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Corrigendum to “Road detection algorithm for Autonomous Navigation Systems based on dark channel prior and vanishing point in complex road scenes” [Robot. Auton. Syst. 85 (2016) 1–11]

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Robotics and Autonomous Systems 2017年 88卷 202-202页

作者： Yong Li Weili Ding Xuguang Zhang Zhaojie Ju Laboratory of Pattern Recognition and Intelligent Systems Key Laboratory of Industrial Computer Control Engineering of Hebei Province Department of Automation Institute of Electrical Engineering Yanshan University Qinghuangdao Hebei 066004 China College of Information Science and Engineering Northeastern University Shenyang Liaoning 110004 China School of Computing University of Portsmouth PO1 3HE UK

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Addressing Security in the ATM Environment: From Identification to Validation of Security Countermeasures with Introduction of New Security Capabilities in the ATM System Context

Addressing Security in the ATM Environment: From Identificat...

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International Conference on Availability, Reliability and Security, ARES

作者： Patrizia Montefusco Rosana Casar Rainer Koelle Tim H. Stelkens-Kobsch Traffic Control System Engineering LEONARDO Naples Italy School of Computing and Communications Lancaster University Lancaster United Kindom Department of Transport and Information Technology ISDEFE Madrid Spain Institute of Flight Guidance German Aerospace Center (DLR) Braunschweig Germany

This paper addresses the full lifecycle of security countermeasures identified in the Security Risk Analysis of the future Air Traffic Management System (ATM). The process establishes new security functions identified in the GAMMA project [1] and their implementations in order to ensure acceptable levels of security for ATM. In this project, ATM Security is addressed by focusing on two dimensions defined by Single European Sky ATM Research [2]: establishing a collaborative support capability by defining a framework embracing three-levels for Security Management (i.e. European, National, and Local) and developing security measures for the self-protection/resilience of the ATM Systems by exploiting automated security-related functions to handle potential threats. This paper concentrates on the second dimension and how the countermeasures are identified, implemented and developed in prototypes. The prototypes will then be validated in an operational scenario, through the new concept introduced by the project. The reader will be accompanied through a practical example of the whole process on how ATM Security needs have been identified. The objective is to protect the core ATM Security functionalities (Primary Assets) and corresponding Supporting Assets. We identified 44 of the most feared threat scenarios in terms of impact on the SESAR Key Performance Areas (KPA). The threat scenario described in this paper is "False ATCO", affecting the Supporting Asset "Voice system". The developed prototype is "SACom" (Secure ATC Communication) that considers the security countermeasures identified in the risk treatment analysis to reduce the risks. The paper concludes with the description of the activities planned for validating the SACom prototype as part of the proposed global solution.

关键词： Europe Security management Prototypes Navigation Risk management Stakeholders

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Variable selection in identification of a high dimensional nonlinear non-parametric system

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control Theory and Technology 2015年第1期13卷 1-16页

作者： Er-Wei BAI Wenxiao ZHAO Weixing ZHENG Department of Electrical and Computer Engineering University of Iowa School of Electronics Electrical Engineering and Computer Science Queen's UniversityBelfast U.K. Key Laboratory of Systems and Control Academy of Mathematics and Systems Science National Center for Mathematics and Interdisciplinary Sciences Chinese Academy of Sciences School of Computing Engineering and Mathematics University of Western Sydney

The problem of variable selection in system identification of a high dimensional nonlinear non-parametric system is described. The inherent difficulty, the curse of dimensionality, is introduced. Then its connections to various topics and research areas are briefly discussed, including order determination, pattern recognition, data mining, machine learning, statistical regression and manifold embedding. Finally, some results of variable selection in system identification in the recent literature are presented.

关键词： System identification variable selection nonlinear non-parametric system curse of dimensionality

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Metric for attractor overlap

arXiv

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arXiv 2017年

作者： Ishar, Rishabh Kaiser, Eurika Morzynski, Marek Fernex, Daniel Semaan, Richard Albers, Marian Meysonnat, Pascal S. Schröder, Wolfgang Noack, Bernd R. Department of Mechanical Engineering Punjab Engineering College Chandigarh160012 India University of Washington Department of Mechanical Engineering Stevens Way Box 352600 SeattleWA98195 United States Poznan University of Technology Chair of Virtual Engineering Jana Pawla II 24 PoznanPL 60-965 Poland Institut für Strömungsmechanik Technische Universität Braunschweig Hermann-Blenk-Str. 37 Braunschweig38108 Germany Institute of Aerodynamics RWTH Aachen University Wüllnerstr. 5a Aachen52062 Germany Forschungszentrum Jülich JARA-High-Performance Computing Jülich52425 Germany Laboratoire d'Informatique pour la Mecanique et les Sciences de l'Ingenieur LIMSI-CNRS Rue John von Neumann Campus Universitaire d'Orsay Bat 508 Orsay Cedex91403 France Technische Universität Berlin Müller-Breslau-Strae 8 Berlin10623 Germany Institute for Turbulence-Noise-Vibration Interaction and Control Harbin Institute of Technology Shenzhen Campus China

We present the first general metric for attractor overlap (MAO) facilitating an unsupervised comparison of flow data sets. The starting point is two or more attractors, i.e., ensembles of states representing different operating conditions. The proposed metric generalizes the standard Hilbert-space distance between two snapshots to snapshot ensembles of two attractors. A reduced-order analysis for big data and many attractors is enabled by coarse-graining the snapshots into representative clusters with corresponding centroids and population probabilities. For a large number of attractors, MAO is augmented by proximity maps for the snapshots, the centroids, and the attractors, giving scientifically interpretable visual access to the closeness of the states. The coherent structures belonging to the overlap and disjoint states between these attractors are distilled by few representative *** employ MAO for two quite different actuated flow configurations: A two-dimensional wake with vortices in a narrow frequency range and three-dimensional wall turbulence with broadband spectrum. In the first application, seven control laws are applied to the uidic pinball, i.e., the two-dimensional flow around three circular cylinders whose centers form an equilateral triangle pointing in the upstream direction. These seven operating conditions comprise unforced shedding, boat tailing, base bleed, high- and low-frequency forcing as well as two opposing Magnus effects. In the second example, MAO is applied to three-dimensional simulation data from an open-loop drag reduction study of a turbulent boundary layer. The actuation mechanisms of 38 spanwise traveling transversal surface waves are investigated. MAO compares and classifies these actuated flows in agreement with physical intuition. For instance, the first feature coordinate of the attractor proximity map correlates with drag for the uidic pinball and for the turbulent boundary layer. MAO has a large spectrum of potential

关键词： Dynamical systems

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Positively invariant manifolds: concept and applications

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Journal of Physics: Conference Series 2017年第1期811卷

作者： Sergei S. Sazhin Elena Shchepakina Vladimir Sobolev Sir Harry Ricardo Laboratories Advanced Engineering Centre School of Computing Engineering and Mathematics University of Brighton Brighton BN2 4GJ UK Department of Technical Cybernetics Samara National Research University 34 Moskovskoye shosse Samara 443086 Russia Department of Differential Equations and Control Theory Samara National Research University 34 Moskovskoye shosse Samara 443086 Russia

In many applications of the system order reduction models, including those focused on spray ignition and combustion processes, it is assumed that all functions in corresponding differential equations are Lipschitzian. This assumption has not been checked in most cases and the cases when these functions were non-Lipschitzian have sometimes been overlooked. This allows us to question the results of application of the conventional theory of integral manifolds to some such systems. The aim of this paper is to demonstrate that even in the case of singular perturbed systems with non-Lipschitzian nonlinearities the order reduction can be performed, using a new concept of positively invariant manifolds. This is illustrated by several examples including the problem of heating, evaporation, ignition and combustion of Diesel fuel sprays.

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Distributed k-means algorithm for sensor networks based on multi-agent consensus theory

Distributed k-means algorithm for sensor networks based on m...

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IEEE International Conference on Industrial Technology (ICIT)

作者： Qiuhong Liu Weiming Fu Jiahu Qin Wei Xing Zheng Huijun Gao Research Institute of Intelligent Control and Systems Harbin Institute of Technology Harbin China Department of Automation University of Science and Technology of China Hefei China School of Computing Engineering and Mathematics Western Sydney University Sydney NSW Australia Harbin Institute of Technology Harbin Heilongjiang CN

ISBN: (纸本)9781467380768

This paper is concerned with developing a distributed k-means algorithm for the wireless sensor networks (WSN) where each node is equipped with sensors. The underlying topology of the WSN is supposed to be strongly connected. The consensus algorithm in multi-agent consensus theory is utilized to exchange the measurement information of the sensors in WSN. To obtain a faster convergence speed as well as a higher possibility of having the global optimum, a distributed k-means++ algorithm is firstly proposed to find the initial centroids before executing the distributed k-means algorithm. The proposed distributed k-means algorithm is capable of partitioning the data observed by the nodes into measure-dependent groups which have small in-group and large out-group distances. Simulation results show that the proposed distributed algorithms can achieve almost the same results as that achieved by the centralized clustering algorithms.

关键词： Clustering algorithms Partitioning algorithms Nickel Wireless sensor networks Computational complexity Distributed databases Network topology

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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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26th Annual Computational Neuroscience Meeting (CNS*2017) of the Organization for Computational Neuroscience Antwerp, Belgium, July 15-20, 2017

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BMC NEUROSCIENCE 2017年第SUPPL 1期18卷 59-59页

作者： [Anonymous] Indiana University Purdue University Indianapolis Indianapolis IN 46032 USA Stark Neurosciences Research Institute Indiana University School of Medicine Indianapolis IN 46032 USA Department of Mathematics East Carolina University Greenville NC 27858 USA Jülich Supercomputing Centre Forschungszentrum Jülich 52425 Jülich Germany Future Systems Swiss National Supercomputing Centre 8092 Zurich Switzerland User Engagement and Support Swiss National Supercomputing Centre 6900 Lugano Switzerland Institut de Neurosciences des Systèmes Aix Marseille Univ 13005 Marseille France Simulation Lab Neuroscience Forschungszentrum Jülich Jülich Germany Department of Experimental Psychology Ghent University 9000 Ghent Belgium Donders Center for Cognitive Neuroimaging Radboud University 6525HR Nijmegen The Netherlands Department of Electrical Computer and Energy Engineering University of Colorado Boulder CO 80309 USA Department of Neurosurgery Johns Hopkins School of Medicine Baltimore MD 21287 USA Department of Neurology Johns Hopkins School of Medicine Baltimore MD 21287 USA Department of Otolaryngology Johns Hopkins School of Medicine Baltimore MD 21287 USA INSERM U968 Paris France Sorbonne Universités UPMC University Paris 06 UMR_S 968 Institut de la Vision Paris France CNRS UMR_7210 Paris France Department of Computer Architecture and Technology University of Granada (CITIC) Granada Spain Sorbonne Universités UPMC Univ Paris 06 INSERM CNRS Institut de la Vision Paris France Department of Adaptive Machine Systems Osaka University Osaka Japan Department of Computer Science University of Cergy-Pontoise Cergy-Pontoise France Department of Physics and Astronomy College of Charleston Charleston SC 29424 USA School of Physics Faculty of Science University of Sydney Sydney NSW 2006 Australia Center of Excellence for Integrative Brain Function Australian Research Council Sydney Australia Max Planck Institute for Human Cognitive and Brain Sciences Saxony Lei

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Cloud computing Platform and Big Data Service for Incubator Cluster

Cloud Computing Platform and Big Data Service for Incubator ...

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2016年IEEE/INFORMS服务运筹、物流与信息化国际会议

作者： Gang Xiong Chi Zhang Tongkai Ji Banji Guan Aobing Sun Yuexiang Hu Timo R.Nyberg Dongguan Research Institute of CASIA Cloud Computing CenterChinese Academy of Sciences The State Key Laboratory of Management and Control for Complex Systems Institute of AutomationChinese Academy of Sciences School of Economic and Management Beijing University of Chemical Technology Department of Industrial Engineering and Management School of Sciences Aalto University

Hundreds of incubators get birthed worldwide every day, and many new ICT technologies developed continuously, like cloud computing and big data. Therefore, it's possible and necessary to develop a cloud computing platform that gathering a variety of data and resources information needed by thousands of incubators and millions of incubated enterprises. In this paper, after comparing three different countries incubator's management procedure, we mainly talk about the way to design a cloud-based incubator platform including its architecture, main functions, and the interface. We assume that the platform can gather and share a variety of information and resource, like money, human resource, equipment, office place and so on. So the platform is beneficial for the incubated enterprises, incubators, and the government. For example, the platform can provide individual services through big data technology. The platform can analysis the situation of incubators and warn the companies in dangerous condition with the history big data. Finally, this paper describes the use case of cloud-based incubator platform for Guangdong Province.

关键词： business incubator cluster open innovation cloud computing platform crowdsourcing big data service

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Device-free human presence detection using frequency domain 1

Device-free human presence detection using frequency domain

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1st IEEE International Workshop on Consumer Electronics - Novi Sad, CE WS 2015

作者： Mrazovac, Bojan Todorovic, Branislav M. Kukolj, Dragan Temerinac, Miodrag Department for Computing and Control Engineering Faculty of Technical Sciences Novi Sad Serbia RT-RK Institute for Computer Based Systems Narodnog fronta 23a Novi Sad Serbia

ISBN: (纸本)9781509042685

Device-free human presence (DFHP) detection is a novel technique, mainly applicable to wireless sensor networks in which a human does not need to carry a wireless transceiver to be detected. Irregularities in the radio signal propagation pattern, expressed in a form of received signal strength indicator's (RSSI) variations can indicate possible presence of a human within the existing radio network. In this paper we propose DFHP detection method based on frequency analysis of RSSI variations. The main advantage of the proposed method compared to human detection method based on information entropy is the possibility to determine human presence from furniture layout change. The main drawback is a disability to recognize the absence from the presence of a motionless person in an environment. However, the combination of these two approaches defines a robust and cost-effective DFHP detection method that can be used in various smart home systems without the need for specific hardware installation and exploitation procedures. © 2015 IEEE.

关键词： Fast Fourier transforms

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