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.

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.

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.

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.

 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.

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.

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).

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. 

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.

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.

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

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

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

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

In order to 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. 

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.

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 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.

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.

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

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.

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 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 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.