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IEEE Annual Conference on Decision and control

作者： Francesca Parise Basilio Gentile Sergio Grammatico John Lygeros Automatic Control Laboratory ETH Zurich Switzerland Control Systems group Department of Electrical Engineering Eindhoven University of Technology The Netherlands

ISBN: (纸本)9781479978878

We consider quasi-aggregative games for large populations of heterogeneous agents, whose interaction is determined by an underlying communication network. Specifically, each agent minimizes a quadratic cost function, which depends on its own strategy and on a convex combination of the strategies of its neighbors, and is subject to heterogeneous convex constraints. We suggest two distributed algorithms that can be implemented to steer the best responses of the rational agents to a Nash equilibrium configuration. The convergence of these schemes is guaranteed under different sufficient conditions depending on the matrices defining the agents' cost functions and on the communication network.

关键词： Games Aggregates Nash equilibrium Sociology Statistics Convergence Cost function

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Constrained linear quadratic deterministic mean field control: Decentralized convergence to Nash equilibria in large populations of heterogeneous agents

Constrained linear quadratic deterministic mean field contro...

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IEEE Annual Conference on Decision and control

作者： Sergio Grammatico Francesca Parise John Lygeros Control Systems group Department of Electrical Engineering Eindhoven University of Technology The Netherlands Automatic Control Laboratory ETH Zurich Switzerland

ISBN: (纸本)9781479978878

This paper considers the linear quadratic deterministic mean field control problem for large populations of heterogeneous agents, subject to convex state and input constraints, and coupled via a quadratic cost function which depends on the average population state. To control the optimal responses of the rational agents to a Nash equilibrium, we propose feedback iterative solutions based on operator theory arguments. Contrary to the state of the art, global convergence is ensured, under mild sufficient conditions on the matrices defining the cost functions, and not on the convex constraints.

关键词： Sociology Statistics Cost function Games Nash equilibrium Aggregates Convergence

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Model Predictive control approaches for Centrifugal Compression systems

Model Predictive Control approaches for Centrifugal Compress...

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IEEE Annual Conference on Decision and control

作者： Giampaolo Torrisi Sergio Grammatico Manfred Morari Roy S. Smith Automatic Control Laboratory ETH Zurich Switzerland Control Systems group Department of Electrical Engineering Eindhoven University of Technology The Netherlands

ISBN: (纸本)9781479978878

Model Predictive control (MPC) techniques are considered for industrial centrifugal compression systems with nonlinear dynamics. We consider the torque provided by an external drive and a recycle valve as control actuators for the system. Closed-loop stability in the presence of control constraints is studied via the contractive control Lyapunov function method. We solve the nonlinear MPC problem to assess a performance benchmark, and then design a Sequential Quadratic Programming (SQP) MPC approach which is computationally affordable. We show in several numerical simulations based on a realistic centrifugal compressor case study that the SQP MPC technique outperforms the classic linearized MPC and performs similarly to the nonlinear MPC approach.

关键词： Valves Recycling Mathematical model Predictive control Torque Surges Actuators

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On the price of being selfish in large populations of plug-in electric vehicles

On the price of being selfish in large populations of plug-i...

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IEEE Annual Conference on Decision and control

作者： Marina Gonzalez Vaya Sergio Grammatico Goran Andersson John Lygeros Power Systems Laboratory ETH Zurich Switzerland Control Systems group Department of Electrical Engineering Eindhoven University of Technology Automatic Control Laboratory ETH Zurich Switzerland

ISBN: (纸本)9781479978878

We consider the problem of optimally scheduling the flexible electricity demand of a fleet of plug-in electric vehicles (PEVs). More specifically, we analyze the solutions of the following charging optimization problems: a) the welfare-optimal problem, where the overall system cost is minimized;b) the fleet-optimal problem, where the charging cost of the fleet as a whole is minimized by a central agent, that is the PEV aggregator;c) the selfish-optimal problem, where the noncooperative PEVs aim at minimizing their individual charging cost. For a homogenous PEV fleet and a simplified problem setup, we show that the solutions of the three different approaches correspond to different valley-filling results. A main insight is that, as the population of PEVs grows, the selfish-optimal solution converges to the welfare-optimal solution. On the other hand, we show that the centralized fleet-optimal solution of the PEV aggregation can be recovered via decentralized selfish-optimal solutions with respect to an appropriate price signal as the population size grows. Finally, we demonstrate our technical results on a realistic PEV fleet case study.

关键词： Electric vehicles Fleet population size optimal solution Prices

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Resource-aware set-valued estimation for discrete-time linear systems

Resource-aware set-valued estimation for discrete-time linea...

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IEEE Annual Conference on Decision and control

作者： F. D. Brunner T. M. P. Gommans W. P. M. H. Heemels F. Allgowert Institute for Systems Theory and Automatic Control (IST) University of Stuttgart 70550 Stuttgart Germany Control System Technology group Department of Mechanical Engineering Eindhoven University of Technology

ISBN: (纸本)9781479978878

In this paper, we propose a self-triggered estimator for discrete-time linear systems subject to unknown but bounded disturbances affecting both the system states and outputs. The proposed self-triggered estimator is a set-valued estimator that employs rollout techniques to reduce the communication between the sensors and the estimator with respect to a periodic sampling strategy. Moreover, at each time instant it provides an estimate of the plant state and a guaranteed bound on the difference between the true plant state and the estimate.

关键词： Linear system discrete-time discrete time systems Unknown Sensors Communication Research

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Identifying the best machine learning algorithms for brain tumor segmentation, progression assessment, and overall survival prediction in the BRATS challenge

arXiv

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arXiv 2018年

作者： Bakas, Spyridon Reyes, Mauricio Jakab, Andras Bauer, Stefan Rempfler, Markus Crimi, Alessandro Shinohara, Russell Takeshi Berger, Christoph Ha, Sung Min Rozycki, Martin Prastawa, Marcel Alberts, Esther Lipkova, Jana Freymann, John Kirby, Justin Bilello, Michel Fathallah-Shaykh, Hassan M. Wiest, Roland Kirschke, Jan Wiestler, Benedikt Colen, Rivka Kotrotsou, Aikaterini Lamontagne, Pamela Marcus, Daniel Milchenko, Mikhail Nazeri, Arash Weber, Marc-Andr Mahajan, Abhishek Baid, Ujjwal Gerstner, Elizabeth Kwon, Dongjin Acharya, Gagan Agarwal, Manu Alam, Mahbubul Albiol, Alberto Albiol, Antonio Albiol, Francisco J. Alex, Varghese Allinson, Nigel Amorim, Pedro H.A. Amrutkar, Abhijit Anand, Ganesh Andermatt, Simon Arbel, Tal Arbelaez, Pablo Avery, Aaron Azmat, Muneeza Pranjal, B. Bai, Wenjia Banerjee, Subhashis Barth, Bill Batchelder, Thomas Batmanghelich, Kayhan Battistella, Enzo Beers, Andrew Belyaev, Mikhail Bendszus, Martin Benson, Eze Bernal, Jose Bharath, Halandur Nagaraja Biros, George Bisdas, Sotirios Brown, James Cabezas, Mariano Cao, Shilei Cardoso, Jorge M. Carver, Eric N. Casamitjana, Adri Castillo, Laura Silvana Cat, Marcel Cattin, Philippe Cérigues, Albert Chagas, Vinicius S. Chandra, Siddhartha Chang, Yi-Ju Chang, Shiyu Chang, Ken Chazalon, Joseph Chen, Shengcong Chen, Wei Chen, Jefferson W. Chen, Zhaolin Cheng, Kun Choudhury, Ahana Roy Chylla, Roger Clrigues, Albert Colleman, Steven Colmeiro, Ramiro German Rodriguez Combalia, Marc Costa, Anthony Cui, Xiaomeng Dai, Zhenzhen Dai, Lutao Daza, Laura Alexandra Deutsch, Eric Ding, Changxing Dong, Chao Dong, Shidu Dudzik, Wojciech Eaton-Rosen, Zach Egan, Gary Escudero, Guilherme Estienne, Tho Everson, Richard Fabrizio, Jonathan Fan, Yong Fang, Longwei Feng, Xue Ferrante, Enzo Fidon, Lucas Fischer, Martin French, Andrew P. Fridman, Naomi Fu, Huan Fuentes, David Gao, Yaozong Gates, Evan Gering, David Gholami, Amir Gierke, Willi Glocker, Ben Gong, Mingming Gonzlez-Vill, Sandra Grosges, T. Guan, Yuanfang Guo, Sheng Gupta, Sudeep Han, Woo-Sup Han, Il Song Harmuth, Ko Center for Biomedical Image Computing and Analytics University of Pennsylvania PhiladelphiaPA United States Department of Radiology Perelman School of Medicine University of Pennsylvania PhiladelphiaPA United States Department of Pathology and Laboratory Medicine Perelman School of Medicine University of Pennsylvania PhiladelphiaPA United States Institute for Surgical Technology and Biomechanics University of Bern Bern Switzerland Center for MR-Research University Children's Hospital Zurich Zurich Switzerland Support Centre for Advanced Neuroimaging Inselspital Institute for Diagnostic and Interventional Neuroradiology Bern University Hospital Bern Switzerland University Hospital of Zurich Zurich Switzerland Center for Clinical Epidemiology and Biostatistics University of Pennsylvania Philadelphia United States Image-Based Biomedical Modeling Group Technical University of Munich Munich Germany Icahn School of Medicine Mount Sinai Health System New YorkNY United States Leidos Biomedical Research Inc. Frederick National Laboratory for Cancer Research FrederickMD21701 United States Cancer Imaging Program National Cancer Institute National Institutes of Health BethesdaMD20814 United States Department of Neurology University of Alabama at Birmingham BirminghamAL United States Department of Diagnostic Radiology University of Texas MD Anderson Cancer Center HoustonTX United States Department of Psychology Washington University St. LouisMO United States Neuroimaging Informatics and Analysis Center Washington University St. LouisMO United States Department of Radiology Washington University St. LouisMO United States Institute of Diagnostic and Interventional Radiology Pediatric Radiology and Neuroradiology University Medical Center Rostock Ernst-Heydemann-Str. 6 Rostock18057 Germany Tata Memorial Centre Homi Bhabha National Institute Mumbai India Shri Guru Gobind Singhji Institute of Engineering and Technology Nanded India NVIDIA Santa Clara

Gliomas are the most common primary brain malignancies, with different degrees of aggressiveness, variable prognosis and various heterogeneous histologic sub-regions, i.e., peritumoral edematous/invaded tissue, necrotic core, active and non-enhancing core. This intrinsic heterogeneity is also portrayed in their radio-phenotype, as their sub-regions are depicted by varying intensity profiles disseminated across multi-parametric magnetic resonance imaging (mpMRI) scans, reflecting varying biological properties. Their heterogeneous shape, extent, and location are some of the factors that make these tumors difficult to resect, and in some cases inoperable. The amount of resected tumor is a factor also considered in longitudinal scans, when evaluating the apparent tumor for potential diagnosis of progression. Furthermore, there is mounting evidence that accurate segmentation of the various tumor sub-regions can offer the basis for quantitative image analysis towards prediction of patient overall survival. This study assesses the state-of-the-art machine learning (ML) methods used for brain tumor image analysis in mpMRI scans, during the last seven instances of the International Brain Tumor Segmentation (BraTS) challenge, i.e., 2012-2018. Specifically, we focus on i) evaluating segmentations of the various glioma sub-regions in pre-operative mpMRI scans, ii) assessing potential tumor progression by virtue of longitudinal growth of tumor sub-regions, beyond use of the RECIST/RANO criteria, and iii) predicting the overall survival from pre-operative mpMRI scans of patients that underwent gross total resection. Finally, we investigate the challenge of identifying the best ML algorithms for each of these tasks, considering that apart from being diverse on each instance of the challenge, the multi-institutional mpMRI BraTS dataset has also been a continuously evolving/growing dataset. Copyright © 2018, The Authors. All rights reserved.

关键词： Tumors

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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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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 image analysis algorithm for soil structure identification

Advances in Intelligent Systems and Computing

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Advances in Intelligent systems and Computing 2015年 323卷 681-692页

作者： Charytanowicz, Malgorzata Kulczycki, Piotr Polish Academy of Sciences Systems Research Institute Centre of Information Technology for Data Analysis Methods Newelska 6 Warsaw01-447 Poland Catholic University of Lublin Institute of Mathematics and Computer Science Konstantynow 1H Lublin20-708 Poland Cracow University of Technology Department of Automatic Control and Information Technology Warszawska 24 Cracow31-155 Poland

Pore space study has been utilized as a general method for defining soil structures. This is because the characteristics particular to pore space impact the majority of physical and physicochemical soil parameters relevant due to plant growth. This paper presents an image segmentation approach for detecting the soil pore structures that have been studied by way of soil tomography sections. In so-doing, a research study was conducted using a density-based clustering method, and in turn, the nonparametric kernel estimation methodology. This overcomes the rigidity of arbitrary assumptions concerning the number or shape of clusters among data, and lets the researcher detect inherent data structures. After a short description of the method, the practical aspects and applications illustrated with a number of soil aggregates are presented. © Springer International Publishing Switzerland 2015.

关键词： Image segmentation

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Coordination of actuators for an A-double heavy vehicle combination using control allocation 17

Coordination of actuators for an A-double heavy vehicle comb...

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2014 17th IEEE International Conference on Intelligent Transportation systems, ITSC 2014

作者： Uhlen, Karin Nyman, Per Eklov, Johan Laine, Leo Sadeghi Kati, Maliheh Fredriksson, Jonas Volvo Group Trucks Technology GöteborgSE-405 08 Sweden Department of Signals and Systems Div. of Automatic Control Automation and Mechatronics Chalmers University of Technology Sweden

ISBN: (纸本)9781479960781

This paper shows how actuator coordination, based on control allocation, can be used in the motion control system for long articulated heavy vehicles. Usage of a control allocation structure is one way to handle an over-actuated system, which is a system where there are more motion actuators than controlled motions. The A-double combination is in focus for the control allocation design, which is based on vehicle modelling using the Lagrange formulation. The control structure is tested in a simulation environment and evaluated by two test scenarios. For the selected configuration with 31 actuators, the control allocation method coordinates the desired motion for both large and small steer and articulation angles. The proposed control structure is shown to be convenient when the vehicle configuration, for example the number of actuators in the combination, is changed. © 2014 IEEE.

关键词： Actuators

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