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10th Annual Cyber and Information Security Research Conference, CISRC 2015

作者： Zhong, Xingsi Arunagirinathan, Paranietharan Ahmadi, Afshin Brooks, Richard Venayagamoorthy, Ganesh Kumar Real-Time Power and Intelligent Systems Laboratory Holcombe Department of Electrical and Computer Engineering Clemson University ClemsonSC29634 United States

ISBN: (纸本)9781450333450

The deployment of synchrophasor devices such as Phasor Measurement Units (PMUs) in an electric power grid enhances real-time monitoring, analysis and control of grid operations. PMU information is sensitive, and any missing or incorrect PMU data could lead to grid failure and/or damage. Therefore, it is important to use encrypted communication channels to avoid any cyber attack. However, encrypted communication channels are vulnerable to side-channel attacks. In this study, side-channel attacks using packet sizes and/or inter-packet timing delays differentiate the stream of packets from any given PMU within an encrypted tunnel. This is investigated under different experimental settings. Also, virtual private network vulnerabilities due to side-channel analysis are discussed. © 2015 ACM.

关键词： Phasor measurement units

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An analysis of sampling effect on bilateral teleoperation system transparency 34

An analysis of sampling effect on bilateral teleoperation sy...

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34th Chinese Control Conference, CCC 2015

作者： Ting, Yang Fu, Yi Li Tavakoi, Mahdi State Key Laboratory of Robotics and Systems Harbin Institute of Technology Harbin Heilongjiang China Department of Electrical and Computer Engineering University of Alberta EdmontonAB Canada

ISBN: (纸本)9789881563897

The performances of continuous-time controlled and discrete-time controlled bilateral teleoperation systems are mathematically and experimentally studied and compared. The results show that under stability conditions, continuous-time controlled teleoperation systems have hybrid matrix parameters that are closer to their ideal values than their discrete-time controlled counterparts. This means better force tracking and position tracking performance under continuous-time control, which can lead to better task performance. © 2015 Technical Committee on Control Theory, Chinese Association of Automation.

关键词： Bilateral teleoperation continuous-time control discrete-time control hybrid parameters system performance

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Design issues for real-time remote robotic science operations support tools: Observations from the field 14th

Design issues for real-time remote robotic science operation...

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14th International Conference on Space Operations, SpaceOps 2016

作者： Kim, Hyunjung Park, Young-Woo Baker, Electa A. Adams, Julie A. Fong, Terrence W. Electronics and Telecommunications Research Institute SWContent Laboratory 218 Gajeong-ro Yuseong-guDaejeon34129 Korea Republic of Ulsan National Institute of Science and Technology Dept. of Industrial Design 50 UNIST-gil 901-10 Engineering Bldg 1. Eonyang-eup UlsanUljugun44919 Korea Republic of Vanderbilt University Dept. of Electrical Engineering and Computer Science VU Station B #351824 NashvilleTN37235 United States NASA Ames Research Center Intelligent Robotics Group MS 269-3 Moffett FieldCA94035 United States

ISBN: (数字)9781624104268

ISBN: (纸本)9781624104268

Real-time remote robotic science operations will be a key part of future planetary exploration missions because the use of robots can reduce operational costs and increase scientific returns. To better understand real-time remote robotic science operations, and to provide practical guidance for improving the design of operations support tools, we conducted in-field observations of a team of 14 scientists remotely operating a planetary rover during a five-day prospecting mission. On the basis of our results, we identify the characteristics of real-time remote robotic science operations. In addition, we discuss the identified challenges, opportunities, and guidelines associated with improving the design of support tools. We expect that our findings will inform future research on robotic operations support tools and future study of additional use cases, including numerous terrestrial field science deployments and a planned 2020 NASA lunar rover mission. © 2016 American Institute of Aeronautics and Astronautics Inc, AIAA. All rights reserved.

关键词： NASA

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Rewinding Strategy Aided by FEA as a Solution to Increase Efficiency of Industrial Motors

Rewinding Strategy Aided by FEA as a Solution to Increase Ef...

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International Conference on electrical Machines

作者： V. P. B. Aguiar R. S. T. Pontes T. R. Fernandes Neto F. J. T. E. Ferreira Motor Driven Systems Laboratory Department of Electrical Engineering Federal University of Ceará Fortaleza CE 60440554 Brazil Institute of Systems and Robotics Department of Electrical and Computer Engineering University of Coimbra Coimbra Portugal

ISBN: (纸本)9781509025398

This paper presents a proper model and methodology to improve the efficiency class of a squirrel-cage induction motor during rewinding. The modeling approach is based on the finite element model, i.e. two-dimensional model coupled with the rotor equivalent circuit (first stage) and the stator equivalent circuit (second stage). Results obtained with finite element analysis are compared with those obtained with analytical calculations. It is demonstrated that, during rewinding, if an improved winding configuration is used, efficiency gains can be obtained, allowing to improve the efficiency class of the motor, from IE2 to IE3, respecting the specified limits of NEMA & IEC performance indicators, namely, breakdown torque, locked-rotor torque, and locked-rotor current. In 79% of the simulated cases the efficiency gain allowed the studied motor to reach the IE3 class.

关键词： Finite element analysis Equivalent circuits IEEE standards IEC standards ANSI standards Stator Induction motors Electromagnetic modeling Frequency-domain analysis

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Author Correction: Why rankings of biomedical image analysis competitions should be interpreted with care

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Nature communications 2019年第1期10卷 588页

作者： Lena Maier-Hein Matthias Eisenmann Annika Reinke Sinan Onogur Marko Stankovic Patrick Scholz Tal Arbel Hrvoje Bogunovic Andrew P Bradley Aaron Carass Carolin Feldmann Alejandro F Frangi Peter M Full Bram van Ginneken Allan Hanbury Katrin Honauer Michal Kozubek Bennett A Landman Keno März Oskar Maier Klaus Maier-Hein Bjoern H Menze Henning Müller Peter F Neher Wiro Niessen Nasir Rajpoot Gregory C Sharp Korsuk Sirinukunwattana Stefanie Speidel Christian Stock Danail Stoyanov Abdel Aziz Taha Fons van der Sommen Ching-Wei Wang Marc-André Weber Guoyan Zheng Pierre Jannin Annette Kopp-Schneider Division of Computer Assisted Medical Interventions (CAMI) German Cancer Research Center (DKFZ) 69120 Heidelberg Germany. l.maier-hein@dkfz.de. Division of Computer Assisted Medical Interventions (CAMI) German Cancer Research Center (DKFZ) 69120 Heidelberg Germany. Centre for Intelligent Machines McGill University Montreal QC H3A0G4 Canada. Christian Doppler Laboratory for Ophthalmic Image Analysis Department of Ophthalmology Medical University Vienna 1090 Vienna Austria. Science and Engineering Faculty Queensland University of Technology Brisbane QLD 4001 Australia. Department of Electrical and Computer Engineering Department of Computer Science Johns Hopkins University Baltimore MD 21218 USA. CISTIB - Center for Computational Imaging & Simulation Technologies in Biomedicine The University of Leeds Leeds Yorkshire LS2 9JT UK. Department of Radiology and Nuclear Medicine Medical Image Analysis Radboud University Center 6525 GA Nijmegen The Netherlands. Institute of Information Systems Engineering TU Wien 1040 Vienna Austria. Complexity Science Hub Vienna 1080 Vienna Austria. Heidelberg Collaboratory for Image Processing (HCI Heidelberg University 69120 Heidelberg Germany. Centre for Biomedical Image Analysis Masaryk University 60200 Brno Czech Republic. Electrical Engineering Vanderbilt University Nashville TN 37235-1679 USA. Institute of Medical Informatics Universität zu Lübeck 23562 Lübeck Germany. Division of Medical Image Computing (MIC) German Cancer Research Center (DKFZ) 69120 Heidelberg Germany. Institute for Advanced Studies Department of Informatics Technical University of Munich 80333 Munich Germany. Information System Institute HES-SO Sierre 3960 Switzerland. Departments of Radiology Nuclear Medicine and Medical Informatics Erasmus MC 3015 GD Rotterdam The Netherlands. Department of Computer Science University of Warwick Coventry CV4 7AL UK. Department of Radiation Oncology Massachusetts General Hospital Boston MA

In the original version of this Article the values in the rightmost column of Table 1 were inadvertently shifted relative to the other columns. This has now been corrected in the PDF and HTML versions of the Article.

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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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Combined actuator sensor unit for interaction with honeybees

Combined actuator sensor unit for interaction with honeybees

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IEEE Sensors Applications Symposium, SAS

作者： Karlo Griparic Tomislav Haus Damjan Miklic Stjepan Bogdan Faculty of Electrical Engineering and Computing Laboratory for Robotics and Intelligent Control Systems Zagreb Croatia

Interacting with a specific animal society by integrating autonomous robot/s into the society, has become a powerful method to influence the behaviour of animals and investigate collective behaviour of both, animal and robot societies. In order to interact with animals, artificial unit/s should be well integrated into their society. In the European project ASSISIbf, a network of static autonomous robots called CASUs (Combined Actuator Sensor Units) for interaction with young honeybees has been designed. In the proposed approach CASUs can affect honeybees using three types of physical stimuli: heat, vibration and light. To provide feedback signals necessary for controlling CASU interaction with honeybees, accurate and reliable measurements of the stimuli are necessary. This paper describes the mechanical and electronic design of CASUs, capable of emitting controllable heat, vibration and light stimulations. Each CASU is equipped with temperature sensors, 3-axis accelerometers, infrared proximity sensors and microcontroller for data processing. Preliminary experimental results with honeybee groups are presented.

关键词： Robot sensing systems Vibrations Temperature measurement Temperature sensors Animals Heating

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Robust Decentralized Control of Interconnected Nonlinear systems Subject to Uncertainties and Disturbances via Generalized Extended State Observers 34

Robust Decentralized Control of Interconnected Nonlinear Sys...

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第三十四届中国控制会议

作者： ZHANG Chuanlin YANG ning HE Shuaipeng JIA Ruting Shanghai Engineering Research Center of Intelligent Management and Control for Power Process College of Automation EngineeringShanghai University of Electric Power Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education Southeast University Electrical and Computer Engineering Department California State University

ISBN: (纸本)9789881563897

This paper studies the problem of robust decentralized control for a class of large-scale interconnected systems subject to uncertainties and disturbances via a generalized active disturbance rejection control method. First, a novel extended state observer(ESO) is constructed separately for each subsystem with less dependence of the precise system information and structure. Second, an explicit formula of the decentralized active disturbance rejection control(DADRC) law is presented by utilizing the output feedback domination approach. It is shown that by a delicate analysis procedure, the closed-loop system can be rendered semi-globally asymptotically stable only using decentralized output feedback anti-disturbance controller. Numerical simulations show the efficiency of the proposed method.

关键词： output feedback control global stabilization disturbance rejection control extended state observer

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Side channel analysis of multiple PMU data in electric power systems

Side channel analysis of multiple PMU data in electric power...

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2015 Clemson University Power systems Conference, PSC 2015

作者： Zhong, Xingsi Arunagirinathan, Paranietharan Ahmadi, Afshin Brooks, Richard Venayagamoorthy, Ganesh Kumar Yu, Lu Fu, Yu Real-Time Power and Intelligent Systems Laboratory Clemson University ClemsonSC29634 United States Holcombe Department of Electrical and Computer Engineering Clemson University ClemsonSC29634 United States

ISBN: (纸本)9781479919512

The deployment of Phasor Measurement Units (PMUs) in an electric power grid will enhance real-time monitoring and analysis of grid operations. The PMU collects bus voltage phasors, branch current phasors, and bus frequency measurements and uses a communication network to transmit the measurements to the respective substation(s)/control center(s). PMU information is sensitive, since missing or incorrect PMU data could lead to grid failure and/or damage. It is important to use encrypted communicate channels to avoid cyber attacks. In this study, a side-channel attack using inter-packet delays to isolate the stream of packets of one PMU from an encrypted tunnel is shown. Also, encryption in power system VPNs and vulnerabilities due to side channel analysis is discussed. © 2015 IEEE.

关键词： Phasor measurement units

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