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DESYNCHRONIZATION OF TWO COUPLED LIMIT-CYCLE OSCILLATORS USING AN ASTROCYTE-INSPIRED CONTROLLER

International Journal of Biomathematics 2014年第1期7卷 1-23页

作者： GHAZAL MONTASERI MOHAMMAD JAVAD YAZDANPANAH Advanced Control Systems Laboratory School of Electrical and Computer EngineeringCollege of Engineering University of Tehran Tehran Iran Advanced Control Systems LaboratoryControl and Intelligent Processing Center of Excellence School of Electrical and Computer Engineering College of Engineering University of Tehran Tehran Iran

Astrocytes have potential to break synchrony between neurons. Authors＇ recent researches reveal that astrocytes vary the synchronization threshold and provide an appropriate feedback control in stabilizing neural activities. In this study, we propose an astrocyte-inspired controller for desynchronization of two coupled limit-cycle oscillators as a minimal network model. The design procedure consists of two parts. First, based on the astrocyte model, the structure of the dynamic controller is suggested. Then, to have an emcient controller, parameters of controller are tuned through an optimization algo- rithm. The proposed bio-inspired controller takes advantages of three important proper- ties：（1） the controller desynchronizes the oscillators without any undesirable effects （e.g. stopping, annihilating or starting divergent oscillations）; （2） it consumes little effort to preserve the desirable desynchronized state; and （3） the controller is robust with respect to parameters＇ variations. Simulation results reveal the ability of the proposed controller.

关键词： Astrocyte dynamic model bio-inspired controller coupled limit-cycle oscillators desynchronization optimization

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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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An Unsupervised Methodology for the Detection of Epileptic Seizures in long-term EEG signals

An Unsupervised Methodology for the Detection of Epileptic S...

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IEEE International Conference on Bioinformatics and Bioengineering

作者： Kostas M. Tsiouris Spiros Konitsiotis Sofia Markoula Dimitrios D. Koutsouris Antonis I. Sakellarios Dimitrios I. Fotiadis Biomedical Engineering Laboratory School of Electrical and Computer Engineering National Technical University of Athens Department of Neurology Medical School University of Ioannina University Hospital of Ioannina Institute of Communication and Computer Systems Unit of Medical Technology and Intelligent Information Systems Department of Materials Science and Engineering and the Biomedical Research Department FORTH-IMBB University of Ioannina

ISBN: (纸本)9781467379847

An unsupervised methodology for the detection of Epileptic seizures in EEG recordings is proposed. The time-frequency content of the EEG signals is extracted using the Short Time Fourier Transform. The analysis focuses on the EEG energy distribution among the well-established delta, theta and alpha rhythms (2-13 Hz), as energy variations in these frequency bands are widely associated with seizure activity. Relying on seizure rhythmicity, the classification is performed by isolating the segments where each rhythm is more clearly and dominantly expressed over the others. For the first time, an unsupervised methodology is evaluated using more than 978 hours of EEG recordings from a public database. The results show that the proposed methodology achieves high seizure detection sensitivity with significantly reduced human intervention.

关键词： Electroencephalography Sensitivity Databases Inspection Visualization Time-frequency analysis Fourier transforms

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Model Predictive Control of a Wheeled Inverted Pendulum Robot

Model Predictive Control of a Wheeled Inverted Pendulum Robo...

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RSI/ISM International Conference on Robotics and Mechatronics

作者： Navid Dini Vahid Johari Majd Intelligent Control Systems Laboratory School of Electrical and Computer Engineering Tarbiat Modares University Tehran Iran Intelligent Control Systems Laboratory School of Electrical and Computer Engineering Tarbiat Modares University Tehran Iran

ISBN: (纸本)9781467372350

In this paper, a model predictive control method, namely the Generalized Predictive Control (GPC), has been applied to a two wheeled inverted pendulum robot. First, the dynamic equations of a Segway-like 2-wheeled inverted pendulum robot has been derived. The GPC method has been employed so that the output of the closed loop system tracks a desired trajectory. The comparison of the results with that of a classical tracking control method shows that the GPC controller has produced a better tracking performance for the robot.

关键词： Wheeled Inverted Pendulum Robot Model Based Predictive Control Generalized Predictive Control Segway Robot

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[email protected] : Analysis and results of evolving competitions for domestic and service robots

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Artificial Intelligence 2015年 229卷 258-281页

作者： Luca Iocchi Dirk Holz Javier Ruiz-del-Solar Komei Sugiura Tijn van der Zant Department of Computer Control and Management Engineering Sapienza University of Rome Italy Autonomous Intelligent Systems Group University of Bonn Germany Department of Electrical Engineering & Advanced Mining Technology Center Universidad de Chile Chile Information Services Platform Laboratory National Institute of Information and Communications Technology Japan Cognitive Robotics Laboratory University of Groningen The Netherlands

Scientific competitions are becoming more common in many research areas of artificial intelligence and robotics, since they provide a shared testbed for comparing different solutions and enable the exchange of research results. Moreover, they are interesting for general audiences and industries. Currently, many major research areas in artificial intelligence and robotics are organizing multiple-year competitions that are typically associated with scientific conferences. One important aspect of such competitions is that they are organized for many years. This introduces a temporal evolution that is interesting to analyze. However, the problem of evaluating a competition over many years remains unaddressed. We believe that this issue is critical to properly fuel changes over the years and measure the results of these decisions. Therefore, this article focuses on the analysis and the results of evolving competitions. In this article, we present the [email protected] competition, which is the largest worldwide competition for domestic service robots, and evaluate its progress over the past seven years. We show how the definition of a proper scoring system allows for desired functionalities to be related to tasks and how the resulting analysis fuels subsequent changes to achieve general and robust solutions implemented by the teams. Our results show not only the steadily increasing complexity of the tasks that [email protected] robots can solve but also the increased performance for all of the functionalities addressed in the competition. We believe that the methodology used in [email protected] for evaluating competition advances and for stimulating changes can be applied and extended to other robotic competitions as well as to multi-year research projects involving Artificial Intelligence and Robotics.

关键词： Robotic competitions Artificial intelligence and robotics Benchmarking

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A New Approach for Voltage Control Area Identification Based on Reactive Power Sensitivities

A New Approach for Voltage Control Area Identification Based...

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North American Power Symposium

作者： Rabindra Maharjan Sukumar Kamalasadan Energy and Intelligent Systems Laboratory Department of Electrical and Computer Engineering University of North Carolina-Charlotte

ISBN: (纸本)9781467373906

This paper presents a new method for determining coherent bus group called voltage control area (VCA) based on reactive power control capabilities of that area. VCA can be used in voltage stability assessment and optimal reactive power allocation. The proposed method is based on the sensitivity of reactive power of generator to reactive power of load which is the direct relation between the production and consumption of reactive power in the system. The results are shown for the New England 39 bus test system and 68 bus test system.

关键词： Voltage Control Area Reactive power and voltage control Voltage stability

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Distributed Adaptive Sensor Fault Tolerant Control for Smart Buildings

Distributed Adaptive Sensor Fault Tolerant Control for Smart...

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IEEE Annual Conference on Decision and Control

作者： Panayiotis M. Papadopoulos Vasso Reppa Marios M. Polycarpou Christos G. Panayiotou KIOS Research Center for Intelligent Systems and Networks Department of Electrical and Computer Engineering University of Cyprus Nicosia 1678 Cyprus Laboratory of Signals and Systems (UMR CNRS 8506) CentraleSupelec-CNRS-University Paris-Sud Univ. Paris-Saclay 91192 Gif-sur-Yvette France

ISBN: (纸本)9781479978878

This paper presents a model-based distributed fault-tolerant control (FTC) scheme with emphasis on compensating the effects of sensor faults in multi-zone heating, ventilating and air-conditioning (HVAC) systems. A bank of local adaptive FTC agents are designed to accommodate possible sensor faults in HVAC systems, modeled as a set of interconnected, nonlinear subsystems. In order to compensate the fault effects that may propagate in the neighboring subsystems, each local FTC utilizes the information generated by a local monitoring agent that diagnoses the sensor faults and performs an adaptive estimation of the isolated sensor faults. The local monitoring and control agents are allowed to exchange information with neighboring agents. Simulation results are used to illustrate the effectiveness of the proposed methodology applied to a seven-zone HVAC system.

关键词： High Voltage Alternating Current systems fault-tolerant control adaptive estimation Sensors Agents SUB-SYSTEM

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Gene expression imputation across multiple brain regions provides insights into schizophrenia risk (vol 51, pg 659, 2019)

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NATURE GENETICS 2019年第6期51卷 1068-1068页

作者： Huckins, Laura M. Dobbyn, Amanda Ruderfer, Douglas M. Hoffman, Gabriel Wang, Weiqing Pardinas, Antonio F. Rajagopal, Veera M. Als, Thomas D. Nguyen, Hoang T. Girdhar, Kiran Boocock, James Roussos, Panos Fromer, Menachem Kramer, Robin Domenici, Enrico Gamazon, Eric R. Purcell, Shaun Demontis, Ditte Borglum, Anders D. Walters, James T. R. O'Donovan, Michael C. Sullivan, Patrick Owen, Michael J. Devlin, Bernie Sieberts, Solveig K. Cox, Nancy J. Im, Hae Kyung Sklar, Pamela Stahl, Eli A. Pamela Sklar Division of Psychiatric Genomics Icahn School of Medicine at Mount Sinai New York NY USA Department of Genetics and Genomics Icahn School of Medicine at Mount Sinai New York NY USA Department of Psychiatry Icahn School of Medicine at Mount Sinai New York NY USA Icahn Institute for Genomics and Multiscale Biology Icahn School of Medicine at Mount Sinai New York NY USA Vanderbilt University Medical Center Nashville TN USA MRC Centre for Neuropsychiatric Genetics and Genomics Cardiff University Cardiff UK Department of Biomedicine Aarhus University Aarhus Denmark The Lundbeck Foundation Initiative for Integrative Psychiatric Research iPSYCH Denmark Center for Integrative Sequencing Aarhus University Aarhus Denmark Department of Human Genetics David Geffen School of Medicine University of California Los Angeles Los Angeles CA USA Human Brain Collection Core National Institute of Mental Health Bethesda MD USA Laboratory of Neurogenomic Biomarkers Centre for Integrative Biology (CIBIO) University of Trento Trento Italy Clare Hall University of Cambridge Cambridge UK University of North Carolina at Chapel Hill Chapel Hill NC USA Karolinska Institutet Stockholm Sweden Department of Psychiatry University of Pittsburgh Pittsburgh PA USA Systems Biology Sage Bionetworks Seattle WA USA Section of Genetic Medicine Department of Medicine University of Chicago Chicago IL USA Integrated Technology Research Laboratories Pharmaceutical Research Division Takeda Pharmaceutical Company Limited Fujisawa Japan Neuropsychiatry Section Department of Psychiatry Perelman School of Medicine University of Pennsylvania Philadelphia PA USA Neuropsychiatric Signaling Program Department of Psychiatry Perelman School of Medicine University of Pennsylvania Philadelphia PA USA Psychiatry JJ Peters Virginia Medical Center Bronx NY USA Department of Neuroscience Icahn School of Medicine at Mount Sinai New York New York NY USA Friedman Brain Institute

Transcriptomic imputation approaches combine eQTL reference panels with large-scale genotype data in order to test associations between disease and gene expression. These genic associations could elucidate signals in complex genome-wide association study (GWAS) loci and may disentangle the role of different tissues in disease development. We used the largest eQTL reference panel for the dorso-lateral prefrontal cortex (DLPFC) to create a set of gene expression predictors and demonstrate their utility. We applied DLPFC and 12 GTEx-brain predictors to 40,299 schizophrenia cases and 65,264 matched controls for a large transcriptomic imputation study of schizophrenia. We identified 413 genic associations across 13 brain regions. Stepwise conditioning identified 67 non-MHC genes, of which 14 did not fall within previous GWAS loci. We identified 36 significantly enriched pathways, including hexosaminidase-A deficiency, and multiple porphyric disorder pathways. We investigated developmental expression patterns among the 67 non-MHC genes and identified specific groups of pre- and postnatal expression.

关键词： Gene expression Genome-wide association studies Schizophrenia Transcriptomics

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Infeasibility-free inverse kinematics method

Infeasibility-free inverse kinematics method

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IEEE/SICE International Symposium on System Integration

作者： Wael Suleiman Fumio Kanehiro Eiichi Yoshida Electrical and Computer Engineering Department Université de Sherbrooke Sherbrooke Quebec Canada Intelligent Systems Research Institute National Institute of Advanced Industrial Science and Technology (AIST) Tsukuba Ibaraki Japan CNRS-AIST JRL (Joint Robotics Laboratory) Tsukuba Ibaraki Japan

ISBN: (纸本)9781467372435

The problem of inverse kinematics is revisited in the present paper. The paper is focusing on the problem of solving the inverse kinematics problem while respecting velocity limits on both the robot's joints and the end-effector. Even-though the conventional inverse kinematics algorithms have been proven to be efficient in many applications, defining an admissible trajectory for the end-effector is still a burdensome task for the user, and the problem can easily become unsolvable. The main idea behind the proposed algorithms is to consider the sampling time as a free variable, hence adding more flexibility to the optimization problem associated with the inverse kinematics. We prove that the reformulated problem has always a solution if the end-effector path is in the reachable space of the robot, thus solving the problem of infeasibility of conventional inverse kinematics methods. To validate the proposed approach, we have conducted three simulations scenarios. The simulation results point that while the conventional inverse kinematics methods fail to track precisely a desired end-effector trajectory, the proposed algorithms always succeed.

关键词： Kinematics Optimization Robots Trajectory Algorithm design and analysis Jacobian matrices Angular velocity

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Real-time optimal scheduling of smart power distribution systems using integrated receding horizon control and convex conic programming

Real-time optimal scheduling of smart power distribution sys...

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2014 IEEE Industry Application Society Annual Meeting, IAS 2014

作者： Moghadasi, Seyedmahdi Kamalasadan, Sukumar Power Energy and Intelligent Systems Laboratory Department of Electrical and Computer Engineering University of North Carolina at Charlotte CharlotteNC United States

ISBN: (纸本)9781479922888

In this paper, we propose a convex Optimal Power Flow (OPF) formulation integrated within Receding Horizon Control (RHC) method using Second Order Conic Programming (SOCP). The main advantages of the proposed method are a) global optimal scheduling with faster computation time b) dynamic equation models with real-time control c) integration of uncertain resources and measurements. The effectiveness of this method is evaluated using a 32 bus power distribution test system, considering various network constraints that include market interaction, energy storage dynamics and uncertain model of wind generation. The efficiency of the proposed method compared to RHC AC Optimal Power Flow (RHC-ACOPF) architecture is also evaluated. The results show that the proposed architecture outperforms the RHC-ACOPF in computation time and guaranties the global optimal solution. Also the proposed method provides effective usage of energy storage system as the RHC integration allows including dynamic modeling of energy storage within the optimization algorithm. © 2014 IEEE.

关键词： Convex optimization

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