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The Analysis of Muscle-Synergy Similarity between Walking and Pedaling Based on Variational Mode Decomposition and Non-Negative Matrix Factorization

The Analysis of Muscle-Synergy Similarity between Walking an...

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IEEE International Conference on engineering, Technology and Innovation

作者： Wangyang Ge Jinhua She Shijie Guo Feng Wang Juan Zhao Seiichi Kawata School of Automation China University of Geosciences Wuhan China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan China Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Wuhan China School of Engineering Tokyo University of Technology Hachioji Tokyo Japan Engineering Research Center of the Ministry of Education for Intelligent Rehabilitation Equipment and Detection Technologies Hebei Key Laboratory of Robot Sensing and Human-Robot Interaction Tianjin China School of Mechanical Engineering Hebei University of Technology Tianjin China

ISBN: (数字)9798350362435

ISBN: (纸本)9798350362442

Pedaling lower-limb rehabilitation robots are widely used in various stages of lower-limb rehabilitation because of their high safety and low requirements for patients' lower-limb strength. researchers found that the activation sequence of the surface electromyographic (sEMG) signals of the muscles of the lower limb during pedaling is very similar to that of walking, which makes it possible to simulate the muscle force generation during walking by changing the pedal load during pedaling training. However, most pedaling lower-limb rehabilitation robots use constant loads, and it is of great importance to study the relationship between load and muscle synergy and to design load phase angle trajectories with higher muscle synergy consistency in the walking-pedaling motion to optimize the training effect of pedal-type rehabilitation robots. In this paper, we used variational mode decomposition (VMD) and nonnegative matrix factorization (NMF) to extract the muscle synergy features in each band of sEMG during walking and pedaling and to study the similarity of pedaling and walking and the influence of pedal load on muscle synergy consistency.

关键词： Legged locomotion Training Technological innovation Time series analysis Muscles Assistive robots Feature extraction Trajectory Safety Matrix decomposition

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Natural Human Skin‑Inspired Wearable and Breathable Nanofiber‑based Sensors with Excellent Thermal Management Functionality

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Advanced Fiber Materials 2024年第6期6卷 1955-1968页

作者： Peng Wang Xiaodan Li Guifen Sun Guoqing Wang Qing Han Chuizhou Meng Zhonghe Wei Yang Li School of Mechanical Engineering University of JinanJinan 250022China School of Information and Control Engineering China University of Mining and TechnologyXuzhou 221116China State Key Laboratory for Reliability and Intelligence of Electrical Equipment Engineering Research Center of Ministry of Education for Intelligent Rehabilitation Device and Detection TechnologyHebei Key Laboratory of Smart Sensing and Human‑Robot InteractionSchool of Mechanical EngineeringHebei University of TechnologyTianjin 300401China Shandong Provincial Key Laboratory of Network‑Based Intelligent Computing School of Information Science and EngineeringUniversity of JinanJinan 250022China Network Information Center Department of Computer ScienceQilu University of Technology(Shandong Academy of Sciences)Jinan 250353China School of Integrated Circuits Shandong UniversityJinan 250101China

Wearable sensors have been rapidly developed for application in various human monitoring ***,the wearing comfort and thermal properties of these devices have been largely ignored,and these characteristics urgently need to be ***,we develop a wearable and breathable nanofiber-based sensor with excellent thermal management functionality based on passive heat preservation and active Joule heating *** multifunctional device consists of a micropatterned carbon nanotube(CNT)/thermoplastic polyurethane(TPU)nanofiber electrode,a microporous ionic aerogel electrolyte and a microstructured Ag/TPU nanofiber *** to the presence of a supercapacitive sensing mechanism and the appli-cation of microstructuration,the sensor shows excellent sensing performance,with a sensitivity of 24.62 ***,due to the overall porous structure and hydrophobicity of TPU,the sensor shows good breathability(62 mm/s)and water repellency,with a water contact angle of 151.2°.In addition,effective passive heat preservation is achieved by combining CNTs with high solar absorption rates(85%)as the top layer facing the outside,aerogel with a low thermal conductivity(0.063 W m-1 k-1)as the middle layer for thermal insulation,and Ag with a high infrared reflectance rate as the bottom layer facing the *** warming,this material yields a higher temperature than ***,the active Joule heat-ing effect is realized by applying current through the bottom resistive electrode,which can quickly increase the temperature to supply controlled warming on *** proposed wearable and breathable sensor with tunable thermal properties is promising for monitoring and heat therapy applications in cold environments.

关键词： Wearable sensor Breathable Personal thermal management Electrospun nanofibers Joule heating

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Adaptive data driven multi period power supply recovery method for distribution networks

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Scientific reports 2025年第1期15卷 19002页

作者： Xi Ye Meng Yang Zhihong Yang Qing Xiang Yazhuo Li Institute of Intelligent Manufacturing Jianghan University Wuhan Hubei China. ***@***. Hubei Engineering Research Center for Intelligent Detection and Control of Specialized Equipment Wuhan China. ***@***. Economics and Technology Research Institute State Grid Hubei Electric Power Company Wuhan 430077 China. Institute of Intelligent Manufacturing Jianghan University Wuhan Hubei China.

In the process of distribution network fault recovery, in order to better address the issues caused by the uncertainty of new energy power output, this paper proposes a multi period power supply recovery method for distribution networks based on adaptive data driven approach. Firstly, this method uses the historical data of new energy power output to construct an ellipsoidal uncertainty set, and forms a data driven convex hull polyhedral set by connecting the vertices of the high-dimensional ellipsoid. Then, aiming at the problem of large conservatism during the reduction process of the convex hull polyhedral set, based on the range of the box set, cutting planes are made starting from the vertices, and a data driven hyperplane polyhedral set model is constructed. Furthermore, considering the constraints of cyber-physical integration, an adaptive data driven power supply recovery model for distribution networks is established, and the column and constraint generation (C&CG) algorithm is adopted to solve the robust scheduling model. Finally, simulations on the improved IEEE-33 bus system and actual example systems verify that the adaptive data driven power supply recovery model for distribution networks can reduce conservatism and improve the robustness of the optimization results. This power supply recovery model can reduce conservatism and enhance the robustness of the optimization results. In the actual distribution network fault recovery scenarios, it can make more efficient use of new energy and ensure the stability of power supply, which strongly demonstrates the effectiveness and application value of the proposed method.

关键词： Convex hull polyhedron set Hyperplane polyhedron set Power supply recovery Two-stage robust optimization

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Cooperative Hierarchical Coverage control of Multi-Agent Systems with Non-holonomic Constraints in Poriferous Environments

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IEEE Transactions on control of Network Systems 2025年

作者： Zou, Jiayu Zhang, Hai-Tao Zhai, Chao Xing, Ning Ma, Yong Liu, Xingjian Huazhong University of Science and Technology School of Artificial Intelligence and Automation The MOE Engineering Research Center of Autonomous Intelligent Unmanned Systems The State Key Laboratory of Digital Manufacturing Equipment and Technology Wuhan430074 China China University of Geosciences School of Automation China The MOE Engineering Research Center of Intelligent Technology for Geo-Exploration Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan430074 China Academician Workstation of China COSCO Shipping Corporation Ltd. Shanghai200120 China Wuhan University of Technology Sanya Science and Education Innovation Park Sanya572000 China Wuhan University of Technology Chongqing Research Institute Chongqing401120 China

It has long posed a challenging task to optimally deploy multi-agent systems (MASs) to cooperatively coverage poriferous environments in real cooperative detection applications. In response to this challenge, this paper proposes a hierarchical coverage control (HCC) protocol for MASs to perform sector-based coverage operations. First, a distributed Voronoi partition-based sweep-and-assign protocol, combined with a sectorial partition method, is developed, enabling the segmentation of a whole poriferous region into multiple sectorial sub-subregions. Following this procedure, an extreme search scheme is developed to determine the optimal amount of agents for each subregion. After this allocation, a distributed controller is proposed to deploy the agents, considering non-holonomic constraints, into designated niche positions. Additionally, sufficient conditions are derived to guarantee the asymptotical stability of the present HCC. The analytical challenge of the present study stems from the non-convex characteristics inherent in the loss function induced by porous environments. This non-convexity renders the task of globally optimizing the assignment of MASs intractable. Finally, numerical simulations are conducted to validate the effectiveness of the present coverage control approach. © 2014 IEEE.

关键词： Nonlinear control systems

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A Highly Sensitive Stretchable Constraint Fiber Mat Sensor with Tailored Near-Zero Poisson's Ratio for Very Early Stage of Battery Over Charge/Overdischarge Monitoring

SSRN

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SSRN 2024年

作者： Liu, Guoqing Zhang, Yingying Li, Bo Guo, Shijie Yuan, Wenjing Meng, Chuizhou School of Materials Science & Engineering Tianjin Key Laboratory of Materials Laminating Fabrication and Interface Control Technology Hebei University of Technology Tianjin300130 China State Key Laboratory for Reliability and Intelligence of Electrical Equipment Engineering Research Center of Ministry of Education for Intelligent Rehabilitation Device and Detection Technology Hebei Key Laboratory of Smart Sensing and Human-Robot Interaction School of Mechanical Engineering Hebei University of Technology Tianjin300401 China

Battery state monitoring is crucial for safe operation and efficient management of lithium-ion battery (LIB) systems, but early stage of timely and accurate diagnosis remains challenging. Herein, we propose to utilize a highly sensitive stretchable strain sensor to in-situ monitor the slight strain variation of cells during charge and discharge, through which the state of the battery can be accurately reflected. The high sensing capability is realized by tailoring the Poisson’s ratio of low-modulus conductive fiber mats through a facile constraint manner. The non-uniform strain distribution together with the near-zero Poisson’s ratio synergistically result in superior sensing performance, including ultralow strain detection limit (0.006%), high strain sensitivity particularly under small strains (gauge factor of 116.1 between 0–2% strain), and high strain resolution under large deformations. When being practically utilized for LIB monitoring, the constraint fiber mat sensor can produce instant resistance response reflecting the cell capacity, and offer timely warning signal once the cell is overcharged or overdischarged. The developed stretchable strain sensor possesses comprehensive advantages of low modulus, tailorable small size, easy integration and conformality to cells, and little temperature interference, which would serve as a promising candidate for future battery state analysis and safety monitoring. © 2024, The Authors. All rights reserved.

关键词： Lithium-ion batteries

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Ultrathin and Highly-Stable Rubber Electrodes Based on Island-Bridge Multi-Filler Conductive Network for Multilayer-Stacked Dielectric Elastomer Artificial Muscles

SSRN

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SSRN 2024年

作者： Yu, Wei Chen, Wenqiang Yuan, Wenjing Li, Guoxian Meng, Chuizhou Guo, Shijie State Key Laboratory for Reliability and Intelligence of Electrical Equipment Engineering Research Center of Ministry of Education for Intelligent Rehabilitation Device and Detection Technology Hebei Key Laboratory of Smart Sensing and Human-Robot Interaction School of Mechanical Engineering Hebei University of Technology Tianjin300401 China School of Materials Science & Engineering Tianjin Key Laboratory of Materials Laminating Fabrication and Interface Control Technology Hebei University of Technology Tianjin300130 China

Dielectric elastomer actuators (DEAs) have shown great promise in applications ranging from millimeter-sized haptic feedback to centimeter-scaled soft robots. However, past progress has been focused on the soft dielectric elastomers, and little attention has been paid to the compliant electrodes. Being stable to maintain conductive while enduring repeated large deformations and compliant to avoid stiffness increasing for the dielectric elastomer remains challenging. Moreover, the fabrication of multilayer DEAs is complex, hindering the practicality. Herein, an ultrathin (~500 nm) and highly stable acrylate rubber electrode based on unique island-bridge multi-filler conductive network is developed with outstanding electrical conductivity of 2.7 S cm-1, excellent stretchability of over 1200%, and superior cycling stability of 500,000 cycles at 30% uniaxial strain. An efficient stacking method based on conductive and dielectric layer co-stretching (CDCS) approach is proposed for assembling multilayer-stacked DEAs. The 5-layer DEA reaches a large actuation strain of >17% for lifting a heavy 1-kg load and can serve as biceps muscle to drive an artificial lower arm by a maximum rotation angle of 8°, demonstrating its potential in actuating humanoid robot in an essentially soft way. This conductive rubber electrode and CDCS method are promising for developing soft robotics and muscle-like actuators. © 2024, The Authors. All rights reserved.

关键词： Electrodes

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Deep Learning-Based Dimension Measurement System for Industrial Part Inspection

Deep Learning-Based Dimension Measurement System for Industr...

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Chinese Automation Congress (CAC)

作者： Xiao-Di Li Xing-Rong Fan Xian-Ming Zhang Han-Zhi Zhang Chun-Jie Huang Cheng Xu Chongqing Engineering Laboratory for Detection Control and Integrated System School of Artificial Intelligence Chongqing Technology and Business University Chongqing China Engineering Research Centre for Waste Oil Recovery Technology and Equipment Ministry of Education Chongqing Key Laboratory Chongqing Technology and Business University Chongqing China School of Computer Science and Information Engineering Chongqing Technology and Business University Chongqing China Chongqing Intelligent Supply Chain Engineering & Technology Research Center Chongqing Jiaotong University Chongqing China Chongqing Aerospace Bashan Motorcycle Manufacturing Co. Ltd. Chongqing China

ISBN: (数字)9798350368604

ISBN: (纸本)9798350368611

This paper develops a deep learning-based system for the dimension measurement of industrial parts. The system employs a general self-supervised image segmentation network to perform dichotomous image segmentation (DIS) on the parts, effectively extracting binary images of the target components from complex backgrounds without requiring preprocessing steps such as filtering or denoising. Following segmentation, contour detection, and dimension measurement are executed. The system utilizes a CMOS area scan camera and was tested on a computer equipped with a GTX1050 graphics card. The tests, conducted on two typical thrust ball bearing components (i.e., the ball retainer and the shaft ring) - yielded ten measurements per part. Comparison with a vernier caliper as the reference standard reveals that under normal operating conditions, the maximum standard deviation is 0.007 mm and the maximum absolute error is 0.017 mm.

关键词： Shafts Image segmentation Accuracy Measurement uncertainty Noise reduction Manuals Size measurement Stability analysis Robustness Standards

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Hybrid Iterative K-Means Clustering with Improved Moth-Flame Optimization

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Hsi-An Chiao Tung Ta Hsueh/Journal of Xi'an Jiaotong University 2020年第9期54卷 32-39页

作者： Huang, He Li, Xinrui Wu, Kun Guo, Lu Wang, Huifeng Ru, Feng School of Electronic Control Chang'an University Xi'an710064 China Shaanxi Road Traffic Intelligent Detection and Equipment Engineering Technology Research Center Xi'an710064 China UAV National Engineering Research Center Northwestern Polytechnical University Xi'an710072 China

A new K-means clustering algorithm with improved moth-flame optimization is proposed to solve the problem that the current K-means clustering (KMC) algorithm has great randomness in selecting the initial clustering center, poor global search ability and low clustering accuracy. The maximum and minimum distance function is adopted to initialize the clustering center to avoid the problem that KMC algorithm is sensitive to the random initial clustering center. Then the moth-flame optimization is improved by spline interpolation to heighten the convergence rate and optimization accuracy. The average distance category is taken as the fitness function to guide interpolation moth-flame optimization to optimize the clustering center in the process of KMC iteration so as to improve the clustering accuracy. Compared with KMC algorithm, K-means++ algorithm and fuzzy c-means clustering algorithm on the international standard data sets Iris, Wine and Seeds, the experimental results show that the IMFO-KMC algorithm achieves the most significant improvement on the Iris data set. Compared with the other algorithms, the accuracy of IMFO-KMC algorithm is improved by 0.67%-4.18%, and the normalized mutual information is improved by 1.5%-4.01%. © 2020, Editorial Office of Journal of Xi'an Jiaotong University. All right reserved.

关键词： K-means clustering

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detection operator for traffic image in hazy weather

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Guangxue Jingmi Gongcheng/Optics and Precision engineering 2017年 25卷 244-249页

作者： Huang, He Song, Jing Sheng, Guang-Feng Xu, Zhe Zhang, Yi-Pu Chang'an University Electronic and Control Engineering School Xi'an710064 China Shaanxi Road Traffic Intelligent Detection and Equipment Engineering Technology Research Center Xi'an710064 China The 20th Research Institute of CETC Xi'an710068 China

A new edge detection method of traffic image in hazy weather was proposed pertinent to the problem that complete edge extraction cannot be achieved for traffic image in hazy weather in current edge detection method of image. Coarse edge of traffic image was extracted with newly proposed eight-direction edge detection operators pertinent to edge detection for traffic image in hazy weather, then weight of pixel points in partial directions were increased to overcome defocus blur due to refraction of haze particles in combination with features of hazy images;weight value was adjusted to design new refining operators of image edge so as to conduct refinement treatment on image edge and reduce quantity of fake edges;finally refined image edge was output for display. Experimental result indicates that most edge details of traffic image in hazy weather can be detected in the method proposed and more fine image edge can be achieved simultaneously compared with existing edge detection methods;it has better distinction degree and more accurate image edge positioning;image edge strength increases by over 50% compared with traditional method, thus effect of edge detection in hazy weather is effectively increased. © 2017, Science Press. All right reserved.

关键词： Edge detection

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Dispatching Algorithm Design for Elevator Group control System with Q-Learning based on a Recurrent Neural Network

Dispatching Algorithm Design for Elevator Group Control Syst...

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第25届中国控制与决策会议

作者： Weipeng Liu Ning Liu Hexu Sun Guansheng Xing Yan Dong Haiyong Chen School of Control Science and Engineering Hebei University of Technology Education Ministry Engineering Research Center of Intelligent Rehabilitation Equipment and Detection Technology Hebei University of Science and Technology

ISBN: (纸本)9781467355339

A dispatching algorithm of elevator group control system is proposed based on reinforcement learning. Elevator dispatching is modeled by a Markov Decision Process. Then an internally recurrent neural network based reinforcement learning method is designed to find the optimal dispatching policy while the state-action value function is iteratively approximated. Finally, several simulated experiments are done to compare the trained dispatching policy with other traditional ones. The experimental results demonstrate the effectiveness of proposed dispatching method.

关键词： Elevator Group control, Dispatching Algorithm, Reinforcement Learning, Neural Network

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