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arXiv 2017年

作者： Yu, Hai Wan, Simin Cai, Wenlong Xu, Ling Zhou, Xiaobo Wang, Jin Wu, Yongpeng Shu, Feng Wang, Jiangzhou Wang, Jianxin School of Electronic and Optical Engineering Nanjing University of Science and Technology Nanjing210094 China faculty of National Key Laboratory of Science and Technology on Aerospace Intelligence Control Beijing Aerospace Automatic Control Institute Beijing China Shanghai Key Laboratory of Navigation and Location-Based Services Shanghai Jiao Tong University Minhang200240 China College of Computer and Information Sciences Fujian Agriculture and Forestry University Fuzhou350002 China School of Engineering and Digital Arts University of Kent CanterburyCT2 7NT United Kingdom

In a directional modulation network, a general power iterative (GPI) based beamforming scheme is proposed to maximize the secrecy rate (SR), where there are two optimization variables required to be optimized. The first one is the useful precoding vector of transmitting confidential messages to the desired user while the second one is the artificial noise (AN) projection matrix of forcing more AN to eavesdroppers. In such a secure network, the paramount problem is how to design or optimize the two optimization variables by different criteria. To maximize the SR (Max-SR), an alternatively iterative structure (AIS) is established between the AN projection matrix and the precoding vector for confidential messages. To choose a good initial value of iteration process of GPI, the proposed Max-SR method can readily double its convergence speed compared to the random choice of initial value. With only four iterations, it may rapidly converge to its rate ceil. From simulation results, it follows that the SR performance of the proposed AIS of GPI-based Max-SR is much better than those of conventional leakage-based and null-space projection methods in the medium and large signal-to-noise ratio (SNR) regions, and its achievable SR performance gain gradually increases as SNR increases. Copyright © 2017, The Authors. All rights reserved.

关键词： Modulation

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Towards an ontology for autonomous robots

Towards an ontology for autonomous robots

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25th IEEE/RSJ International Conference on Robotics and Intelligent Systems, IROS 2012

作者： Paull, Liam Severac, Gaetan Raffo, Guilherme V. Angel, Julian Mauricio Boley, Harold Durst, Phillip J. Gray, Wendell Habib, Maki Nguyen, Bao Ragavan, S. Veera Saeedi G., Sajad Sanz, Ricardo Seto, Mae Stefanovski, Aleksandar Trentini, Michael Li, Howard COBRA Department of Electrical and Computer Engineering University of New Brunswick Fredericton NB E3B 5A3 Canada ONERA-University of Toulouse France Department of Automation and Systems Federal University of Santa Catarina Brazil Department of Computer Engineering Pontificia Universidad Javeriana Bogot Colombia National Research Council Canada US Army ERDC 3909 Halls Ferry Road Vicksburg MS 39180 United States Mechanical Engineering Department School of Sciences and Engineering American University in Cairo New Cairo Egypt Defence Research and Development Canada-CORA Ottawa ON Canada Monash University Sunway Campus Australia Systems Engineering and Automatic Control Autonomous Systems Laboratory Research Group Universidad Politcnica de Madrid Madrid Spain Defence Research and Development Canada-Atlantic Dartmouth NS Canada Department of Computer Science George Washington University United States Defence R and D Canada-Suffield PO Box 4000 Station Main Medicine Hat AB T1A 8K6 Canada

ISBN: (纸本)9781467317375

The IEEE RAS Ontologies for Robotics and Automation Working Group is dedicated to developing a methodology for knowledge representation and reasoning in robotics and automation. As part of this working group, the Autonomous Robots sub-group is tasked with developing ontology modules for autonomous robots. This paper describes the work in progress on the development of ontologies for autonomous systems. For autonomous systems, the focus is on the cooperation, coordination, and communication of multiple unmanned aerial vehicles (UAVs), unmanned ground vehicles (UGVs), and autonomous underwater vehicles (AUVs). The ontologies serve as a framework for working out concepts of employment with multiple vehicles for a variety of operational scenarios with emphasis on collaborative and cooperative missions. © 2012 IEEE.

关键词： Ontology

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BAYESIAN APPROACH TO MODELLING OF QUASI-PERIODIC INTERMITTENT DEMAND

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IFAC Proceedings Volumes 2005年第1期38卷 343-348页

作者： Alexandre Dolgui Anatoly Pashkevich Maxim Pashkevich Division for Industrial Engineering and Computer Sciences (G2I) Ecole de Mines de Saint Etienne 158 Cours Fauriel 42023 Saint Etienne France Robotic Laboratory Department of Automatic Control Belarusian State University of Informatics and Radioelectronics 6 P.Brovka St. Minsk 220027 Belarus Department of Mathematical Modeling and Data Analysis Belarusian State University Skarina Av. 4 Minsk 220050 Belarus

The paper focuses on the stochastic modelling of the quasi-periodic intermittent demand patterns, which arise in the inventory management of the “slow moving items” such as service parts or high-priced capital goods. It is proposed a new stochastic model, which describes the demand patterns with essentially non-exponential distribution of the inter-arrival times. The model is based on generalized beta-binomial distribution and the Bayesian inference using the historical data array describing the demand repeatability within the time periods. For this model, there were derived explicit expressions for the forecast distributions, its moments and relevant Bayesian risk. The efficiency of the proposed approach is confirmed by computer simulation and an application example.

关键词： inventory control demand modelling statistical inference parameter estimation prediction methods

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Modeling Demand for Inventory Management of Slow-Moving Items in Case of Reporting Errors

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IFAC Proceedings Volumes 2004年第4期37卷 27-32页

作者： Alexandre Dolgui Anatoly Pashkevich Maxim Pashkevich Scientific Methods for Industrial Management Dept. (MSGI) Industrial Engineering and Computer Sciences Center (G2I) Ecole de Mines de Saint Etienne 158 Cours Fauriel 42023 Saint Etienne France Robotic Laboratory Department of Automatic Control Belarusian State University of Informatics and Radioelectronics 6 P.Brovka St. Minsk 220027 Belarus Department of Mathematical Modeling and Data Analysis Faculty of Applied Mathematics and Computer Science Belarusian State University Skarina Av. 4 Minsk 220050 Belarus

The paper is devoted to the problem of modeling demand for inventory management of slow-moving items in the case of reporting errors. It is proposed a generalization of the beta-binomial demand model that takes into account possible reporting errors in the learning sample. For the new model, there are developed identification and forecasting algorithms that provide consistent estimators of the model parameters and mean square optimal forecasts. The efficiency of the proposed approach is illustrated by an application example for slow-moving car parts.

关键词： inventory control error correction statistical inference parameter estimation prediction methods

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Guest Editorial

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IEEE Transactions on Industrial Electronics 2011年第4期58卷 1074-1080页

作者： Marco Liserre Josep Balcells Thomas Basso Jan T. Bialasiewicz Carlo Cecati Sudipta Chakraborty Josep M. Guerrero Mehrdad Kazerani Friederich Kupzog Adel Nasiri Peter Palensky José Rodriguez Pedro Rodríguez Thilo Sauter Remus Teodorescu Polytechnic University of Bari Bari Italy Universitat Politéchnica de Catalunya Barcelona Spain National Renewable Energy Laboratory (NREL) Golden CO USA Electrical Engineering Department University of Colorado Denver CO USA Department of Electrical and Information Engineering University of L'Aquila L'Aquila Italy National Renewwable Energy Laboratory (NREL) Golden CO USA Department of Automatic Control Systems and Computer Engineering Technical University of Catalonia Barcelona Spain Department of Electrical and Computer Engineering University of Waterloo Waterloo ON Canada Institue of Computer Technology Vienna Technical University Vienna Austria Department of Electrical Engineering and Computer Science University of Wisconsin Milwaukee WI USA Sustainable Building Technologies Austrian Institue of Technology (AIT) Vienna Austria Department of Electronics Engineering Universidad Técnica Federico Santa María Valparaiso Chile Research Group on Renewable Electrical Energy Systems Department of Electrical Engineering Universitat Politécnica de Catalunya (UPC) Barcelona Spain Austrian Academy of Sciences Institute of Integraded Sensor Systems Wiener Neustadt Austria

The 20 papers in this second part of this special section are classified by energy sources (wind, photovoltaic, hydro, etc.), grid-related issues (islanding, synchronization, microgrid, and smart grid), and converters... 详细信息

关键词： Special issues and sections Smart grids Renewable energy resources Photovoltaic systems Power conversion Wind energy Hydroelectric power generation Power grids

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Sensorless Speed control With MRAS for Induction Motor Drive

Sensorless Speed Control With MRAS for Induction Motor Drive

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International Conference on Electrical Machines

作者： Y. Agrebi Zorgani Y. Koubaa M. Boussak Laboratory of Sciences and Techniques of Automatic control & computer engineering (Lab-STA) National School of Engineering of Sfax University of Sfax-Postal Box 1173 3038 Sfax Tunisia the Laboratoire des Sciences de l'Information et des Systèmes (LSIS) UMR CNRS 7296 - Ecole Centrale Marseille (ECM) - 38 rue Joliot Curie Technop?le de Chateau Gombert 13451 Marseille Cedex 20 France

ISBN: (纸本)9781467301435

Usually In the speed sensorless of the induction motor, the machine parameters (especially rotor resistance Rr) have a strong influence on the speed estimation. This paper presents simultaneous estimation of speed and rotor resistance in sensorless ISFOC induction Motor drive based on a Model Reference System (MRAS). The MRAS has been formed to estimate the rotor speed and the rotor resistance which are tuned to obtain high-performance ISFOC induction motor drive. The error between the reference and adjustable models, developed in stationary stator reference frame, is used to drive a suitable adaptation mechanism that generates the estimate wr and Rr from measured terminal voltages and currents. The proposed algorithm has been tested by numerical simulation, showing the capability of driving active load and stability is preserved. Experimental results for the simultaneous estimation are presented in order to validate the effectiveness of the proposed scheme. The control algorithm has been implemented using a digital signal processor based on dSPACE DS1104.

关键词： Induction motor drive indirect stator field oriented control (ISFOC) model reference adaptive system (MRAS) rotor speed estimation rotor resistance estimation

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Towards an Ontology for Autonomous Robots

Towards an Ontology for Autonomous Robots

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2012 IEEE/RSJ International Conference on Intelligent Robots and Systems

作者： Liam Paull Gaetan Severac Guilherme V. Raffo Julian Mauricio Angel Harold Boley Phillip J Durst Wendell Gray Maki Habib Bao Nguyen S. Veera Ragavan Sajad Saeedi G. Ricardo Sanz Mae Seto Aleksandar Stefanovski Michael Trentini Howard Li COBRA Department of Electrical and Computer Engineering University of New Brunswick Fredericton Canada ONERA - University of Toulouse France Department of Automation and Systems Federal University of Santa Catarina Brazil Department of Computer Engineering Pontificia Universidad Javeriana Bogot Colombia National Research Council Canada US Army ERDC Vicksburg Ms Mechanical Engineering Department School of Sciences and Engineering The American University in Cairo New Cairo Egypt Defence Research and Development Canada - CORA Ottawa ON Canada Monash University Sunway Campus Systems Engineering and Automatic Control Autonomous Systems Laboratory research group Universidad Politcnica de Madrid Madrid Spain Defence Research and Development Canada - Atlantic NS Canada Department of Computer Science The George Washington University Defence R&D Canada - Suffield Medicine Hat AB Canada

ISBN: (纸本)9781467317375

关键词： Ontologies Robot kinematics Sensors Planning Mobile robots Navigation

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Introduction to the issue on biophotonics

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IEEE Journal on Selected Topics in Quantum Electronics 2014年第2期20卷 4-7页

作者： Ilev, Ilko K. Boppart, Stephen A. Andersson-Engels, Stefan Kim, Beop-Min Perelman, Lev Tuchin, Valery Center for Devices and Radiological Health U.S. Food and Drug Administration Silver Spring MD 20993 United States Beckman Institute for Advanced Science and Technology Departments of Bioengineering Electrical and Computer Engineering and Medicine University of Illinois Urbana IL 61801 United States Division of Atomic Physics Lund University Lund SE-22100 Sweden Department of Biomedical Engineering Korea University Seoul 136-703 Korea Republic of Center for Advanced Biomedical Imaging and Photonics Beth Israel Deaconess Medical Center Harvard University Boston MA 02215 United States Division of Optics and Biophotonics Saratov State University Saratov 410012 Russia Institute of Precision Mechanics and Control Russian Academy of Sciences Saratov 410028 Russia Optoelectronics and Measurement Techniques Laboratory University of Oulu Oulu FI-90014 Finland

The objective of this JSTQE Issue on Biophotonics is to highlight recent progress and trends in innovative biophotonics technology development. The papers published in this issue cover a broad range of advanced biophotonics areas summarized in the following sections: 1) bioimaging: optical coherence tomography and microscopy, nonlinear optical imaging, and novel multimodality imaging approaches; 2) biosensing: multifunctional biosensing, and novel biosensing approaches; 3) biophotonic science: mechanisms of light-cell and lighttissue interactions, advanced modeling methods in biophotonics, and light induced biophotonics effects; 4) biophotonic technology: advanced technologies in biophotonic diagnostics and therapeutics, infrared technology in biophotonics, and novel laser, biomedical and fiber optics tools and devices in biophotonics. These key research topics are highlighted as comprehensive overviews of the current status and future trends, as well as original results and recent developments in the field of biophotonics. This issue contains 33 papers, including 18 invited and 15 contributed papers authored by well-established research groups and promising scientists from all over the world. The invited papers include extended reviews on recent biophotonic developments and clinical applications in the areas of highresolution multispectral bioimaging, combined multifunctional microscopy, nonlinear deep tissue imaging, single molecule imaging, optical coherence elastography, cellular and molecular mechanisms of photobiomodulation, tissue optical clearing, clinical ultrafast laser surgery, infrared laser nerve stimulation, and novel tapered diode lasers for biophotonics. The contributed papers cover a broad variety of key biophotonic research areas including recently obtained original results on multimodal bioimaging and sensing, high resolution phase-sensitive magnetomotive optical coherence microscopy and elastography, and laser-assisted typing of polymorphic human

关键词： Special issues and sections Biophotonics

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An Early Stage Researcher's Primer on Systems Medicine Terminology

Network and Systems Medicine

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Network and Systems Medicine 2021年第1期4卷 2-50页

作者： Zanin, Massimiliano Aitya, Nadim A.A. Basilio, José Baumbach, Jan Benis, Arriel Behera, Chandan K. Bucholc, Magda Castiglione, Filippo Chouvarda, Ioanna Comte, Blandine Dao, Tien-Tuan Ding, Xuemei Pujos-Guillot, Estelle Filipovic, Nenad Finn, David P. Glass, David H. Harel, Nissim Iesmantas, Tomas Ivanoska, Ilinka Joshi, Alok Boudjeltia, Karim Zouaoui Kaoui, Badr Kaur, Daman Maguire, Liam P. McClean, Paula L. McCombe, Niamh De Miranda, João Luís Moisescu, Mihnea Alexandru Pappalardo, Francesco Polster, Annikka Prasad, Girijesh Rozman, Damjana Sacala, Ioan Sanchez-Bornot, Jose M. Schmid, Johannes A. Sharp, Trevor Solé-Casals, Jordi Spiwok, Vojtěch Spyrou, George M. Stalidzans, Egils Stres, Blaa Sustersic, Tijana Symeonidis, Ioannis Tieri, Paolo Todd, Stephen Van Steen, Kristel Veneva, Milena Wang, Da-Hui Wang, Haiying Wang, Hui Watterson, Steven Wong-Lin, Kongfatt Yang, Su Zou, Xin Schmidt, Harald H.H.W. Centro de Tecnología Biomédica Universidad Politécnica de Madrid Madrid Spain Intelligent Systems Research Centre School of Computing Engineering and Intelligent Systems Ulster University Ulster United Kingdom Center for Physiology and Pharmacology Institute of Vascular Biology and Thrombosis Research Medical University of Vienna Vienna Austria TUM School of Life Sciences Weihenstephan Technical University of Munich Freising Germany Holon Israel CNR National Research Council IAC Institute for Applied Computing Rome Italy Lab of Computing Medical Informatics and Biomedical Imaging Technologies School of Medicine Aristotle University of Thessaloniki Thessaloniki Greece Université Clermont Auvergne INRAE UNH Plateforme d'Exploration du Métabolisme MetaboHUB Clermont Clermont-Ferrand France Université de Technologie de Compiègne Compiègne France Labex MS2T Control of Technological Systems-of-Systems CNRS and Université de Technologie de Compiègne Compiègne France Faculty of Engineering University of Kragujevac Kragujevac Serbia Kragujevac Serbia Steinbeis Advanced Risk Technologies Institute Doo Kragujevac Kragujevac Serbia Pharmacology and Therapeutics School of Medicine Galway Neuroscience Centre National University of Ireland Galway Ireland School of Computing Ulster University Ulster United Kingdom Holon Israel Department of Mathematics and Natural Sciences Kaunas University of Technology Kaunas Lithuania Faculty of Computer Science and Engineering Ss. Cyril and Methodius University Skopje Macedonia Medicine Faculty Université Libre de Bruxelles CHU de Charleroi Charleroi Belgium Northern Ireland Centre for Stratified Medicine Biomedical Sciences Research Institute Ulster University Ulster United Kingdom Escola Superior de Tecnologia e Gestão Instituto Politécnico de Portalegre Portalegre Portugal Instituto Superior Técnico Universidade de Lisboa Lisboa Portugal Faculty of Automatic Control and Computers University Politehnica of B

Background: Systems Medicine is a novel approach to medicine, that is, an interdisciplinary field that considers the human body as a system, composed of multiple parts and of complex relationships at multiple levels, and further integrated into an environment. Exploring Systems Medicine implies understanding and combining concepts coming from diametral different fields, including medicine, biology, statistics, modeling and simulation, and data science. Such heterogeneity leads to semantic issues, which may slow down implementation and fruitful interaction between these highly diverse fields. Methods: In this review, we collect and explain more than100 terms related to Systems Medicine. These include both modeling and data science terms and basic systems medicine terms, along with some synthetic definitions, examples of applications, and lists of relevant references. Results: This glossary aims at being a first aid kit for the Systems Medicine researcher facing an unfamiliar term, where he/she can get a first understanding of them, and, more importantly, examples and references for digging into the topic. © Massimiliano Zanin et al., 2021;Published by Mary Ann Liebert, Inc. 2021.

关键词： Semantics

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26th Annual Computational Neuroscience Meeting (CNS*2017): Part 3 Antwerp, Belgium. 15-20 July 2017 Abstracts

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BMC NEUROSCIENCE 2017年第SUPPL 1期18卷 95-176页

作者： [Anonymous] Department of Neuroscience Yale University New Haven CT 06520 USA Department Physiology & Pharmacology SUNY Downstate Brooklyn NY 11203 USA NYU School of Engineering 6 MetroTech Center Brooklyn NY 11201 USA Departament de Matemàtica Aplicada Universitat Politècnica de Catalunya Barcelona 08028 Spain Institut de Neurobiologie de la Méditerrannée (INMED) INSERM UMR901 Aix-Marseille Univ Marseille France Center of Neural Science New York University New York NY USA Aix-Marseille Univ INSERM INS Inst Neurosci Syst Marseille France Laboratoire de Physique Théorique et Modélisation CNRS UMR 8089 Université de Cergy-Pontoise 95300 Cergy-Pontoise Cedex France Department of Mathematics and Computer Science ENSAT Abdelmalek Essaadi’s University Tangier Morocco Laboratory of Natural Computation Department of Information and Electrical Engineering and Applied Mathematics University of Salerno 84084 Fisciano SA Italy Department of Medicine University of Salerno 84083 Lancusi SA Italy Dipartimento di Fisica Università degli Studi Aldo Moro Bari and INFN Sezione Di Bari Italy Data Analysis Department Ghent University Ghent Belgium Coma Science Group University of Liège Liège Belgium Cruces Hospital and Ikerbasque Research Center Bilbao Spain BIOtech Department of Industrial Engineering University of Trento and IRCS-PAT FBK 38010 Trento Italy Department of Data Analysis Ghent University Ghent 9000 Belgium The Wellcome Trust Centre for Neuroimaging University College London London WC1N 3BG UK Department of Electronic Engineering NED University of Engineering and Technology Karachi Pakistan Blue Brain Project École Polytechnique Fédérale de Lausanne Lausanne Switzerland Departement of Mathematics Swansea University Swansea Wales UK Laboratory for Topology and Neuroscience at the Brain Mind Institute École polytechnique fédérale de Lausanne Lausanne Switzerland Institute of Mathematics University of Aberdeen Aberdeen Scotland UK Department of Integrativ

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