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2024 China automation Congress, CAC 2024

作者： Chen, Zhaoang Su, Tingting Li, Guotao Liang, Xu Liu, Shengda Xiao, Wendong Xie, Shoulie Zhang, Jie He, Guangping The Department of Mechanical and Electrical Engineering North China Universily of Technology Beijing100144 China The School of Information Science and Technology Beijing University of Technology Beijing100124 China The State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation Chinese Academy of Sciences Beijing100190 China School of Automation and Intelligence Beijing Jiaotong University Beijing100044 China School of Automation and Electrical Engineering University of Science and Technology Beijing100083 China Institute for Infocomm Research A*Star Singapore

ISBN: (纸本)9798350368604

Rehabilitation training is vital to patient’s recovery after stroke surgery. However, due to the inability to effectively absorb and buffer external shocks, rigid exoskeletons are more likely to cause damage to the patient’s joints and muscles. In this paper, the index finger exoskeleton with elastic actuator (IFEEA) is studied. First, the IFEEA is designed to realize the flexion-extension (F/E) movements of MCP joint of index finger. Second, the kinematic model of IFEEA is analyzed. Finally, the motion experiment was carried out. The experimental results show that the IFEEA has good spatial motion ability and great potential in future applications. © 2024 IEEE.

关键词： Exoskeleton (Robotics)

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Bearing Fault Diagnosis Under Variable Working Conditions Based on Domain Adaptation and Improved Depthwise Separable Convolution Network

Bearing Fault Diagnosis Under Variable Working Conditions Ba...

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IEEE International Conference on Industrial technology (ICIT)

作者： Zehui Kuang Fan Mao Xingyu Zhao Xiongbo Wan Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education School of Automation China University of Geosciences Wuhan China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education School of Future Technology China University of Geosciences Wuhan China Ministry of Education 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 Wuhan China

ISBN: (数字)9798331521950

ISBN: (纸本)9798331521967

Domain adaptation methods and appropriate feature extractors are usually applied to solve the problem that the variable working conditions of bearings affect the effectiveness of the fault diagnostic framework. However, the existing feature extractors are too complex to be deployed in real industries. In this paper, we propose a lightweight framework called improved depthwise separable convolution network (IDSCN) for the diagnosis of bearing faults in variable operating conditions. IDSCN is built on wavelet convolution and depthwise separable residual convolution (DSRC), where the wavelet convolution is used to extract frequency features from the source and target domains, and DSRC guarantees the performance of feature extraction with reduced parameters and computation. To minimise the distribution difference between the source and target domains, the local maximum mean difference is incorporated into the proposed framework. The performance of the proposed framework is validated on the Paderborn University dataset, involving a total of 6 transfer tasks. The experiment shows that the proposed framework achieves satisfactory results and outperforms other frameworks in bearing fault diagnosis.

关键词： Employee welfare Fault diagnosis Vibrations Industries Adaptation models Wavelet domain Convolution Frequency-domain analysis Feature extraction Complexity theory

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Parameters Extraction of Photovoltaic Cell Using Particle Swarm Optimization 4th

Parameters Extraction of Photovoltaic Cell Using Particle Sw...

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4th International Conference on Electronic engineering and Renewable Energy systems, ICEERE 2024

作者： Chermite, Charaf Douiri, Moulay Rachid Elamri, Oumaymah Control and Computer Science for Intelligent Systems and Green Energy Department of Applied Physics Faculty of Science and Technology Cadi Ayyad University Marrakech Morocco LAMIGEP EMSI of Marrakech Systems and Applications Engineering Laboratory National School of Applied Sciences Cadi Ayyad University Marrakech Marrakesh Morocco

ISBN: (纸本)9789819799749

Extracting parameters accurately and effectively from solar photovoltaic (PV) models is crucial for detailed simulation, evaluation, and management of PV systems. Although there has been an increase in the development of analytical, numerical, and metaheuristic methods targeting this task, accurately determining these parameters continues to be a significant challenge. This study focuses on applying Particle Swarm Optimization (PSO) for the extraction of key parameters of RTC France solar cells, a critical step towards enhancing PV system efficiency and reliability. Additionally, the PSO's performance is evaluated against other metaheuristic algorithms including Enhanced Chaotic JAYA (CJAYA), Performance-Guided JAYA (PGJAYA), and Enhanced Gradient-Based Optimizer (EGBO), underscoring the effectiveness of the proposed PSO-based approach. Utilizing MATLAB-SIMULINK, comprehensive analysis and results are presented, shedding light on the efficacy of PSO in addressing the intricacies of parameter extraction in PV systems. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Particle swarm optimization (PSO)

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3D Reconstruction of Weakly Textured Objects Based On Denoising Polarized Images 41

3D Reconstruction of Weakly Textured Objects Based On Denois...

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第41届中国控制会议

作者： Youai Wang Yi Liu Chi Xu 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 Eduction CRRC Zhuzhou Locomotive Co. Ltd.

ISBN: (数字)9789887581536

ISBN: (纸本)9781665482561

Traditional 3D visual reconstruction technology based on feature extraction is difficult to estimate the surface depth information of a smooth object with a single texture,while the polarization 3 D reconstruction technology can recover the surface normal information from the image pixel by pixel,which has the characteristics of high precision,low texture dependence and strong material ***,the normals recovered from the polarization information have the problem of multi-value ambiguity,which needs to be corrected before the accurate normal information can be *** the same time,the image collected by the polarization camera contains a lot of noise,which will greatly affect the accuracy of reconstruction *** this paper,the characteristics of polarization information are used to denoise the polarization image,and the reference normal information is used as the prior information for correcting the polarization ***,the degree of polarization and angle of polarization are calculated based on the polarized image,then the two are fused and denoised by the Block Matching threedimensional(BM3D) method,and then the multi-valued ambiguity of polarization normal is corrected according to the reference normal ***,the corrected polarization normal is integrated to obtain a high-precision 3D shape of the object.

关键词： Polarization Denoising 3D Reconstruction Weakly Textured Objects Normal Multi-value Correction

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Investigating Optimal Tracking Intervals for Solar Tracking systems to Enhance Efficiency and Performance

Investigating Optimal Tracking Intervals for Solar Tracking ...

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2024 IEEE International Conference on Artificial Intelligence and Green Energy, ICAIGE 2024

作者： Dahli, Khadidja Ilinca, Adrian Benallal, Abdellah Cheggaga, Nawel University of Blida 1 Electrical and Remote Control Systems Laboratory LabSET Faculty of Technology Blida Algeria Ecole de Technologie Superieure Department of Mechanical Engineering MontrealQC Canada University of Quebec at Rimouski Wind Energy Laboratory RimouskiQC Canada

ISBN: (纸本)9798350389838

Conventional solar trackers continuously adjust solar panels to optimize energy production, a process that can consume significant energy, potentially surpassing the energy gained. This research examines the most effective tracking interval for solar tracking systems to mitigate this issue. Through testing with intervals ranging from 5 minutes to 5 hours, it was determined that setting tracking intervals no shorter than 1 hour and no longer than 2 hours and 30 minutes is recommended to achieve a balance between energy efficiency and production. Additionally, this study suggests that elevation and azimuth angles should not be tracked using the same time periods;instead, a 2-hour interval for elevation tracking and a 3-hour interval for azimuth angle tracking are advised for optimal performance. The proposed discrete tracking method consumes less than 0.15% of the energy produced, thereby enhancing energy efficiency, reducing unnecessary energy consumption, and improving the overall reliability and lifespan of solar tracking systems. This approach has the potential to significantly impact the future of solar power by enhancing the efficiency and reliability of solar tracking systems, thereby increasing the viability and appeal of solar energy as a primary renewable resource. © 2024 IEEE.

关键词： discrete tracking energy efficiency renewable resource solar power solar trackers

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Vital Signs Detection Based on Scale-Space Representation and Empirical Wavelet Transform for FMCW Radar

Vital Signs Detection Based on Scale-Space Representation an...

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2022 Chinese automation Congress, CAC 2022

作者： Wu, Dongchang Xue, Wei Yang, Xinya Li, Tin Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education School of Automation China University of Geosciences Hubei Key Laboratory of Advanced Control Intelligent Automation for Complex Systems Wuhan430074 China

ISBN: (纸本)9781665465335

With the development of radar technology, frequency modulated continuous wave (FMCW) radar has been used for non-contact vital signs detection. In order to suppress the environmental noise and interference of breathing harmonics on heartbeat signal, this paper proposes a new vital sign detection method based on scale-space representation (SSR) and empirical wavelet transform (EWT) for the FMCW radar. First, a low-pass filter is set for the high-frequency noise elimination outside the frequency range of vital sign signals. Second, SSR is used to adaptively segment the spectrum of signals to obtain the initial frequency boundaries, and the kurtosis of the spectrum is applied to segment the spectrum again and obtain the final frequency boundaries to further eliminate noise. Third, the empirical wavelet filter bank is constructed by using the determined boundaries and EWT is used to decompose the signals to several components. Finally, according to the frequency ranges of components and the correlation between the components and vital sign signals, the components are selected to reconstruct the breathing and heartbeat signals. The experimental results show that the proposed method achieves a better detection performance than the classical EWT and empirical mode decomposition (EMD). © 2022 IEEE.

关键词： Wavelet transforms

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Dynamics Modeling Method of Offshore Drilling Experimental System Based on Double-Layer Stewart Parallel Mechanism 42

Dynamics Modeling Method of Offshore Drilling Experimental S...

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42nd Chinese control Conference, CCC 2023

作者： Wang, Chenxuan Ma, Zhejiaqi Lu, Chengda Du, Sheng Wu, Min School of Future Technology China University of Geosciences Wuhan430074 China School of Automation China University of Geosciences Wuhan430074 China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan430074 China Ministry of Education Engineering Research Center of Intelligent Technology for Geo-Exploration Wuhan430074 China

ISBN: (纸本)9789887581543

Establishing the dynamics model of the offshore drilling experimental system can better complete the offshore drilling test in the laboratory environment and reduce the cost of testing. A dynamical modeling method for the offshore drilling experimental system built on the double-layer Stewart parallel mechanism is proposed. Firstly, the kinematic and dynamical characteristics of the double-layer Stewart parallel mechanism are combined with the Lagrange method and the virtual work method to establish the dynamics model of the mechanism. Then a parameter identification scheme is designed using a nonlinear gray system estimation method based on the trust-domain reflection algorithm, and the model parameters are identified. The model is downscaled to improve the feasibility of the identification scheme and the accuracy of the identified parameters. The actual experimental system data verify this model's correctness and the model parameters' accuracy. © 2023 Technical Committee on control Theory, Chinese Association of automation.

关键词： Dynamics

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Fatigue Driving Detection of EEG Signals by LSTM Deep Neural Network with LPSD and DE 20

Fatigue Driving Detection of EEG Signals by LSTM Deep Neural...

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20th IEEE International Conference on Mechatronics and automation, ICMA 2023

作者： Zhang, Hao Dong, Enzeng Tong, Jigang Yang, Sen Du, Shengzhi Tianjin University of Technology Tianjin Key Laboratory for Control Theory & Applications in Complicated Systems Intelligent Robot Laboratory 391 Binshui West Road Tianjin China Tshwane University of Technology Department of Electrical Engineering Pretoria0001 South Africa

ISBN: (纸本)9798350320831

Fatigue driving detection has always been an urgent social problem to be solved, and many complex factors increase the difficulty of detection. Nowadays, more scholars choose to use multi-channel EEG signals for fatigue detection, which is accompanied by the development of deep learning networks. Here, we propose the deep Long Short Term Memory (LSTM) neural network with the Differential Entropy (DE) and Logarithm Power Spectrum Density (LPSD). We first divide the EEG signal into 8-s Hanning windows with 50% overlap, and then use the Short Time Fourier Transform(STFT) on the extracted time window to obtain 50 frequency sub-bands. Finally, LPSD and DE features are extracted from the frequency offspring obtained from the STFT and input into the deep LSTM neural network. The network can choose to learn more task related information by adding soft attention and extract effective time information from EEG signals,thereby improving the model's performance in detecting driving fatigue. In addition, we also collected EEG signals from 8 subjects in both awake and fatigued states,and used the PERCLOS index to judge the degree of fatigue. Using the collected dataset, we compared the effectiveness of the deep LSTM neural network with five other methods. The results indicate that the deep LSTM neural network has a better classification accuracy of 93.12%. It proves that the network has good performance in fatigue detection of EEG signals. © 2023 IEEE.

关键词： Long short-term memory

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RESEARCH ON DAMAGE LOCALIZATION TECHNIQUES FOR REUSABLE CARRIER TANK STRUCTURES

RESEARCH ON DAMAGE LOCALIZATION TECHNIQUES FOR REUSABLE CARR...

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2024 CSAA/IET International Conference on Aircraft Utility systems, AUS 2024

作者： Ben, Xu Mei, Yuan Shaopeng, Dong Jiutian, Zhang Fuel System Department Nanjing Engineering Institute of Aircraft Systems AVIC Nanjing China School of Automation Science and Electrical Engineering Beihang University Beijing China Aviation Key Laboratory of Science and Technology on Aero Electromechanical System Integration Nanjing China Ningho Institute of Technology Beihang University Ningbo China

Lamb waves, characterized by slow attenuation, short wavelength, and sensitivity to damage, have been widely utilized in the field of composite material damage detection. In addressing the issue of damage localization in composite curved tank structures, this study proposes a composite tank localization algorithm based on convolutional neural networks (CNNs) using Lamb wave data. The approach transforms the damage localization regression problem into a classification problem through partitioning, achieving damage zone localization in composite tank structures with fewer sensors. The accuracy of the algorithm is more than 98% in the dataset constructed from physical experiments, which verifies its accuracy and applicability. © The Institution of engineering & technology 2024.

关键词： Tanks (containers)

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Convex Equilibrium-Free Stability and Performance Analysis of Discrete-Time Nonlinear systems

arXiv

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arXiv 2024年

作者： Koelewijn, Patrick J.W. Weiland, Siep Tóth, Roland Control Systems Group Department of Electrical Engineering Eindhoven University of Technology Netherlands Systems and Control Laboratory HUN-REN Institute for Computer Science and Control Hungary

This paper considers the equilibrium-free stability and performance analysis of discrete-time nonlinear systems. We consider two types of equilibrium-free notions. Namely, the universal shifted concept, which considers stability and performance w.r.t. all equilibrium points of the system, and the incremental concept, which considers stability and performance between trajectories of the system. In this paper, we show how universal shifted stability and performance of discrete-time systems can be analyzed by making use of the time-difference dynamics. Moreover, we extend the existing results for incremental dissipativity for discrete-time systems based on dissipativity analysis of the differential dynamics to more general state-dependent storage functions for less conservative results. Finally, we show how both these equilibrium-free notions can be cast as a convex analysis problem by making use of the linear parameter-varying framework, which is also demonstrated by means of an example. Copyright © 2024, The Authors. All rights reserved.

关键词： Nonlinear systems

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