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IEEE Symposium on Intelligent Vehicle

作者： Teng Liu Chao Yang Chuanzheng Hu Hong Wang Li Li Dongpu Cao Fei-Yue Wang Vehicle Intelligence Pioneers Inc. Qingdao Shandong China Jiangsu XCMG Construction Machinery Research Institute Ltd. Xuzhou Jiangsu China Mechanical and Mechatronics Engineering Department Waterloo University Canada Faculty of Department of Automation Tsinghua University China State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China

This paper proposes a learning-based predictive control technique for self-driving hybrid electric vehicle (HEV). This approach is a hierarchical framework. The higher-level is a human-like driver model, which is applied to predict accelerations in the car following situation to replicate a human driver's demonstrations. The lower-level is a reinforcement learning (RL)-based controller, which enforces the battery and fuel consumption constraints to improve energy efficiency of HEV. In addition, we present induced matrix norm (IMN) to handle cases that the training data cannot provide sufficient information on how to operate in current driving situation. Simulation results illustrate that the proposed method can reproduce human driver's driving style and promote fuel economy.

关键词： Acceleration Hybrid electric vehicles Trajectory Hidden Markov models Predictive control Energy efficiency

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Social Energy:Mining Energy From the Society

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IEEE/CAA Journal of Automatica Sinica 2017年第3期4卷 466-482页

作者： Jun Jason Zhang David Wenzhong Gao Yingchen Zhang Xiao Wang Xiangyang Zhao Dongliang Duan Xiaoxiao Dai Jun Hao Fei-Yue Wang Department of Electrical and Computer Engineering Ritchie School of Engineering and Computer ScienceUniversity of Denver IEEE National Renewable Energy Laboratory Qingdao Academy of Intelligent Industries(QAII) QingdaoShandongChinaand SKL-MCCSCASIA School of Automation Science and Electrical Engineering Beihang University Department of Electrical and Computer Engineering College of Engineering and Applied ScienceUniversity of Wyoming State Key Laboratory of Management and Control for Complex Systems Institute of AutomationChinese Academy of Sciences(SKL-MCCSCASIA) Research Center of Computational Experiments and Parallel Systems National University of Defense Technology

The inherent nature of energy,i.e.,physicality,sociality and informatization,implies the inevitable and intensive interaction between energy systems and social *** this perspective,we define "social energy" as a complex sociotechnical system of energy systems,social systems and the derived artificial virtual systems which characterize the intense intersystem and intra-system *** recent advancement in intelligent technology,including artificial intelligence and machine learning technologies,sensing and communication in Internet of Things technologies,and massive high performance computing and extreme-scale data analytics technologies,enables the possibility of substantial advancement in socio-technical system optimization,scheduling,control and *** this paper,we provide a discussion on the nature of energy,and then propose the concept and intention of social energy systems for electrical power.A general methodology of establishing and investigating social energy is proposed,which is based on the ACP approach,i.e., "artificial systems"(A), "computational experiments"(C) and "parallel execution"(P),and parallel system methodology.A case study on the University of Denver(DU) campus grid is provided and studied to demonstrate the social energy *** the concluding remarks,we discuss the technical pathway,in both social and nature sciences,to social energy,and our vision on its future.

关键词： ACP distribution locational marginal pricing intelligent operation in power systems parallel control parallel intelligence parallel management social energy socio-technical energy systems

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Optimal control Method of High-Voltage Frequency Converters with Damaged Cells

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IOP Conference Series: Materials Science and Engineering 2020年第1期985卷

作者： V Busher O Chornyi O Glazeva V G Kuznetsov V Tytiuk M Tryputen Department of Ship's Electromechanics and Electrotechnics National University 'Odessa Maritime Academy' 8 Didrikhson str. Odessa 65029 Ukraine Institute of Electromechanics Energy Saving and Automatic Control Systems Kremenchuk Mykhailo Ostrohradskyi National University 20 Pershotravneva Street Kremenchuk Ukraine Electric Power Department Railway Research Institute 50 Chlopickiego str. Warsaw Poland Department of electromechanics Kryvyj Rih National University 11 Vitaly Matusevich str Kryvyi Rih Ukraine Department of Automation and Instrumentation Dnipro University of Technology 19 D. Yavornitsky ave. Dnipro Ukraine

Application of high-voltage frequency converters is one of the most prospective trends in the development of current powerful electric drives, i.e. in high-speed trains. In terms of such converters, a multilevel cascaded H-bridge converter is becoming the most widespread one owing to its increased reliability, survival in terms of one or more damaged cells, and quick repairability due to its modular structure. The research proposes a control method, which provides maximum possible linear voltage in case of one or several damaged cells owing to the shift of zero point and turning of phase voltages. It is also demonstrated how turning angles of phase voltages should be set to preserve spatial position of the linear voltage vectors which provides the least complicated electromagnetic transients in terms of nominal rotation frequency; in case of low or middle frequencies, damage of the cells may have no effect on the motor operation. Fast algorithm to calculate turning points of phase voltages is proposed; the algorithm may be applied in the industrial microprocessor control systems. Algorithm of the operations, which provides implementation of the method, is described.

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Commentaries on Viewpoint: Physiology and fast marathons

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Journal of applied physiology (Bethesda, Md. : 1985) 2020年第4期128卷 1069-1085页

作者： Jordan Santos-Concejero Fernando González-Mohíno José María González-Ravé Stephane Perrey Arthur H. Dewolf Brandon A. Yates Anton Ušaj Tadej Debevec José Manuel González-Rayas Ana Lilia Rayas-Gómez José Manuel González-Yáñez Romuald Lepers Paul Stapley Julien Louis Felix Proessl P T Nikolaidis B Knechtle D Muniz-Pumares B Hunter L Bottoms Bastien Bontemps Pedro L. Valenzuela Daniel Boullosa Juan Del Coso Richard C. Blagrove Philip R. Hayes Gregoire P. Millet Davide Malatesta Yuri de Almeida Costa Campos Miller Pereira Guimarães Jeferson Macedo Vianna Sandro Fernandes da Silva Paulo Henrique Silva Marques de Azevedo Hunter L. Paris Margaret A. Leist Mast T. Lige William Malysa Alicia S. Oumsang Erin C. Sinai Rasmus K. Hansen Niels H. Secher Stefanos Volianitis Laura Hottenrott Kuno Hottenrott Thomas Gronwald Jonathon W. Senefeld Ricardo J. Fernandes João Paulo Vilas-Boas Alain Riveros-Rivera Dieter Böning Daniel H. Craighead Shalaya Kipp Rodger Kram Christoph Zinner Billy Sperlich Hans-Christer Holmberg Borja Muniz-Pardos Shaun Sutehall Konstantinos Angeloudis Fergus M. Guppy Andrew Bosch Yannis Pitsiladis David C. Andrade Rodrigo Del Rio Rodrigo Ramirez-Campillo Thiago Ribeiro Lopes Bruno Moreira Silva Stephen J. Ives Peter G. Weyand Cayque Brietzke Paulo Estevão Franco-Alvarenga Tony Meireles dos Santos Flávio Oliveira Pires Gwenael Layec Wouter Hoogkamer Christopher S. Balestrini Curtis S. Goss Mikaela C. Gabler Albaro Escalera Shane A. Bielko Robert F. Chapman Department of Physical Education and Sport University of the Basque Country UPV/EHU Vitoria-Gasteiz Spain University of Castilla-La Mancha Sport Training Lab Toledo Spain Facultad de Lenguas y Educación Universidad Nebrija Madrid Spain EuroMov Digital Health in Motion University of Montpellier IMT Mines Ales Montpellier France Department of Systems Medicine and Center of Space Biomedicine University of Rome Tor Vergata Rome Italy Department of Kinesiology Indiana University-Purdue University of Indianapolis Indianapolis Indiana Indiana Center for Musculoskeletal Health Indiana University School of Medicine Indianapolis Indiana Faculty of Sport University of Ljubljana Ljubljana Slovenia Department of Automation Biocybernetics and Robotics Jozef Stefan Institute Ljubljana Slovenia School of Medicine and Health Sciences Monterrey Institute of Technology and Higher Education Monterrey México Hospital San José de Querétaro Querétaro México INSERM UMR1093-CAPS UFR des Sciences du Sport Université Bourgogne Franche-Comté Dijon France Neural Control of Movement Laboratory School of Medicine Faculty of Science Medicine and Health Illawarra Health and Medical Research Institute University of Wollongong New South Wales Australia Research Institute for Sport and Exercise Sciences Liverpool John Moores University Liverpool United Kingdom Neuromuscular Research Laboratory University of Pittsburgh Pittsburgh Pennsylvania School of Health and Caring Sciences University of West Attica Athens Greece Institute of Primary Care University of Zurich Zurich Switzerland Department of Psychology and Sport Sciences School of Life and Medical Sciences University of Hertfordshire Hatfield United Kingdom Unité de recherche Impact de l’Activité Physique sur la Santé (UR IAPS Number 201723207F) University of Toulon Toulon France Department of Systems Biology University of Alcalá Madrid Spain Department of Sport and Health Spanish Agency for Health Protection in Sport (AEPSAD)

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Distributed Least Squares Solver for Network Linear Equations

arXiv

引用

arXiv 2018年

作者： Yang, Tao George, Jemin Qin, Jiahu Yi, Xinlei Wu, Junfeng State Key Laboratory of Synthetical Automation for Process Industries Northeastern University Shenyang110819 China U.S. Army Research Laboratory AdelphiMD20783 United States Department of Automation University of Science and Technology of China Hefei230027 China Division of Decision and Control Systems School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Stockholm100 44 Sweden College of Control Science and Engineering Zhejiang University Hangzhou310027 China

In this paper, we study the problem of finding the least square solutions of over-determined linear algebraic equations over networks in a distributed manner. Each node has access to one of the linear equations and holds a dynamic state. We first propose a distributed least square solver over connected undirected interaction graphs and establish a necessary and sufficient on the step-size under which the algorithm exponentially converges to the least square solution. Next, we develop a distributed least square solver over strongly connected directed graphs and show that the proposed algorithm exponentially converges to the least square solution provided the step-size is sufficiently small. Moreover, we develop a finite-time least square solver by equipping the proposed algorithms with a finite-time decentralized computation mechanism. The theoretical findings are validated and illustrated by numerical simulation examples. Copyright © 2018, The Authors. All rights reserved.

关键词： Linear equations

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Automatic Sleep Spindle Detection with EEG Based on Complex Demodulation Method and Decision Tree Model

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Journal of Biomedical Science and Engineering 2017年第5期10卷 10-17页

作者： Jiabin Li Bei Wang Takenao Sugi Yu Zhang Masatoshi Nakamura Department of Automation East University of Science and Technology Shanghai China Department of Electrical and Electronic Engineering Saga University Saga Japan Research Institute of Systems Control Institute for Advanced Research and Education Saga University Saga Japan

Sleep spindle is the characteristic waveform of electroencephalogram (EEG) which is important for clinical diagnosis. In this study, an automatic sleep spindle detection method was developed. The EEG signals were recorded based on the standard polysomnogram (PSG) measurement. A preprocessing procedure is introduced to exclude the unnecessary data segments and normalized the necessary data segments. Complex demodulation method is adopted to detect the candidate sleep spindle waveforms and calculate the features. The sleep spindles are recognized based on a decision tree model. Finally, the detected sleep spindles were utilized to amend the sleep stage recognition results. The sleep EEG data from 3 patients with sleep disorders were analyzed. The obtained results showed that the detected sleep spindles in EEG signal improved the accuracy of sleep stage recognition.

关键词： Sleep Spindle Detection Complex Demodulation Method Decision Tree Mod-el EEG

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Author Correction: A consensus protocol for functional connectivity analysis in the rat brain

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Nature neuroscience 2023年 2023 Apr 18页

作者： Joanes Grandjean Gabriel Desrosiers-Gregoire Cynthia Anckaerts Diego Angeles-Valdez Fadi Ayad David A Barrière Ines Blockx Aleksandra Bortel Margaret Broadwater Beatriz M Cardoso Marina Célestine Jorge E Chavez-Negrete Sangcheon Choi Emma Christiaen Perrin Clavijo Luis Colon-Perez Samuel Cramer Tolomeo Daniele Elaine Dempsey Yujian Diao Arno Doelemeyer David Dopfel Lenka Dvořáková Claudia Falfán-Melgoza Francisca F Fernandes Caitlin F Fowler Antonio Fuentes-Ibañez Clément M Garin Eveline Gelderman Carla E M Golden Chao C G Guo Marloes J A G Henckens Lauren A Hennessy Peter Herman Nita Hofwijks Corey Horien Tudor M Ionescu Jolyon Jones Johannes Kaesser Eugene Kim Henriette Lambers Alberto Lazari Sung-Ho Lee Amanda Lillywhite Yikang Liu Yanyan Y Liu Alejandra López-Castro Xavier López-Gil Zilu Ma Eilidh MacNicol Dan Madularu Francesca Mandino Sabina Marciano Matthew J McAuslan Patrick McCunn Alison McIntosh Xianzong Meng Lisa Meyer-Baese Stephan Missault Federico Moro Daphne M P Naessens Laura J Nava-Gomez Hiroi Nonaka Juan J Ortiz Jaakko Paasonen Lore M Peeters Mickaël Pereira Pablo D Perez Marjory Pompilus Malcolm Prior Rustam Rakhmatullin Henning M Reimann Jonathan Reinwald Rodrigo Triana Del Rio Alejandro Rivera-Olvera Daniel Ruiz-Pérez Gabriele Russo Tobias J Rutten Rie Ryoke Markus Sack Piergiorgio Salvan Basavaraju G Sanganahalli Aileen Schroeter Bhedita J Seewoo Erwan Selingue Aline Seuwen Bowen Shi Nikoloz Sirmpilatze Joanna A B Smith Corrie Smith Filip Sobczak Petteri J Stenroos Milou Straathof Sandra Strobelt Akira Sumiyoshi Kengo Takahashi Maria E Torres-García Raul Tudela Monica van den Berg Kajo van der Marel Aran T B van Hout Roberta Vertullo Benjamin Vidal Roël M Vrooman Victora X Wang Isabel Wank David J G Watson Ting Yin Yongzhi Zhang Stefan Zurbruegg Sophie Achard Sarael Alcauter Dorothee P Auer Emmanuel L Barbier Jürgen Baudewig Christian F Beckmann Nicolau Beckmann Guillaume J P C Becq Erwin L A Blezer Radu Bolbos Susann Boretius Sandrine Bouvard Eike Budinger Joseph D Buxbaum Diana Cash Victor Donders Institute for Brain Behaviour and Cognition Radboud University Nijmegen The Netherlands. joanes.grandjean@radboudumc.nl. Department for Medical Imaging Radboud University Medical Center Nijmegen The Netherlands. joanes.grandjean@radboudumc.nl. Cerebral Imaging Centre Douglas Mental Health University Institute Verdun QC Canada. Integrated Program in Neuroscience McGill University Montreal QC Canada. Bio-imaging Lab University of Antwerp Antwerp Belgium. µNEURO Research Centre of Excellence University of Antwerp Antwerp Belgium. Instituto de Neurobiología Universidad Nacional Autónoma de México Campus Juriquilla Querétaro Mexico. Biological and Biomedical Engineering McGill University Montreal QC Canada. McConnell Brain Imaging Centre McGill University Montreal QC Canada. Montreal Neurological Institute McGill University Montreal QC Canada. UMR INRAE/CNRS 7247 Physiologie des Comportements et de la Reproduction Physiologie de la reproduction et des comportements Centre de recherche INRAE de Nouzilly Tours France. Department of Neurology and Neurosurgery McGill University Montreal QC Canada. Center for Animal MRI University of North Carolina at Chapel Hill Chapel Hill NC USA. Department of Neurology University of North Carolina at Chapel Hill Chapel Hill NC USA. Biomedical Research Imaging Center University of North Carolina at Chapel Hill Chapel Hill NC USA. Preclinical MRI Champalimaud Research Champalimaud Centre for the Unknown Lisbon Portugal. Laboratoire des Maladies Neurodégénératives Molecular Imaging Research Center (MIRCen) Université Paris-Saclay Commissariat à l'Énergie Atomique et aux Énergies Alternatives (CEA) CNRS Fontenay-aux-Roses France. Departamento de Neurobiología Conductual y Cognitiva Instituto de Neurobiología Universidad Nacional Autónoma de México Campus Juriquilla Querétaro México. Translational Neuroimaging and Neural Control Group High-Field Magnetic Resonance Max Planck Institute for Biological Cyberneti

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Automatic Sleep Spindle Detection with EEG Based on Complex Demodulation Method and Decision Tree Model

Automatic Sleep Spindle Detection with EEG Based on Complex ...

引用

The 11th International Conference on Bioinformatics and Biomedical Engineering (ICBBE 2017)

作者： Jiabin Li Bei Wang Takenao Sugi Yu Zhang Masatoshi Nakamura Department of Automation East University of Science and Technology Department of Electrical and Electronic Engineering Saga University Research Institute of Systems Control Institute for Advanced Research and Education Saga University

Sleep spindle is the characteristic waveform of electroencephalogram(EEG) which is important for clinical diagnosis. In this study, an automatic sleep spindle detection method was developed. The EEG signals were recorded based on the standard polysomnogram(PSG) measurement. A preprocessing procedure is introduced to exclude the unnecessary data segments and normalized the necessary data segments. Complex demodulation method is adopted to detect the candidate sleep spindle waveforms and calculate the features. The sleep spindles are recognized based on a decision tree model. Finally, the detected sleep spindles were utilized to amend the sleep stage recognition results. The sleep EEG data from 3 patients with sleep disorders were analyzed. The obtained results showed that the detected sleep spindles in EEG signal improved the accuracy of sleep stage recognition.

关键词： Sleep Spindle Detection Complex Demodulation Method Decision Tree Model EEG

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Inside Cover: A novel in vivo Cerenkov luminescence image-guided surgery on primary and metastatic colorectal cancer (J. Biophotonics 3/2020)

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Journal of Biophotonics 2020年第3期13卷

作者： Zeyu Zhang Yawei Qu Yu Cao Xiaojing Shi Hongbo Guo Xiaojun Zhang Sheng Zheng Haifeng Liu Zhenhua Hu Jie Tian Engineering Research Center of Molecular and Neuro Imaging of Ministry of Education School of Life Science and Technology Xidian University Xi'an China CAS Key Laboratory of Molecular Imaging Beijing Key Laboratory of Molecular Imaging The State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China Department of Gastroenterology the Third Medical Centre Chinese PLA General Hospital Beijing China Department of Control Science and Engineering Harbin Institute of Technology Harbin China Department of Anorectal the Third medical Centre Chinese PLA General Hospital Beijing China University of Chinese Academy of Sciences Beijing China School of Information Sciences and Technology Northwest University Xi'an China Department of Nuclear Medicine Chinese PLA General Hospital Beijing China Beijing Advanced Innovation Center for Big Data-Based Precision Medicine School of Medicine Beihang University Beijing China

This study presents a novel intraoperative in vivo imaging approach which harnessed Cerenkov luminescence (CL) to detect primary and metastatic colorectal cancer (CRC) using clinically approved radiopharmaceuticals. In the mice and swine experiments, the proposed approach effectively improved the effect of CRC surgery. The approach is believed to be promising for utilizing CL in open surgery. Further details can be found in the article by Zeyu Zhang, Yawei Qu, Yu Cao et al. ( e201960152 )

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Theranostics: Boosting Postsurgical Outcomes of Orthotopic Hepatocellular Carcinoma via an EpCAM-Targeting Theranostic Nanoparticle (Part. Part. Syst. Charact. 8/2019)

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Particle & Particle systems Characterization 2019年第8期36卷

作者： Min Hu Kun Wang Wenting Shang Ziyu Han Hongmei Jiang Chihua Fang Jie Tian Department of Hepatobiliary Surgery Zhujiang Hospital Southern Medical University Guangzhou 510280 China CAS Key Laboratory of Molecular Imaging The State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing 100190 China Department of Hepatobiliary Surgery The First Affiliated Hospital of Jinan University Guangzhou 510630 China Institute of Automation University of Chinese Academy of Sciences Beijing 100080 China Guangdong Provincial Clinical and Engineering Technology Research Center of Digital Medicine Guangzhou 510280 China

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