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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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An ensemble multivariate detection method for steady-state drift in process industries

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Science China(Information Sciences) 2025年

作者： Wenshuo SONG Weihua CAO Yan YUAN Wenkai HU School of Automation China University of Geosciences Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education

In process industries, it is crucial to maintain operational parameters within a designated steady-state operating point to ensure product quality and operational efficiency. However,steady-state drift(a gradual shift in key parameters occurs over time even when the system is intended to be under stable conditions) can lead to significant production losses,safety risks, and increased operational costs. Therefore, accurately detecting steady-state drift is essential for maintaining stable, safe, and optimized operating conditions.

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Dim-light image processing strategy rooted in improved dual-branch generative adversarial network

Dim-light image processing strategy rooted in improved dual-...

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2024 International Conference on Image Processing and Artificial Intelligence, ICIPAl 2024

作者： Li, keyi Wei, Zongshou Key Laboratory of Opt-Electronic Technology and Intelligent Control of Ministry of Education Lanzhou Jiaotong University Lanzhou730070 China Key Laboratory of Plateau Traffic Information Engineering and Control of Gansu Province Lanzhou Jiaotong University Lanzhou730070 China

ISBN: (纸本)9781510681514

Images taken in dim lighting conditions usually suffer from drawbacks such as weak contrast along with unwanted interference. As a solution to this issue, this paper suggests a dim-light image processing strategy rooted in an improved dual-branch adversarial generative model. This method utilizes a generator network that combines a dim-light processing module and an image segmentation module, along with a patch discriminator network, trained using an adversarial loss function. The experiments demonstrate that the suggested approach for improving low-light image quality produces visually more captivating results when applied to real-world datasets. Moreover, it surpasses existing methods in terms of both preserving image structure and reducing noise. © 2024 SPIE.

关键词： Generative adversarial networks

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Research on Waxiang Dialect Recognition Based on Wav2vec2.0 Pre-Training and Transfer Learning

Research on Waxiang Dialect Recognition Based on Wav2vec2.0 ...

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2024 International Conference on Computing, Robotics and System Sciences, ICRSS 2024

作者： Deng, Mixuan Tang, Pengju Gao, Yanxia Li, Huixuan Zhen, Guodong Fu, Kaifeng Wu, Xiaotian Huaihua University Huaihua China Key Laboratory of Wuling-Mountain Health Big Data Intelligent Processing and Application in Hunan Province Universities China Key Laboratory of Intelligent Control Technology for Wuling-Mountain Ecological Agriculture in Hunan Province Huaihua China

ISBN: (纸本)9798331511296

As a unique local dialect, Waxiang dialect carries rich regional culture and historical information. However, with the acceleration of modernization and the increase of population mobility, the inheritance and protection of Waxiang dialect are facing severe challenges. By constructing the 'Xuewatong' APP- Waxiang dialect digital protection platform, based on cloud storage technology, digital resource display, multimedia integration and other technologies, and providing learning resources through big data analysis of user needs, it can effectively promote the digital recording and preservation of Waxiang dialect language resources, and significantly improve the dissemination and educational effect of Waxiang dialect culture, showing the great potential of digital technology in dialect protection. © 2024 IEEE.

关键词： Linguistics

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Prediction of NO_(x)concentration using modular long short-term memory neural network for municipal solid waste incineration

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Chinese Journal of Chemical Engineering 2023年第4期56卷 46-57页

作者： Haoshan Duan Xi Meng Jian Tang Junfei Qiao Faculty of Information Technology Beijing University of TechnologyBeijing 100124China Beijing Laboratory of Smart Environmental Protection Beijing 100124China Beijing Key Laboratory of Computational Intelligence and Intelligent System Beijing 100124China Engineering Research Center of Intelligence Perception and Autonomous Control Ministry of Education Beijing 100124China

Air pollution control poses a major problem in the implementation of municipal solid waste incineration(MSWI).Accurate prediction of nitrogen oxides(NO_(x))concentration plays an important role in efficient NO_(x)emission *** this study,a modular long short-term memory(M-LSTM)network is developed to design an efficient prediction model for NO_(x)***,the fuzzy C means(FCM)algorithm is utilized to divide the task into several sub-tasks,aiming to realize the divide-and-conquer ability for complex ***,long short-term memory(LSTM)neural networks are applied to tackle corresponding sub-tasks,which can improve the prediction accuracy of the ***,a cooperative decision strategy is designed to guarantee the generalization performance during the testing or application ***,after being evaluated by a benchmark simulation,the proposed method is applied to a real MSWI *** the experimental results demonstrate the considerable prediction ability of the M-LSTM network.

关键词： Municipal solid waste incineration NO_(x)concentration prediction Modular neural network Model

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Finite-Time Observer-Based Path-Following control of an Underactuated Surface Vehicle with Actuator Saturation and Fault

Finite-Time Observer-Based Path-Following Control of an Unde...

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2024 International Conference on Fuzzy Theory and Its Applications, iFUZZY 2024

作者： Zhang, Chengxiang Wang, Ning School of Marine Engineering Dalian Maritime University Dalian China State Key Laboratory of Maritime Technology and Safety China Dalian Key Laboratory of Green Power Control and Test for Intelligent Ships China

ISBN: (纸本)9798350352788

In this paper, a finite-time observer-based fault-tolerant control (F-FTC) scheme is proposed to address path following problem of an underactuated surface vehicle suffering from input saturation and actuator gain degradation. First, a finite-time observer is designed to accurately detect faults and complex system uncertainties within the faulty system. Second, a smooth dead-zone operator-based model is applied to avoid the potential non-differentiability in controller. Finally, the F-FTC is established by virtue of a first-order filtering system. Lyapunov theory is used to verify the closed-loop system stability while simulations experiments validate the merits of the proposed F-FTC scheme. © 2024 IEEE.

关键词： Dead zones

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Linear Active Disturbance Rejection Contour control of X-Y Motion Platform Driven by Dual PMSMs 24

Linear Active Disturbance Rejection Contour Control of X-Y M...

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6th International Conference on control and Computer Vision, ICCCV 2024

作者： Zhang, Xiuyun Hu, Haohao Wang, Heyu Geng, Liqing Wang, Zhiqiang Key Laboratory of Information Sensing and Intelligent Control Tianjin University of Technology and Education Tianjin China School of Automobile and Transportation Tianjin University of Technology and Education Tianjin China

ISBN: (纸本)9798400718045

In order to solve the problem that the contour processing of X-Y motion platform is easily affected by load disturbance, friction disturbance and fluctuation of motor parameters, this paper proposes a linear active disturbance rejection contour control strategy of X-Y motion platform driven by dual permanent magnet synchronous motors. The anti-disturbance performance of the position loop is improved by establishing a linear extended state observer to compensate the disturbance. At the same time, the differential feedforward control is added to improve the rapidity of the system, so that the position loop can ensure a certain anti-disturbance while taking into account the rapidity performance. On this basis, a cross-coupled position synchronous compensator is added to improve the synchronous performance of the system. The stability and anti-disturbance performance of the system are studied by the Bode diagram analysis method in the frequency domain, and the tracking performance and contour performance are analyzed. Finally, the simulation research is carried out to verify the effectiveness of the proposed control algorithm. © 2024 Copyright held by the owner/author(s). Publication rights licensed to ACM.

关键词： Disturbance rejection

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Broadband Chaos Generation and Synchronization Induced by Integrated Tri-section Laser

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IEEE Access 2024年 13卷 1-1页

作者： Guo, Yuanyuan Wei, Wuyin Gao, Hua Zhang, Yuanye Zhao, Tong Jia, Zhiwei Chang, Pengfa Wang, Longsheng Wang, Anbang Wang, Yuncai Key Laboratory of Advanced Transducers and Intelligent Control System Ministry of Education and Shanxi Province Taiyuan China College of Information Engineering Shanxi Vocational University of Engineering Science and Technology Taiyuan China School of Information Engineering Guangdong University of Technology Guangzhou China

Broadband chaos generation is numerically demonstrated using an integrated tri-section laser which is composed of mutually coupled distributed feedback (DFB) lasers with a phase section, and the broadband chaos synchronization of two DFB lasers is achieved using the integrated laser as a driver. The simulation results indicate that the integrated tri-section laser with optical feedback can generate broadband chaos with a bandwidth up to 34 GHz, based on dual-mode beating effect. The influence of feedback strength and frequency interval on the chaos bandwidth are studied in detail. As a common driving signal, the broadband chaos signal can induce the response DFB lasers to generate chaos signals with a bandwidth of no less than 25 GHz and meanwhile achieve chaos synchronization with a correlation coefficient beyond 0.98. The low correlation coefficient between the drive signal and the response signal ensures the security of the chaos carrier from the public channel. Moreover, the dynamic properties of the integrated laser and the influence of the external parameters on chaotic synchronization are also investigated. Authors

关键词： Distributed feedback lasers

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A decentralised federated learning scheme for heterogeneous devices in cognitive IoT

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International Journal of Cognitive Computing in Engineering 2024年第1期5卷 357-366页

作者： Ge, Huanhuan Yang, Xingtao Wang, Jinlong Lyu, Zhihan School of Information and Control Engineering Qingdao University of Technology Qingdao266520 China Anhui Province Key Laboratory of Intelligent Building & Building Energy Saving Anhui Jianzhu University Hefei Anhui China Uppsala University Sweden

Cognitive Internet of Things (IoT) technologies typically rely on substantial data collected from edge devices for data analysis and decision-making. However this reliance often leads to the inadvertent exposure of private data from smart edge devices. Federated learning (FL) is a distributed machine learning framework that protects user privacy by performing collaborative training without uploading private data. Nevertheless, applying classical FL to cognitive IoT systems to preserve privacy preservation faces significant challenges, such as central server failure and communication burden. Furthermore, when edge devices with heterogeneous data and systems participate in federated training, the learning process becomes slow and the performance of edge devices is compromised. To address these challenges, we propose a decentralised FL framework for cognitive IoT, termed DFL–MKF. In DFL– MKF, the centralised server is eliminated and each edge device is dynamically connected. We initialised models for edge devices based on computational and storage capabilities to accommodate system heterogeneity. Edge devices learned from the knowledge of multiple neighbours via knowledge transfer, and knowledge fusion was employed to aggregate the knowledge of multiple neighbours, thereby improving the performance of local models, and addressing data heterogeneity. Comprehensive experiments were performed on three image classification tasks. The results of these experiments demonstrate that the proposed method achieved superior performance compared to various baselines and improved communication efficiency. © 2024 The Authors

关键词： Internet of things

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Calibration and measurement method of PMD based on the optimization algorithm of swarm intelligent body

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Applied Optics 2025年第9期64卷 2102-2111页

作者： Xu, Xingnan Yuan, Zihang Zhu, Jiaxin Zhou, Hong Sun, Wenqing Key Laboratory of Intelligent Optoelectronic Devices and Chips Jiangsu Higher Education Institutions School of Physical Science and Technology Suzhou University of Science and Technology Suzhou215009 China Advanced Technology Research Institute Taihu Photon Center School of Physical Science and Technology Suzhou University of Science and Technology Suzhou215009 China Suzhou Huadian Electric Technology Service Co. Ltd. Suzhou215000 China

Traditional phase measuring deflectometry (PMD) systems encounter challenges related to the stability of calibration parameter optimization, often resulting in insufficient reconstruction accuracy and low computational efficiency. In response to this technical challenge, this paper proposes an optimization method for the two-step calibration of the PMD system based on a swarm-intelligent body optimization algorithm, aiming to significantly improve the reconstruction accuracy and processing efficiency of the system. First, a traditional calibration method is used for the initial calibration of the measurement system. Subsequently, the dung beetle optimization (DBO) algorithm—to our knowledge, a novel swarm intelligence optimization algorithm known for its rapid convergence and global search capabilities—is introduced to construct a multi-objective optimization model with the three-dimensional system reconstruction error as the objective function. By applying the DBO to solve this model, a set of optimal calibration parameters is obtained. Experimental results demonstrate that the DBO algorithm achieves significant improvements in both reconstruction accuracy and stability compared to traditional calibration methods, thereby verifying its effectiveness and practicality in PMD systems. © 2025 Optica Publishing Group. All rights, including for text and data mining (TDM), Artificial Intelligence (AI) training, and similar technologies, are reserved.

关键词： Swarm intelligence

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