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International Conference on Information Fusion

作者： Ruilong Chen Matthew Hawes Lyudmila Mihaylova Jingjing Xiao Wei Liu Department of Automatic Control and Systems Engineering University of Sheffield S1 3JD UK School of Electronics Electrical and Computer Engineering University of Birmingham B15 2TT UK Department of Electronic and Electrical Engineering University of Sheffield S1 3JD UK

ISBN: (纸本)9781509020126

An efficient recognition framework requires both good feature representation and effective classification methods. This paper proposes such a framework based on a spatial Scale Invariant Feature Transform (SIFT) combined with a logistic regression classifier. The performance of the proposed framework is compared to that of state-of-the-art methods based on the Histogram of Orientation Gradients, SIFT features, Support Vector Machine and K-Nearest Neighbours classifiers. By testing with the largest vehicle logo data-set, it is shown that the proposed framework can achieve a classification accuracy of 99.93%, the best among all studied methods. Moreover, the proposed framework shows robustness when noise is added in both training and testing images.

关键词： Histograms Vehicles Logistics Dictionaries Support vector machines Robustness Training histograms vehicle Dictionaries as Topic Support Vector Network Logistics Functional training Logistic Regression Robustness Frameworks Power grids Training Classification Methods

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A motor imagery based brain-computer interface system via swarm-optimized fuzzy integral and its application

A motor imagery based brain-computer interface system via sw...

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IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)

作者： Shang-Lin Wu Yu-Ting Liu Kuang-Pen Chou Yang-Yin Lin Jie Lu Guangquan Zhang Chun-Hsiang Chuang Wen-Chieh Lin Chin-Teng Lin Brain Research Center Institute of Electrical Control Engineering Institute of Computer Science and Engineering Electronic Systems Research Division National Chung-Shan Institute of Science and Technology (NCSIST) Taoyuan Taiwan Faculty of Engineering and Information Technology University of Technology Sydney Sydney Australia Department of Computer Science National Chiao-Tung University Hsinchu Taiwan

ISBN: (纸本)9781509006274

A brain-computer interface (BCI) system provides a convenient means of communication between the human brain and a computer, which is applied not only to healthy people but also for people that suffer from motor neuron diseases (MNDs). Motor imagery (MI) is one well-known basis for designing Electroencephalography (EEG)-based real-life BCI systems. However, EEG signals are often contaminated with severe noise and various uncertainties, imprecise and incomplete information streams. Therefore, this study proposes spectrum ensemble based on swam-optimized fuzzy integral for integrating decisions from sub-band classifiers that are established by a sub-band common spatial pattern (SBCSP) method. Firstly, the SBCSP effectively extracts features from EEG signals, and thereby the multiple linear discriminant analysis (MLDA) is employed during a MI classification task. Subsequently, particle swarm optimization (PSO) is used to regulate the subject-specific parameters for assigning optimal confidence levels for classifiers used in the fuzzy integral during the fuzzy fusion stage of the proposed system. Moreover, BCI systems usually tend to have complex architectures, be bulky in size, and require time-consuming processing. To overcome this drawback, a wireless and wearable EEG measurement system is investigated in this study. Finally, in our experimental result, the proposed system is found to produce significant improvement in terms of the receiver operating characteristic (ROC) curve. Furthermore, we demonstrate that a robotic arm can be reliably controlled using the proposed BCI system. This paper presents novel insights regarding the possibility of using the proposed MI-based BCI system in real-life applications.

关键词： Electroencephalography Feature extraction Wireless sensor networks Wireless communication Electrodes Particle swarm optimization Microcontrollers

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Driving fatigue prediction with pre-event electroencephalography (EEG) via a recurrent fuzzy neural network

Driving fatigue prediction with pre-event electroencephalogr...

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IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)

作者： Yu-Ting Liu Shang-Lin Wu Kuang-Pen Chou Yang-Yin Lin Jie Lu Guangquan Zhang Wen-Chieh Lin Chin-Teng Lin Brain Research Center Institute of Electrical Control Engineering Institute of Computer Science and Engineering Electronic Systems Research Division National Chung-Shan Institute of Science and Technology (NCSIST) Taoyuan Taiwan FacuIty of Engineering and Information Technology University of Technology Sydney Sydney Australia Department of Computer Science National Chiao-Tung University Hsinchu Taiwan

ISBN: (纸本)9781509006274

We propose an electroencephalography (EEG) prediction system based on a recurrent fuzzy neural network (RFNN) architecture to assess drivers' fatigue degrees during a virtual-reality (VR) dynamic driving environment. Prediction of fatigue degrees is a crucial and arduous biomedical issue for driving safety, which has attracted growing attention of the research community in the recent past. Meanwhile, combined with the benefits of measuring EEG signals facilitates, many EEG-based brain-computer interfaces (BCIs) have been developed for use in real-time mental assessment. In the literature, EEG signals are severely blended with stochastic noise; therefore, the performance of BCIs is constrained by low resolution in recognition tasks. For this rationale, independent component analysis (ICA) is usually used to find a source mapping from original data that has been blended with unrelated artificial noise. However, the mechanism of ICA cannot be used in real-time BCI design. To overcome this bottleneck, the proposed system in this paper utilizes a recurrent self-evolving fuzzy neural work (RSEFNN) to increase memory capability for adaptive noise cancellation when assessing drivers' mental states during a car driving task. The experimental results without the use of ICA procedure indicate that the proposed RSEFNN model remains superior performance compared with the state-of-the-arts models.

关键词： Electroencephalography Fatigue Real-time systems Vehicles Fuzzy neural networks Signal processing algorithms Integrated circuits

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Integrated optimal power flow for distribution networks in local and urban scales

Integrated optimal power flow for distribution networks in l...

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UKACC International Conference on control (control)

作者： Xiandong Xu Kang Li Yang Liu Hongjie Jia School of Electronics Electrical Engineering and Computer Science Queen's University Belfast Belfast UK Department of Control Science and Engineering Harbin Institute of Technology Harbin China Key Laboratory of Smart Grid Tianjin University Tianjin China

This paper develops an integrated optimal power flow (OPF) tool for distribution networks in two spatial scales. In the local scale, the distribution network, the natural gas network, and the heat system are coordinated as a microgrid. In the urban scale, the impact of natural gas network is considered as constraints for the distribution network operation. The proposed approach incorporates unbalance three-phase electrical systems, natural gas systems, and combined cooling, heating, and power systems. The interactions among the above three energy systems are described by energy hub model combined with components capacity constraints. In order to efficiently accommodate the nonlinear constraint optimization problem, particle swarm optimization algorithm is employed to set the control variables in the OPF problem. Numerical studies indicate that by using the OPF method, the distribution network can be economically operated. Also, the tie-line power can be effectively managed.

关键词： Microgrids Cogeneration Optimization Natural gas Energy conversion

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Weak Resilience of Networked control Systems

Weak Resilience of Networked Control Systems

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European control Conference

作者： Tomonori Sadamoto Henrik Sandberg Bart Besselink Takayuki Ishizaki Jun-ichi Imura Karl Henrik Johansson Department of Mechanical and Environmental Informatics Graduate School of Information Science and Engineering Tokyo Institute of Technology 2-12-1 Meguro Tokyo Japan School of Electrical Engineering Automatic Control Royal Institute of Technology (KTH) SE-100 44 Stockholm Sweden

ISBN: (纸本)9781509025923

In this paper, we propose a method to establish a networked control system that maintains its stability in the presence of certain undesirable incidents on local controllers. We call such networked control systems weakly resilient. We first derive a necessary and sufficient condition for the weak resilience of networked systems. Networked systems do not generally satisfy this condition. Therefore, we provide a method for designing a compensator which ensures the weak resilience of the compensated system. Finally, we illustrate the efficiency of the proposed method by a power system example based on the IEEE 14-bus test system.

关键词： Weak Resilience Networked control Systems proposed method

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Improvement of Resting-state EEG Analysis Process with Spectrum Weight-Voting based on LES

arXiv

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arXiv 2017年

作者： Ye, Yumeng Liu, Haichun Zhang, TianHong Pan, Changchun Yang, Genke Wang, JiJun Qiu, Robert C. Key Laboratory of System Control and Information Processing Shanghai Jiaotong University Ministry of Education of China Shanghai200240 China Department of Automation and Shanghai Key Laboratory of Navigation and Location Based Services Shanghai Key Laboratory of Psychotic Disorders Shanghai Mental Health Center Shanghai Jiaotong University School of Medicine Shanghai200030 China Brain Science and Technology Research Center Shanghai Jiao Tong University Shanghai200240 China Shanghai Jiaotong University Shanghai200240 China Department of Electrical Engineering Shanghai Jiaotong University Shanghai200240 China Department of Electrical and Computer Engineering Tennessee Technological University CookevilleTN38505 United States

Electroencephalography is a non-invasive technique for recording brain bioelectric activity, which has potential applications in various fields such as human-computer interaction and neuroscience. Among them, analysis of the risk of schizophrenia using EEG data is a relatively new research topic. However, there are many difficulties in analyzing EEG data, including its complex composition, low amplitude as well as low signal-to-noise ratio. Some of the existing methods of analysis are based on feature extraction and machine learning to differentiate the phase of schizophrenia (First-episode schizophrenia, Healthy controls or Clinical high-risk) that samples belong to. However, medical research requires the use of machine learning not only to give more accurate classification results, but also to give the results that can be applied to pathological studies. The main purpose of this study is to obtain the weight values as the representation of influence of each frequency band on the classification of schizophrenia phases on the basis of a more effective classification method using the LES feature extraction, and then the weight values are processed and applied to improve the accuracy of machine learning classification. We propose a method called weight-voting to obtain the weights of sub-bands features by using results of classification for voting to fit the actual categories of EEG data, and using weights for reclassification. Through this method, we can first obtain the influence of each band in distinguishing three schizophrenia phases, and analyze the effect of band features on the risk of schizophrenia contributing to the study of psychopathology. In addition, the weights applied to the original classifier can achieve the upgrade of the classification effect, which contributes to the BCI-assisted system of diagnosis and treatment. Our results show that there is a high correlation between the change of weight of low gamma band and the difference between HC, CHR an

关键词： Electroencephalography

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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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Distributed Weighted Least-Squares Estimation for Power Networks

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IFAC-PapersOnLine 2015年第28期48卷 562-567页

作者： Marelli, Damián Ninness, Brett Fu, Minyue School of Electrical Engineering and Computer Science University of Newcastle University Drive Callaghan NSW 2308 Australia Department of Control Science and Engineering Zhejiang University 38 Zhe Da Road Hangzhou310007 China

The paper presents a fully distributed scheme to optimal parameter estimation, withapplication in multi-area interconnected power systems. In this scheme, each area performs itsown local parameter estimation based on low-dimensional local and boundary measurementsas well as the estimate of boundary parameters from its neighbors. We show, under certainassumptions, that the distributed estimation scheme provides the same accurate unbiasedestimate as the centralized weighted least-squares estimate with finite time convergence. Thedistributed estimation scheme is robust to communication link failures, delays, and asynchronismof control centers in different areas. A simulation using the IEEE 118-bus system is included todemonstrate the performance. © 2015

关键词： Parameter estimation Electric power transmission networks Standby power systems Distributed estimation Distributed parameter Finite time convergence Least squares estimation Optimal parameter estimation Smart power applications Weighted least squares Weighted least squares estimation

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Improving BDD-based attractor detection for synchronous boolean networks 15

Improving BDD-based attractor detection for synchronous bool...

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7th Asia-Pacific Symposium on Internetware, Internetware 2015

作者： Qu, Hongyang Yuany, Qixia Pangz, Jun Mizera, Andrzej Department of Automatic Control and Systems Engineering University of Sheffield United Kingdom Faculty of Science Technology and Communication University of Luxembourg Luxembourg

ISBN: (纸本)9781450336413

Boolean networks are an important formalism for modelling biological systems and have attracted much attention in recent years. An important direction in Boolean networks is to exhaustively find attractors, which represent steady states when a biological network evolves for a long term. In this paper, we propose a new approach to improve the efficiency of BDD-based attractor detection. Our approach includes a monolithic algorithm for small networks, an enumerative strategy to deal with large networks, and two heuristics on ordering BDD variables. We demonstrate the performance of our approach on a number of examples, and compare it with one existing technique in the literature. © 2015 ACM.

关键词： Binary decision diagrams

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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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