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International Conference on Mechatronics and Semiconductor Materials (ICMSCM 2013)

作者： Jiang Guixiu Shu Jie Chinese Acad Sci Key Lab Renewable Energy Guangzhou Guangdong Peoples R China Chinese Acad Sci Guangzhou Inst Energy Convers Guangzhou Peoples R China

ISBN: (纸本)9783037859391

The current control method based on double hysteresis current control and space vector is taken, so that the current tracing control of three phase VSR can realized. The predictive current control based on bp algorithm is presented, and the drawback that there is beat during the control of SVPWM based on hysteresis can be made up, which has serious influence on the current tracing when the difference value changed violently. Without the rise of sampling frequency, the reference offset current of next time is predicted using of historical current. The simulation results show that the predictive current control based on bp algorithm is correct and valid. The deadbeat control of SVPWM based on hysteresis is realized, less harmonic current and better tracing are got.

关键词： electronic load bp algorithm predictive current control

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3-D Imaging Under Low-Rank Constraint With Radio Signals 24

3-D Imaging Under Low-Rank Constraint With Radio Signals

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IEEE 24th International Workshop on Multimedia Signal Processing (MMSP)

作者： He, Ying Zhang, Dongheng Sun, Qibin Chen, Yan Univ Elect Sci & Technol China Sch Informat & Commun Engn Chengdu Peoples R China Univ Sci & Technol China Sch Cyber Sci & Technol Chengdu Peoples R China

ISBN: (数字)9781665471893

ISBN: (纸本)9781665471893

Compared with optical imaging, radio frequency (RF) imaging enables object imaging in an all-weather, privacy-preserving, and cost-effective way. However, the existing heuristic solutions such as back projection (bp) and range migration (RM), suffer from the sidelobe interference due to limited antenna aperture. In this paper, we propose a super-resolution 3-D imaging algorithm to enhance imaging performance. Mathematically, the reconstruction problem is an inverse problem that can be formulated as an optimization problem. The low-rank property of the object is exploited to regularize the imaging problem by nuclear norm minimization. We evaluate the proposed algorithm with both simulations and measurements. With a frequency band range from 2.7-4.1 GHz and a 16 x 6 multiple-input-multiple-output (MIMO) antenna array, simulation results show high imaging quality with a median boundary keypoint precision of 2 cm, and experimental results validate the feasibility of the proposed algorithm in a real-world environment.

关键词： 3-D imaging bp algorithm low-rank

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Spaceborne Synthetic Aperture Radar Aerial Moving Target Detection Based on Two-Dimensional Velocity Search

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REMOTE SENSING 2024年第13期16卷 2392页

作者： Hao, Jialin Yan, He Liu, Hui Xu, Wenshuo Min, Zhou Zhu, Daiyin Nanjing Univ Aeronaut & Astronaut Coll Elect & Informat Engn Nanjing 210016 Peoples R China

Synthetic aperture radar (SAR) can detect moving targets on the ground/sea, and high-resolution imaging on the ground/sea has critical applications in both military and civilian fields. This paper attempts to use a spaceborne SAR system to detect and image moving targets in the air for the first time. Due to the high velocity of aerial targets, they usually appear as two-dimensional range and azimuth direction defocus in SAR images, and clutter will also have a profound impact on target detection. To solve the above problems, a method of detecting and focusing on a spaceborne SAR target based on a two-dimensional velocity search is proposed by combining the bp algorithm. According to the current environment of the aerial target and the number of system channels, the clutter suppression methods are set and combined with two-dimensional velocity search with different precision, the Shannon entropy under different search velocity groups is used to obtain the search velocity group closest to the actual velocity and realize the integrated processing of moving target detection-focused imaging parameter estimation. Combined with simulation data, the effectiveness of the proposed method is verified.

关键词： aerial moving target detection clutter suppression synthetic aperture radar bp algorithm velocity search minimum Shannon entropy criterion

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Correlation analysis of aeroengine operation monitoring using deep learning

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SOFT COMPUTING 2021年第1期25卷 551-562页

作者： Xie, Chuan Zhang, Peng Yan, Zhi Air Force Engn Univ Aeronaut Engn Coll Xian 710038 Peoples R China Air Force Engn Univ Grad Coll Xian 710038 Peoples R China

To analyze the reliability of aeroengine more accurately, based on the analysis of operation reliability and complex reliability, the deep learning method is adopted to deal with the nonlinear and time-varying problems between the state parameters and operation reliability of aeroengine, and the condition monitoring method and deep learning of aeroengine are discussed. The results show that, based on the deep learning integrated network, the remaining useful life of aeroengine is predicted, and the key parameters of aeroengine are fitted and predicted by the back propagation (bp) algorithm. The artificial neural network method is used to predict the aeroengine parameters. For the collected aeroengine monitoring parameters, the greedy layer by layer training algorithm is used to mine the relationship between the parameters, extract the evaluation features, and evaluate the performance degradation, which verify the statistical significance and robustness of the conclusions. The proposed algorithm is more accurate and robust than the results of back bp neural network and support vector machine. It can prevent the over-fitting of small samples in aeroengine condition monitoring and further improve its nonlinear processing and generalization ability.

关键词： Artificial neural network bp algorithm Predictive performance Aeroengine Deep learning

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Parallel Deep Learning with a hybrid bp-PSO framework for feature extraction and malware classification

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APPLIED SOFT COMPUTING 2022年 131卷

作者： Al-Andoli, Mohammed Nasser Tan, Shing Chiang Sim, Kok Swee Lim, Chee Peng Goh, Pey Yun Multimedia Univ Fac Informat Sci & Technol Cyberjaya Selangor Malaysia Multimedia Univ Fac Engn & Technol Cyberjaya Selangor Malaysia Deakin Univ Inst Intelligent Syst Res & Innovat Melbourne Vic Australia

Malicious software (Malware) is a key threat to security of digital networks and systems. While traditional machine learning methods have been widely used for malware detection, deep learning (DL) has recently emerged as a promising methodology to detect and classify different malware variants. As the DL training algorithm is oriented on gradient descent optimization, i.e. the Backpropagation (bp) algorithm, several shortcomings are encountered, e.g., local suboptimal solutions and high computational cost. We develop a new DL-based framework for malware detection. In this regard, we introduce a hybrid DL optimization method by exploiting the integration of bp and Particle Swarm Optimization (PSO) algorithms to provide optimal solutions for malware detection. Many hybrid DL optimization methods in the literature are not implemented under a parallel computing setup. In this paper, we develop an efficient distributed parallel computing framework for implementing the proposed DL-based method to improve efficiency and scalability. The experimental results on several benchmark data sets indicate efficacy of the proposed solution in malware detection, which significantly outperforms other machine learning methods in terms of effectiveness, efficiency and scalability.(c) 2022 Elsevier B.V. All rights reserved.

关键词： Malware detection Machine learning Deep learning Parallel computing bp algorithm PSO algorithm Hybrid optimization

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Fast Assessment of Requests Schedulability in Data Relay Satellite Systems: A Multi-layer Neural Network Method

Fast Assessment of Requests Schedulability in Data Relay Sat...

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IEEE/CIC International Conference on Communications in China (ICCC)

作者： Wang, Lei Yang, Jingyu Zheng, Xinhua Natl Def Univ Beijing Peoples R China Beijing Space Informat Relay & Transmiss Technol Beijing Peoples R China Sun Yat Sen Univ Guangzhou Peoples R China

ISBN: (数字)9781665459778

ISBN: (纸本)9781665459778

In data relay satellite (DRS) systems, the widely adopted centralized scheduling mode usually results in varying degrees of feedback delay to users, which is detrimental to the efficiency of the whole system and users'QoE (quality of experience). To solve this problem, we investigate the periodicity of such system's mission planning and the actual characteristics of relay missions, and then construct a multi-layer neural network model to simulate the mapping relationship between the inputs (i.e., the mission requests) and outputs (i.e., the scheduling results). The model has a simple and practical structure, composed of the input layer, the hidden layer and the output layer, which benefits from the accurate identification and extraction of relay mission' characteristic parameters. Last, we utilize a back propagation (bp) algorithm to train the neural network. The proposed method can quickly assess the schedulability of users' requests with the average accuracy 85% in multiple scenarios, which effectively improve the efficiency of the DRS system and the QoE.

关键词： data relay satellite(DRS) request schedulability multi-layer feedforward neural network scheduling,quality of experience(QoE) bp algorithm

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Study on the Prediction System of Shrimp Field Distribution in the East China Sea Based on Big Data Analysis of Fishing Trajectories

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Journal of Ocean University of China 2021年第1期20卷 228-234页

作者： GUAN Huanhuan ZHAO Xueli Management College Ocean University of ChinaQingdao 266100China College of Information Science and Engineering Ocean University of ChinaQingdao 266100China

In recent years,China has paid more and more attention to the development of marine economy and the management and protection of fishery *** management departments at all levels regulate and manage the fishing behavior of fishing vessels through the data of fishing *** this paper,the distribution of shrimp farms in the East China Sea is predicted by studying the trajectories and behavior patterns of shrimp boats in the system of fishing *** the same time,a set of shrimp farm distribution management system based on Back Propagation algorithm is *** can monitor the trajectories of fishing boats and the distribution of shrimp groups in real time,which effectively improves the work efficiency and management mode of the management *** also plays a positive role in regulating the behavior of fishing boats at sea.

关键词： bp algorithm big data shrimp farm distribution fishery management

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The optimization method of CNC lathe performance based on Morris sensitivity analysis and improved GA algorithm

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JOURNAL OF VIBROENGINEERING 2024年第2期26卷 438-454页

作者： Li, Xianyi NingboTech Univ Sch Mechatron & Energy Engn Ningbo 315100 Peoples R China

This study achieved the goal of guiding bed design and optimization by conducting multi-objective optimization research on the performance of CNC lathe beds. In this study, Morris analysis was first performed on the sensitivity of the parameters, and then out to optimize the parameters using a combination of neural network and genetic algorithm. The loss function value, RMSE error accumulation, recall, sensitivity and specificity of the ASSGA-bp optimization model were better. The maximum error between the predicted and true values of the ASSGA-bp model was 0.28 mm. In the performance study of the multi-objective optimization method based on the Morris sensitivity analysis and the improved GA algorithm, the average MAE value is 0.91 %. The average RMSE value is 0.59 %. Also, the new model is significantly better than the NSGA-II, EGA, and FGA algorithms in terms of both the number of final non-dominated solutions and the speed of reaching convergence. The above results demonstrate that the model proposed in this study has high performance, can achieve faster convergence and has the best stability of the convergence state. The innovation of this article lies in the use of the Morris method to screen and evaluate numerous parameters in order to improve the accuracy of the calculation results and ensure the effectiveness of the optimization results. The improved algorithm overcomes the problems of bp neural network and can effectively improve the generalization performance of the neural network, thereby improving the prediction accuracy of the model.

关键词： Morris sensitivity GA algorithm bp algorithm CNC lathe parameter optimization

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Remote Sports Injury Monitoring using Wireless Sensor Networks

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MOBILE NETWORKS & APPLICATIONS 2023年第6期28卷 2030-2040页

作者： Song, Ying Srivastava, Gautam Wuhan Univ Engn Sci Coll Humanities Wuhan Peoples R China Brandon Univ Dept Math & Comp Sci Brandon MB Canada China Med Univ Res Ctr Interneural Comp Taichung Taiwan

When remote sports injuries are traditionally monitored, the feedback is not timely, and there are some problems such as low running speed and poor accuracy of sports injury monitoring. Therefore, this paper designs a remote sports injury monitoring method based on a wireless sensor network. Firstly, the terminal node of the sports injury monitoring process is designed, and three-terminal devices are tied to the experimental object's body to collect motion information, to realize the collection of human motion information based on ZigBee wireless sensor network;Secondly, the USB module circuit interface is designed to realize the series connection of each line, and the local processing ability of network nodes is used to make a centralized decision. Then, the skeleton coordinate system is constructed, and the rotation of the human skeleton is measured by an inertial sensor. Through a variety of posture fitting, the error of remote sports injury monitoring is reduced from the two directions of joint error and muscle error. Finally, the training sample set is learned through the bp algorithm, the fitness function of the genetic algorithm is obtained, the external structural parameters of the adaptive neural network model are adjusted, the discrimination deviation and fitness function are calculated, the adaptive neural network model with the best generalization ability is output, and the local processing ability of remote sports injury monitoring method is improved combined with wireless sensor network technology, The design of remote sports injury monitoring method based on wireless sensor network is realized. The experimental results show that the accuracy of the method is 99%, the average time delay is 1 s, and the accuracy of the method is 92% even with noise. Therefore, the method can effectively improve the running speed of the remote sports injury monitoring method and improve the accuracy of sports injury monitoring.

关键词： ZigBee wireless sensor network Adaptive neural network model Multi pose fitting bp algorithm Sports injury Remote monitoring

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Belief Propagation Optimization for Lossy Compression Based on Gaussian Source

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SENSORS 2023年第21期23卷 8805-8805页

作者： Deng, Huan Song, Dan Xu, Zhiping Sun, Yanglong Wang, Lin Jimei Univ Nav Collage Xiamen 361021 Peoples R China Xiamen Univ Key Lab Underwater Acoust Commun & Marine Informat Minist Educ Xiamen 361005 Peoples R China Jimei Univ Sch Ocean Informat Engn Xiamen 361021 Peoples R China Xiamen Univ Dept Informat & Commun Engn Xiamen 361005 Peoples R China Hangzhou Microimage Software Co Ltd Hangzhou 310051 Peoples R China

In the Internet of Things, sensor nodes collect environmental information and utilize lossy compression for saving storage space. To achieve this objective, high-efficiency compression of the continuous source should be studied. Different from existing schemes, lossy source coding is implemented based on the duality principle in this work. Referring to the duality principle between the lossy source coding and the channel decoding, the belief propagation (bp) algorithm is introduced to realize lossy compression based on a Gaussian source. In the bp algorithm, the log-likelihood ratios (LLRs) are iterated, and their iteration paths follow the connecting relation between the check nodes and the variable nodes in the protograph low-density parity-check (P-LDPC) code. During LLR iterations, the trapping set is the main factor that influences compression performance. We propose the optimized bp algorithms to weaken the impact of trapping sets. The simulation results indicate that the optimized bp algorithms obtain better distortion-rate performance.

关键词： lossy source coding P-LDPC code trapping set bp algorithm

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