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23rd Mediterranean Conference on control and Automation, MED 2015

作者： Rodriguez, J.M. Castilla, M. Alvarez, J.D. Rodriguez, F. Berenguel, M. University of Almería Agrifood Campus of International Excellence CeiA3 CIESOL Joint Center University of Almería CIEMAT Automatic Control Robotics and Mechatronics Research Group Ctra. Sacramento s/n La-Cañada04120 Spain

ISBN: (纸本)9781479999361

In general, human beings receive most part of information through eyesight. Therefore, light is an essential element which allows people to distinguish the shape, colour and perspective of the environment around them. Moreover, energy efficiency in buildings is a problem that is being widely analysed since energy consumption inside them represents approximately 40% of total world energy consumption. In addition, illumination supposes around 20% of total energy consumption in houses and between 40% and 70% in shops and offices. This paper presents a fuzzy logic controller which allows maintaining visual comfort inside a meeting-room through an efficient use of both natural and artificial lights. In order to probe the effectiveness of the proposed control system, suitable real results obtained in a meeting-room of the CIESOL bioclimatic building are included and commented. © 2015 IEEE.

关键词： Energy utilization

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Guest Editorial for Special Issue on Cyber-Physical systems

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IEEE/CAA Journal of Automatica Sinica 2015年第3期2卷 233-234页

作者： Youxian Sun Xinping Guan Jiming Chen Yilin Mo Zhejiang University the Chinese Academy of Engineering the Institute of Industrial Process Control and the National Engineering Research Center of Industrial Automation Zhejiang University Department of Automation Shanghai Jiao Tong University University Research Management Office Shanghai Jiao Tong University the Key Laboratory of Systems Control and Information Processing Ministry of Education of China the prestigious Innovative Research Team of the National Natural Science Foundation of China(NSFC) Department of Control Science and Engineering group of Networked Sensing and Control in the State Key laboratory of Industrial Control Technology Institute of Industrial Process Control at Zhejiang University the School of Electrical and Electronic Engineering at Nanyang Technological University

Ⅰ.Introduction CYBER-PHYSICAL system is a system of collaborating computational elements to control physical *** coordination and the tight link between computational,virtual and physical resources in cyber-physical system will have a pervasive effect on our everyday *** development of cyber-physical system will create new opportunities for the introduction of services that will enhance the quality of life

关键词： for WSAN Guest Editorial for Special Issue on Cyber-Physical systems of into on WSN is

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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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Micrometeoroid Events in LISA Pathfinder

arXiv

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arXiv 2019年

作者： Thorpe, J.I. Slutsky, J. Baker, John G. Littenberg, Tyson B. Hourihane, Sophie Pagane, Nicole Pokorny, Petr Janches, Diego Armano, M. Audley, H. Auger, G. Baird, J. Bassan, M. Binetruy, P. Born, M. Bortoluzzi, D. Brandt, N. Caleno, M. Cavalleri, A. Cesarini, A. Cruise, A.M. Danzmann, K. de Deus Silva, M. de Rosa, R. Di Fiore, L. Diepholz, I. Dixon, G. Dolesi, R. Dunbar, N. Ferraioli, L. Ferroni, V. Fitzsimons, E.D. Flatscher, R. Freschi, M. Marirrodriga, C. García Gerndt, R. Gesa, L. Gibert, F. Giardini, D. Giusteri, R. Grado, A. Grimani, C. Grzymisch, J. Harrison, I. Heinzel, G. Hewitson, M. Hollington, D. Hoyland, D. Hueller, M. Inchauspé, H. Jennrich, O. Jetzer, P. Johlander, B. Karnesis, N. Kaune, B. Korsakova, N. Killow, C.J. Lobo, J.A. Lloro, I. Liu, L. López-Zaragoza, J.P. Maarschalkerweerd, R. Mance, D. Martín, V. Martin-Polo, L. Martino, J. Martin-Porqueras, F. Madden, S. Mateos, I. McNamara, P.W. Mendes, J. Mendes, L. Nofrarias, M. Paczkowski, S. Perreur-Lloyd, M. Petiteau, A. Pivato, P. Plagnol, E. Prat, P. Ragnit, U. Ramos-Castro, J. Reiche, J. Robertson, D.I. Rozemeijer, H. Rivas, F. Russano, G. Sarra, P. Schleicher, A. Shaul, D. Sopuerta, C.F. Stanga, R. Sumner, T. Texier, D. Trenkel, C. Tröbs, M. Vetrugno, D. Vitale, S. Wanner, G. Ward, H. Wass, P. Wealthy, D. Weber, W.J. Wissel, L. Wittchen, A. Zambotti, A. Zanoni, C. Ziegler, T. Zweifel, P. Barela, P. Cutler, C. Demmons, N. Dunn, C. Girard, M. Hsu, O. Javidnia, S. Li, I. Maghami, P. Marrese-Reading, C. Mehta, J. O’Donnell, J. Romero-Wolf, A. Ziemer, J. Gravitational Astrophysics Lab NASA Goddard Space Flight Center 8800 Greenbelt Road GreenbeltMD20771 United States NASA Marshall Space Flight Center HuntsvilleAL35812 United States University of Michigan Johns Hopkins University Code 674 NASA Goddard Space Flight Center 8800 Greenbelt Road GreenbeltMD20771 United States Catholic University of America European Space Astronomy Centre European Space Agency Villanueva de la Cañada Madrid28692 Spain Albert-Einstein-Institut Max-Planck-Institut für Gravitationsphysik und Leibniz Universität Hannover Callinstraße 38 Hannover30167 Germany APC Univ Paris Diderot CNRS/IN2P3 CEA/lrfu Obs de Paris Sorbonne Paris Cité France High Energy Physics Group Physics Department Imperial College London Blackett Laboratory Prince Consort Road LondonSW7 2BW United Kingdom Dipartimento di Fisica Università di Roma "Tor Vergata" INFN sezione Roma Tor Vergata RomaI-00133 Italy Department of Industrial Engineering University of Trento Trento and Trento Institute for Fundamental Physics and Application INFN via Sommarive 9 38123 Italy Airbus Defence and Space Claude-Dornier-Strasse Immenstaad88090 Germany European Space Technology Centre European Space Agency Keplerlaan 1 Noordwijk2200 AG Netherlands Dipartimento di Fisica Università di Trento and Trento Institute for Fundamental Physics and Application / INFN Povo Trento38123 Italy School of Physics and Astronomy University of Birmingham Birmingham United Kingdom Dipartimento di Fisica Università di Napoli "Federico II" INFN - Sezione di Napoli NapoliI-80126 Italy INFN - Sezione di Napoli NapoliI-80126 Italy Airbus Defence and Space Gunnels Wood Road StevenageHertfordshireSG1 2AS United Kingdom Institut für Geophysik ETH Zürich Sonneggstrasse 5 ZürichCH-8092 Switzerland UK Astronomy Technology Centre Royal Observatory Edinburgh Blackford Hill EdinburghEH9 3HJ United Kingdom Campus UAB Carrer de Can Magrans s/n Cerdanyola del Vallès08193 Spain I

The zodiacal dust complex, a population of dust and small particles that pervades the Solar System, provides important insight into the formation and dynamics of planets, comets, asteroids, and other bodies. Efforts to understand this system have relied on analytic and numerical models anchored by observational data. Much of this observational data is concentrated in the near-Earth regime, where the planet’s gravitational effects mask some of the subtle but important differences between models. Here we present a new set of data obtained using a novel technique: direct measurements of momentum transfer to a spacecraft from individual particle impacts. This technique is made possible by the extreme precision of the instruments flown on the LISA Pathfinder spacecraft, a technology demonstrator for a future space-based gravitational wave observatory that operated near the first Sun-Earth Lagrange point from early 2016 through Summer of 2017. Pathfinder employed a technique known as drag-free control whereby the spacecraft was commanded to maintain a constant position and attitude relative to a free-flying test mass within the instrument. This required rejection of external disturbances, including particle impacts, using a micropropulsion system. Using a simple model of the impacts and knowledge of the control system, we show that it is possible to detect impacts and measure properties such as the transferred momentum (related to the particle’s mass and velocity), direction of travel, and location of impact on the spacecraft. In this paper, we present the results of a systematic search for impacts during 4348 hours of Pathfinder data. We report a total of 54 candidates with momenta ranging from 0.2 µNs to 230 µNs. We furthermore make a comparison of these candidates with models of micrometeoroid populations in the inner solar system including those resulting from Jupiter-family comets, Oort-cloud comets, Hailey-type comets, and Asteroids. We find that our measured popula

关键词： Dust

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A robotic concept for the NASA asteroid-capture mission

A robotic concept for the NASA asteroid-capture mission

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AIAA SPACE Conference and Exposition, 2015

作者： Flores-Abad, Angel Crespoy, Luis G. Universidad Auntonoma de Ciudad Juarez Ciudad JuarezChih32310 Mexico NASA Langley Research Center MS 308 NASA LaRC HamptonVA23681 United States Mechatronics Department Mexico Dynamic Systems and Control Branch United States

ISBN: (数字)9781624103346

ISBN: (纸本)9781624103346

This paper proposes an approach for capturing and despinning a tumbling asteroid using a space robot. The capturing and despining approach consists of four phases. In the first phase, the capturing system is controlled to match the linear and angular velocities of the asteroid while the path of its center of mass (CM) is made to follow the path of the asteroid's CM. The second phase is divided into two subphases. In the first subphase, a robotic manipulator is deployed until a camera mounted at the end effector (EE) faces the target landing location. During the second phase, the EE of manipulator approaches a chosen point on the surface of the asteroid. Attitude controllers driving the thrusters at the base of the spacecraft ensure that the matching velocity errors remain bounded while the robotic arm is deployed. Cameras mounted on the arm time such a deployment in order to minimize the impact forces to be experienced at contact. In the third phase a rigid connection between the asteroid and the end-effector is established. In the last phase, the position of the base of the space- craft relative to contact point is autonomously adjusted to minimize the energy consumption required to despin and stabilize the joint system by maximizing the torque's arm. This procedure relaxes the need for accurate estimates of the inertial properties of the asteroid. The concept proposed is illustrated by simulating the capture of a rigid asteroid subject to a one axis rotation with no precession or nutation. © 2015, American Institute of Aeronautics and Astronautics Inc, AIAA. All Rights Reserved.

关键词： Energy utilization

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Nonlinear Model Predictive control with Constraint Satisfactions for a Quadcopter

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Journal of Physics: Conference Series 2017年第1期783卷

作者： Ye Wang Andres Ramirez-Jaime Feng Xu Vicenç Puig Advanced Control Systems (SAC) Research Group at Institut de Robòtica i Informàtica Industrial CSIC-UPC Universitat Politècnica de Catalunya-BarcelonaTech (UPC) C/. Llorens i Artigas 4-6 08028 Barcelona Spain Departamento de Ingeniería Eléctrica y Electrónica Universidad de los Andes Carrera 1 No. 18A-10 Colombia Center of Intelligent Control and Telescience Graduate School at Shenzhen Tsinghua University 518055 Shenzhen P.R. China

This paper presents a nonlinear model predictive control (NMPC) strategy combined with constraint satisfactions for a quadcopter. The full dynamics of the quadcopter describing the attitude and position are nonlinear, which are quite sensitive to changes of inputs and disturbances. By means of constraint satisfactions, partial nonlinearities and modeling errors of the control-oriented model of full dynamics can be transformed into the inequality constraints. Subsequently, the quadcopter can be controlled by an NMPC controller with the updated constraints generated by constraint satisfactions. Finally, the simulation results applied to a quadcopter simulator are provided to show the effectiveness of the proposed strategy.

关键词：

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In-flight suppression of a destabilized F/A-18 structural mode using the space launch system adaptive augmenting control system

In-flight suppression of a destabilized F/A-18 structural mo...

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

作者： Wall, John H. VanZwieten, Tannen S. Gilligan, Eric T. Miller, Christopher J. Hanson, Curtis E. Orr, Jeb S. Guidance Navigation & Control Group Dynamic Concepts Inc HuntsvilleAL35806 United States NASA Engineering and Safety Center NASA Marshall Space Flight Center HuntsvilleAL35812 United States Control Systems and Analysis Branch NASA Marshall Space Flight Center HuntsvilleAL35812 United States Flight Controls & Dynamics Branch NASA Armstrong Flight Research Center EdwardsCA93523 United States Dynamics & Control The Charles Stark Draper Laboratory Inc HuntsvilleAL35806 United States Jacobs ESSSA Group United States

ISBN: (纸本)9781510801097

NASA’s Space Launch System (SLS) Flight control System (FCS) includes an Adaptive Augmenting control (AAC) component which employs a multiplicative gain update law to enhance the performance and robustness of the baseline control system for extreme off-nominal scenarios. The SLS FCS algorithm including AAC has been flight tested utilizing a specially outfitted F/A-18 fighter jet in which the pitch axis control of the aircraft was performed by a Non-linear Dynamic Inversion (NDI) controller, SLS reference models, and the SLS flight software prototype. This paper describes test cases from the research flight campaign in which the fundamental F/A-18 airframe structural mode was identified using frequency-domain reconstruction of flight data, amplified to result in closed loop instability, and suppressed in-flight by the SLS adaptive control system. © 2015, E-flow American Institute of Aeronautics and Astronautics (AIAA). All rights reserved.

关键词： Flight control systems

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A fuzzy controller for visual comfort inside a meeting-room

A fuzzy controller for visual comfort inside a meeting-room

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Mediterranean Conference on control and Automation (MED)

作者： J.M. Rodríguez M. Castilla J.D. Álvarez F. Rodríguez M. Berenguel Robotics and Mechatronics Research Group ceiA3 CIESOL Joint Center University of Almería - CIEMAT and Automatic Control La Cañada Almería España

关键词： Visualization Fuzzy logic Indexes Lighting Actuators Buildings

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A control-oriented dynamic wind farm flow model: 'WFSim'

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Journal of Physics: Conference Series 2016年第3期753卷

作者： S. Boersma P.M.O. Gebraad M. Vali B.M. Doekemeijer J.W. van Wingerden Delft Center for Systems and Control Delft University of Technology The Netherlands Siemens Wind Power Denmark Wind Energy System Research Group ForWind University of Oldenburg Germany

In this paper, we present and extend the dynamic medium fidelity control-oriented Wind Farm Simulator (WFSim) model. WFSim resolves flow fields in wind farms in a horizontal, two dimensional plane. It is based on the spatially and temporally discretised two dimensional Navier-Stokes equations and the continuity equation and solves for a predefined grid and wind farm topology. The force on the flow field generated by turbines is modelled using actuator disk theory. Sparsity in system matrices is exploited in WFSim, which enables a relatively fast flow field computation. The extensions to WFSim we present in this paper are the inclusion of a wake redirection model, a turbulence model and a linearisation of the nonlinear WFSim model equations. The first is important because it allows us to carry out wake redirection control and simulate situations with an inflow that is misaligned with the rotor plane. The wake redirection model is validated against a theoretical wake centreline known from literature. The second extension makes WFSim more realistic because it accounts for wake recovery. The amount of recovery is validated using a high fidelity simulation model Simulator fOr Wind Farm Applications (SOWFA) for a two turbine test case. Finally, a linearisation is important since it allows the application of more standard analysis, observer and control techniques.

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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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