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 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.

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. 

 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.

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.

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.

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 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. 

 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. 

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% . 

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

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

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. 

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. 

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. 

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. 

 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. 

 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. 

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. 

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

To 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. 

 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. 

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 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.