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Multi-stream fusion network for continuous gesture recognition based on sEMG

International Journal of Wireless and Mobile Computing 2024年第4期26卷 374-383页

作者： Li, Jun Zou, Chunlong Tang, Dalai Sun, Ying Fan, Hanwen Li, Boao Tang, Xinjie Hubei Key Laboratory of Mechanical Transmission and Manufacturing Engineering Wuhan University of Science and Technology Hubei Wuhan China College of Mechanical Engineering Hubei University of Automotive Technology Hubei Shiyan China Inner Mongolia University of Finance and Economics Hohhot China Research Centre for Biomimetic Robot and Intelligent Measurement and Control Wuhan University of Science and Technology Hubei Wuhan China Key Laboratory of Metallurgical Equipment and Control Technology of Ministry of Education Wuhan University of Science and Technology Hubei Wuhan China

Dynamic gesture recognition is an essential step in human-computer interaction. However, dynamic gesture recognition based on Surface Electromyography (sEMG) signals faces issues such as incomplete feature extraction and incomplete recognition of temporal information between continuous gestures. Therefore, this paper proposes a multi-stream fusion network (MSK-LCNN) for dynamic gesture recognition to improve accuracy. We combine CNN and LSTM models into a unified framework which extracts spatio-temporal information both globally and in depth, and combines feature fusion to retain essential information. This framework uses an attention mechanism (SKNet) to learn more intricate feature information. The gesture recognition accuracy of this method on our data set is 95.23%, and on the NinaPro DB1 data set, it is 91.45%, which outperforms other similar networks in recent years. Applying this algorithm to prosthetic hand control has achieved flexible and stable control of the prosthetic hand. © 2024 Inderscience Enterprises Ltd.

关键词： Gesture recognition

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Research on Parallel Multimodal Current Sharing Based on Merged Coupled Inductance 1st

Research on Parallel Multimodal Current Sharing Based on Mer...

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1st Electrical Artificial Intelligence Conference, EAIC 2024

作者： Gong, Xiangqian Guo, Ruiyu Victoria Park Collegiate Institute TorontoONM3A 2V1 Canada Department of College of Measurement and Communication Engineering Harbin University of Science and Technology Harbin150080 China

ISBN: (纸本)9789819640621

When multiple SiC MOSFETs are paralleled, the differentiation of internal parameters can lead to imbalanced currents in the parallel branches, causing individual devices to overcurrent and even damage. Existing current sharing technologies and methods suffer from issues such as parameter drift, poor dynamic performance, and high system complexity. To enhance the reliability and stability of parallel current sharing, this paper first analyzes the unbalanced current distribution caused by transient and steady-state modes under parallel switching conditions, obtaining the internal parameters of the devices and the external circuit parameters, and extracting the key parameters for transient and static current imbalance. Secondly, a method for autonomous current sharing in four branches based on the merging approach of coupled inductance is proposed. The magnetic coupling effect of the coupled inductance in the equal distribution of current is deeply analyzed theoretically, and the magnetic and circuit parameters of the coupled inductance are optimized. Finally, parallel simulation and experimental testing in the branches of a three-phase rectification circuit show that the current balance achieved with merged inductance is below 1%, and the scheme has been verified to have superior robustness under load disturbance. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Electromagnetic transients

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A Fuzzy Logic controller for Path Navigation of Brain-controlled Mobile Robots in Complex Simulated Environments 1

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control Instrumentation System Conference, CISCON 2023

作者： Bhattacharjee, Rituraj Mukherjee, Subhradip Borah, Siddhanta Department of Instrumentation and Control Engineering Manipal Institute of Technology Manipal Academy of Higher Education Karnataka Manipal576104 India Department of Electronics and Instrumentation Engineering National Institute of Technology Nagaland Nagaland Chumukedima797103 India Department of Electronics and Communication Engineering MVJ College of Engineering Bengaluru India Department of Electronics and Communication Engineering Assam Engineering College Assam Guwahati India

ISBN: (数字)9789819758661

ISBN: (纸本)9789819758654

Electroencephalogram (EEG)-based mobile robots can be powerful everyday aids for persons with severe disabilities, especially if they need assistance in moving voluntarily. With the amalgamation of EEG signal processing, artificial intelligence algorithm and mobile robotics trajectory planning, it is possible to drive a mobile robot using noninvasively recorded brain activity. In this research work, with the help of MATLAB simulations, mobile robot path navigation has been shown where the target location was retrieved using EEG signal data processing. A fuzzy logic controller has been designed and presented to conduct the obstacle avoidance mechanism based on fuzzy logic. The fuzzy logic approach has been made functional with 25 sets of rules, dictating the movement positions. The autonomous navigation system uses this position to direct the mobile robot to the intended place while avoiding collisions with outside barriers. This approach could prove very useful in effective mobile robot path planning in brand new and unpredictable situations. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Mobile robots

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Graph Convolutional Networks with Visual Masking and Social Interaction Models for Pedestrian Trajectory Prediction 5

Graph Convolutional Networks with Visual Masking and Social ...

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5th International Conference on Consumer Electronics and Computer engineering, ICCECE 2025

作者： Zeng, Guopei Wang, Luping School of Electronics and Communication Engineering Sun Yat-sen University Shenzhen China Zhejiang Aerospace Runbo Measurement and Control Technology Co. Ltd Zhejiang China

ISBN: (纸本)9798331533113

Pedestrian trajectory prediction is critical for autonomous driving, crowd analysis, and urban surveillance. To address insufficient interaction modeling in existing methods, we propose two enhancements. First, a field-of-view(FoV)-aware masking mechanism filters irrelevant interactions by dynamically adjusting to pedestrian distances and motion directions. Second, we introduce a more detailed modeling of the influence among pedestrians, replacing the traditional modeling based on reciprocal of distance. This model, integrated with FoV masks to construct sparse adjacency matrices for graph convolution. A temporal convolutional network then predicts trajectories as bivariate Gaussian distributions. Evaluations on ETH and UCY benchmarks demonstrate the effectiveness of our method. © 2025 IEEE.

关键词： GCN social-force trajectory prediction

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Air-Ground Collaborative control for Angle-Specified Heterogeneous Formations

IEEE Transactions on Intelligent Vehicles

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IEEE Transactions on Intelligent Vehicles 2024年 1-13页

作者： Chen, Liangming Xiao, Jiaping Teo, Clarence Wei Rui Li, Jianqing Feroskhan, Mir School of System Design and Intelligent Manufacturing and Shenzhen Key Laboratory of Control Theory and Intelligent Systems Southern University of Science and Technology Shenzhen China school of Mechanical and Aerospace Engineering Nanyang Technological University Singapore Singapore College of Communication Engineering Hangzhou Dianzi University Hangzhou China

The need for better search and rescue capabilities has led to the increased demand for collaborative aerial-ground multi-robot deployments. However, most existing solutions require high communication bandwidth or bulky sensor equipment that is not suitable especially for aerial robots. To make the execution of these tasks more efficient, this paper proposes an angle-specified heterogeneous leader-follower formation framework consisting of unmanned aerial vehicles (UAVs) flying in a 3D space and ground robots moving in a 2D plane. The UAVs are controlled to maintain a desired angle-specified formation with the first three UAVs as the leaders forming a triangular shape and the remaining UAVs as the followers. The follower UAVs only need direction measurements to track the leader UAVs. For the ground robots, two leader robots track the UAV group and determine the orientation and scale of the ground formation, while the follower robots track the leader robots using only direction measurements. The proposed heterogeneous formation framework is energy-efficient as most robots only require low-cost and lightweight direction measurements. The stability of the proposed formation control algorithms is proved and validated through various physical application experiments. IEEE

关键词： Multi agent systems

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A data-driven control method for vehicle lateral stability in snow and ice-rutted conditions under aperiodic sampling 7

A data-driven control method for vehicle lateral stability i...

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7th CAA International Conference on Vehicular control and Intelligence, CVCI 2023

作者： Zhao, Xu Guo, Jingzheng Wang, Lianbing Guo, Hongyan Liu, Zhenze Zhang, Yiming College of Communication Engineering Jilin University The Department of Aircraft Control and Information Engineering Jilin Institute of Chemical Technology Changchun China College of Communication Engineering Jilin University Changchun China State Key Laboratory of Automotive Simulation and Control The College of Communication Engineering Jilin University Changchun China SAIC Motor R&D Innovation Headquarter Jilin University Shanghai China

ISBN: (纸本)9798350340488

Designing a controller to ensure vehicle stability under snow and ice-rutted road conditions, in the absence of accurate model knowledge and with potential sampling data delay, poses a significant challenge. To address this, this paper presents a novel data-driven parametric approach that integrates linear matrix inequalities (LMIs) methods. This approach allows for the determination of an appropriate sampling time interval. By iteratively solving LMIs, a state-feedback controller is derived, which guarantees both system stability and robustness. The effectiveness of the proposed approach is demonstrated through numerical simulations conducted on the MATLAB/CarSim platform. The results highlight the good control performance and robustness achieved by the proposed method. © 2023 IEEE.

关键词： data-driven control linear matrix inequality sampling time interval vehicle stability control

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STRUCTURAL RELIABILITY ANALYSIS BASED ON IMPROVED PARALLEL SAMPLING ALGORITHM 10

STRUCTURAL RELIABILITY ANALYSIS BASED ON IMPROVED PARALLEL S...

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10th International Symposium on Test Automation and Instrumentation, ISTAI 2024

作者： Yu, Yifan Wang, Liyong Zhang, Ximing College of Mechanical & Energy Engineering Beijing University of Technology Beijing China Key Laboratory of Modern Measurement & Control Technology Ministry of Education Beijing China China North Vehicle Research Institute Beijing China

ISBN: (纸本)9781837242672

Aiming at the problem of computational inefficiency of traditional reliability analysis methods when dealing with large-scale structural systems, this paper proposes a reliability analysis method that improves the parallel sample algorithm. The method adopts the Kriging model to fit the performance function and introduces the mean square error to evaluate the uncertainty of the sample points;In addition, the positional relationship between the sample points is taken into consideration to avoid overfitting the model;To improve the efficiency of the model fitting, the sample points are divided into multiple clusters using the Mini batch k-means clustering algorithm in parallel computation. Finally, it is verified by multiple random variable cases that the method can significantly improve the computational efficiency while guaranteeing the relative error compared with other reliability analysis methods. © The Institution of engineering & technology 2024.

关键词： Data reliability

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Home Monitoring System using Internet of Things 3

Home Monitoring System using Internet of Things

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3rd International Conference on Artificial Intelligence and Smart Energy, ICAIS 2023

作者： Ramesh, A. Mohanraj, K. Venugopal, E. Sivakumar, P. Sri Eshwar College of Engineering Department of Electronics and Communication Engineering Coimbatore India Sri Sairam Engineering College Department of Instrumentation and Control Engineering Chennai India Psg College of Technology Department of Electrical and Electronics Engineering Coimbatore India

ISBN: (纸本)9781665462167

People are now keen to monitor home from anywhere in the world. Communicating the monitoring information immediately to the user has always been a difficult task. The emerging technological advances helps to tackle this challenge. The Internet serves as a platform to ease the monitoring process by continually communicating the status of household appliances to the users. This study has developed and implemented a novel prototype for kitchen monitoring system. The proposed prototype is composed of sensors, processing device, and an android application. Here, the monitoring process has been carried out virtually via a mobile application. In addition to, the software tools significantly accelerate the process of prototype development. © 2023 IEEE.

关键词： HTTP

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Fast Finite-Time Adaptive Neural control for Pure-Feedback Stochastic Nonlinear Systems

Fast Finite-Time Adaptive Neural Control for Pure-Feedback S...

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2023 IEEE International Conference on Memristive Computing and Applications, ICMCA 2023

作者： Zhao, Ximei Zhang, Hanyu Shao, Lijun Huang, Lili Li, Jiao School of Intelligent Manufacturing and Control Engineering Qilu Institute of Technology Jinan China Qilu Institute of Technology Jinan China College of Computer and Communication Engineering Qilu Institute of Technology Jinan China

ISBN: (纸本)9798350316230

In this article, we solve the fast finite-time stabilization as well as adaptive neural control design issues for a class uncertain stochastic nonlinear systems. By employing the mean value theorem, the pure-feedback form can be transformed into strict-feedback case. Firstly, a new fast finite-time control scheme is proposed in virtue of generalizing the fast finite-time adaptive control strategy for deterministic systems to the stochastic case. Next, a novel adaptive neural control strategy is developed to simultaneously deal with the stochastic nonlinear systems with completely unknown nonlinearities as well as the disturbances term. Then, stability analysis have been given based on a Jensen's inequality. © 2023 IEEE.

关键词： Feedback

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In-Vehicle Network Injection Attacks Detection Based on Feature Selection and Classification

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Automotive Innovation 2024年第1期7卷 138-149页

作者： Haojie Ji Liyong Wang Hongmao Qin Yinghui Wang Junjie Zhang Biao Chen Key Laboratory of Modern Measurement and Control Technology Ministry of EducationBeijing Information Science and Technology UniversityBeijing 100192China State Key Laboratory of Advanced Design and Manufacturing for Vehicle Body College of Mechanical and Vehicle EngineeringHunan UniversityChangsha 410082China School of Transportation Science and Engineering Beihang UniversityBeijing 100191China Hefei Innovation Research Institute Beihang UniversityHefei 230012China

Detecting abnormal data generated from cyberattacks has emerged as a crucial approach for identifying security threats within in-vehicle *** transmission of information through in-vehicle networks needs to follow specific data for-mats and communication protocols ***,statistical algorithms are employed to learn these variation rules and facilitate the identification of abnormal ***,the effectiveness of anomaly detection outcomes often falls short when confronted with highly deceptive in-vehicle network *** this study,seven representative classification algorithms are selected to detect common in-vehicle network attacks,and a comparative analysis is employed to identify the most suitable and favorable detection *** consideration of the communication protocol characteristics of in-vehicle networks,an optimal convolutional neural network(CNN)detection algorithm is proposed that uses data field characteristics and classifier selection,and its comprehensive performance is *** addition,the concept of Hamming distance between two adjacent packets within the in-vehicle network is introduced,enabling the proposal of an enhanced CNN algorithm that achieves robust detection of challenging-to-identify abnormal *** paper also presents the proposed CNN classifica-tion algorithm that effectively addresses the issue of high false negative rate(FNR)in abnormal data detection based on the timestamp feature of data *** experimental results validate the efficacy of the proposed abnormal data detection algorithm,highlighting its strong detection performance and its potential to provide an effective solution for safeguarding the security of in-vehicle network information.

关键词： Classification algorithm Anomaly detection In-vehicle network Feature extraction Injecting attack

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