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IEEE Power and Energy Society General Meeting

作者： Huakun Liu Xiaorong Xie Jingbo He Hui Liu Yunhong Li State Key Labor. of Power System Department of Electrical Engineering Tsinghua University National Power Dispatching and Control Center North China Electric Power Research Institute

ISBN: (纸本)9781509041695

Subsynchronous resonance (SSR) issue, namely subsynchronous control interaction (SSCI), was observed in doubly-fed induction generators (DFIGs) based wind farms connected to fixed series compensation. This paper proposes a novel scheme to deactivate SSR by adding subsynchronous suppression filters (SSFs) to DFIG converter controllers. Based on the impedance model analysis, a quantitative index is defined and implemented to identify the "best" location of converter controllers for embedding a SSF. A specific SSF scheme is recommended for practical application, which is to embed a SSF into d-axis channel in inner current loop of rotor-side converter. This scheme has been applied to a practical series-compensated DFIG-based wind farm system located in North China, which has experienced real SSR events. Both impedance analysis and time-domain simulation have validated the effectiveness of the proposed scheme. This control scheme has a broad application prospect due to its easiness to implement and robustness to changeable operating conditions.

关键词： Doubly-fed induction generator (DFIG) Fixed series compensation (FSC) Subsynchronous resonance (SSR) Subsynchronous control interaction (SSCI) Subsynchronous suppression filter PPAN gene semiconductor relays Converters series compensation controllers Current rings impedance analysis Wind farms

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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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Attitude synchronization of multiple rigid bodies in SE(3) over proximity networks

Attitude synchronization of multiple rigid bodies in SE(3) o...

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第35届中国控制会议

作者： Juan Deng Zhixin Liu Lin Wang John S.Baras Key Laboratory of Systems and Control Academy of Mathematics and Systems ScienceCAS Department of Automation Shanghai Jiao Tong Universityand the Key Laboratory of System Control and Information ProcessingMinistry of Education Department of Electrical and Computer Engineering and the Institute for Systems Research University of MarylandCollege Park

ISBN: (纸本)9781509009107

This paper investigates the attitude synchronization of a group of moving rigid bodies whose states including positions and rotation matrices evolve in the Lie group SE(3).The commonly used circular neighborhood is adopted,and the neighbor graphs are described by proximity *** order to avoid introducing discussions on the abrupt changes of neighbor graphs,a dwell time is *** distributed sampled-data control law for the angular velocity is *** the assumptions on the initial states,we provide sufficient conditions for the attitude synchronization of multiple rigid body *** with the existing results,our condition depends only on the neighborhood radius,the moving velocity and the dwell time.

关键词： rigid body SE(3) attitude synchronization sampled-data control proximity network

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Improving a two-equation eddy-viscosity turbulence model to predict the aerodynamic performance of thick wind turbine airfoils

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Journal of Physics: Conference Series 2018年第1期974卷

作者： Galih Bangga Tri Kusumadewi Go Hutomo Ahmad Sabila Taurista Syawitri Herlambang Setiadi Muhamad Faisal Raditya Wiranegara Yongki Hendranata Dwi Lastomo Louis Putra Stefanus Kristiadi Institute of Aerodynamics and Gas Dynamics University of Stuttgart Stuttgart Germany Mechanical Engineering Department Institut Teknologi Sepuluh Nopember Surabaya Indonesia School of Manufacturing System and Mechanical Engineering Sirindhorn International Institute of Technology Thammasat University Thailand Toyota SO Department Astra International Ltd. Indonesia Civil Engineering Department Universitas Brawijaya Malang Indonesia Mechanical Engineering Department Universitas Muhammadiyah Surakarta Surakarta Indonesia School of Information Technology & Electrical Engineering The University of Queensland Brisbane Australia Non-Installation Top Range Department Berca Schindler Lifts Ltd. Indonesia Graduate Institute of Automation and Control National Taiwan University of Science and Technology Taiwan School of Aerospace Transport and Manufacturing Cranfield University United Kingdom Mechanical Engineering Department Texas A&M University College Station United States PUI-PT Mechatronics and Industrial Automation Research Center Intitut Teknologi Sepuluh Nopember Surabaya Indonesia Mechanical Engineering Department Politecnico di Milano Milan Italy Aeronautics Department Imperial College of Science Technology and Medicine London United Kingdom

Numerical simulations for relatively thick airfoils are carried out in the present studies. An attempt to improve the accuracy of the numerical predictions is done by adjusting the turbulent viscosity of the eddy-viscosity Menter Shear-Stress-Transport (SST) model. The modification involves the addition of a damping factor on the wall-bounded flows incorporating the ratio of the turbulent kinetic energy to its specific dissipation rate for separation detection. The results are compared with available experimental data and CFD simulations using the original Menter SST model. The present model improves the lift polar prediction even though the stall angle is still overestimated. The improvement is caused by the better prediction of separated flow under a strong adverse pressure gradient. The results show that the Reynolds stresses are damped near the wall causing variation of the logarithmic velocity profiles.

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State estimation with quantized innovations in wireless sensor networks: Gaussian mixture estimator and posterior Cramér–Rao lower bound

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Chinese Journal of Aeronautics 2015年第6期28卷 1735-1746页

作者： Zhang Zhi Li Jianxun Liu Liu Liu Zhaolei Han Shan Department of Automation Shanghai Jiao Tong University Key Laboratory of System Control and Information Processing Ministry of Education of China Nanjing Research Institute of Electronics Technology Engineering Research Center of Wideband Wireless Communication Technology Ministry of EducationNanjing University of Posts and Telecommunications

Since the features of low energy consumption and limited power supply are very impor- tant for wireless sensor networks （WSNs）, the problems of distributed state estimation with quan- tized innovations are investigated in this paper. In the first place, the assumptions of prior and posterior probability density function （PDF） with quantized innovations in the previous papers are analyzed. After that, an innovative Gaussian mixture estimator is proposed. On this basis, this paper presents a Gaussian mixture state estimation algorithm based on quantized innovations for WSNs. In order to evaluate and compare the performance of this kind of state estimation algo- rithms for WSNs, the posterior Cram6r-Rao lower bound （CRLB） with quantized innovations is put forward. Performance analysis and simulations show that the proposed Gaussian mixture state estimation algorithm is efficient than the others under the same number of quantization levels and the performance of these algorithms can be benchmarked by the theoretical lower bound.

关键词： Posterior Cramer-Rao lower bounds Quantiation State estimation Target tracking Wireless sensor network

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A robust terrain-based road vehicle localization algorithm

A robust terrain-based road vehicle localization algorithm

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IEEE Symposium on Intelligent Vehicle

作者： Tianyi Li Ming Yang Xujin Zhou Chunxiang Wang Department of Automation Key Laboratory of System Control and Information Processing Shanghai China Department of Instrument Science and Engineering Shanghai Jiao Tong University Shanghai China Research Institute of Robotics Shanghai Jiao Tong University Shanghai China

ISBN: (纸本)9781509018222

Terrain-based localization is an alternate to the global positioning system (GPS) in signal blocked areas. However, terrain-based localization technique may suffer from low accuracy or even fail when brake vibration occurs. This paper presents a real-time algorithm for vehicle localization which is robust against brake vibration. The input includes a reference map of pitch difference and measurements from rear wheel encoders and inertial measurement units (IMU). This method consists of two steps. In the first step, terrain map is generated using pitch difference at equidistant intervals. After that, the Bayesian inference and particle filters are adopted in the second step to identify the vehicle location during travel. To enhance system stability, we propose dynamic distributions of filter variances according to acceleration input. Experimental results demonstrate that the localization method with dynamic distributions can localize the vehicle quickly with high accuracy even when a quite severe shuddering happens.

关键词： vehicles Roads Brakes Acceleration Heuristic algorithms Vibrations Standards

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Towards multi-functional light-weight long-term real-time coastal ocean observation system 11

Towards multi-functional light-weight long-term real-time co...

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11th ACM International Conference on Underwater Networks and systems, WUWNet 2016

作者： Jiang, Zhenfeng Ma, Yanming Chen, Jiali Wang, Zigeng Peng, Zheng Liu, Jun Han, Guitao Smart Ocean Industry Research Institute Nantong Jiangsu226000 China Computer Science and Engineering Department University of Connecticut StorrsCT06269 United States Computer Science Deparment City College of New York NY10031 United States Computer Science and Technology Department Jilin University Changchun Jilin130012 China State Key Laboratory of Robotics Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai200240 China

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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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A Two-Step Fault Diagnosis Framework for Rolling Element Bearings with Imbalanced Data

A Two-Step Fault Diagnosis Framework for Rolling Element Bea...

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2016 13th International Conference on Ubiquitous Robots and Ambient Intelligence (URAI)

作者： Yuan Lan Weiwei Zong Xiaojian Ding Xiaoyan Xiong Xiaohang Han Jiahai Huang Bing Ma Key Laboratory of Ministry of Education in Advance Transducers and Intelligent Control System Institute of Mechatronics Engineering under School of Mechanical EngineeringTaiyuan University of Technology Department of Computer Science Wayne State University Science and Technology on Information Systems Engineering Laboratory The Second Research Institute of China Electronic Technology Group Corporation

Rolling element bearings constitute the key parts on rotating machinery and their fault diagnosis are of great importance. In this paper, a novel Two-Step fault diagnosis framework is proposed to diagnose the status of rolling element bearings with imbalanced data. The Wavelet Packet Transform(WPT) is used to determine the feature vectors. 16-dimensional wavelet packet node energies were extracted from the original datasets as the feature vectors prepared to input to the ***, our proposed framework consists of two steps for the fault diagnosis, where Step One makes use of Weighted Extreme Learning Machine(weighted ELM) in an effort to classify the normal or abnormal categories, and Step Two further diagnoses the underlying anomaly in details. The effectiveness of our proposed approach is testified on the raw data collected from the rolling element bearing experiments conducted in our institute,and the empirical results showed that our approach is really fast and can achieve the diagnosis accuracies more than 95%.

关键词： Two-Step fault diagnosis framework Weighed ELM ELM Bearing fault diagnosis

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A Two-Step Fault Diagnosis Framework for Rolling Element Bearings with Imbalanced Data

A Two-Step Fault Diagnosis Framework for Rolling Element Bea...

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International Conference on Ubiquitous Robots and Ambient Intelligence

作者： Yuan Lan Weiwei Zong Xiaojian Ding Xiaoyan Xiong Xiaohang Han Jiahai Huang Bing Ma Key Laboratory of Ministry of Education in Advance Transducers and Intelligent Control System Institute of Mechatronics Engineering under School of Mechanical Engineering Taiyuan University of Technology Department of Computer Science Wayne State University Science and Technology on Information Systems Engineering Laboratory The Second Research Institute of China Electronic Technology Group Corporation

ISBN: (纸本)9781509008223

Rolling element bearings constitute the key parts on rotating machinery and their fault diagnosis are of great importance. In this paper, a novel Two-Step fault diagnosis framework is proposed to diagnose the status of rolling element bearings with imbalanced data. The Wavelet Packet Transform (WPT) is used to determine the feature vectors. 16-dimensional wavelet packet node energies were extracted from the original datasets as the feature vectors prepared to input to the classifiers. Next, our proposed framework consists of two steps for the fault diagnosis, where Step One makes use of Weighted Extreme Learning Machine (weighted ELM) in an effort to classify the normal or abnormal categories, and Step Two further diagnoses the underlying anomaly in details. The effectiveness of our proposed approach is testified on the raw data collected from the rolling element bearing experiments conducted in our institute, and the empirical results showed that our approach is really fast and can achieve the diagnosis accuracies more than 95%.

关键词： Two-Step fault diagnosis framework Weighed ELM ELM Bearing fault diagnosis

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