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Asilomar Conference on Signals, systems & computers

作者： Brian Swenson Soummya Kar João Xavier Department of Electrical and Computer Engineering Carnegie Mellon University Pittsburgh PA USA Institute for Systems and Robotics (ISR/IST) Universidade de Lisboa Portugal

ISBN: (纸本)9781538639559

Fictitious Play (FP) is a popular algorithm known to achieve Nash equilibrium learning in certain large-scale games. However, for games with many players, the computational demands of the FP algorithm can be prohibitive. Sampled FP (SFP) is a variant of FP that mitigates computational demands via a Monte Carlo approach. While SFP does mitigate the complexity of FP, it can be shown that SFP still uses information in an inefficient manner. The paper generalizes the SFP convergence result and studies a stochastic-approximation-based variant that significantly reduces the complexity of SFP.

关键词： Games Convergence Monte Carlo methods Approximation algorithms Complexity theory Probability distribution Nash equilibrium

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Stator Winding Short-Circuit Fault Diagnosis in Induction Motors using Random Forest

Stator Winding Short-Circuit Fault Diagnosis in Induction Mo...

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IEEE International Electric Machines and Drives Conference

作者： Tiago dos Santos Fernando J. T. E. Ferreira Joao Moura Pires Carlos Damasio Department of Computer Science NOVA-LINCS Nova University of Lisbon (FCT/UNL) Lisbon Portugal Dep. of Electrical and Computer Engineering Institute of Systems and Robotics University of Coimbra Coimbra Portugal

ISBN: (纸本)9781509042821

In this paper, an approach to detect stator winding short-circuit faults in squirrel-cage induction motors based on Random Forest and Park's Vector is proposed. This is accomplished by scoring the unbalance in the current and voltage waveforms as well as in Park's Vector, both for current and voltage. To score the unbalance in the d-q space, a Principal Component Analysis is applied to Park's Vector and with the first two principal components the eccentricity is calculated, while the first principal component is used to determine the phase in short-circuit. The proposed strategy has been experimentally tested on a special 400-V, 50-Hz, 4-pole, 2.2-kW induction motor with reconfigurable stator windings in which it was possible to emulate different types of inter-turn short-circuits. The results are quite promising, even only using 1-kHz sampling frequency to acquire the current and voltage waveforms in the three phases, and the use of the Fast Fourier Transform is avoided. The developed solution may be used for tele-monitoring of the motor condition and to implement advanced predictive maintenance strategies.

关键词： Fault diagnosis Induction motor Inter-turn short-circuit Stator winding PCA Predictive maintenance Machine learning Classification Random forest

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Social activity recognition based on probabilistic merging of skeleton features with proximity priors from RGB-D data

Social activity recognition based on probabilistic merging o...

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IEEE/RSJ International Conference on Intelligent Robots and systems

作者： Claudio Coppola Diego R. Faria Urbano Nunes Nicola Bellotto L-CAS School of Computer Science University of Lincoln (UK) Institute of Systems and Robotics Department of Electrical and Computer Engineering University of Coimbra Portugal

ISBN: (纸本)9781509037636

Social activity based on body motion is a key feature for non-verbal and physical behavior defined as function for communicative signal and social interaction between individuals. Social activity recognition is important to study human-human communication and also human-robot interaction. Based on that, this research has threefold goals: (1) recognition of social behavior (e.g. human-human interaction) using a probabilistic approach that merges spatio-temporal features from individual bodies and social features from the relationship between two individuals;(2) learn priors based on physical proximity between individuals during an interaction using proxemics theory to feed a probabilistic ensemble of activity classifiers;and (3) provide a public dataset with RGB-D data of social daily activities including risk situations useful to test approaches for assisted living, since this type of dataset is still missing. Results show that using the proposed approach designed to merge features with different semantics and proximity priors improves the classification performance in terms of precision, recall and accuracy when compared with other approaches that employ alternative strategies.

关键词： Feature extraction Skeleton Mixture models Robots Covariance matrices Probabilistic logic

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A contribution of demand response for the reliability of a power system

A contribution of demand response for the reliability of a p...

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IEEE International Conference on Environment and electrical engineering

作者： Claudia Gaspar Alvaro Gomes Institute of Systems and Robotics Department of Electrical and Computer Engineering University of Coimbra 3030-290 Coimbra Portugal INESC Coimbra Department of Electrical and Computer Engineering University of Coimbra 3030-290 Coimbra Portugal

ISBN: (纸本)9781509023219

Reliability is a crucial issue in modern power systems, being, in simple words, the ability to continuously and efficiently supply loads without neglecting the quality of service while keeping the power grid stable. In general, reliability can be improved by using more reliable components (generators) or by adding redundancy, being this extra generation known as reserves. Insufficient reserve margin may originate instability and severe problems in power systems. Adequacy is related with the provision of sufficient generation resources to meet the demand and the reserves requirements. The higher the redundancy level the higher the costs, thus a trade-off between the reliability degree and the investment requirements is needed. In this work is investigated if controllable demand can be actively used as an alternative to generation units for guaranteeing adequate reliability indices in short term operation of power systems. The goal is assessing the feasibility of using demand side resources -controllable demand- instead of generation for improving the reliability indices. An evolution strategy has been implemented to design adequate control actions, to be applied over end-use loads, and their impacts on the reliability indices have been assessed. The obtained results showed that, when adequately designed, demand control actions can effectively contribute for a more reliable operation of the power system, being an alternative to the redundant generation in short term.

关键词： alternative generation units guaranteeing

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DAC-h3: A proactive robot cognitive architecture to acquire and express knowledge about the world and the self

arXiv

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arXiv 2017年

作者： Moulin-Frier, Clément Fischer, Tobias Petit, Maxime Pointeau, Grégoire Puigbo, Jordi-Ysard Pattacini, Ugo Low, Sock Ching Camilleri, Daniel Nguyen, Phuong Hoffmann, Matej Chang, Hyung Jin Zambelli, Martina Mealier, Anne-Laure Damianou, Andreas Metta, Giorgio Prescott, Tony J. Demiris, Yiannis Dominey, Peter Ford Verschure, Paul F.M.J. Laboratory for Synthetic Perceptive Emotive and Cognitive Systems Universitat Pompeu Fabra Barcelona08002 Spain Barcelona Spain Personal Robotics Laboratory Department of Electrical and Electronic Engineering Imperial College London SW7 2AZ United Kingdom Robot Cognition Laboratory INSERM U846 Stem Cell and Brain Research Institute Bron69675 France Italian Institute of Technology iCub Facility Via Morego 30 Genova Italy Department of Cybernetics Faculty of Electrical Engineering Czech Technical University in Prague Department of Computer Science University of Sheffield United Kingdom

This paper introduces a cognitive architecture for a humanoid robot to engage in a proactive, mixed-initiative exploration and manipulation of its environment, where the initiative can originate from both the human and the robot. The framework, based on a biologically-grounded theory of the brain and mind, integrates a reactive interaction engine, a number of state-of-the art perceptual and motor learning algorithms, as well as planning abilities and an autobiographical memory. The architecture as a whole drives the robot behavior to solve the symbol grounding problem, acquire language capabilities, execute goal-oriented behavior, and express a verbal narrative of its own experience in the world. We validate our approach in human-robot interaction experiments with the iCub humanoid robot, showing that the proposed cognitive architecture can be applied in real time within a realistic scenario and that it can be used with naive users. Copyright © 2017, The Authors. All rights reserved.

关键词： Human robot interaction

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Automation of Humanitarian Demining: The 2016 Humanitarian robotics and Automation Technology Challenge

Automation of Humanitarian Demining: The 2016 Humanitarian R...

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International Conference on robotics and Automation for Humanitarian Applications

作者： Lino Marques Edson Prestes Sedat Dogru Jose Prado Joao Pedro Macedo Renata Neuland Mathias Mantelli Renan Maffei Raj Madhavan Institute of Systems and Robotics Department of Electrical and Computer Engineering University of Coimbra Informatics Institute Federal University of Rio Grande do Sul Humanitarian Robotics Technologies (HRT) LLC

ISBN: (纸本)9781509052042

This paper introduces the problem of detection of landmines and unexploded ordnances. This is a serious humanitarian problem affecting dozens of countries across the world. A solution using robots for cleaning affected areas would have a huge positive impact for the life of persons living there and has for long been foreseen, but this approach has its own difficulties. The paper presents a Challenge aiming to engage researchers across the world into this problem and helping to improve robotics demining. The different phases of the Humanitarian robotics and Automation Technology Challenge (HRATC) are described and the progresses achieved after three years of running the Challenge are analyzed and discussed.

关键词： robotics and automation Automation Humanitarian demining ANTITANK MINES robotics Robot Developing Countries Persons Demining

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Influence of slot geometry on air gap magnetic flux density of rotating machines

Influence of slot geometry on air gap magnetic flux density ...

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IEEE International Conference on Environment and electrical engineering

作者： Rodrigo Alves de Lima A. C. Paulo Coimbra Tony Almeida Viviane Margarida Gomes Thiago Martins Pereira Aylton José Alves Wesley Pacheco Calixto Program in Electronic Systems and Automation PGEA Department of Electrical Engineering ENE University of Brasilia UnB Institute of Systems and Robotics ISR University of Coimbra UC Experimental & Technological Research and Study Group NExT Federal Institute of Goias IFG

ISBN: (纸本)9781509023219

The objective of this work is to investigate the influence of slotted air gap constructive parameters on magnetic flux density of rotating machines. For this purpose, different approaches were used to solve the air gap field diagram using finite element method and the magnetic field distribution uniformity was evaluated by Carter's factor calculation. Sensitivity analysis of slot constructive parameters was performed and results show that slot geometry modifies the magnetic flux on air gap and shifts the air gap magnetic equipotential midline of double slotted machines.

关键词： Carter's factor finite elements method rotating machines

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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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Optimal search strategies for pollutant source localization

Optimal search strategies for pollutant source localization

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IEEE/RSJ International Conference on Intelligent Robots and systems

作者： Behzad Bayat Naveena Crasta Howard Li Auke Ijspeert BioRobotics Laboratory Ecole Polytechnique Federale de Lausanne (EPFL) Switzerland Institute for Systems and Robotics (ISR) Instituto Superior Tecnico (IST) University of Lisbon Portugal COBRA Department of Electrical and Computer Engineering University of New Brunswick Fredericton E3B 5A3 Canada

ISBN: (纸本)9781509037636

This paper is aimed at developing optimal motion planning for a single autonomous surface vehicle (ASV) equipped with an on-board pollutant sensor that will maximize the sensor-related information available for source seeking. The ASV uses a nonlinear diffusion model of the pollutant source to estimate the intensity/level of the pollution at the present ASV location. The rate of detection of particles depends on the relative distance between the ASV and the source. First, we use a probabilistic map of the source location built through the sensor information for a dynamic motion planning of source seeking based on an entropy reduction formulation, where an appropriately defined Fisher information matrix (FIM) is used for entropy reduction or information gain. We derive the FIM for the set-up and investigate optimal trajectories. Next, we present an online nonlinear Monte Carlo algorithm that uses the obtained sensor information about pollutant at different vehicle locations to update a probabilistic uncertainty map of pollutant source location. As the mission unfolds the ASV motion is computed by considering a moving-horizon interval of decision, which will allow for the inclusion of new information available for optimal motion planning. The proposed motion planning approach is extended to take into account external disturbances and it is able to minimize the uncertainty in the pollutant source. Finally, we provide two case studies to demonstrate efficacy of the proposed motion planning algorithm.

关键词： Position measurement Pollution measurement Uncertainty Planning Probabilistic logic Robot sensing systems Pollution

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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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