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2022 International Conference on Scientific Computation and Applied Statistics, ICSCAS 2022

作者： Tao, Xueying Shao, Huaizong Li, Qiang Pan, Ye Fu, Zhongqi School of Information and Communication Engineering University of Electronic Science and Technology of China Chengdu China Laboratory of Electromagnetic Space Cognition and Intelligent Control Beijing China Peng Cheng Laboratory Shenzhen China

This paper considers the modulation classification of radio frequency (RF) signals. An external attention mechanism-based convolution neural network (EACNN) is proposed. Thanks to the external attention layers, the EACNN network can capture the potential correlations of different modulation data, which helps reduce computational consumption and memory costs efficiently during training. Moreover, to account for the variation of the signals induced by channel fading, we further propose a customized batch normalization (BN) layer in EACNN to improve the classification accuracy with less training time. Numerical experiments on RML2016.a dataset shows that the proposed method outperforms the baseline method CNN2 by 7% in terms of classification accuracy. © Published under licence by IOP Publishing Ltd.

关键词： Fading channels

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The Battery Management System with DNN Deployed in STM32

The Battery Management System with DNN Deployed in STM32

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Chinese Automation Congress (CAC)

作者： Dongfeng Liang Ling Xiong Wei Li Ji Xiao School of Information Science and Engineering Wuhan University of Science and Technology Engineering Research Center for Metallurgical Automation and Measurement Technology of Ministry of Education Wuhan China Wuhan University of Science and Technology Electronic Control Equipment Co. Ltd Wuhan China

ISBN: (数字)9798350368604

ISBN: (纸本)9798350368611

Accurate prediction of the state of charge (SOC) is crucial for extending the lifespan of batteries. While traditional ampere-hour integration methods are simple and practical, they suffer from large cumulative errors and are not well-suited for complex application scenarios. This paper presents a smart battery management system based on the STM32F407VET6 microcontroller, which incorporates a deep neural network (DNN) to effectively model the nonlinear relationship between key parameters such as current, voltage, and SOC. The system features autonomous operation without requiring a host computer, enabling precise measurement of remaining battery capacity and real-time data display on an OLED screen. Furthermore, it supports data transmission in industrial environments via an RS485 module using the Modbus RTU protocol, as well as communication through CAN bus and ESP8266-MQTT for IoT applications. Experimental results demonstrate that this system can accurately predict battery SOC while facilitating multiplatform data communication.

关键词： Computational modeling Battery management systems Artificial neural networks Organic light emitting diodes Real-time systems Battery charge measurement Batteries State of charge Internet of Things Data communication

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Obstacle Avoidance Algorithm of Handling Robot in Cultural Complex

Obstacle Avoidance Algorithm of Handling Robot in Cultural C...

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2022 International Conference on Culture-Oriented Science and technology, CoST 2022

作者： Ren, Hui Li, Mengnan Shen, Xiaochen School of Information and Commmunication Engineering Communication University of China Beijing100024 China Key Laboratory of Acoustic Visual Technology and Intelligent Control System Beijing100024 China Beijing100024 China

ISBN: (数字)9781665462488

ISBN: (纸本)9781665462488

In order to solve the problem of rapid switching of space equipment in the cultural complex, this paper combines Moth-Flame optimization Algorithm (MFO) and Dynamic Window Approach (DWA) to realize the hybrid path planning of handling robots in dynamic environments. Compared with the mainstream algorithms, the Moth-Flame optimization algorithm has the best performance. Different paths were classified and judged, and the global path planning based on MFO was proposed. The intersection of the dynamic obstacle and the global path were detected, and the dynamic window approach was used for local path planning in the road section where the collision occurs. Finally the combination of the global path planning and the local dynamic obstacle avoidance was realized. The effect of the hybrid path planning algorithm in the complex environment of the cultural complex had been verified. © 2022 IEEE.

关键词： Optimization

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Integrated Airborne Radar-communication Waveform Design Based on the Complementary P4-OFDM Signal

Integrated Airborne Radar-Communication Waveform Design Base...

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2024 International Conference on Optical communication and Optoelectronic technology, OCOT 2024

作者： Zhao, Ziang Wen, Hong Song, Huanhuan Wang, Yongfeng Yao, Ruixiang Ma, Wendi Pang, Yv School of Aeronautics and Astronautics University of Elec. Science and Tech. of China Chengdu611731 China Aircraft Swarm Intelligent Sensing and Cooperative Control Key Lab. of Sichuan Province UESTC 611731 China Intelligent IoT Communication Technology Engineering Research Center of Sichuan Province UESTC 611731 China

ISBN: (纸本)9781510683266

To meet the specific demands of airborne radar and communication systems, this study introduces an integrated radar-communication waveform design based on selecting the optimal dual sequence of complementary P4-OFDM signals. This waveform leverages the cyclic shift counts of complementary P4 codes to carry communication information, achieving dual functionality in target detection and data transmission. To enhance the data transmission rate, the study innovatively developed a dual cyclic shift signal, which expands the number of states represented by P4 codes using a pair of primary and secondary original sequences. This signal offers exceptionally high speed and distance resolution for radar detection and maintains a low bit error rate in communications, making it particularly suitable for airborne environments where the priority is on communication reliability over efficiency. © 2024 SPIE.

关键词： Bit error rate

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Data Augmentation Integrated with Feature-Enhanced Convolutional Neural Network for Imbalanced Fault Diagnosis in Rolling Bearings

Data Augmentation Integrated with Feature-Enhanced Convoluti...

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Data Driven control and Learning Systems (DDCLS)

作者： Li Wang Qilin Qu Yanwei Wang David Shan-Hill Wong Ying Zheng China -Belt and Road Joint Laboratory On Measurement and Control Technology School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan China School of Mechanical & Electrical Engineering Wuhan Institute of Technology Wuhan Department of Chemical Engineering National Tsing-Hua University Hsinchu Taiwan

ISBN: (数字)9798350361674

ISBN: (纸本)9798350361681

Imbalance in fault samples is a key problem with most rolling bearing datasets in real industrial processes, limiting the performance of fault detection. In addition, existing fault diagnosis methods for rolling bearings do not adopt a modular division between the initial layer and the classification layer. This lack of modularization inhibits the full activation of the network's feature extraction capabilities and impedes subsequent model improvements. In this paper, a novel fault diagnosis method based on data augmentation integrated with a feature-enhanced convolutional neural network (DA-FECNN) is proposed. First, Variational Autoencoder-Generative Adversarial Network (VAE-GAN) is utilized to expand the number of fault samples and improve the diversity of few-shot. Second, a uniform feature-enhanced structure is designed with a feature extraction module, a multi-scale classification module, and a reconstruction module. The network's ability on feature extraction is improved by focusing on high quality features. Thereby, accurate fault classification is realized simultaneously. Experiments on real world data show the effectiveness of the proposed method on fault diagnosis task.

关键词： Fault diagnosis Learning systems Accuracy Limiting Rolling bearings Feature extraction Data augmentation

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Interpretable Bearing Stage Division based on Shapelet and SVM

Interpretable Bearing Stage Division based on Shapelet and S...

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Data Driven control and Learning Systems (DDCLS)

作者： Xinye Li Xiaoyu Yang Hong Zhang Yanwei Wang Ying Zheng The Belt and Road Joint Laboratory on Measurement and Control Technology School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan China School of Mechanical & Electrical Engineering Wuhan Institute of Technology Wuhan

The stage division of bearings is of great significance to ensure the safe operation of heavy machinery. By dividing bearing stages, early degradation points can be identified for bearing remaining useful life (RUL) prediction, and the accuracy of RUL prediction can be improved. Although the existing stage division methods can effectively divide the stages of bearings, most of the methods lack interpretability. In this paper, an interpretable rolling bearing stage division method based on shapelet and Support vector machine (SVM) is proposed. Firstly, a Manhattan distance-based shapelet is used to extract the bearing degradation features, which better discriminates the difference of bearing signals in different health states. Then, SVM is utilized to stage the degradation features. Finally, shapelet evolution diagrams are constructed to characterize the rolling bearing vibration signals at different stages, visually interpreting the bearing stage classification results. Based on the experimental study on the PRONOSTIA platform, the effectiveness and interpretability of the proposed method are verified. The results show that the proposed method can effectively classify the bearing health stages and visualize the interpretation of the stage classification results.

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Acceleration of Formation control Based on Hessian Matrix of Artificial Potential Field

Acceleration of Formation Control Based on Hessian Matrix of...

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第42届中国控制会议

作者： Haoran Han Zhilong Xi Maolong Lv Jian Cheng School of Information and Communication Engineering University of Electronic Science and Technology of China Air Traffic Control and Navigation College Air Force Engineering University

The formation control of multi-agent system has become a hotspot in recent years. One of the most commonly used methods is to use artificial potential field(APF) to generate the attractive and repulsive force to organize the agents into a low-energy and stable formation. In this paper, we regard the APF as the difference in the desired position, and introduce the derivative of the gradient of the APF with respect to time, which is simplified as the Hessian matrix of the APF, into the formation control from the perspective of trajectory tracking. The energy-based Lyapunov function is selected to prove the stability and collision avoidance ability of the proposed control law. Moreover, we show that the maneuverability of the multi-agent system is enormously improved with the additional Hessian matrix term.

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Research on Risk Analysis for Lighting and Sound Equipment of Temporarily Constructed Stage

Research on Risk Analysis for Lighting and Sound Equipment o...

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Culture-Oriented Science and technology (CoST), International Conference on

作者： Xueying Zhang Zhibin Su Huiqin Wang Yujian Jiang School of Information and Communication Engineering Communication University of China Key Laboratory of Acoustic Visual Technology and Intelligent Control System Ministry of Culture and Tourism Beijing China

With the increasing demand for stage performance activities, the quality and safety of performance equipment, especially for the temporarily constructed stage, have become important topics that cannot be ignored. In order to enhance the safety of performance equipment and improve the service quality, this article mainly conducts risk analysis and safety assessment on the lighting and sound equipment for stage of temporary perform site without protective measures, and uses Analytic Hierarchy Process (AHP) with Fuzzy Comprehensive Evaluation (FCE) to conduct risk analysis and evaluation. The calculated risk level of stage lighting and sound equipment is given, and relevant safety measures are proposed to reduce the potential risks.

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Grip strength forecast and rehabilitative guidance based on adaptive neural fuzzy inference system using sEMG

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Personal and Ubiquitous Computing 2022年第4期26卷 1215-1224页

作者： Jiang, Du Li, Gongfa Sun, Ying Kong, Jianyi Tao, Bo Chen, Disi Key Laboratory of Metallurgical Equipment and Control Technology Ministry of Education Wuhan University of Science and Technology Wuhan430081 China Institute of Precision Manufacturing Wuhan University of Science and Technology Wuhan University of Science and Technology Wuhan430081 China Research Center for Biomimetic Robot and Intelligent Measurement and Control Wuhan University of Science and Technology Wuhan430081 China Hubei Key Laboratory of Mechanical Transmission and Manufacturing Engineering Wuhan University of Science and Technology Wuhan430081 China School of Computing University of Portsmouth PortsmouthPO1 3HE United Kingdom

In order to resolve the problem of unstable control of force in human–computer interaction based on surface EMG signals, the adaptive neural fuzzy inference system is designed to achieve the grip strength assessment. As we know, the acquisition of surface EMG signal is non-invasive, which provides a better evaluation index for rehabilitation training in the medical process. By establishing the relationship between grip force and surface electromechanical signals, the effect of rehabilitation training can be evaluated directly while reducing the types of sensors used. Firstly, the experimental equipment are introduced, which are utilized to carry out simultaneous acquisition of surface EMG signals and forces. Then, the traditional features of sEMG and the corresponding algorithms are illustrated, based on this, supplementing the energy eigenvalue with wavelet analysis and fuzzy entropy. In which, fuzzy entropy is effective in characterizing muscle fatigue that can effectively reduce the impact of muscle fatigue on force assessment. Finally, combining fuzzy logic implication and neural network, the adaptive neural fuzzy inference system is designed, which is trained by extracted feature vectors. The experimental result shows the method used in this paper can effectively predict the grip force. Further, force prediction based on sEMG can be used to guide rehabilitation therapy in virtual space, combined with an electrical stimulator. © 2019, Springer-Verlag London Ltd., part of Springer Nature.

关键词： Eigenvalues and eigenfunctions

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False Data Injection Attacks Detection in Smart Grids Based on TCN Prediction Model

False Data Injection Attacks Detection in Smart Grids Based ...

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2024 International Conference on Optical communication and Optoelectronic technology, OCOT 2024

作者： Ma, Wendi Wen, Hong Hou, Wenjing Yao, Ruixiang Zhu, Yanxu Yan, Dibao Zhao, Ziang School of Aeronautics and Astronautics University of Elec. Science and Tech. of China Chengdu611731 China Aircrat Swarm Intelligent Sensing and Cooperative Control Key Lab. of Sichuan Province UESTC 611731 China Intelligent IoT Communication Technology Engineering Research Center of Sichuan Province UESTC 611731 China

ISBN: (纸本)9781510683266

The injection of false data attack (FDIA) poses a serious threat to smart grids, and accurate detection of FDIAs is crucial for the secure and stable operation of the grid. A promising approach for FDIA detection based on edge computing is to adopt a scheme that involves prediction before classification, taking into account the temporal correlations of measurement data. This paper proposes a FDIA detection model based on temporal convolutional network (TCN). The model effectively captures long-term dependencies in sequential data, thereby enhancing data prediction performance. Simulation results demonstrate that TCN exhibits superior detection performance under common neural network architectures, compared to typical recurrent neural network (RNN) models. This improvement facilitates highlighting the differences between injected false data and genuine data during the subsequent classification stage, making anomaly detection algorithms more capable of identifying abnormal situations and thereby enhancing the accuracy of FDIA detection. © 2024 SPIE.

关键词： Spatio-temporal data

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