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design of wearable squat booster for automobile assembler based on ergonomics

Journal of Physics: Conference Series 2020年第1期1635卷

作者： Zhewen Chen Zhifeng Qiao Tianxu Bao Fuchao Dou Xinpu Chen Tianjin University of Technology Tianjin Key Laboratory for Advanced Mechatronic System Design and Intelligent Control School of Mechanical Engineering National Demonstration Center for Experimental Mechanical and Electrical Engineering Education Tianjin China

Aiming at the problem that automobile assembler need to squat for a long time, a wearable squatting assistance system with low cost, simple control and easy to squat and walk is designed. Based on ergonomics and finite element theory, UG was used to draw a three-dimensional solid model of the wearable squatting power system for automobile assemblers. With the help of ANSYS software, the statics analysis of key components is carried out to check their strength and stiffness. It is concluded that the wearable squatting power assist device for automotive fitters based on ergonomics has reached the design requirements in terms of function use and strength and stiffness.

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Research on Logistics Simulation and Optimization of Die Forging Production Line Based on Flexsim

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Journal of Physics: Conference Series 2020年第2期1624卷

作者： Qiang Cheng Hongchao Shen Hongyan Chu Zhifeng Liu Caixia Zhang Jiaxiang Ren Institute of Advanced Manufacturing and Intelligent Technology Beijing University of Technology Beijing 100124 China Beijing Key Laboratory of Advanced Manufacturing Technology Beijing University of Technology Beijing 100124 China Mechanical Industry Key Laboratory of Heavy Machine Tool Digital Design and Testing Beijing University of Technology Beijing 100124 China

Long forging production cycle and low equipment utilization are the important problems currently facing domestic die forging ring production lines. To solve this problem, a simulation optimization method for die forging production line based on Flexsim was proposed. Firstly, the ECRS theory method was used to analyze the bottleneck process of the production line. Then, built a logistics simulation model of die forging production line based on Flexsim, The parallel and cycle processing steps was set reasonable parameters and simulated. Next, through logic analysis, methods such as adding processing equipment, improving equipment efficiency and balancing equipment handling tasks were used to optimize and improve the production line. Finally, compared and analyzed the original and optimized model. The simulation results show that the optimized production line equipment task allocation tends to be rationalized, the equipment utilization rate is increased by 4.34%, and the product cycle time is reduced by 27.65%, and optimized planning and design are achieved.

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An Improved Correlation-Based Just-in-Time Modeling Method Using Dynamic Partial Least Squares and Adaptive Local Domain Partition 29

An Improved Correlation-Based Just-in-Time Modeling Method U...

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第29届中国控制与决策会议

作者： Xiaolong Chen Zhizhong Mao Runda Jia Dong Xiao Xiaojun Wang College of Information Science & Engineering Northeastern University State Key Laboratory of Synthetical Automation for Process Industries Northeastern University Key Laboratory of Advanced Design and Intelligent Computing Dalian university

ISBN: (纸本)9781509046584

This paper proposes an improved correlation-based just-in-time modeling method,referring to as the ICo JIT,for improving the prediction accuracy and real-time performance of the conventional correlation-based just-in-time(CoJIT) modeling *** achieve this objective,a novel adaptive local domain partition method has been developed based on the moving window technique and the fitting precision,which takes into account the input and output information simultaneously and has potentially the capabilities of obtaining the optimal local domain partition adaptively and capturing new process states by adding new local *** dynamic partial least squares and adaptive local domain partition method,multiple local domains and corresponding local models can be obtained during the offline operation *** online computation burden is reduced compared to CoJIT modeling *** addition,the proposed ICoJIT modeling method can efficiently deal with nonlinearity and time-varying behavior of processes as well as the CoJIT modeling *** effectiveness of the proposed method is demonstrated through a real industrial process dataset in sulfur recovery unit process.

关键词： Soft sensor Correlation-based just-in-time modeling Partial least squares Local domain partition Moving window

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Kinematic Analysis of a Redundant Actuated Five Degrees of Freedom Parallel Mechanism

Kinematic Analysis of a Redundant Actuated Five Degrees of F...

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2018 IEEE International Conference on Robotics and Biomimetics, ROBIO 2018

作者： Xiaolong, Yin Bin, Li Xinhua, Zhao Tianjin Key Laboratory for Advanced Mechatronic System Design and Intelligent Control Tianjin University of Technology China Fac. of Natl. Demonstration Center for Experimental Mechanical and Electrical Engineering Education Tianjin University of Technology Tianjin300384 China

ISBN: (纸本)9781728103761

A 5 degree of freedom parallel mechanism with 6 branched redundant actuators is proposed. The mechanism is composed of 4 -SPS and 2-RPS kinematic branches. The mechanism can achieve three rotational and two translational motion. The performance of the forward and inverse kinematics, singularity and workspace, is analyzed, firstly, using the vector algebra method and Newton iteration method, the forward and inverse kinematics model were obtained from numerical solution and analytic solution;on this basis, get the Jacobian matrix of mechanism, and on the basis of the three kinds of performance parameters of matrix the singularity of the mechanism was analyzed;finally with the help of Monte Carlo method, obtaining the workspace of the mechanism. The related kinematic analysis will lay a theoretical foundation for the future study of the dynamics. © 2018 IEEE.

关键词： Mechanisms

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Variational auto-encoder based approximate bayesian computation uncertian inverse method for sheet metal forming problem

arXiv

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arXiv 2019年

作者： Wang, Jiaquan Zeng, Yang Jiang, Xinchao Wang, Hu Li, Enying Li, Guangyao State Key Laboratory of Advanced Design and Manufacturing for Vehicle Body Hunan University Changsha410082 China Joint Center for Intelligent New Energy Vehicle Shanghai201804 China College of Mechanical & Electrical Engineering Central South University of Forestry and Teleology Changsha41004 China

In this study, an image-assisted Approximate Bayesian Computation (ABC) parameter inverse method is proposed to identify the design parameters. In the proposed method, the images are mapped to a low-dimensional latent space by Variational AutoEncoder (VAE), and the information loss is minimized by network training. Therefore, an effective trade-off between information loss and computational cost can be achieved by using the latent variables of VAE as summary statistics of ABC, which overcomes the difficulty of selecting summary statistics in the ABC. Besides, for some practical engineering problems, processing the images as objective function can effective show the response result. Meanwhile, the relationship between design parameters and the latent variables is constructed by Least Squares Support Vector Regression (LSSVR) surrogate model. With the well-constructed LSSVR model, the simulation coefficient vectors under given parameters will be determined effectively. Then, the parameters to be identified are determined by comparing the simulated and observed coefficient vectors in ABC. Finally, a sheet forming problem is investgated by the suggested method. The material parameters of the blank and the process parameters of the forming process are identified. Results show that the method is feasibility and effective for the identification of sheet forming parameters. Copyright © 2019, The Authors. All rights reserved.

关键词： Bayesian networks

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Correction to: A method of VR‑EEG scene cognitive rehabilitation training

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Health information science and systems 2021年第1期9卷 7页

作者： Wenjun Tan Yang Xu Pan Liu Chunyan Liu Yujin Li Yanrui Du Chao Chen Yuping Wang Yanchun Zhang Key Laboratory of Intelligent Computing in Medical Image Ministry of Education Northeastern University Shenyang 110189 China. Cyberspace Institute of Advanced Technology Guangzhou University Guangzhou 510006 China. Department of Neurology Xuanwu Hospital Capital Medical University Beijing 100053 China. Key Laboratory of Complex System Control Theory and Application Tianjin University of Technology Tianjin 300384 China. Institute for Sustainable Industries and Liveable Cities Victoria University Melbourne VIC 8001 Australia.

[This corrects the article DOI: 10.1007/s13755-020-00132-6.].

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Classification of ECG signals based on 1D convolution neural network 19

Classification of ECG signals based on 1D convolution neural...

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19th IEEE International Conference on e-Health Networking, Applications and Services, Healthcom 2017

作者： Li, Dan Zhang, Jianxin Zhang, Qiang Wei, Xiaopeng Key Lab of Advanced Design and Intelligent Computing Ministry of Education Dalian University Dalian China

ISBN: (纸本)9781509067046

Recently, with the obvious increasing number of cardiovascular disease, the automatic classification research of Electrocardiogram signals (ECG) has been playing a significantly important part in the clinical diagnosis of cardiovascular disease. In this paper, a 1D convolution neural network (CNN) based method is proposed to classify ECG signals. The proposed CNN model consists of five layers in addition to the input layer and the output layer, i.e., two convolution layers, two down sampling layers and one full connection layer, extracting the effective features from the original data and classifying the features automatically. This model realizes the classification of 5 typical kinds of arrhythmia signals, i.e., normal, left bundle branch block, right bundle branch block, atrial premature contraction and ventricular premature contraction. The experimental results on the public MIT-BIH arrhythmia database show that the proposed method achieves a promising classification accuracy of 97.5%, significantly outperforming several typical ECG classification methods. © 2017 IEEE.

关键词： Neural network models

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Kinematics Analysis of Walking Robot Based on Double Stewart Parallel Mechanism

Kinematics Analysis of Walking Robot Based on Double Stewart...

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Chinese Automation Congress (CAC)

作者： Shuo Jia Wei Chen Wenchang Zhang Hang Wu Weixiao Li Tianjin Key Laboratory for Advanced Mechatronic System Design and Intelligent Control Tianjin University of Technology Tianjin China Academy of Military Sciences of Chinese PLA Institute of Medical Support Technology Tianjin China Information Management and Information System Nanjing University of Posts and Telecommunications Nanjing China

Parallel mobile robots have recently attracted wide attention due to their strong load capacity, high running stability and rapid response speed. This paper conducts the kinematics research based on a pair of novel mobile legs that consist of a dual Stewart parallel mechanism. Firstly, we describe its motion mechanism and analyze the DOF of the mechanism in detail. Secondly, the forward and inverse kinematic solutions of the mechanism are computed and analyzed, and then the workspace is obtained by Matlab simulation. Thirdly, according to the principle of kinematics, we carry out dynamic performance optimization research to analyze and compare various motion control rules. Finally, the human-computer interface is designed for ease of analysis and displaying the kinematic rules of robot motion.

关键词： Legged locomotion Robot kinematics Mobile robots Motion control Stability

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Strategic priorities for transformative progress in advancing biology with proteomics and artificial intelligence

arXiv

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arXiv 2025年

作者： Sun, Yingying Jun, A. Liu, Zhiwei Sun, Rui Qian, Liujia Payne, Samuel H. Bittremieux, Wout Ralser, Markus Li, Chen Chen, Yi Dong, Zhen Perez-Riverol, Yasset Khan, Asif Sander, Chris Aebersold, Ruedi Vizcaíno, Juan Antonio Krieger, Jonathan R. Yao, Jianhua Wen, Han Zhang, Linfeng Zhu, Yunping Xuan, Yue Sun, Benjamin Boyang Qiao, Liang Hermjakob, Henning Tang, Haixu Gao, Huanhuan Deng, Yamin Zhong, Qing Chang, Cheng Bandeira, Nuno Li, Ming Weinan, E. Sun, Siqi Yang, Yuedong Omenn, Gilbert S. Zhang, Yue Xu, Ping Fu, Yan Liu, Xiaowen Overall, Christopher M. Wang, Yu Deutsch, Eric W. Chen, Luonan Cox, Jürgen Demichev, Vadim He, Fuchu Huang, Jiaxing Jin, Huilin Liu, Chao Li, Nan Luan, Zhongzhi Song, Jiangning Yu, Kaicheng Wan, Wanggen Wang, Tai Zhang, Kang Zhang, Le Bell, Peter A. Mann, Matthias Zhang, Bing Guo, Tiannan Affiliated Hangzhou First People’s Hospital State Key Laboratory of Medical Proteomics School of Medicine Westlake University Zhejiang Province Hangzhou China Westlake Center for Intelligent Proteomics Westlake Laboratory of Life Sciences and Biomedicine Zhejiang Province Hangzhou China Biology Department Brigham Young University ProvoUT84602 United States Department of Computer Science University of Antwerp Antwerp2020 Belgium Department of Biochemistry CharitéUniversitätsmedizin Berlin Berlin Germany Biomedicine Discovery Institute Department of Biochemistry and Molecular Biology Monash University MelbourneVICVIC 3800 Australia Wellcome Genome Campus Hinxton CambridgeCB10 1SD United Kingdom Harvard Medical School Ludwig Center at Harvard United States Harvard Medical School Broad Institute Ludwig Center at Harvard Dana-Farber Cancer Institute United States Department of Biology Institute of Molecular Systems Biology ETH Zürich Zürich Switzerland Bruker Ltd. MiltonONL9T 6P4 Canada AI for Life Sciences Lab Tencent Shenzhen518057 China State Key Laboratory of Medical Proteomics AI for Science Institute Beijing100080 China Beijing Institute of Lifeomics Beijing102206 China Thermo Fisher Scientific GmbH Hanna-Kunath Str. 11 Bremen28199 Germany Informatics and Predictive Sciences Research Bristol Myers Squibb United States Department of Chemistry Fudan University Songhu Road 2005 Shanghai200438 China Department of Computer Science Luddy School of Informatics Computing and Engineering Indiana University IN47408 United States ProCan® Children’s Medical Research Institute Faculty of Medicine and Health The University of Sydney WestmeadNSW Australia La Jolla CA United States Central China Institute of Artificial Intelligence University of Waterloo Canada AI for Science Institute Center for Machine Learning Research School of Mathematical Sciences Peking University China Research Institute of Intelligent Complex Systems Fudan U

Artificial intelligence (AI) is transforming scientific research, including proteomics. Advances in mass spectrometry (MS)-based proteomics data quality, diversity, and scale, combined with groundbreaking AI techniques, are unlocking new challenges and opportunities in biological discovery. Here, we highlight key areas where AI is driving innovation, from data analysis to new biological insights. These include developing an AI-friendly ecosystem for proteomics data generation, sharing, and analysis;improving peptide and protein identification and quantification;characterizing protein-protein interactions and protein complexes;advancing spatial and perturbation proteomics;integrating multi-omics data;and ultimately enabling AI-empowered virtual cells. © 2025, CC BY.

关键词： Biotic

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Development of electric cart for improving walking ability — application of control theory to assistive technology

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Science China(Information Sciences) 2017年第12期60卷 262-270页

作者： Jinhua SHE Yasuhiro OHYAMA Min WU Hiroshi HASHIMOTO School of Engineering Tokyo University of Technology School of Automation China University of Geosciences Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Master Program of Innovation for Design & Engineering Advanced Institute of Industrial Technology

This paper explains the development of an electric cart that helps the elderly maintain or improve their physical strength. Unlike commercially available ones, it has a pedal unit that provides some exercise for a user in training his lower limbs. An impedance model describes the feeling of pushing the pedals. The largest pedal load is determined based on a pedaling experiment. An H∞ controller is designed for each of the largest pedal load and virtually no load. A control law, which is based on the concept of dynamic parallel distributed compensation, is designed using the rating of perceived exertion of a driver as a criterion to choose a pedal load between the largest and almost zero. Five university students and twelve elderly people participated experiments to verify the system design and the validity of the system.

关键词： aging Borg’s scale dynamic parallel distributed compensation electrical cart H_∞ control Karvonen formula lower limbs pedaling rating of perceived exertion

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