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IEEE International Electric Machines and Drives Conference, IEMDC 2015

作者： Ferreira, Fernando J. T. E. Dept. Electrical and Computer Engineering Institute of Systems and Robotics University of Coimbra Polo II Coimbra3030-290 Portugal

ISBN: (纸本)9781479979417

When a three-phase squirrel-cage induction motor is to be started with an electronic soft-starter, the respective stator winding can be designed for nominal power with delta, star or star-delta connection. If the motor has two sets of windings sharing the same slots, which may be connected in series or parallel, more connection modes are possible. In this paper, on the basis of a theoretical approach, simulations, and some experiments, a comparative analysis of the tolerance to voltage unbalance of the delta, star and star-delta stator winding connections is presented, offering a new perspective on this issue. It is demonstrated that, under unbalanced voltage supply condition, these stator winding connection modes have a different performance in terms of phase voltage and current unbalance ratios. © 2015 IEEE.

关键词： Delta Connection Star Connection Star-Delta Connection Stator Windings Three-Phase Induction Motor Voltage Unbalance

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On the Star, Delta and Star-Delta Stator Winding Connections Tolerance to Voltage Unbalance

On the Star, Delta and Star-Delta Stator Winding Connections...

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IEEE International Electric Machines and Drives Conference

作者： Fernando J. T. E. Ferreira Dept. Electrical and Computer Engineering Institute of Systems and Robotics University of Coimbra

ISBN: (纸本)9781479979424

关键词： Delta Connection Star Connection Star-Delta Connection Stator Windings Three-Phase Induction Motor Voltage Unbalance

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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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Comparison of protection requirements in IE2-, IE3-, and IE4-class motors

Comparison of protection requirements in IE2-, IE3-, and IE4...

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IEEE International Electric Machines and Drives Conference, IEMDC 2015

作者： Ferreira, Fernando J. T. E. Leprettre, Benoit Duarte, Andre Q. De Almeida, Anibal T. Dept. Electrical and Computer Engineering Institute of Systems and Robotics University of Coimbra Polo II Coimbra3030-290 Portugal Schneider Electric Electropole 38EQI 38 Eybens France

ISBN: (纸本)9781479979417

In this paper, an overview of the main motor features influencing the protection devices settings is presented, pointing out key differences between three-phase direct-on-line motors of different efficiency classes. The results of experimental tests and simulations are presented and discussed in the scope of the motor protection against overload, short-circuit, current unbalance and locked-rotor conditions, for 7.5-kW, 4-pole, IE2- and IE3-class squirrel-cage induction motors and for IE4-class line-start permanent-magnet motors, offering important data related to their electrical and thermal behavior. Two commercial digital thermal-magnetic motor protection devices, whose thermal models were in most cases designed for IE1- and IE2-class motors, are evaluated in terms of operation effectiveness in the tested motors. This work is important for industry due to the significant penetration of IE3-class induction motors, as well as to the recent introduction in the market of IE4-class induction motors and line-start permanent magnet motors. © 2015 IEEE.

关键词： Circuit Breakers Induction Motor Induction Motor Inrush Current Line-Start PM Motor Locked Rotor Motor Protection Switchgears Undervoltage Voltage Unbalance

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Active pedal exerciser for leg rehabilitation

Active pedal exerciser for leg rehabilitation

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Bioengineering (ENBENG), Portuguese Meeting in

作者： Filipa Garcia João P. Ferreira Paulo Ferreira Stephane Cruz Manuel Crisóstomo A. Paulo Coimbra Universidade de Coimbra Coimbra Coimbra PT Institute of Systems and Robotics Dept. of Electrical and Computer Engineering Univ. of Coimbra Dept. of Electrical Engineering Superior Institute of Engineering of Coimbra Coimbra Portugal

Summary form only given. Given the importance of the ability to drive the lower limbs to perform most daily activities for all people and knowing that there is a constant need to develop new ways to help people who do not fully make use of this ability, either by external or physical causes, a tool for motion rehabilitation is being developed. In the market there are already some products for leg rehabilitation. Pedal exercisers and static bicycles are the most common. Mostly, they are very rudimentary because its practise can't be controlled by the health staff through software. A number of them have virtual environments for simulation of outdoor exercise. Some products that aren't yet on the market have new and important features, like reacting to the patient's performance to optimize the rehabilitation process, through an adjustable resistant motor [1]. Other can stimulate patient's legs movement because of their motor, and alternate between cycling forward or backward [2]. There is also a prototype of one device that trains lower limbs bilaterally [3]. The rehabilitation device presented here is based on a kind of motor assisted bicycle, which will gradually be triggered, depending on the pressure exerted on the force sensors existing on the pedals, allowing to compensate the leg with mobility problems, helping it to perform the expected cycling movement. There is also a sensor to monitor the patient's heart rate. To obtain the pressure sensor and heart rate values a data acquisition system is used. It is connected to a computer and to the motor controller. With it, it is possible to perform motor control to ensure the efficiency of the treatment and the patient safety. There is also the capability of exercising each leg with different parameters, which represents a very useful advantage for stroke patients, and can also compensate for a missing or impaired limb by mimicking the performance of the healthy leg. A computer interface allows the physiotherapist in c

关键词： Biomedical engineering Legged locomotion computers Bicycles Performance evaluation Prototypes Monitoring

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Applying probabilistic Mixture Models to semantic place classification in mobile robotics

Applying probabilistic Mixture Models to semantic place clas...

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IEEE/RSJ International Conference on Intelligent Robots and systems

作者： C. Premebida D. R. Faria F. A. Souza U. Nunes Department of Electrical and Computer Engineering’ Institute of Systems and Robotics University of Coimbra Portugal Dept. of Electrical Engineering Univ. Federal of Ceara Brazil

In this paper a study is made of the problem of classifying scenarios, in terms of semantic categories, based on data gathered from sensors mounted on-board mobile robots operating indoors. Once the data are transformed to feature space, supervised classification is performed by a probabilistic approach called Dynamic Bayesian Mixture Models (DBMM). This approach combines class-conditional probabilities from supervised learning models and incorporates past inferences. In this work, several experiments on multi-class semantic place classification are reported based on publicly available datasets. Such experiments were conducted in a such way that generalization aspects are emphasized, which is particularly important in real-world applications. Benchmark results show the effectiveness and competitive performance of the DBMM method, in terms of classification rates, using features extracted from 2D range data and from a RGB-D (Kinect) sensor.

关键词： Feature extraction Semantics Robot kinematics Mixture models Sensors Probabilistic logic

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Selection of 5 kW converter leg for power electronic system for residential buildings

Selection of 5 kW converter leg for power electronic system ...

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European Conference on Power Electronics and Applications

作者： Marcin Zygmanowski Jaroslaw Michalak Michal Jelen Grzegorz Jarek Jan Popczyk Faculty of Electrical Engineering Dept. of Power Electronics Electrical Drives and Robotics SILESIAN UNIVERSITY OF TECHNOLOGY ul. B. Krzywoustego 2 Gliwice Poland Faculty of Electrical Engineering Institute of Power Systems and Control SILESIAN UNIVERSITY OF TECHNOLOGY ul. B. Krzywoustego 2 Gliwice Poland

ISBN: (纸本)9781509014101

Three converter leg variants are analyzed for low power converter used for power electronic system for residential buildings. The two-level Si-IGBT and SiC-MOSFET converters are compared with Si-IGBT three-level T-type converter. Power losses generated in each of these converters over a predicted period of 20 years of operation are contrasted with the cost of converter options. The detailed selection procedure for output inductor is presented in this paper. This procedure shows the influence of the inductor parameters like number of turns, air gap length on its losses, cost and size. Theoretical approach is verified with simulations and experimental results.

关键词： Design IGBT Silicon Carbide (SiC) MOSFET Microgrid

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Comparison of Protection Requirements in IE2-, IE3-, and IE4-Class Motors

Comparison of Protection Requirements in IE2-, IE3-, and IE4...

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IEEE International Electric Machines and Drives Conference

作者： Fernando J. T. E. Ferreira Benoit Leprettre Andre Q. Duarte Anibal T. De Almeida Dept. Electrical and Computer Engineering Institute of Systems and Robotics University of Coimbra Schneider Electric

ISBN: (纸本)9781479979424

关键词： Circuit Breakers Inrush Current Induction Motor Line-Start PM Motor Motor Protection Switchgears Undervoltage Voltage Unbalance Locked Rotor

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25th Annual Computational Neuroscience Meeting CNS-2016, Seogwipo City, South Korea, July 2-7, 2016 Abstracts

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BMC NEUROSCIENCE 2016年第1期17卷 1-112页

作者： [Anonymous] Computational Neurobiology Laboratory The Salk Institute for Biological Studies San Diego USA UNIC CNRS Gif sur Yvette France The European Institute for Theoretical Neuroscience (EITN) Paris France ATR Computational Neuroscience Laboratories Kyoto Japan Krembil Research Institute University Health Network Toronto Canada Department of Physiology University of Toronto Toronto Canada Department of Medicine (Neurology) University of Toronto Toronto Canada Department of Physics University of New Hampshire Durham USA Department of Neurophysiology Nencki Institute of Experimental Biology Warsaw Poland Department of Theory Wigner Research Centre for Physics of the Hungarian Academy of Sciences Budapest Hungary Department of Mathematical Sciences KAIST Daejoen Republic of Korea Department of Mathematics University of Houston Houston USA Department of Biochemistry & Cell Biology and Institute of Biosciences and Bioengineering Rice University Houston USA Department of Biology and Biochemistry University of Houston Houston USA Grupo de Neurocomputación Biológica Dpto. de Ingeniería Informática Escuela Politécnica Superior Universidad Autónoma de Madrid Madrid Spain Department of Biological Sciences University of Southern California Los Angeles USA Center for Neuroscience Korea Institute of Science and Technology Seoul South Korea Department of Neurology Albert Einstein College of Medicine Bronx USA Center for Neuroscience KIST Seoul South Korea Department of Neuroscience University of Science and Technology Daejon South Korea Systems Neuroscience Group QIMR Berghofer Medical Research Institute Herston Australia Department of Psychology Yonsei University Seoul South Korea Department of Psychiatry Kyung Hee University Hospital at Gangdong Seoul South Korea Department of Psychiatry Veterans Administration Boston Healthcare System and Harvard Medical School Brockton USA Department of Electrical and Electronic Engineering The University of Melbourne Parkvil

A1 Functional advantages of cell-type heterogeneity in neural circuits Tatyana O. Sharpee A2 Mesoscopic modeling of propagating waves in visual cortex Alain Destexhe A3 Dynamics and biomarkers of mental disorders Mitsuo Kawato F1 Precise recruitment of spiking output at theta frequencies requires dendritic h-channels in multi-compartment models of oriens-lacunosum/moleculare hippocampal interneurons Vladislav Sekulić, Frances K. Skinner F2 Kernel methods in reconstruction of current sources from extracellular potentials for single cells and the whole brains Daniel K. Wójcik, Chaitanya Chintaluri, Dorottya Cserpán, Zoltán Somogyvári F3 The synchronized periods depend on intracellular transcriptional repression mechanisms in circadian clocks. Jae Kyoung Kim, Zachary P. Kilpatrick, Matthew R. Bennett, Kresimir Josić O1 Assessing irregularity and coordination of spiking-bursting rhythms in central pattern generators Irene Elices, David Arroyo, Rafael Levi, Francisco B. Rodriguez, Pablo Varona O2 Regulation of top-down processing by cortically-projecting parvalbumin positive neurons in basal forebrain Eunjin Hwang, Bowon Kim, Hio-Been Han, Tae Kim, James T. McKenna, Ritchie E. Brown, Robert W. McCarley, Jee Hyun Choi O3 Modeling auditory stream segregation, build-up and bistability James Rankin, Pamela Osborn Popp, John Rinzel O4 Strong competition between tonotopic neural ensembles explains pitch-related dynamics of auditory cortex evoked fields Alejandro Tabas, André Rupp, Emili Balaguer-Ballester O5 A simple model of retinal response to multi-electrode stimulation Matias I. Maturana, David B. Grayden, Shaun L. Cloherty, Tatiana Kameneva, Michael R. Ibbotson, Hamish Meffin O6 Noise correlations in V4 area correlate with behavioral performance in visual discrimination task Veronika Koren, Timm Lochmann, Valentin Dragoi, Klaus Obermayer O7 Input-location dependent gain modulation in cerebellar nucleus neurons Maria Psarrou, Maria Schilstra, Neil Davey, Benjamin Torben-Ni

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Speeding Up Rao-Blackwellized Particle Filter SLAM with a Multithreaded Architecture

Speeding Up Rao-Blackwellized Particle Filter SLAM with a Mu...

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IEEE/RSJ International Conference on Intelligent Robots and systems

作者： Bruno D. Gouveia David Portugal Lino Marques Institute of Systems and Robotics Dept. Electrical and Computers Eng. University of Coimbra 3030-290 Coimbra

ISBN: (纸本)9781479969357

In this work we explore multiprocessor computer architectures to propose an effective method for solving the Simultaneous Localization and Mapping Problem. The proposed method makes use of multithreading to parallelize a Rao-Blackwellized Particle Filter approach. By applying the method in common computers found in robots, it is shown that a significant gain in efficiency can be obtained. Furthermore, the parallel method enables us to raise the number of particles up to values that would not be possible in a single threaded solution, thus gaining in localization precision and map accuracy. In order to analyze SLAM results, frequently used datasets by the robotics community were used, and a benchmarking metric was applied.

关键词： Speeding Multithreading Particle filters simultaneous localization and mapping Robot Computer Architecture

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