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Parametric Estimation of Interconnected Nonlinear Systems Described by Input-output Mathematical Models

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International Journal of Automation and computing 2016年第4期13卷 364-381页

作者： Mourad Elloumi Samira Kamoun Laboratory of Sciences and Techniques of Automatic Control & Computer Engineering National Engineering School of Sfax (ENIS) University of Sfax Tunisia B.P. 1173 3038 Sfax Tunisia

In this paper, two types of mathematical models are developed to describe the dynamics of large-scale nonlinear systems, which are composed of several interconnected nonlinear subsystems. Each subsystem can be described by an input-output nonlinear discrete-time mathematical model, with unknown, but constant or slowly time-varying parameters. Then, two recursive estimation methods are used to solve the parametric estimation problem for the considered class of the interconnected nonlinear systems. These methods are based on the recursive least squares techniques and the prediction error method. Convergence analysis is provided using the hyper-stability and positivity method and the differential equation approach. A numerical simulation example of the parametric estimation of a stochastic interconnected nonlinear hydraulic system is treated.

关键词： Large-scale nonlinear systems interconnected nonlinear systems deterministic systems stochastic systems input-outputmathematical models parametric estimation algorithm convergence analysis.

Multi-view hybrid embedding: A divide-and-conquer approach

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arXiv 2018年

作者： Xu, Jiamiao Yu, Shujian You, Xinge Leng, Mengjun Jing, Xiao-Yuan Chen, C. L. Philip School of Electronic Information and Communications Huazhong University of Science and Technology Wuhan430074 China Department of Electrical and Computer Engineering University of Florida GainesvilleFL32611 United States Department of Computer Science University of Houston HoustonTX77204 United States State Key Laboratory of Software Engineering School of Computer Wuhan University China Department of Computer and Information Science Faculty of Science and Technology University of Macau Macau99999 China Dalian Maritime University Dalian116026 China State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing100080 China

We present a novel cross-view classification algorithm where the gallery and probe data come from different views. A popular approach to tackle this problem is the multiview subspace learning (MvSL) that aims to learn a latent subspace shared by multi-view data. Despite promising results obtained on some applications, the performance of existing methods deteriorates dramatically when the multi-view data is sampled from nonlinear manifolds or suffers from heavy outliers. To circumvent this drawback, motivated by the Divideand-Conquer strategy, we propose Multi-view Hybrid Embedding (MvHE), a unique method of dividing the problem of cross-view classification into three subproblems and building one model for each subproblem. Specifically, the first model is designed to remove view discrepancy, whereas the second and third models attempt to discover the intrinsic nonlinear structure and to increase discriminability in intra-view and interview samples respectively. The kernel extension is conducted to further boost the representation power of MvHE. Extensive experiments are conducted on four benchmark datasets. Our methods demonstrate overwhelming advantages against the stateof-the-art MvSL based cross-view classification approaches in terms of classification accuracy and robustness. Copyright © 2018, The Authors. All rights reserved.

关键词： Embeddings

Satellite-Relayed Intercontinental Quantum Network

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Physical Review Letters 2018年第3期120卷 030501-030501页

作者： Sheng-Kai Liao Wen-Qi Cai Johannes Handsteiner Bo Liu Juan Yin Liang Zhang Dominik Rauch Matthias Fink Ji-Gang Ren Wei-Yue Liu Yang Li Qi Shen Yuan Cao Feng-Zhi Li Jian-Feng Wang Yong-Mei Huang Lei Deng Tao Xi Lu Ma Tai Hu Li Li Nai-Le Liu Franz Koidl Peiyuan Wang Yu-Ao Chen Xiang-Bin Wang Michael Steindorfer Georg Kirchner Chao-Yang Lu Rong Shu Rupert Ursin Thomas Scheidl Cheng-Zhi Peng Jian-Yu Wang Anton Zeilinger Jian-Wei Pan Hefei National Laboratory for Physical Sciences at the Microscale and Department of Modern Physics University of Science and Technology of China Hefei 230026 China Chinese Academy of Sciences (CAS) Center for Excellence and Synergetic Innovation Center in Quantum Information and Quantum Physics University of Science and Technology of China Shanghai 201315 China Vienna Center for Quantum Science and Technology Faculty of Physics University of Vienna Vienna 1090 Austria Institute for Quantum Optics and Quantum Information Austrian Academy of Sciences Vienna 1090 Austria School of Computer National University of Defense Technology Changsha 410073 China Key Laboratory of Space Active Opto-Electronic Technology Shanghai Institute of Technical Physics Chinese Academy of Sciences Shanghai 200083 China National Astronomical Observatories Chinese Academy of Sciences Beijing 100012 China Key Laboratory of Optical Engineering Institute of Optics and Electronics Chinese Academy of Sciences Chengdu 610209 China Shanghai Engineering Center for Microsatellites Shanghai 201203 China State Key Laboratory of Astronautic Dynamics Xi’an Satellite Control Center Xi’an 710061 China Xinjiang Astronomical Observatory Chinese Academy of Sciences Urumqi 830011 China National Space Science Center Chinese Academy of Sciences Beijing 100080 China Space Research Institute Austrian Academy of Sciences Graz 8042 Austria

We perform decoy-state quantum key distribution between a low-Earth-orbit satellite and multiple ground stations located in Xinglong, Nanshan, and Graz, which establish satellite-to-ground secure keys with ∼kHz rate per passage of the satellite Micius over a ground station. The satellite thus establishes a secure key between itself and, say, Xinglong, and another key between itself and, say, Graz. Then, upon request from the ground command, Micius acts as a trusted relay. It performs bitwise exclusive or operations between the two keys and relays the result to one of the ground stations. That way, a secret key is created between China and Europe at locations separated by 7600 km on Earth. These keys are then used for intercontinental quantum-secured communication. This was, on the one hand, the transmission of images in a one-time pad configuration from China to Austria as well as from Austria to China. Also, a video conference was performed between the Austrian Academy of sciences and the Chinese Academy of sciences, which also included a 280 km optical ground connection between Xinglong and Beijing. Our work clearly confirms the Micius satellite as a robust platform for quantum key distribution with different ground stations on Earth, and points towards an efficient solution for an ultralong-distance global quantum network.

关键词： Quantum channels Quantum communication Quantum cryptography Quantum networks Quantum optics

The Importance of System-Level Information in Multiagent Systems Design: Cardinality and Covering Problems

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arXiv 2017年

作者： Paccagnan, Dario Marden, Jason R. Department of Electrical and Computer Engineering University of California Santa Barbara United States Automatic Control Laboratory Swiss Federal Institute of Technology Zurich

A fundamental challenge in multiagent systems is to design local control algorithms to ensure a desirable collective behaviour. The information available to the agents, gathered either through communication or sensing, naturally restricts the achievable performance. Hence, it is fundamental to identify what piece of information is valuable and can be exploited to design control laws with enhanced performance guarantees. This paper studies the case when such information is uncertain or inaccessible for a class of submodular resource allocation problems termed covering problems. In the first part of this work we pinpoint a fundamental risk-reward tradeoff faced by the system operator when conditioning the control design on a valuable but uncertain piece of information, which we refer to as the cardinality, that represents the maximum number of agents that can simultaneously select any given resource. Building on this analysis, we propose a distributed algorithm that allows agents to learn the cardinality while adjusting their behaviour over time. This algorithm is proved to perform on par or better to the optimal design obtained when the exact cardinality is known a priori. Copyright © 2017, The Authors. All rights reserved.

关键词： Multi agent systems

Publisher Correction: A vaccine targeting the RBD of the S protein of SARS-CoV-2 induces protective immunity

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Nature 2021年第7844期590卷 E23页

作者： Jingyun Yang Wei Wang Zimin Chen Shuaiyao Lu Fanli Yang Zhenfei Bi Linlin Bao Fei Mo Xue Li Yong Huang Weiqi Hong Yun Yang Yuan Zhao Fei Ye Sheng Lin Wei Deng Hua Chen Hong Lei Ziqi Zhang Min Luo Hong Gao Yue Zheng Yanqiu Gong Xiaohua Jiang Yanfeng Xu Qi Lv Dan Li Manni Wang Fengdi Li Shunyi Wang Guanpeng Wang Pin Yu Yajin Qu Li Yang Hongxin Deng Aiping Tong Jiong Li Zhenling Wang Jinliang Yang Guobo Shen Zhiwei Zhao Yuhua Li Jingwen Luo Hongqi Liu Wenhai Yu Mengli Yang Jingwen Xu Junbin Wang Haiyan Li Haixuan Wang Dexuan Kuang Panpan Lin Zhengtao Hu Wei Guo Wei Cheng Yanlin He Xiangrong Song Chong Chen Zhihong Xue Shaohua Yao Lu Chen Xuelei Ma Siyuan Chen Maling Gou Weijin Huang Youchun Wang Changfa Fan Zhixin Tian Ming Shi Fu-Sheng Wang Lunzhi Dai Min Wu Gen Li Guangyu Wang Yong Peng Zhiyong Qian Canhua Huang Johnson Yiu-Nam Lau Zhenglin Yang Yuquan Wei Xiaobo Cen Xiaozhong Peng Chuan Qin Kang Zhang Guangwen Lu Xiawei Wei Laboratory of Aging Research and Cancer Drug Target State Key Laboratory of Biotherapy and Cancer Center National Clinical Research Center for Geriatrics West China Hospital Sichuan University Chengdu China. National Kunming High-level Biosafety Primate Research Center Institute of Medical Biology Chinese Academy of Medical Sciences and Peking Union Medical College Yunnan China. Key Laboratory of Human Disease Comparative Medicine Chinese Ministry of Health Beijing Key Laboratory for Animal Models of Emerging and Remerging Infectious Diseases Institute of Laboratory Animal Science Chinese Academy of Medical Sciences and Comparative Medicine Center Peking Union Medical College Beijing China. National Institutes for Food and Drug Control (NIFDC) Beijing China. National Chengdu Center for Safety Evaluation of Drugs State Key Laboratory of Biotherapy and Cancer Center West China Hospital Sichuan University Collaborative Innovation Center for Biotherapy Chengdu China. Shanghai Key Laboratory of Chemical Assessment and Sustainability School of Chemical Science & Engineering Tongji University Shanghai China. Treatment and Research Center for Infectious Diseases The Fifth Medical Center of PLA General Hospital National Clinical Research Center for Infectious Diseases Beijing China. Center for Biomedicine and Innovations Faculty of Medicine Macau University of Science and Technology Macau China. Department of Computer Science and Technology Tsinghua University Beijing China. Department of Applied Biology and Chemical Technology Hong Kong Polytechnic University Hong Kong China. Sichuan Provincial Key Laboratory for Human Disease Gene Study and Institute of Laboratory Medicine Sichuan Provincial People's Hospital University of Electronic Science and Technology of China Chengdu China. State Key Laboratory of Medical Molecular Biology Chinese Academy of Medical Sciences School of Basic Medicine Peking Union Medical College Beijing China. Center for Biomedicine and Innovation

Small P values may not yield robust findings:an example using REST-meta-PD

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Science Bulletin 2021年第21期66卷 2148-2152,M0003页

作者： Xi-Ze Jia Na Zhao Hao-Ming Dong Jia-Wei Sun Marek Barton Roxana Burciu Nicolas Carrière Antonio Cerasa Bo-Yu Chen Jun Chen Stephen Coombes Luc Defebvre Christine Delmaire Kathy Dujardin Fabrizio Esposito Guo-Guang Fan Federica Di Nardo Yi-Xuan Feng Brett W.Fling Saurabh Garg Moran Gilat Martin Gorges Shu-Leong Ho Fay BHorak Xiao Hu Xiao-Fei Hu Biao Huang Pei-Yu Huang Ze-Juan Jia Christina Jones Jan Kassubek Lenka Krajcovicova Ajay Kurani Jing Li Qing Li Ai-Ping Liu Bo Liu Hu Liu Wei-Guo Liu Renaud Lopes Yu-Ting Lou Wei Luo Tara Madhyastha Ni-Ni Mao Grainne McAlonan Martin J.McKeown Shirley Pang Andrea Quattrone Irena Rektorova Alessia Sarica Hui-Fang Shang James M.Shine Priyank Shukla Tomas Slavicek Xiao-Peng Song Gioacchino Tedeschi Alessandro Tessitore David Vaillancourt Jian Wang Jue Wang Z.Jane Wang Lu-Qing Wei Xia Wu Xiao-Jun Xu Lei Yan Jing Yang Wan-Qun Yang Nai-Lin Yao De-Long Zhang Jiu-Quan Zhang Min-Ming Zhang Yan-Ling Zhang Cai-Hong Zhou Chao-Gan Yan Xi-Nian Zuo Mark Hallett Tao Wu Yu-Feng Zang Center for Cognition and Brain Disorders the Affiliated HospitalHangzhou Normal UniversityHangzhou 310015China Zhejiang Key Laboratory for Research in Assessment of Cognitive Impairments Hangzhou 311121China National Basic Science Data Center Beijing 100190China State Key Laboratory of Cognitive Neuroscience and Learning&McGovern Institute for Brain Research Beijing Normal UniversityBeijing 100875China School of Information and Electronics Technology Jiamusi UniversityJiamusi 154007China Neuroscience Program Central European Institute of TechnologyCEITECMasaryk UniversityBrno 62500Czech Republic Department of Applied Physiology and Kinesiology University of FloridaGainesville FL 32611USA Univ.Lille InsermCHU LilleU1172-LilNCog-Lille Neuroscience&CognitionLille F-59000France The Institute for Biomedical Research and Innovation National Research CouncilMangone CS 87050Italy Department of Radiology The First Affiliated Hospital of China Medical UniversityShenyang 110001China Department of Radiology Guangdong Provincial Hospital of Chinese MedicineGuangzhou 510120China Department of Medicine Surgery and DentistryScuola Medica SalernitanaUniversity of SalernoFiscianoSA 132-84084Italy Department of Advanced Medical and Surgery Sciences University of Campania“Luigi Vanvitelli”Caserta 81100Italy Eye Center of the 2nd Affiliated Hospital Medical College of Zhejiang UniversityHangzhou 310020China Zhejiang Provincial Key Lab of Ophthalmology Hangzhou 310020China Department of Health and Exercise Science Colorado State UniversityFort Collins CO 80523USA Pacific Parkinson’s Research Centre University of British ColumbiaVancouver BC V6E 2M6Canada Department of Medicine(Neurology)University of British Columbia Vancouver BC V6T 1B7Canada Brain and Mind Center The University of SydneySydney NSW 2006Australia Department of Neurology Ulm UniversityUlm 89081Germany Division of Neurology Department of MedicineQueen Mary HospitalUniversity of Hong KongHong Kong 999077China Department of

Thousands of resting state functional magnetic resonance imaging(RS-f MRI)articles have been published on brain *** precise localization of abnormal brain activity,a voxel-level comparison is *** of the large number of voxels in the brain,multiple comparison correction(MCC)must be performed to reduce false positive rates,and a smaller P value(usually including either liberal or stringent MCC)is widely recommended[1].

关键词：帕金森病 precise correction

Main belt asteroids taxonomical information from Dark Energy Survey data

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arXiv 2023年

作者： Carruba, Valerio Camargo, Júlio I.B. Aljbaae, Safwan Ferreira, F.S. Lin, E. Figueiredo-Peixoto, V. Banda-Huarca, M.V. Pieres, A. Boufleur, R.C. da Costa, L.N. Abbott, T.M.C. Aguena, M. Allam, Sahar S. Alves, O. Bernardinelli, P.H. Bertin, E. Brooks, D. Carnero Rosell, A. Carretero, J. Pereira, M.E.S. Davis, T.M. De Vicente, J. Desai, S. Doel, P. Ferrero, I. Friedel, D. Frieman, J. García-Bellido, J. Gatti, M. Giannini, G. Gruen, D. Gruendl, R.A. Herner, K. Hinton, S.R. Hollowood, D.L. James, D.J. Kent, S. Kuehn, K. Lahav, O. Marshall, J.L. Mena-Fernández, J. Miquel, R. Palmese, A. Plazas Malagón, A.A. Rodríguez-Monroy, M. Sanchez, E. Santiago, B. Schubnell, M. Smith, M. Suchyta, E. Swanson, M.E.C. Tarle, G. Walker, A.R. Weaverdyck, N. Wiseman, P. School of Natural Sciences and Engineering Brazil Laboratório Interinstitucional de e-Astronomia - LIneA Brazil Observatório Nacional Rua Gal. José Cristino 77 RJ Rio de Janeiro20921-400 Brazil Division of Space Mechanics and Control C.P. 515 SP São José dos Campos12227-310 Brazil Department of Physics University of Michigan Ann ArborMI48109 United States IGeo - Universidade Federal do Rio de Janeiro Av. Athos da Silveira Ramos 274 RJ Rio de Janeiro21941-916 Brazil Cerro Tololo Inter-American Observatory NSF’s National Optical-Infrared Astronomy Research Laboratory Casilla 603 La Serena Chile Fermi National Accelerator Laboratory P. O. Box 500 BataviaIL60510 United States Astronomy Department University of Washington Box 351580 SeattleWA98195 United States CNRS UMR 7095 Institut d’Astrophysique de Paris ParisF-75014 France Sorbonne Universités UPMC Univ Paris 06 UMR 7095 Institut d’Astrophysique de Paris ParisF-75014 France Department of Physics & Astronomy University College London Gower Street LondonWC1E 6BT United Kingdom Instituto de Astrofisica de Canarias Tenerife La LagunaE-38205 Spain Universidad de La Laguna Dpto. AstrofÃsica Tenerife La LagunaE-38206 Spain The Barcelona Institute of Science and Technology Campus UAB Bellaterra Barcelona08193 Spain Hamburger Sternwarte Universität Hamburg Gojenbergsweg 112 Hamburg21029 Germany School of Mathematics and Physics University of Queensland BrisbaneQLD4072 Australia Madrid Spain Department of Physics IIT Hyderabad Telangana Kandi502285 India Institute of Theoretical Astrophysics University of Oslo. P.O. Box 1029 Blindern OsloNO-0315 Norway Center for Astrophysical Surveys National Center for Supercomputing Applications 1205 West Clark St. UrbanaIL61801 United States Kavli Institute for Cosmological Physics University of Chicago ChicagoIL60637 United States Instituto de Fisica Teorica UAM/CSIC Universidad Autonoma de Madrid Madrid28049 Spain Department

While proper orbital elements are currently available for more than 1 million asteroids, taxonomical information is still lagging behind. Surveys like SDSS-MOC4 provided preliminary information for more than 100,000 objects, but many asteroids still lack even a basic taxonomy. In this study, we use Dark Energy Survey (DES) data to provide new information on asteroid physical properties. By cross-correlating the new DES database with other databases, we investigate how asteroid taxonomy is reflected in DES data. While the resolution of DES data is not sufficient to distinguish between different asteroid taxonomies within the complexes, except for V-type objects, it can provide information on whether an asteroid belongs to the C- or S-complex. Here, machine learning methods optimized through the use of genetic algorithms were used to predict the labels of more than 68 000 asteroids with no prior taxonomic information. Using a high-quality, limited set of asteroids with data on gri slopes and i − z colors, we detected 409 new possible V-type asteroids. Their orbital distribution is highly consistent with that of other known V-type objects. © 2023, CC BY.

关键词： Taxonomies

A generalized concept-cognitive learning: A machine learning viewpoint

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arXiv 2018年

作者： Mi, Yunlong Shi, Yong Li, Jinhai School of Computer and Control Engineering University of Chinese Academy of Sciences Beijing101408 China Key Laboratory of Big Data Mining and Knowledge Management Chinese Academy of Sciences Beijing100190 China Research Center on Fictitious Economy and Data Science Chinese Academy of Sciences Beijing100190 China College of Information Science and Technology University of Nebraska at Omaha NE68182 United States Faculty of Science Kunming University of Science and Technology Kunming650500 China

Concept-cognitive learning (CCL) is a hot topic in recent years, and it has attracted much attention from the communities of formal concept analysis, granular computing and cognitive computing. However, the relationship among cognitive computing (CC), concept-cognitive computing (CCC), CCL and concept-cognitive learning model (CCLM) is not clearly described. To this end, we first explain the relationship of CC, CCC, CCL and CCLM. Then, we propose a generalized concept-cognitive learning (GCCL) from the point of view of machine learning. Finally, experiments on some data sets are conducted to verify the feasibility of concept formation and concept-cognitive process of GCCL. Copyright © 2018, The Authors. All rights reserved.

关键词： Machine learning

On the nonexistence of event-based triggers that preserve Gaussian state in presence of package-drop

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On the nonexistence of event-based triggers that preserve Ga...

American control Conference

作者： Enoch Kung Junfeng Wu Dawei Shi Ling Shi Electronic and Computer Engineering Hong Kong University of Science and Technology Department of Automatic Control The Royal Institute of Technology (KTH) State Key Laboratory of Intelligent Control and Decision of Complex Systems School of Automation Beijing Institute of Technology

ISBN: (纸本)9781509045839

State estimation is a core objective in cyber-physical systems. In the state estimation problem over linear systems, the Kalman filter is the standard solution. The filter is the format on which the solutions to subsequent estimation problems are based. Among these problems are the estimation problem in the presence of packet drops and estimation problem involving event-based triggers. We study in this paper both phenomena simultaneously. In an attempt to find the Kalman-like filter, which proves the Gaussianity of the state and offers a set of update equations, our paper shows that no such filter exists. More precisely, one cannot find an event-based trigger such that under possible packet drops, the state variable remains a Gaussian variable. This conclusion can be reasonably extended to a more general setting.

关键词： State estimation Packet drops Event-based triggers State estimation Event-based Filters State variable TRIGGERS Gauss