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arXiv 2017年

作者： Geraee, Shiva Mohammadbagherpoor, Hamed Shafiei, Mehdi Valizadeh, Majid Montazeri, Farshad Feyzi, Mohammad Reza Faculty of Electrical and Computer Engineering University of Tabriz Faculty of Electrical and Computer Engineering North Carolina State University United States Department of Electrical and Electronic Engineering and Computer Science Queensland University of Technology Department of Electrical and Electronic Engineering University of Ilam Center of Excellence for Power System Automation and Operation Electronic Engineering Iran University of Science and Technology

This paper represents a novel regenerative braking approach for the Electric Vehicles. The proposed method solves the short-range problem which is corresponding to the charge of the battery pack. The DTC switching algorithm has been modified to recover the electrical energy from electrical Vehicle (EV), driven by Brushless DC motor, without using the additional power converter or the other electrical energy storage devices. During regenerative braking process, different switching pattern is applied to the inverter to convert the mechanical energy to the electrical energy through the reverse diodes. This switching pattern is different from the normal operation due to the special arrangement of voltage vectors which is considered to convert the mechanical energy to electrical energy. The state of charge of the battery is used as a performance indicator of the method. Simultaneously, a model reference adaptive system has been designed to tune the system's parameters. Several simulations are carried out to validate the performance and effectiveness of the proposed methods. The results show the high capability and performance of the designed method. Copyright © 2017, The Authors. All rights reserved.

关键词： Brushless DC motors

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Model-driven deep learning for physical layer communications

arXiv

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arXiv 2018年

作者： He, Hengtao Jin, Shi Wen, Chao-Kai Gao, Feifei Li, Geoffrey Ye Xu, Zongben National Mobile Communications Research Laboratory Southeast University Nanjing210096 China Institute of Communications Engineering National Sun Yat-sen University Kaohsiung804 Taiwan State Key Lab of Intelligent Technologies and Systems Tsinghua University Department of Automation Tsinghua University Beijing China School of Electrical and Computer Engineering Georgia Institute of Technology AtlantaGA30332 United States Institute for Information and System Sciences Xian Jiaotong University Xi'an710049 China

Intelligent communication is gradually becoming a mainstream direction. As a major branch of machine learning, deep learning (DL) has been applied in physical layer communications and demonstrated an impressive performance improvement in recent years. However, most of the existing works related to DL focus on data-driven approaches, which consider the communication system as a black box and train it by using a huge volume of data. Training a network requires sufficient computing resources and extensive time, both of which are rarely found in communication devices. By contrast, modeldriven DL approaches combine communication domain knowledge with DL to reduce the demand for computing resources and training time. This article discusses the recent advancements in model-driven DL approaches in physical layer communications, including transmission schemes, receiver design, and channel information recovery. Several open issues for future research are also highlighted. Copyright © 2018, The Authors. All rights reserved.

关键词： Deep learning

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Repeated Strike Process During Disconnector Operation in Ultra-High Voltage Gas-Insulated Switchgear

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Plasma Science and Technology 2016年第3期18卷 247-253页

作者：关永刚蔡元纪陈维江刘卫东李志兵岳功昌张俊民 State Key Lab of Control and Simulation of Power System and Generation Equipments Department of Electrical EngineeringTsinghua UniversityBeijing 100084China State Grid Corporation of China Beijing 100031China China Electric Power Research Institute Beijing 100192China School of Automation Science and Electrical Engineering Beihang UniversityBeijing 100191China

Very fast transient over-voltage （VFTO）, induced by disconnector operations in gas- insulated switchgears, has become the limiting dielectric stress at ultra-high voltage levels. Much work has been done to investigate single-strike waveforms of VFTO. However, little study has been carried out investigating the repeated strike process, which would influence VFTO significantly. In this paper, we carried out 450 effective experiments in an ultra-high voltage test circuit, and conducted calculations through the Monte Carlo simulation method, to investigate the repeated strike process. Firstly, the mechanism of the repeated strike process is proposed, based on the ex- perimentai results. Afterwards, statistical breakdown characteristics of disconnectors are obtained and analyzed. Finally, simulations of the repeated strike process are conducted, which indicate that the dielectric strength recovery speed and polarity effect factor have a joint effect on VFTO. This study enhances the understanding of the nature of VFTO, and may help to optimize the disconnector designed to minimize VFTO.

关键词： disconnector （DS） repeated strike process discharge statistical breakdowncharacteristics ultra-high voltage （UHV） very fast transient over-voltage （VFTO）

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Repeated Strike Process During Disconnector Operation in Ultra-High Voltage Gas-Insulated Switchgear

Repeated Strike Process During Disconnector Operation in Ult...

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作者： Guan, Yonggang Cai, Yuanji Chen, Weijiang Liu, Weidong Li, Zhibing Yue, Gongchang Zhang, Junmin State Key Lab of Control and Simulation of Power System and Generation Equipments Department of Electrical Engineering Tsinghua University Beijing100084 China State Grid Corporation of China Beijing100031 China China Electric Power Research Institute Beijing100192 China School of Automation Science and Electrical Engineering Beihang University Beijing100191 China

Very fast transient over-voltage (VFTO), induced by disconnector operations in gas-insulated switchgears, has become the limiting dielectric stress at ultra-high voltage levels. Much work has been done to investigate single-strike waveforms of VFTO. However, little study has been carried out investigating the repeated strike process, which would influence VFTO significantly. In this paper, we carried out 450 effective experiments in an ultra-high voltage test circuit, and conducted calculations through the Monte Carlo simulation method, to investigate the repeated strike process. Firstly, the mechanism of the repeated strike process is proposed, based on the experimental results. Afterwards, statistical breakdown characteristics of disconnectors are obtained and analyzed. Finally, simulations of the repeated strike process are conducted, which indicate that the dielectric strength recovery speed and polarity effect factor have a joint effect on VFTO. This study enhances the understanding of the nature of VFTO, and may help to optimize the disconnector designed to minimize VFTO.

关键词： Monte Carlo methods

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A Practical Compound Controller Design for Solid Oxide Fuel Cells

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IFAC-PapersOnLine 2015年第30期48卷 445-449页

作者： Sun, Li Li, Donghai Wu, Guiying Lee, Kwang Y. Xue, Yali State Key Lab of Power System Department of Thermal Engineering Tsinghua University Beijing China Department of Electrical and Computer Engineering Baylor University WacoTX76798 United States

Solid oxide fuel cell (SOFC) has emerged as a promising technology in the family of clean electricity generation methods. However, it is challenging to control the output voltage at the desired value due to the strong nonlinearity, slow dynamics and actuator saturation. The biggest difficulty comes from the constraint on the fuel utilization in a transient response. This paper is dedicated to develop an engineering friendly control structure to fulfill the comprehensive objectives. A basic PI feedback controller is utilized to accommodate the nonlinearity. The dynamic response is accelerated via a feedforward action. A compensation block is designed to satisfy the constraint requirement. The simulation shows that the resulting performance is similar to that of the latest model predictive control (MPC) design, demonstrating that the various difficulties can be effectively handled under the framework of conventional control. © 2015, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved.

关键词： Solid oxide fuel cells (SOFC)

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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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Smart Grid Data Mining and Visualization

Smart Grid Data Mining and Visualization

引用

2016 IEEE International Conference on Progress in Informatics and Computing

作者： Yingyao Zhou Ping Li Yuning Xiao Anum Masood Qichen Yu Bin Sheng Information department China Southern Power Grid Department of Mathematics and Information Technology The Education University of Hong Kong Department of Computer Science and Engineering Shanghai Jiao Tong University State Key Lab of Computer Science Institute of Software Chinese Academy of Sciences Department of Computer Science COMSATS Institute of Information Technology Department of Electrical Engineering and Automation Shanghai Jiao Tong University

The power industry innovation has increasingly become a top concern for current reforms. power systems feature scattered data storage, incapable data analysis ability, poor computing capability, and ineffective interaction interface. To resolve these issues, we need multiple data mining techniques to extract information for analytical capacity improvement. Secondly, we need visualization techniques to analyze and optimize interaction. Lastly, we need distributed technologies for unified data management to increase computing capability and system scalability. Considering China's smart grid information, this paper proposes solutions to problems, such as the existing underdeveloped power management systems, a lack of automation methods, low data visualization, and poor data management. The electric power industry has functional requirements for this research. Based on existing data mining, visualization and understanding of distributed technologies, we discussed the functions of each part of the implementation in a smart grid management system: the data mining module, visualization module and data management module.

关键词： smart grid data mining visualization geographic information systems distributed computing

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Smart meter data aggregation against wireless attacks: A game-theoretic approach

Smart meter data aggregation against wireless attacks: A gam...

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IEEE International Conference on Communications (ICC)

作者： Yang Cao Dongliang Duan Liuqing Yang Zhi Sun Haochuan Zhang Rong Yu State Key Lab. of Networking & Switching Technol Beijing University of Posts & Telecommunications China Guangdong Electric Power Design Institute (GEDI) Guangzhou China Department of Electrical and Computer Engineering University of Wyoming Laramie WY USA Department of Electrical and Computer Engineering Colorado State University CO USA Department of Electrical Engineering State University of New York Buffalo NY USA School of Automation Guangdong University of Technology Guangzhou China

The efficient design of evolving smart grid faces serious challenges of a variety of malicious attacks. In this paper, the complex decision making processes between a network of electricity users that perform wireless meter data aggregation via other users and multiple sophisticated wireless attackers that are able to act as eavesdroppers and as jammers are investigated. We model the interactions among users and attackers as a hybrid network formation-nonzero sum game. On the one hand, each user seeks to choose the next-hop user that can minimize the electricity use cost which reflects the security and reliability of its meter data transmission. On the other hand, the objective of the attackers is to choose whether to eavesdrop, jam, or use a combination of both strategies, in a way to increase the total network costs. To solve this game, we devised an algorithm based on fictitious play to reach a mixed-strategy Nash equilibrium. Simulation results suggested that the proposed meter data aggregation scheme enables the electricity users to significantly decrease their expected costs as well as adapt to sophisticated wireless attacks in the smart grid.

关键词： Wireless communication Communication system security Reliability Security Smart grids Games

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Smart grid data mining and visualization

Smart grid data mining and visualization

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IEEE International Conference on Progress in Informatics and Computing (PIC)

作者： Yingyao Zhou Ping Li Yuning Xiao Anum Masood Qichen Yu Bin Sheng Information department China Southern Power Grid Guangzhou China Department of Mathematics and Information Technology Education University of Hong Kong Hong Kong China Department of Computer Science and Engineering Shanghai Jiao Tong University Shanghai China State Key Lab of Computer Science Chinese Academy of Sciences Beijing China Department of Computer Science COMSATS Institute of Information Technology Wah Cantt Pakistan Department of Electrical Engineering and Automation Shanghai Jiao Tong University Shanghai China

ISBN: (纸本)9781509034857

关键词： Data visualization Data mining Smart grids Steady-state Interpolation Distributed databases Euclidean distance

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The 5th International Conference on Biomedical engineering and Biotechnology (ICBEB 2016) Abstracts

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BMC MEDICAL IMAGING 2016年第SUPPL 1期16卷 65-65页

作者： [Anonymous] Department of MRI Shandong Medical Imaging Research Institute Affiliated to Shandong University Jinan Shandong 250021 People’s Republic of China Department of Interventional Radiology Shandong Provincial Hospital Affiliated to Shandong University Jinan Shandong 250021 People’s Republic of China College of Information Science and Technology Engr. Research Center of Digitized Textile & Fashion Tech. for Ministry of Education Donghua University Shanghai 201620 China Intelligent multimedia information processing Lab College of Software Northeastern University Shenyang Liaoning Province 110004 China Institute of Biomedical and Health Engineering Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen 518055 China School of Computer Science and Technology Nanjing Normal University Nanjing China Department of Electrical Engineering The City College of New York CUNY New York USA Jiangsu Key Laboratory of 3D Printing Equipment and Manufacturing Nanjing China School of Electronic Science and Engineering Nanjing University Nanjing Jiangsu 210046 China College of Engineering Nanyang Technological University Singapore 639798 Singapore School of Electronic Information Shanghai Dianji University Shanghai China School of Natural Sciences and Mathematics Shepherd University Shepherdstown WV 25443 USA Davis College of Agriculture Natural Resources and Design West Virginia University Morgantown WV 26505 USA State Key Laboratory of Millimeter Waves Southeast University Nanjing 210096 China Center of Medical Physics and Technology Hefei Institutes of Physical Science Chinese Academy of Sciences Hefei China College of Agricultural and Life Sciences University of Florida Gainesville FL 32611 USA Courant Institute of Mathematical Sciences New York University New York NY 10012 USA Translational Imaging Division & MRI Unit Columbia University and New York State Psychiatric Institute New York NY 10032 USA Guangxi Key Laboratory of Manufacturing System & Adv

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