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IEEE International Conference on Mechatronics and Automation

作者： Xu Chen Fei Meng Xiaopeng Chen Qiang Huang Intelligent Robotics Institute Beijing Institute of Technology Beijing China Beijing Advanced Innovation Center for Intelligent Robots and System Beijing Institute of Technology Beijing China Key Laboratory of Biomimetic Robots and Systems Beijing Institute of Technology Beijing China International Joint Research Laboratory of Biomimetic Key Laboratory of Intelligent Control and Decision of Comp lex System Beijing China

ISBN: (纸本)9781509023974

This paper presents an online self-tuning Smith Predictor for the First Order Plus Dead Time Model (FOPDT). It can tune time delay through oscillated input and output. Realtime phase difference detection is used to obtain the phase difference between the input and the output. An online tuner is used to minimize the phase difference. Once the phase difference is minimized, the exact time delay is completely compensated. Our method is suitable for both systems with constant time delay or variant time delay.

关键词： Delay effects Temperature control Delays Predictive models Oscillators Robots

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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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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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control system for neuro-prostheses integrating induced and volitional effort 9

Control system for neuro-prostheses integrating induced and ...

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9th IFAC Symposium on Biological and Medical systems, BMS 2015

作者： Ambrosini, E. Schauer, T. Klauer, C. Pedrocchi, A. Ferrigno, G. Ferrante, S. NeuroEngineering and Medical Robotics Laboratory NearLab Department of Electronics Information and Bioengineering Politecnico di Milano Italy Control Systems Group Technische Universität Berlin Germany Physical Medicine and Rehabilitation Unit Scientific Institute of Lissone Institute of Care and Research Salvatore Maugeri Foundation IRCCS Lissone Italy

To increase the rehabilitation outcomes of stroke patients with weak residual muscle activity, a novel control strategy for Functional Electrical Stimulation (FES) was designed and tested during a tracking task. The system includes an electromyography (EMG)-based feedback controller and a trial-to-trial adaptation of the FES support. The first modulates the stimulation intensity to achieve a desired amount of FES-evoked muscle activity corresponding to a desired FES-induced angular support trajectory. The second iteratively updates this trajectory in order to minimize the error between the target displayed to the patient and the measured angle trajectory. This support adaptation only takes place in case the subject actively contributes to the movement. If the estimated volitional effort is too low, FES support is quickly withdrawn to remind the subject to actively contribute to the tracking task. The EMG signal of the stimulated muscle is used to estimate both the FES-induced and the volitional muscle activity. The feasibility of the proposed control scheme was experimentally investigated in one healthy subject by stimulating the medial deltoid muscle to support shoulder abduction. First, the controller was tested simulating different volitional contributions of a potential patient. The control system adjusted the amount of FES-support as expected. Then, the compensation of muscle fatigue by the feedback controller was demonstrated by repeatedly applying the same desired FES-induced angular trajectories, while the subject was asked to be relaxed. As a result, the control system was able to provide the desired support for about 7 minutes until the stimulation intensity saturated. © 2015, IFAC (International Federation of Automatic control) Hosting by Elsevier Ltd. All rights reserved.

关键词： Electromyography

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A novel IEG strategy for realistically modeled seeker-less interceptors

A novel IEG strategy for realistically modeled seeker-less i...

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AIAA Guidance, Navigation, and control Conference 2015, MGNC 2015 - Held at the AIAA SciTech Forum 2015

作者： Saroj Kumar, G. Ghose, Debasish Vengadarajan, A. Electronics and Radar Development Establishment Bangalore560093 India Guidance Control and Decision Systems Laboratory Dept. of Aerospace Engineering Indian Institute of Science Bangalore560012 India Defence Research and Development Organization Bangalore560093 India Dept. of Aerospace Engineering IISc Bangalore560012 India

ISBN: (纸本)9781510801097

In this paper, an integrated estimation/guidance (IEG) strategy, that combines interactive multiple model (IMM) estimator with differential game guidance law (DGL), is proposed for realistically modeled seeker-less interceptor. IEG strategy takes the performance advantage of differential guidance laws, DGL/1 and DGL/0 law, into account, employs the change of model probability of IMM algorithm to detect evasive maneuver, and then selects the appropriate guidance law to reduce the guidance delay for homing performance. IEG was proposed earlier but tested against kinematic models. This paper presents a more realistic study by using aerodynamics, gravity, atmospheric density, and mass and velocity variation of the missile. An extensive Monte Carlo simulation study is carried out for combined IMM and modified DGL (IMM/modified DGL) which exhibits the superior performance and viability of the algorithm reducing the chattering phenomenon. © 2015, E-flow American institute of Aeronautics and Astronautics (AIAA). All rights reserved.

关键词： Monte Carlo methods

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Coherency-independent structured model reduction of power systems

Coherency-independent structured model reduction of power sy...

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IEEE General Meeting Power& Energy Society

作者： Christopher Sturk Luigi Vanfretti Yuwa Chompoobutrgool Henrik Sandberg Automatic Control KTH Royal Institute of Technology Research and Development Statnett SF Electric Power Systems KTH Royal Institute of Technology

This paper proposes a new model reduction algorithm for power systems based on an extension of balanced truncation. The algorithm is applicable to power systems which are divided into a study area which requires a high-fidelity model and an external area, making up most of the power system, which is to be reduced. The division of the power system can be made arbitrarily and does not rely on the identification of coherent generators. The proposed algorithm yields a reduced order system with a full nonlinear description of the study area and a reduced linear model of the external area.

关键词： Power systems Reduced order systems Generators

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Coherency-independent structured model reduction of power systems

Coherency-independent structured model reduction of power sy...

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Innovative Smart Grid Technologies (ISGT)

作者： Christopher Sturk Luigi Vanfretti Yuwa Chompoobutrgool Henrik Sandberg Automatic Control Lab KTH Royal Institute of Technology Research & Development Statnett SF Electric Power Systems KTH Royal Institute of Technology

关键词： Power systems Reduced order systems Generators

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The Economics of Using Electric Vehicles for Vehicle to Building Applications Considering the Effect of Battery Degradation

The Economics of Using Electric Vehicles for Vehicle to Buil...

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Annual Conference of the IEEE Industrial Electronics Society

作者： Ahmad GHADERI Amir Ali FOROUGH NASSIRAEI Research & Development Department Automotive Motor and Electronic Control Group Graduate School of Life Science and Systems Engineering Kyushu Institute of Technology

ISBN: (纸本)9781479917631

It is expected that the number of electric drive vehicles (EDVs) would grow rapidly in the coming years. Considering cars are parked over 92% of the time, the excess batteries capacity of EVs could be used to support power grid, while they are plugged into the grid. Although this technology, which is called vehicle to grid (V2G), has several benefit, vehicle to building (V2B) systems have a higher potential to be implemented commercially and economically. In this paper potential benefit of V2B considering the battery degradation cost is evaluated. A V2B system, with 6 electric vehicles, is simulated, and the effect of the battery charging/discharging time and the battery replacement cost has been studied. The results of this study prove that V2B has a relatively significant profit even considering the battery cost degradation.

关键词： V2B Vehicle to building V2G Vehicle to grid Electric vehicles Hybrid vehicles V2H Vehicle to home Smart grid Battery degradation Degradation cost Electric vehicles Hybrid electric vehicles Smart grids vehicle BUILDING APPLICATION Batteries economics

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development and control of Hybrid Hardware Engine Simulator

Development and Control of Hybrid Hardware Engine Simulator

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Conference of the Society of Instrument and control Engineers of Japan

作者： K.W. Seo Y. Kaneda M. Yamakita N. Kamamichi J. Ishikawa A. Ohata K. Furuta Department of Mechanical Control Systems Eng. Tokyo Institute of Technology Tokyo Metropolitan Industrial Technology Research Institute Department of Robotics and Mechatronics Tokyo Denki University Toyota Motor Co. Higashifuji Technical Center

ISBN: (纸本)9781479985845

In this paper a novel real-time engine simulator which is combined with partial mechanical hardware system and numerical simulator. In the hardware, the actual piston-clank mechanism is used, and the thrust force is generated by a liner motor. In the numerical simulator, admission and combustion dynamics are calculated, and the motion of the piston is realized by a kind of model matching or following *** this paper, we introduce the concept of the proposed simulator system, and explain the control systems.

关键词： Engine Model Engine Simulator Model Following control

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