Perovskite Solar Cells). A strategy for optimizing SnO2 using a small-molecule chelator is proposed to address the problem of agglomeration-prone commercial SnO2 aqueous dispersions and the need to enhance the electrical and surface properties of SnO2 films. The PSCs with the device structure of ITO/ SnO2+SC / FA1-x MAx PbI3 / Spiro-OMeTAD/ Au are prepared by doping the SnO2 transport layer with a low-cost chelator, SC( Sodium Citrate). After the introduction of SC with an optimized concentration, the open-circuit voltage and fill factor of PSCs can reach up to 1. 135 V and 78. 23% , respectively, with a power conversion efficiency of 21. 53% . This represents a significant improvement compared to the devices without the introduction of SC. The characterization of the films and devices revealed that the doping of SC could enhance the electrical and surface properties of the SnO2 films, which in turn improves perovskite crystallization. As a result, defects in the device are reduced, recombination loss is lowered, and charge transport is promoted.

To reduce the influence of complex and changeable electromagnetic environment on vehicles, the cable model is built in the engine compartment by using the three-dimensional electromagnetic field simulation software CST(Computer Simulation Technology) to study the influence of different factors on the electromagnetic coupling effect of vehicles. Through simulation, the peak relationship curves between induced voltage and induced current in the cable are drawn when the parameters such as cable length, cable height from the bottom of the car, cable relative distance, cable terminal resistance, conductor radius, and insulation layer thickness change. These conclusions can provide theoretical guidance for the design of vehicle wire harnesses, and provide a basis for the conductor radius selection, cable relative distance, height from the ground, and cable length in electromagnetic protection design. 

To address the problem that traditional language models can not solve the problem of deep bidirectional representation and the problem that classification models can not adequately capture salient features of text, a text classification model based on BERT-BiGRU-CNN ( Bidirectional Encoder Representation from Transformers-Bidirectional Gating Recurrent Unit-Convolutional Neural Networks) is proposed. Firstly, the BERT pre-training model is used for text representation; secondly, the output data of BERT is input into BiGRU to capture the global semantic information of text. The results of BiGRU layer again are input into CNN to capture the local semantic features of text. Finally, the feature vectors are input into Softmax layer to obtain the classification results. The Chinese news text headlines dataset is used, and the experimental results show that the BERT-BiGRU-CNN based text classification model achieves an F1 value of 0. 948 5 on the dataset, which is better than other baseline models, proving that the BERT-BiGRU-CNN model can improve theshort text classification performance. 

In order to solve the problem that experiments of communication engineering professional course focus mainly on simulation, and practice are insufficient, a set of optical camera communication experimental system is designed. Using the host computer to edit information, the system employs a STM32 single-chip microcomputer to drive the high-frequency flicker of the strip-typed LED(Light Emitting Diode) illumination via an amplifying circuit, thus realizing the modulated transmission of information. And the mobile phone with a smart camera acts as a receiver to collect, demodulate and decode the strip information to get real-time information to display, which is implemented by a mobile application using Android Studio. The system integrates a variety of professional and practical technologies, such as embedded software, hardware development and Android development, to stimulate students' interest in learning. The experimental platform realizes coding, modulation, transmission, demodulation and decoding step by step, so that students can have a deep understanding of the principle of optical camera communication system through experiments, which is conducive to the study of professional education theory courses. 

When the current method is used to detect the three-dimensional human motion image, it can not accurately obtain the target area in the image, resulting in the problems of low detection integrity, low detection accuracy, high false detection rate and low detection efficiency. Therefore, an intelligent detection algorithm of 3D human motion image based on adaptive projection is proposed. Firstly, the background rough extraction model is constructed, and the background region of 3D human motion image is extracted by using the model to obtain the human motion target region of the image. Secondly, the features of the target region are extracted by adaptive projection method, and the optimal classification function is constructed on the basis of support vector machine. The target region feature is input into the optimal classification function to complete the intelligent detection of three-dimensional human motion image. Experimental results show that the proposed algorithm has high detection integrity, high detection accuracy, low false detection rate and high detection efficiency.

Network spoofing attack detection is an indispensable link in maintaining the normal operation of campus monitoring network, but the detection process is easily disturbed by problems such as signal strength, monitoring configuration and router performance. Therefore, a spoofing attack detection method of campus monitoring network based on deep learning algorithm is proposed. The self encoder in the deep learning network is used to reduce the dimension of the campus monitoring network traffic data, and the stack encoder composed of the self encoder is used to extract the features of the reduced dimension traffic data, the extracted features into is input the confidence neural network, the type of network spoofing attack is judged according to the comparison between the output confidence value and the fixed threshold, and the detection of campus monitoring network spoofing attack is completed. The experimental results show that the proposed method has the advantages of short detection time, high detection rate and low false alarm rate. 

With the increasing complexity of computer network space, in order to effectively reduce the losses caused by network security events, a multi classification prediction model based on the quantum particle swarm lightweight gradient descent algorithm (QPSO LightGBM: Quantum Particle Swarm Optimization-Light Gradient Boosting Machine) is proposed to evaluate vulnerabilities of high-risk network assets. Synthetic MOTE(Minority Oversampling) technique is used to balance the data, QPSO(Quantum Particle Swarm Optimization) is used for automatic parameter optimization is realized, and LightGBM is used for modeling. Multi-classification prediction of network asset vulnerability is realized. In order to verify the rationality of the model, the proposed model is compared with the model constructed by other algorithms. The results show that the proposed model is better in various performance indexes.

Extractive text summarization is a method of extracting key text fragments from the input text to serve as the summary. In order to solve the problem of requiring sentence-level labels during training, extractive text summarization is modeled as a Q-Learning problem and DQN(Deep Q-Network) to learn the Q value function. The document representation method is crucial for the quality of the generated summarization. To effectively represent the document, we adopt a hierarchical document representation method, which uses Bidirectional Encoder Representations from Transformers to obtain sentence-level vector representation and uses Transformer to obtain document-level vector representation. The decoder considers the sentence information enrichment, saliency, position, and redundancy degree between a sentence and the current summarization. This method does not require sentence-level labels when extracting sentences, which significantly reduces workload. Experiments on CNN( Cable News Network) / DailyMail data sets show that, compared with other extraction models, this model achieves the best Rouge-L(38. 35) and comparable Rouge-1(42. 07) and Rouge-2(18. 32) performance.

The unique advantage of dynamic fuzzy logic programming language is that it can process dynamic fuzzy data, but the existing compilers are difficult to effectively parse dynamic fuzzy data. To solve this problem, a new dynamic fuzzy logic programming language compiler is designed by extending the structure of supervised command program and introducing the formal description of dynamic fuzziness. The example shows that the compiler can correctly parse dynamic fuzzy data. Furthermore, it can reduce the difficulty of dynamic fuzzy logic program debugging and improve the efficiency of dynamic fuzzy logic program development.

In order to reduce the inspection burden of electric power workers and realize intelligent inspection in substation, the algorithm of substation equipment defect detection is studied. Firstly, the data augmentation method is used to expand the initial dataset and various image processing method is used to generate the dataset with complex illumination environment. Then, the adaptive spatial feature fusion method is used to mitigate the inconsistency of different scale features in the feature pyramid, and the loss function of confidence is changed to Focal loss function to mitigate the imbalance between positive and negative samples. Based on the improved YOLOX-s(You Only Look Once X-s) network model, the algorithm of substation defect detection is designed. Finally, the detection effect of the improved YOLOX-s model is compared with that of other deep learning algorithms. Under the designed data set, the experiment shows that the comprehensive detection effect of the improved YOLOX-s network model is good, and the accuracy and real-time performance is satisfied. 

Information privacy of database is vulnerable to illegal attacks. It has low security and less storage space. In order to solve the problems an encryption storage algorithm database information privacy for based on chaotic mapping is proposed. The chaotic sequence is generated by disturbing the control parameters of Logistic chaotic mapping, and the sequence is mixed with the chaotic sequence generated by other systems to generate a new chaotic sequence. Then, the non-linear transformation is carried out through the dynamic coding algorithm. The output sequence is used as the database plaintext key to encrypt the plaintext. The index field for database encryption information query is constructed, compressed by hash function, and stored in the database together with the encrypted information realizing the information privacy encrypted storage of searchable chaotic mapping database. The experimental results show that the proposed method can shorten the time of encryption and decryption, reduce the space occupation and reduce the energy consumption.

 In order to improve the high definition of multi frame plane image and improve the quality of multi frame plane image, an detail enhancement algorithm of multi frame plane image texture based on visual communication is proposed. According to egdnet algorithm, the noise interference of multi frame plane image, strengthen the image edge information, supplement the image color information with color mapping equation is eliminated, completing color correction and ensuring the accuracy of color in visual communication. The image high-frequency information is extracted with Gaussian fuzzy algorithm, the super-resolution multi frame plane image is obtained through interpolation, and the super-resolution multi frame plane image is synthesized through windowing operation. And the texture detail enhancement of multi frame plane image is accomplished. The experimental results show that the image texture detail enhancement has higher definition and better image quality. 

For the course “ error theory and data processing ”, which is highly theoretical and has many calculation formulas, while traditional teaching focuses on theory and ignores practical problems, based on Matlab APP(Application) Designer, a comprehensive experimental system APP is designed. The basic concepts such as random error, systematic error and gross error and typical algorithms such as least square fitting are realized and visualized respectively. Based on the actual engineering data of ground nuclear magnetic resonance, the methods for removing systematic errors and gross errors are presented. This comprehensive experimental platform cultivates students ' application ability and completes the organic combination of scientific research and teaching. The experimental platform is convenient for students to understand and master abstract concepts, and improve students ' interest in learning.

In order to reduce the interference of common mode noise and improve the SNR (Signal-to-Noise Ratio) of laser self-mixing grating interference signal under the condition of signal loss, a method of measuring the interference displacement of laser self-mixing grating based on differential technology is proposed. The +1 diffraction light is reflected by the vibrating object and returns to the laser cavity, where self-mixing interference occurs and is received by the photodetector PD1 encapsulated in the laser, while the 0 diffraction light is received by the external photodetector PD2 . According to the principle that there is phase difference in the forward and backward output of semiconductor laser, the phase of the self-mixing interference signal carried by the two beams is opposite, and the superimposed noise is partially correlated, so the difference between the two interference signals can eliminate part of the noise. By processing the differential self-mixing signal, the displacement can be reconstructed. The experimental results show that the mean error of the reconstructed vibration waveform is smaller, the signal is more stable, and the signal-to-noise ratio of the interference signal is improved by no less than 3 dB.

It is difficult to detect the tampering evidence after the images with tampering operations such as splicing and synthesis are compressed. In order to achieve tamper detection of compressed images, a compressed image stitching and synthesis detection method based on camera calibration is proposed. The imaging law of compressed image is analyzed, and the spatial transformation matrix is used as the detection evidence. Based on the camera calibration parameters, the homography matrix is estimated. Based on the external parameters of the camera, the authenticity of the image is determined by rotating the spatial transformation matrix of the image, and the detection of stitched synthetic compressed image is completed. The experimental results demonstrate that this method can detect images that have been spliced, synthesized, and tampered with, with a detection accuracy of 75% .

 In order to solve the problems such as large resistance and low efficiency of the traditional hydraulic power steering system during the driving process of commercial vehicles, improper maintenance after long-term use will lead to leakage, and may cause accidents, an electric auxiliary steering motor control is adopted. The electric auxiliary steering motor selects the permanent magnet synchronous motor. By using the fuzzy PID (Proportional Integral Derivative) algorithm, it can be modulated to the reference value accurately and quickly . The extended Kalman filter is used to estimate the rotor position of the permanent magnet synchronous motor, reduce the volume of the controller and reduce the cost. In the drive system, the diversity of large current coupling paths seriously affects the electromagnetic compatibility performance of the motor control system, so the drive system EMC ( Electro Magnetic Compatibility) design is adopted. From the experimental results, it is concluded that the permanent magnet synchronous motor control based on fuzzy PID algorithm and extended Kalman filter can be applied to the electric auxiliary steering motor control of commercial vehicles.

 In order to enhance the security factor of patients’ private information and reduce the risk of data leakage, a hierarchical encryption algorithm for massive medical information based on chaotic cellular neural network and wavelet transform was proposed under the premise of considering the importance of privacy. The word repetition rate of medical information is calculated by the matching degree of word segmentation and weight matching degree, and similar data in massive information is eliminated, so as to reduce the workload of subsequent information encryption. The importance of data privacy is evaluated by grades such as medical importance, number of visits, and data size, and medical text information and image information are distinguished by data attributes. Using the chaotic features of cellular neural networks, the original medical information is converted into a parameter matrix. Logistic mapping is used to obtain the key chaotic sequence, the medical text information after primary encryption is output, the image signal is analyzed in time domain by wavelet transform to achieve secondary encryption, and the result of secondary encryption is fused to complete the hierarchical encryption of medical information. The experimental results show that the proposed algorithm has the advantages of good encryption effect, fast execution speed and high security factor, and is a suitable solution for the safe storage of medical information.

Aiming at the problem that the current method does not consider the judgment of fault current, resulting in poor fault location and detection effect and low fault location accuracy, a fault locating algorithm for distribution network operation detection based on topological decoupling is proposed. According to the topological structure principle of distribution network with equivalent decoupling, based on the topological adjacency matrix of distribution network, several trunk networks with equivalent decoupling of distribution network are combined, and FTU(Feeder Terminal Unit) feeder terminal equipment is added to each trunk network to divide the single branch of distribution network with FTU. Kirchhoff current law is used to calculate the fault line and judge the fault current direction in the single branch. According to the judgment results, the fault interval judgment matrix is used to locate the operation inspection fault of distribution network. The experimental results show that this method has small error between fault location and actual location, more fault nodes and less false detection times, and its fault location and detection effect is good, which can effectively improve the fault location accuracy.

Eliminating duplicate data is an indispensable step to ensure efficient network operation. But this process is susceptible to interference from signal strength, network device, router performance and other problems. Therefore, a minimum-hashing algorithm for single channel data elimination is proposed. First the hash function in the hash algorithm network is used for single channel data clustering, and then supervision discriminant projection algorithm is applied for clustering of data dimension reduction after processing, finally the algebraic sign estimate is used to guarantee the data between the computing cost minimum and to construct minimum hash tree generated calibration value, in the update to heavy tags. The repeated data in a single channel is completely eliminated by double-layer culling mechanism. Experimental results show that the algorithm has short execution time and low computation and storage cost.

Even subgraph covers is an important subject in graph theory. Inorder to study the shortest cycle covers conjecture, using the connection between integer flows and even subgraph covers, a new upper boundary of shortest cycle covers of bridgeless graphs is obtained by means of the conclusion of expanding an integer 4-flow in a circuit of a graph. The result improves Fan's conclusion.

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. 

Aiming at the problem that the tracking ability decreases due to occlusion during the tracking of video human moving objects, an adaptive tracking algorithm for video human moving objects considering the occlusion factor is proposed. The Kalman filter and the Meanshift tracking algorithm are used to track the video human moving target. When the target scale, rotation and light change there poor tracking results. The SIFT( Scale Invariant Feature Transform) algorithm is introduced to improve the tracking ability and achieve anti-occlusion video human moving target adaptive tracking. The experimental results show that the tracking accuracy of this method is high, and the tracking success rate can reach about 75% when the occlusion degree is 0. 5. The average tracking frame rate is 28. 9 frame / s, and the real-time performance is strong.

A network intrusion detection algorithm that combines systematic data pre-processing and hybrid sampling is proposed for the problem of class imbalance in intrusion detection datasets. Based on the feature distribution of the intrusion detection dataset, the feature values are systematically processed as follows: for the three categorical features, “Proto’’,“Service’’ and “State’’, minor categories within each feature are combined to reduce the total dimension of one-hot encoding; the 18 extremely distributed numerical features are processed with logarithm and then standardized according to the numerical distribution. The class imbalance processing technology, which combines Nearmiss-1 under-sampling and SMOTE ( Synthetic Minority Over-sampling Technique) is designed. Each class of samples in the training dataset is divided into sub-classes based on the “Proto’’,“ Service’’ and “ State’’ categorical features, and each sub-class is under-sampled or oversampled in equal proportion. The intrusion detection model PSSNS-RF ( Nearmiss and SMOTE based on Proto, Service, State-Random Forest) is built, which achieves a 97. 02% multiclass detection rate in the UNSW-NB15 dataset, resolving the data imbalance problem and significantly improving the detection rate of minority classes.

In view of the large scale networking of three-dimensional lightning detectors, the location data of reception and reconciliation have been greatly increased, and there is no software specially designed for monitoring, displaying and processing three-dimensional lightning data in China, therefore a three-dimensional lightning data graphic display system is developed using B / S(Browser/ Server) architecture, ArcGIS geographic information platform, Oracle, SQLite database and other technical methods. The state monitoring subsystem is designed by using state data receiving module, analytical input module, real-time state statistics module and other functional modules. The real-time display subsystem is designed by using the function modules of location data real-time statistics, historical inquiry and three-dimensional display, and the product display subsystem is composed of a product production subsystem with data service product generation function, and the system function and operation state are verified by the national three-dimensional lightning detection network as the practical application background. The results show that the three-dimensional lightning data graphic display system is intuitive, friendly, timely display, full-featured, and rich in products. It is a useful tool in the field of lightning research.

 Current material identification methods are mainly based on X-ray technology, ultrasound technology and radio frequency technology. However, X-ray technology relies on special equipment to transmit high frequency signals and is highly radioactive; ultrasonic technology equipment is bulky and inconvenient to carry; and RF(Radio Frequency) technology mainly relies on costly RFID(Radio Frequency Identification Devices). In order to meet the daily use in home and office scenarios, MateFi system for material identification is proposed based on WiFi(Wireless Fidelity), aiming to establish a new theoretical model to describe more specifically the attenuation state of electromagnetic waves as they penetrate different materials. The theoretical model is used to build a more robust and accurate material recognition system by combining material characteristics with machine learning techniques. The performance of the MateFi system is tested and validated in real-life scenarios. The experiments show that MateFi can recognise six different materials: wood, cardboard, nickel, thin wood, iron and titanium, with an average recognition accuracy of 96. 70% , demonstrating the system’s ability to identify materials accurately. 

Because the existing algorithms fail to calculate the semantic similarity, the detection results are not ideal, and an automatic detection algorithm for the deviation of the composition subject for college English teaching is proposed. In the college English teaching environment, combining distributed semantic space and structured semantic space, a semantic representation model is constructed to obtain the semantic similarity between English words and phrases. Through the LDA(Latent Dirichlet Allocation) model, all documents are trained, and the probabilistic weighted summation of each subject and feature words in the document is carried out, and the composition of the subject deviation is detected according to the set reasonable threshold. The results of simulation experiments show that the proposed algorithm can obtain high-precision automatic detection results of composition subject deviation.

A GA(Genetic Algorithm) based C-IoT(Cognitive Internet of Things) power adaptive PIS( Partial Interference Steering) algorithm is proposed for the interference management problem in C-IoT(Cognitive Internet of Things) systems under the concurrent spectrum access model. The algorithm can improve the spectrum fficiency of the system while ensuring the quality of service for both the PU ( Primary User) and the CU (Cognitive User). The simulation results show that the algorithm can converge quickly in seeking the optimal spectral efficiency of the system and calculate the optimal transmitting power of the PU and CU desired signals. In the scenario where the relative positions of the primary transmitter, PU and CU are determined, the optimal spatial distribution of the cognitive transmitters with access to the authorized spectrum can be solved based on the average degree of constraint violation D_{cv_ave} by the users.

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.

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.

Chinese chemical named entities do not have strict word formation rules to follow, and the recognition entities contain letters, numbers, special symbols and other forms, and the traditional word vector model can not effectively distinguish between nested entities and ambiguous entities in chemical terms. The named entities of high school chemistry test resources are devided into four categories: substances, properties, quantities, and experiments, constructing a vocabulary of chemistry subjects to assist manual labeling. Then, the ALBERT pre- training model is used to extract text features and generate dynamic word vectors, and the named entity recognition is performed on the text of high school chemistry questions combined with the BILSTM-CRF (Bidirectional Long Short-Term Memory with Conditional Random Field) model. The accuracy, recall and F1 values of the proposed model reached 95. 24% ,95. 26% and 95. 25% , respectively. 

Aiming at the insufficient robustness problem of weak signal detection and modulation recognition in multi-sensor distributed reception systems, a new joint processing method based on deep learning is proposed. The proposed method adopts the distributed soft information fusion processing strategy where the signal detection and modulation recognition are integrated into a multi-variate hypothesis test problem. With the help of the excellent function approximation ability of DNN(Deep Neural Network), a method of joint pos terior probability solution and classification based on deep neural network DNN is proposed based on the analysis of network structure, objective function and network input and output. Finally, the performance of the proposed method is verified by simulation experiments, and compared with the existing methods. The results show that the proposed method can effectively fuse multiple sensor signals, and can significantly improve the classification accuracy with the increase of the number of receiving units. Compared to the existing confidence fusion methods based on equal weight combination, the proposed method has better performance, which is more obvious at low SNR(Signal-to-Noise Ratio) values, short signal lengths and large receiving units numbers.

An improved artificial potential field method is proposed to solve the problems of local minimum and unable to reach the target in the path planning of mobile robots. Firstly, in order for the robot to reach the target point when there are obstacles near the target point due to the large repulsive force, a safe distance factor is introduced into the potential field, and this parameter is optimized, so that the robot can maintain a proper distance from the obstacles and reach the target point smoothly. Secondly, in order to solve the local minimum problem, the local minimum discriminant condition is introduced, and the local minimum region is circum- navigated when the condition is triggered, so that the robot can reach the target point smoothly. The simulation results show that the improved algorithm has strong robustness when operating in the map environment with different number of obstacles. The proposed algorithm can make the robot bypass the local minimum area in the U-shaped obstacle environment, and successfully solve the local minimum problem in the mobile robot path planning.

In order to better protect the ecological environment and increase the economic value of recyclable waste, to solve the problems faced by the existing garbage identification methods, such as the complex classification background and the variety of garbage target forms, a residual semantic enhancement network for garbage image classification is proposed, which can strip foreground semantic objects from complex backgrounds. Based on the backbone residual network, the network uses visual concept sampling, inference and modulation modules to achieve visual semantic extraction, and eliminates the gap between semantic level and spatial resolution and visual concept features through the attention module, so as to be more robust to the morphological changes of garbage targets. Through experiments on the Kaggle open source 12 classified garbage dataset and TrashNet dataset, the results show that compared with the backbone network ResNeXt-50 and some other deep networks, the proposed algorithms have improved performance and have good performance in garbage image classification. 

The estimation error of the time difference of the frequency hopping signal in the spread spectrum communication network is too high, leading to a decrease in signal positioning and tracking ability, and signal propagation stagnation in multiple fields. In order to enhance the propagation efficiency of the frequency hopping signal in the spread spectrum communication network and improve the signal detection ability of the radiation source tracking and positioning system. A method for estimating the time difference of hopping signals in spread spectrum communication networks is proposed based on FRFT ( Fractional Fourier Transform ) and blind separation. The blind separation method is used to obtain two channels that meet the time difference estimation conditions of frequency hopping signals, namely flat fading channel and frequency selective fading channel. The two channel characteristics are estimated by FRFT. The time difference estimation of frequency hopping signals in spread spectrum communication networks is realized by combining the characteristics of flat fading channel and frequency selective fading channel with the maximum likelihood block detection algorithm. The experimental results show that the root mean square error of the proposed method is at a low level and the estimation success rate is at a high level whether in normal environment or noise interference environment.

The Artificial Bee Colony algorithm ( ABC: Artificial Bee Colony) suffers from the problems of difficult convergence and difficulty in maintaining the diversity of candidate solutions. In order to solve the Multi- Objective Optimization Problem (MOP: Multi-Objective Problem) the solution strategy of each part is improved. Based on the ABC algorithm framework, a multi-objective ABC algorithm based on an adaptive solution strategy is designed to compare the performance of the improved multi-objective ABC with other typical swarm intelligence algorithms in the practical application of engineering design problem of electromechanical actuator design. The experimental verification shows that the proposed MOABC / DD(Multi-Objective Artificial Bee Colony Based on Dominance and Decomposition) algorithm has better problem solving accuracy compared with typical algorithms in solving the benchmark test case of electromechanical actuator design problem. The experimental results of MOABC / DD are more stable, thus proving that MOABC / DD has high solution stability and robustness.

In order to solve the problem of unclear face information in face recognition under face occlusion, and optimize the face recognition system, a local occlusion face image recognition method based on face edge details is proposed. The face image denoised according to the sparse expression, the edge of the face image is detected according to the principle of image gray transformation, the edge region is segment, and its threshold is calculated to obtain the edge information of the face image. The face feature is marked points to enhance the accuracy of information recognition, the feature descriptor of face image is extracted, it is input into support vector machine model, and local occlusion face image recognition is realized through training. The experimental results show that the average recognition rate of face image under the application of the proposed method is higher than 73% and the recognition time is less than 20 s. 

In order to improve the accuracy of short-term power load prediction, a CNN-BiLSTM-Att (Convolutional Neural Network-Bidirectional Long Short-Term Memory-Attention) short-term load prediction model based on variational mode decomposition VMD(Variational Mode Decomposition) is proposed. In this model, the historical load data is decomposed into multiple sub-sequence loads using VMD and combined with weather, date, type of working day and other factors as input characteristics. The predicted value of each sub- sequence load is predicted by this model, and then added and reconstructed to form the actual load prediction curve. By comparison with other models, the VMD-CNN-BiLSTM-Att model has a decrease in the test set. In the continuous weekly load prediction, the average absolute percentage error of daily load prediction is basically maintained between 1% ~ 2% . In the non-working days with complex load changes, the mean absolute percentage error is reduced by 0. 13% compared with the CNN-LSTM model. It is proved that VMD-CNN- BiLSTM-Att short-term load forecasting model can improve the accuracy of power load forecasting. 

 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

 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.

A new data filtering and fusion algorithm are proposed for the problems of ineffective energy consumption and short network lifetime in oil and gas IoT( Internet of Things). This algorithm can adaptively judge the degree of data abnormality, filter and fusion data, avoiding redundant network information and excessive energy consumption. The algorithm adaptively determines the abnormality of the data by judging the deviation degree between the monitoring data and the normal data, performs intra-cluster filtering and inter-cluster fusion on the data. Compared with the traditional scheme, the proposed scheme can effectively improve the communication quality and energy consumption efficiency of oil and gas IoT.

 For the fault diagnosis problem of the indicator diagram of pumping wells, the feature information in the image is extracted by convolution neural network. In order to ensure the diagnostic performance of the network model, the structure complexity of the network model is reduced. Based on the lightweight convolution neural network, the attention mechanism is introduced to improve the diagnostic performance of the lightweight network model. First, in the network infrastructure the MobileNet-V2 network is used, and the attention ECA (Efficient Channel Attention Module) module is embedded in the inverse residual module of MobileNet-V2. Compared with the ordinary residual network, the features retained after convolution are more complete, so the fault diagnosis capability of the model is improved. Then, the ECA uses 1D convolution to achieve local cross- channel information interaction between adjacent channels and obtain the dependencies between local channels. The resulting channel attention re-calibration weights are multiplied by the corresponding channels of the input feature map of the module, and the attention-weighted feature map is obtained. The MobileNet-V2 accuracy rate is 90. 6% , and the improved MobileNet-V2 diagnostic accuracy rate is 97. 60% . 

The cloud data of metal part surface damage point requires high local feature analysis ability of the segmentation network, and the traditional algorithm with weak local feature analysis ability can not achieve the ideal segmentation effect for the data set. The relative damage volume and other features are selected to classify the metal surface damage, and the damage is divided into six categories. This paper proposes a method to extract the attention feature of 3D map containing spatial scale area information. The obtained spatial scale area feature is used in the design of feature update network module. Based on the feature update module, a feature updated dynamic graph convolution network is constructed for point cloud semantic segmentation. The experimental results show that the proposed method is helpful for more effective point cloud segmentation to extract the local features of point cloud. In metal surface damage segmentation, the accuracy of this method is better than pointnet++, DGCNN(Dynamic Graph Convolutional Neural Networks) and other methods, which improves the accuracy and effectiveness of segmentation results. 

In the face of problems such as low resolution and image blurring in drone inspection images, a super-resolution reconstruction method is proposed for drone inspection images using the theory of VDRCNN(Very Deep Residual Convolutional Neural Network). The algorithm model consists of a VDSR( Very Deep Network for Super-Resolution) and a residual structure. Based on the VDSR, the algorithm is improved by adding a residual structure to enhance convergence speed, while combining batch group normalization and Adam optimizer to achieve better reconstruction effects. On this basis, an electric power component detection dataset is constructed, and high-resolution reconstruction of blurred electric power component images is achieved by properly setting the network parameters. The experimental results show that the super-resolution method based on VDRCNN can reconstruct images with richer textures and more realistic visual effects, with improvements of 2. 95 dB and 3. 79% in peak signal-to-noise ratio and structural similarity respectively, compared to traditional detection methods. Therefore, the proposed VDRCNN-based super-resolution reconstruction method has certain potential application value in solving practical problems in power inspection.

Failure diagnosis of oil pumping machine has low identification accuracy due to various faults and complex system, which increases the difficulty of fault diagnosis. After clarifying the working principle of SVM, the ant colony algorithm is carefully studied to adjust the penalty coefficient of SVM(Support Vector Machine) and the kernel function parameters. The ant colony algorithm has the problem of easy to fall into the local optimal solution, which introduces the idle ant to update the pheromone again after the ant colony algorithm fails to enable the ant group to obtain new paths. In order to further reduce the problem of local optimal solution of ant colony algorithm and improve the search speed of ordinary ants in the early stage of optimization, idle ants are optimized by using chaotic initialization and chaotic perturbation. The test data of the pumping machine shows that the proposed fault diagnosis system has high fault identification accuracy.

Due to the influence of complex terrain and canopy structure in forest, the accuracy of snow depth retrieval based on passive microwave remote sensing data is generally low. Based on the representative semi- empirical snow depth retrieval algorithm and combined with meteorological observation data, an optimization algorithm of semi-empirical snow depth retrieval in forest area in Northeast China was established in this paper. In this algorithm, the permittivity of vegetation varies with temperature and the accuracy of snow depth retrieval in forest is greatly improved. Compared with other representative semi-empirical algorithms, the RMSE(Root- Mean-Square Error) of the proposed algorithm is reduced by 2. 3 cm, Bias by 3. 7 cm on average and correlation (R) improved by 0. 11 on average. Compared with the commonly used snow depth retrieval algorithm based on machine learning, the RMSE of the proposed algorithm is reduced by 2. 17 cm, Bias by 1. 67 cm on average and R improved by 0. 22 on average.

 

 Microseismic inversion is an important way to complete main task of microseismic monitoring by inverting and inferring information such as the location of the epicenter, the time of occurrence of the earthquake, the true magnitude, the initial amplitude of the hypocenter, the focal mechanism and the medium parameters based on the microseismic monitoring data. Through the study of microseismic inversion technology, more accurate microseismic information can be obtained, thereby improving the reliability of reservoir fracture evaluation, reducing development costs and improving oil and gas recovery. We review microseismic inversion from three aspects: microseismic focal location inversion, microseismic focal mechanism inversion, and microseismic multi-parameter joint inversion. The research progress of microseismic inversion technology in recent years is reviewed, and the principle, advantages and disadvantages of various microseismic inversion methods are summarized, the improvement and application of various microseismic inversion techniques are summarized, and the future research ideas and development directions are prospected. It provide reference for further development of microseismic inversion in the future.

 In order to cope with large-scale new energy access, a two-stage optimization scheduling model of an active distribution network is constructed. Based on the IEEE 33-node distribution network model, a day-to-day scheduling model of an active distribution network is constructed by adding dispatchable loads such as electric vehicles and energy storage devices, and reactive power regulating devices such as capacitor banks, static reactive power compensation, and on-load voltage regulating transformers. In the pre-day stage, the output of each equipment in the distribution network in the next 24 hours is scheduled with the optimization goal of comprehensive operation cost. In the intra-day rolling optimization scheduling stage, day-ahead scheduling results of energy storage equipment, electric vehicles, and capacitor banks are taken as constraint conditions, and voltage stability, network loss, and new energy utilization are taken as optimization objectives. The rolling optimization cycle is 4 hours. After dispatching load access, the new energy consumption rate of day-ahead dispatching increased by 4. 41% , and the power purchase cost decreased from 472. 03 yuan to 446. 90 yuan. Compared with the day-ahead scheduling, the absorption rate of new energy in day-ahead scheduling is reduced by 0. 53% , and the maximum voltage offset of each node is reduced from 0. 060 0 to 0. 025 3. Experimental results show that the proposed two-stage scheduling model has a lower voltage offset and higher new energy utilization. 

In order to solve the problems of high packet loss rate, long link delay and low utilization in traditional wired hybrid network, a method of congestion optimization is proposed. Based on the detection results of one-way delay and available bandwidth, Markov model is used to predict the congestion status of wired hybrid network transmission, which takes resource consumption and link utilization as the target of optimization control. When the path of optimal control of the target meets the requirements, the congestion optimization control of data transmission of wired hybrid network is completed. The experimental results show that the packet loss rate of the proposed method is within 4% , the link delay is controlled within 0. 05 s, and the packet loss rate is low, the link delay is short and the link utilization is high.

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.

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.

 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 the process of predicting the intrusion intention of abnormal information in the hospital network, there is no dimension reduction processing for the hospital network data, resulting in a long prediction time and a low prediction accuracy. Therefore, an algorithm for predicting the intrusion intention of abnormal information in the hospital network based on the improved RBF(Radical Basis Function) model is proposed. The redundancy of hospital network data is removed and sorted through correlation analysis, and the sorted data attributes are selected by RBF multilayer neural network to complete the dimensionality reduction of hospital network data. According to the data preprocessing results, the Bayesian attack graph is constructed to obtain the potential network intrusion attack path. The alarm correlation strength is calculated in the path, the intrusion alarm evidence data is extracted, the information intrusion probability is determined through the monitoring of the alarm evidence, and the prediction result of the abnormal information intrusion intention of the hospital network is obtained. The experimental results show that the proposed method has high efficiency, high accuracy and good overall effect.

Aiming at the problem that the traditional distribution strategy has a difference in available capacity in the hybrid energy storage system, and the hybrid energy storage system will be out of service due to insufficient available capacity, a power distribution strategy using improved sparrow algorithm is proposed. The ratio of the effective storage capacity to the overall capacity in the system is the optimization objective. And the improved sparrow algorithm can better solve the power distribution problem between lithium batteries and super capacitors. Aiming at the characteristics of high power and low energy density of supercapacitors, and the problem of insufficient available capacity in practical work, a method using lithium-ion batteries to adjust the residual effective energy storage capacity of supercapacitors according to the transfer current is proposed. The controlled transfer current solution method ensures that the supercapacitor always maintains a certain effective energy storage capacity, thereby enhancing the continuous operation capability of the supercapacitor. Finally, the rapidity, stability and effectiveness of the strategy proposed are verified by simulation.

Measure planning is the main method to increase oil and control water in oilfield. In order to accurately predict the effect of various measures to increase oil production, a measure effect prediction model based on HDCNN(Hybrid Dilated Convolutional Neural Network)-BIGRU-Attention is proposed with monthly oil production and water content as the prediction targets. The model extracts multi-scale global features of production data through HDCNN. Aiming at the characteristics of strong timing and large volatility of measure production data, the BIGRU(Bidirectional Gated Recurrent Unit) is used to fully mine the long-term dependence between data to improve the utilization rate of time series information and the learning effect. The scaled dot- product attention mechanism (Attention) is introduced, and the weight adjustment strategy is used to make the network focus on the feature dimension with large correlation with the prediction target. In order to verify the effectiveness of the proposed model, LSTM(Long Short-Term Memory), CNN(Convolutional Neural Network)- LSTM and LSTM-attention are taken as experimental comparisons. The results show that the proposed model has lower prediction error and better generalization ability. 

 In order to facilitate the testing of students’ English language application ability, many schools hope to apply computers to language testing, but there are doubts about the effect of computer-assisted language testing. In view of this situation, a computer-aided language testing validity evaluation algorithm based on entropy weight method is studied. The validity evaluation index is selected by gray correlation analysis, and the evaluation index system is constructed. The entropy weight method is used to calculate the weight of each index. Through fuzzy comprehensive evaluation, the index weight and the index membership degree are fuzzy synthesized to obtain the validity evaluation score. The validity grade is obtained by referring to the principle of maximum membership degree. The results show that the validity of computer-aided language testing system in junior and senior high schools has reached a very high level, while in universities the validity has decreased, but it still reaches a high level, which shows that computers perform well in computer-aided language testing and have strong practicability.

 Due to differences in camera parameters, shooting environment, and angles for pedestrian photography, the accuracy of pedestrian recognition algorithms still needs to be improved. To this end, a pedestrian re recognition algorithm based on pedestrian semantic perception information and deep learning is proposed. Firstly, super-resolution reconstruction of pedestrian views enhances the detailed features of pedestrian views, extracts the overall feature values of pedestrians, and uses them to identify pedestrians with significant body differences. Secondly, the Semantic information of pedestrian images is perceived, and the feature values of pedestrian Semantic information are extracted according to the above results to identify pedestrians with the same or similar body shape. Then, the macroscopic feature values of the human body and the semantic perception information feature values in the pedestrian video are fused into a comprehensive feature value. Use the generated feature values to calculate the distance between them and the video feature values of different individuals, and identify massive character images. Finally, this article validated the performance of the algorithm in different datasets. The experimental results show that the language perception based pedestrian recognition algorithm has the highest mAP and rand-1 values.

 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. 

This work aims to deploy a CNN onto resource-constrained MCUs(Microcontroller Units) to achieve image classification and recognition for scenarios that require simple image recognition tasks, low image recognition accuracy, and low cost. Firstly, a lightweight deployment strategy on resource-constrained MCUs is proposed. To reduce the number of model parameters, a lightweight neural network algorithm is proposed. To ensure that the model size can fit into limited RAM(Random Access Memory), a storage replacement algorithm is presented based on FLASH ( Flash Memory ) sectors. Secondly, the strategy on embedded devices is implemented. The camera peripheral circuit is designed for image quality, but the acquisition speed does not match. The collected images are binarized by an adaptive threshold based on Gaussian distribution and the integrity of image samples is verified. Experimental results show that the system can achieve better image classification and recognition accuracy when applied in the above practical scenarios. 

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.

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% . 

For the problem that LLE(Local Linear Embedding) fails to adequately preserve the structure between

neighborhoods in high-dimensional data, a new local linear embedding algorithm is proposed for fused

neighborhood distribution properties. The algorithm calculates the neighborhood distribution of each sample data,

then calculates the respective nearest neighborhood distribution difference of the KL ( Kullback-Leibler)

divergence measure between the different neighborhood point and its central sample, and finally optimizes the

reconstructed weight coefficient to obtain more accurate low-dimensional motor data. The effectiveness of the

algorithm is verified by three evaluations of visualization, Fisher measurement and identification accuracy.

Automatic analysis of lumbar disc herniation requires precise segmentation of the multifidus muscle’s fatty infiltration site in spinal MRI ( Magnetic Resonance Imaging) images. An attention-based approach for segmenting the multifidus muscle in lumbar disc herniation patients is proposed to address issues including ambiguous boundaries between segmentation targets and adjacent components. The network utilizes an encoder- decoder structure, and the addition of an attention mechanism module to increase the network segmentation accuracy. After feature extraction, an atrous spatial pyramid pooling module is added to combine contextual data improving the performance of the network model. In comparison to the traditional U-Net algorithm, the experimental results demonstrate that this model improves the segmentation accuracy of the fatty infiltrated regions of multifidus muscle by improving the Dice coefficient by 7. 8% , Jaccard similarity coefficient by 10. 1% , and Hausdorff Distance by 69. 5% . 

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.

To avoid the gradient vanishing or exploding issue in the RNN(Recurrent Neural Network)-based sequential recommenders, a gated recurrent unit based sequential recommender DeepGRU is proposed which introduces the residual connection, layer normalization and feed forward neural network. The proposed algorithm is verified on three public datasets, and the experimental results show that DeepGRU has superior recommendation performance over several state-of-the-art sequential recommenders ( averagely improved by 8. 68% ) over all compared metrics. The ablation study verifies the effectiveness of the introduced residual connection, layer normalization and feedforward layer. It is empirically demonstrated that DeepGRU effectively alleviates the unstable training issue when dealing with long sequences. 

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.

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. 

In order to solve the chattering phenomenon in sliding mode control of nonlinear the ball and plate system and the multi-disturbance problem in trajectory tracking, we studied a sliding mode control scheme based on a three-step method. Firstly, establish the ball and plate system state-space model, and design the sliding mode control law to overcome the uncertain disturbance of the system. Secondly, combining the three-step control law with the sliding mode control algorithm, design a three-step sliding mode controller to avoid the chattering phenomenon in the sliding mode control. Finally, prove the closed-loop control system stability by Lyapunov function. Compared with the simulation results, this method's trajectory tracking control effectiveness is further verified. 

In order to solve the problems of fuzzy boundary information and noise influence in identifying special geological bodies such as seismic faults and gas chimney in seismic images, the LBP / ENT edge detection algorithm is proposed, which is an improved combining LBP( Local Binary Patterns) algorithm and local ENT (Entropy). Rotation invariant unified local binary pattern is used to construct the traditional LBP. The transverse discontinuity of abnormal geological bodies is adapted in seismic images, local entropy is used to describe the local discrete characteristics of seismic images to improve the robustness of seismic noise. Compared with Canny operator, LBP Operator, local VAR(Variance) and LBP / VAR operator, the proposed LBP / ENT method is used to study the complex Marmousi theoretical model and the gas chimney on the actual seismic image. The results show that LBP / ENT can more clearly describe the edge information of seismic image and have better robustness to noise. It is concluded that the proposed LBP / ENT algorithm provides a feasible method and technology for detecting the edge information of special geological bodies on seismic images. 

Affected by the stability of the hospital network, the data of paperless office network data is vulnerable to attack. Therefore, an adaptive encryption algorithm based on chaotic sequence is proposed. The key is automatically generated by random numbers, and the chaotic sequence is generated by three-dimensional chaotic system to generate the scrambled and grouped key sequence. Based on the service expectation, a node scheduling algorithm is designed to schedule the nodes of hospital paperless office network to ensure that the key encryption can be scheduled to the appropriate network nodes. Through cloud storage and improved knapsack algorithm, the adaptive encryption of hospital paperless office network is realized. In the paperless office network of a hospital, text data, image data and video data are selected for testing. The test result shows that the encrypted ciphertext presents a digital state, which is not easy to attract the attention of attackers. The mean square error between the ciphertext and plaintext of the three kinds of data is large, up to 258. 63. The data correlation of the three kinds of data after encryption is greatly weakened, which shows that the design algorithm can destroy the original correlation of the data and has good network data encryption ability.

The propagation of digital media images is widely used in daily life. At present, the fuzzy image de duplication method still has the problems of unclear image and low quality after processing. In order to solve the problems, an adaptive fuzzy de duplication algorithm for digital media images based on wavelet domain is proposed. Firstly, the digital media image is denoised by wavelet domain method. Secondly, the digital media image is divided into protected area and unprotected area by using the method of gradually labeling significant area, in which the protected area is the significant area. Finally, the image is processed by significance regularization, and the image adaptive fuzzy de duplication algorithm is completed. The experimental results show that the image noise is low, the image quality is high, the image information is rich, and the definition is good. 

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. 

 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 improve the QoS(Quality of Service) of users in mobile communications and to solve the problem that the total energy efficiency of the system and the quality of service of users can not be reconciled, a resource allocation scheme based on the quality of service and energy efficiency of users is proposed for the application scenario of SCMA(Sparse Code Multiple Access) downlink. The capacity and energy consumption of the system are taken as the main directions for optimisation. A greedy algorithm is used to allocate subscribers to the codebook, which is transformed into a convex optimization problem using the GDA(Generalized Dinkelbach’s Algorithm) for iterative solution. The user code book and energy consumption are calculated in rotation until the minimum user communication speed in the system changes below a preset value or reaches the maximum number of operations, so that the capacity and energy consumption of the system reach a better situation. The theoretical analysis and simulation results show that the proposed SCMA allocation scheme can effectively improve the communication rate and system energy efficiency of the users in the downlink.

In order to improve the torque ripple and flux ripple in the model predictive control system of PMSM (Permanent Magnet Synchronous Motor), a control system scheme is designed by learning the basic structure and control methods of PMSM. The scheme adjusts the duty cycle and voltage vector synchronously. The optimal expected voltage vector and action time at a certain sampling time are selected, and the optimal expected voltage vector and action time at the current sampling time are added to adjust the duty cycle coefficient of the sampling time. The feasibility and effectiveness of this method in improving the control performance of PMSM are verified by comparative analysis of the simulation model.

At present, due to environmental interference in the process of digital image acquisition and transmission, low-pixel images will appear, resulting in poor image reconstruction effect. For this reason, a digital image super-resolution reconstruction algorithm based on multi-scale residual is proposed. Use bilateral filtering algorithm to complete the dehazing processing of digital images. The brightness feature information and color information of digital images are classfied, and the distance threshold denoising method is used to denoise. To set convolution kernels of multiple sizes. In the process of image feature extraction, digital image features are obtained, and back-projection operations are performed on them. Based on the residual learning idea, the features extracted by the up-sampling and down-sampling processes are connected to realize digital image super-resolution reconstruction. The experimental results show that the proposed algorithm has high structural similarity, high PSNR (Peak Signal-to-Noise Ratio) and good reconstruction effect for image reconstruction. 

In the case of zero sample remote sensing image scene segmentation without any observation data, there is no response reference, which results in long segmentation time and low accuracy. Therefore, a zero sample urban remote sensing image scene segmentation algorithm based on convolutional neural network is proposed. PCA ( Principal Component Analysis) and K-SVD ( K-Singular Value Decomposition) are used to denoise remote sensing images to suppress the patch effect. The denoised image is input into the Retinex enhancement algorithm to further improve the enhancement effect of zero sample urban remote sensing image. The mean shift algorithm is used to segment the remote sensing image scene to obtain the relationship between its pixels, and the convolution neural network is used to complete the accurate segmentation image scene. The experimental results show that the algorithm has high accuracy, high recall, high F-score rate and short consumption time.

Traditional cloud access security monitoring methods can not identify the pseudo characteristics of access data, and the server is vulnerable to storage space constraints, resulting in poor monitoring effect and data confidentiality. In order to detect network attacks and make timely response plans, a virtual machine access security monitoring algorithm based on active energy consumption and idle energy consumption is proposed. Using discrete wavelet transform method to process intrusion information of virtual machine access pages, the data autocorrelation function is obtained, and the amplification factor is the same, which is divided into different aggregation sequences. By calculating the energy consumption of nodes, the energy consumption of active and idle states are obtained, increasing the number of active slots of access path nodes, balancing the network energy load, and extending the network life cycle. A multi angle analysis model of virtual machine network is formed based on the characteristics of network access, its characteristic functions and processing forms are clarified. All virtual machine access effective domain data are obtained, the processor application rate is improved, the time average is calculated, and the security status of virtual machine access is perceived. Experimental results show that the proposed algorithm can monitor the attack situation with lower false positive rate and close to 100% detection accuracy, which is superior to the other two algorithms and proves the effectiveness of the proposed method.

A novel decomposition method that combines the improved LHBA (Levy Honey Badger Algorithm) and VMD ( Variational Mode Decomposition) algorithm is proposed. This method is designed to solve the unsatisfactory noise reduction effect caused by inaccurate parameter selection of the VMD algorithm during signal decomposition. Firstly, the LHBA algorithm is utilized to optimize the decomposition mode number K and penalty factor α of VMD. Secondly, the optimized parameters are applied to decompose the VMD signal. Finally, the effective modal component for signal denoising is chosen after calculating the HD ( Hausdorff Distance) between each modal component and the original signal. The experimental results indicate that the proposed method can entirely distinguish the signal component from the noise for simulation signals. Thus, the four evaluation indices of the method are superior when compared to HBA( Honey Badger Algorithm) -VMD, GA(Genetic Algorithm) -VMD, and PSO( Particle Swarm Optimization) -VMD, demonstrating the algorithm's efficiency and superiority. 

To solve the problem that brute-force attack poses a major risk to network security, this paper proposes an open source brute-force attack recognition algorithm for big data in cyberspace. The open source network space data information model is constructed, the set of parameter vectors is obtained, and the calculation results of model variables are optimized. Based on ant colony algorithm, the feature optimization is transformed into a path search problem. First, the brute-force attack feature is regarded as a location to be visited by ants, and then the state transition probability is selected to refine part of the search to obtain the global optimal feature. Using the information gain method to measure features, the gain of each feature information in the data set is obtained. By calculating the function of the values between single data sets, the sample difference is measured, the outlier value in the data set is reduced, and the attack behavior is identified by comparing with the threshold value. The experimental results show that the proposed algorithm can accurately identify the brute-force attack, the recognition rate is above 95% , the false positive rate is low, and the recognition effect is the best. 

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.

When collecting spatial multidimensional data, collection devices are often discrete, and due to equipment failures, environmental factors, and other factors, there may be omissions or anomalies in spatial multidimensional data, a spatial multidimensional data calibration model based on the Lagrange interpolation algorithm is proposed. Firstly, Star shaped and snowflake shaped spatial multidimensional database structure is established to clarify the data distribution characteristics. Then, the initial data is preprocessed, and the consistent division of data dimension is realized through parameter initialization operation, so as to improve the data quality. Then the data classification is completed through the processes of information entropy ant colony clustering, optimization and merging. And the data with the same characteristics are gathered into the same cluster to reduce outliers. Finally, the Lagrange interpolation polynomial is established by using the basis function. And the normalization idea is introduced to ensure that the value floats in a certain range, avoid Runge phenomenon, and generate a new interpolation polynomial. The polynomial calculation result is the calibrated data value. The experimental results show that this method has good data preprocessing ability and can effectively reduce the calibration error.

For the problem that the K-means algorithm requires manual determination of the cluster numbers and random selection of initial clustering centers, which can fall into local optima, an improved parameter-free K-means algorithm is proposed by combining the density peak-based clustering algorithm CFSFDP(Clustering by Fast Search and Find of Density Peaks). First, the local density and dispersion of the sample points are calculated, then a decision diagram is established, and a vector of two parameters is composed. The distance from each point to the surrounding 5 points is calculated, and those with a distance greater than 2 times the mean square error and a density greater than the average density are filtered out. The filtered point is used as the initial clustering center of the algorithm. The number of statistical clustering centers k is used as the number of clusters, and the initial number of clusters k and the initial clustering centers are used as the initial parameters of the K-means algorithm to cluster data. The algorithm is tested on different types of data sets, including artificially created Gaussian data sets, UCI(University of California, Irvine) data sets, and real tool vibration data sets. The results show that the proposed algorithm maintains the global optimality of the traditional algorithm and validates its effectiveness. Since K-means is an unsupervised clustering method, it can reduce the workload and computational cost of manual data calibration, supervised training, etc. , while obtaining better tool state recognition results, which is of high practical significance for accurate real-time extraction of the operating state of the tool for computerized numerical control machine tools.

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. 

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. 

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.

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. 

 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. 

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.

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.

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.

 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. 

To ensure stable network access and reduce resource waste, an open-source big data load balancing optimization algorithm based on real-time node load is proposed. An open-source big data node computing capability model is established, timely feedback and adjustments based on the size of node load are provided, the next action based on the number of requests received by servers in the region is predicted, exponential smoothing method is used to calculate the predicted number of server requests per second, the lag deviation problem of first- order exponential smoothing method is improved, and the comprehensive server load is calculated. Add a load agent and load monitor on the node to balance the number of blocks and the load of sharded nodes, and place undeleted shards and blocks into the minimum unit candidate list to achieve load balancing optimization. Through experiments, it has been proven that the proposed algorithm can improve network resource utilization and load balancing, ensuring a more stable and secure network during access.

Due to the excessive number of path hops and the large distance in the optical network, it is more difficult to find the available spectrum resources, which leads to lower dynamic spectrum utilization, less network benefits and higher blocking rate in the optical network. Therefore, a dynamic spectrum allocation method based on frog jumping game optimization algorithm is proposed for the optical network. The OHM(Optimized Link State Routing Protocol using the Highway Model ) routing algorithm is used to select the candidate path that corresponds to the service request and meets the minimum hops and the highest modulation level. The available spectrum resources are found. According to the obtained spectrum resources in the optical network, the minimum of the maximum frequency slot number in all links is used as the target to construct the objective function of the dynamic spectrum allocation of the optical network. Under the constraint conditions, the frog jump game optimization algorithm is used to solve the objective function. The obtained solution is the optimal result of dynamic spectrum allocation in optical networks. The experimental results show that the proposed method has low blocking rate, high spectrum utilization and high network revenue, and is practical. 

Most of the existing research on degree prediction in colleges and universities focuses on the construction of performance prediction models, ignoring the importance of degree early warning. Therefore, a degree early warning model based on support vector machine is proposed. A large number of experiments are carried out on the real data of 5 majors, including Broadcast and Television Directing Major, Chinese Language and Literature Major, Chemistry Major, Accounting Major and Mathematics and Applied Mathematics Major, in a university of 2018. The experimental results show that the constructed early warning model has good accuracy and practicality,which can become an important part of improving the teaching quality, and provide practical reference support for teachers to improve the teaching plan and for students to change their learning habits.

 In order to solve the large difference between the results of multi-attribute group decision making, the convergence of measurable functions is studied based on intuitionistic fuzzy measures. Based on the possibility theory and intuitionistic fuzzy set theory, the intuitionistic fuzzy possibility measure is studied, and the boundedness, monotonicity, additivity and intuitionistic fuzzy additive properties of the intuitionistic fuzzy measure are determined. The intuitionistic fuzzy measure is based on the three decision-making rules of receiving decision, delaying decision and rejecting decision. Based on the above research results, the convergence of measurable functions in terms of measure is clarified. It is known that there exists a convergence state of set- valued bizero asymptotically additive set function in measurable space. The example shows that this method can effectively calculate the intuitionistic fuzzy possibility measure of different decisions under different attribute characteristics, and can effectively obtain the best decision scheme, can effectively solve the multi-attribute group decision-making problem with good practicality. 

 The output power of a photovoltaic array exhibits a multi-peak state under partial shading, and traditional MPPT(Maximum Power Point Tracking) control can not solve the multi-peak problem, resulting in the system being trapped in a local optimum affecting the photovoltaic power generation efficiency. To address this issue, a hybrid algorithm is proposed for photovoltaic maximum power point tracking. This method optimizes the initial population of the sparrow algorithm and combines with reverse learning strategy to enhance the algorithm's global search ability. When the algorithm searches near the maximum power point of photovoltaic power generation, the perturbation observation method is used to quickly search the maximum power point by utilizing its fast convergence characteristics. Using Simulink simulation and hardware experimentation, the global search ability and fast convergence ability of the proposed hybrid algorithm are verified. Compared with the sparrow algorithm and perturbation observation method, the hybrid algorithm has significantly improved accuracy and speed

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.

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.

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.

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.

There are many kinds of raw materials required, and the market price of materials is affected by the price of raw materials. Under such conditions, the price of raw materials has changed, but the market price of materials has not changed. In order to improve the effect of prediction, it is necessary to obtain the time delay of price. This study uses binary density clustering method combined with DTW ( Dynamic Time Warping ) algorithm, to calculate the similarity between the market prices of commodities over different time intervals and the trends in raw material prices. It has been determined that the market prices of commodities lag behind the fluctuations in raw material prices by 11 weeks. As a result, the market price of cable materials can be predicted based on the trends in raw material prices from 11 weeks ago. This information can assist the procurement department of businesses in formulating rational procurement strategies.

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 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 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 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. 

 Passive acoustic monitoring is carried out during a continuous day and night for cage culture fish in a large semi-submersible platform, named ‘Penghu爷. Basing on the results of time-frequency analysis, the noise frequency band and sound pressure levels of four moments, e. g, manual feeding, automatic feeding by machine, ship disturbance, and quiet state in the middle of the night, are obtained. The results show that the activity of fish measured under large-scale automatic feeding is obviously higher than that of other states, and the sound intensity is about 30 dB higher than the background noise of the marine environment. When the ship passing by, the fish noise intensity is about 9 dB higher than that of the artificial feeding moments. Fish are in a quiet state and not active during late night, and noise sound pressure level is about 70 ~ 75 dB. The time-domain signal is decomposed by VMD( Variational Mode Decomposition). The obtained Hilbert spectrum analysis shows that IMF1 is the high-frequency noise component caused by fish flapping and swimming noise. IMF2 is the vocalization of golden pomfret with a frequency band of 1 100-3 000 Hz. The frequency band of grouper sound is 300 ~ 1 100 Hz, which is the main component of the third order IMF (Intrinsic Mode Function) component. The peak of Hilbert marginal spectrum is ranged in the 600 ~ 700 Hz, which is the frequency band that the highest energy proportion of bio-noise produced by fish. It is expected to provide a basis for bait regulation and population classification of the fish cultured in the large cage by studying the relationship between the vocal characteristics, behavioral state and environmental background.

 In response to the problem of many class hours and many students attending classes in public computer laboratories each semester, a set of public computer laboratory information system has been developed independently. The system consists of three parts: student side, teacher side and server. The student side is a desktop program based on Python. The teacher side and the server are implemented in a web project written by JSP(Java Server Pages). In terms of function, the platform can be divided into three basic modules: student sign in, lost and found, feedback. It integrates other common functions. The application results indicate that this system can utilize information technology to provide convenience for students to learn in the laboratory. It truly implements the teaching concept of “student-oriented”

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. 

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).

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. 

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. 

 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.

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.

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.

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. 

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 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. 

Aiming at the problems that dynamic objects (such as pedestrins, vehicles, animals) appear in visual SLAM(Simultaneous Localization and Mapping) in real scenes, affect the accuracy of algorithm positioning and mapping, the YOLOv3-ORB-SLAM3(Oriented FAST and Rotated BRIEF-Simultaneous Localization and Mapping 3) algorithm is proposed based on ORB-SLAM3. The algorithm adds a semantic thread on the basis of ORB- SLAM3, and the thread uses YOLOv3 to perform semantic recognition target detection on dynamic objects in the scene. The outliers are removed from the extracted feature points on the tracking thread, and the static environment area extracted by the ORB feature, thereby the positioning accuracy of the visual SLAM algorithm is improved. The TUM(Technical University of Munich) data set is used to verify the positioning accuracy of the algorithm in monocular and RGB-D(Red, Green and Blue-Depth) modes. The verification results show that the dynamic sequence of the YOLOv3-ORB-SLAM3 algorithm in monocular mode is about 30% lower than that of the ORB-SLAM3 algorithm in RGB-D mode, the dynamic sequence decreases by 10% , and the static sequence does not decrease significantly.

When the host has intrusion data with delayed response characteristics, the existing judgment mode is disconnected from the delayed data, resulting in distorted judgment of data association confidence between nodes and failure of intrusion detection. A method to judge the confidence of intrusion data association is proposed. Under the host security protection framework, the host firewall packet filtering technology is used to eliminate abnormal data. The security node is placed in the host by distributed deployment, and intrusion detection is carried out by using mathematical model technology. By analyzing the association between normal data, the association confidence between data is determined, and then the intrusion judgment is completed. The experimental results show that the security and effectiveness of the host security protection system are verified by testing the successful times of virus and Trojan attacks with delay characteristics, the time used for packet monitoring, and the functional coverage. 

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. 

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.

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.

Due to the current rigidity of network data planes, which leads to imbalanced data flow in the network, a programmable load balancing algorithm based on data planes is proposed. Firstly, INT ( In band Network Telemetry) technology is used to obtain real-time network status information, and then the proposed BD- ECMP(Bandwidth and Delay Equal-Cost Multi-Path Routing) algorithm is used to select the optimal transmission path for the data stream. Using P4(Programming Protocol Independent Packet Processors) language to optimize the data flow of SDN network data plane, network load balancing is achieved. The simulation results show that compared with the traditional ECMP algorithm, the BD-ECMP algorithm has significant advantages in terms of average flow completion time, network throughput, and network packet loss rate.

 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 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.

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.

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.

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. 

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. 

In traditional VSG (Virtual Synchronous Generator) voltage and current double closed-loop control, the anti-disturbance performance is poor, and the influence of system parameter changes and uncertainties is great. In order to solve the problems, a nonlinear PID(Proportion Integration Differentiation) based on tracking differentiator is used to control the outer voltage loop and the inner current loop, and the dynamic response of the system is adjusted in real time according to the feedback value of the output signal, so as to achieve the effect of stable output. The correctness and effectiveness of the double closed-loop control of microgrid virtual synchronous generator based on nonlinear PID are verified by simulation. 

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.

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. 

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 problem of poor classification performance faced by current imbalanced big data classification algorithms, a random forest model based imbalanced big data classification algorithm is proposed. Firstly, the SVM(Support Vector Machine) algorithm is used to filter information on imbalanced big data, and then the anti k-nearest neighbor method is used to detect and eliminate outliers. The singularity of the covariance matrix in imbalanced big data is removed through incremental principal component analysis. And based on the entropy method, weight analysis is carried out to extract imbalanced big data feature information. The CART (Classification and Regression Trees) decision tree is used as the base classifier for imbalanced big data, and a random forest decision tree classifier is constructed. The extracted imbalanced big data feature information is input into the classifier to achieve imbalanced big data classification. The experimental results show that the proposed algorithm has good sampling performance, high classification accuracy, high stability, and high performance for imbalanced big data. 

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.

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 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.