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

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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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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Comparison of Adaptive Neuro-Fuzzy Inference systems and Echo State Networks for PV Power Prediction

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Procedia computer Science 2015年 53卷 92-102页

作者： I. Jayawardene G.K. Venayagamoorthy Real-Time Power and Intelligent Systems (RTPIS) Laboratory The Holcombe Department of Electrical and Computer Engineering Clemson University SC 29634 USA

Use of photo-voltaic (PV) power as a source of electricity has been rapidly growing. Integration of large PV power into the grid operation introduces several challenges. Uncertainty of PV power generation causes frequency fluctuations and power system instabilities. Due to this, short term PV power prediction has become an important area of study. Short term PV power prediction supports proper decision-making in control centers. Power generation output of a PV plant is highly dependent on different weather conditions such as solar irradiance, temperature and cloud covers. Weather data analysis and prediction can be considered as big data due to its complexity and dynamically changing characteristics. In this paper, an adaptive neuro-fuzzy inference system (ANFIS) is developed and compared with an echo state network (ESN) for short term PV power prediction. The ANFIS approach consists of three ANFIS modules for predicting solar irradiance and temperature, and then estimating the PV power, respectively. The ESN on the other hand predicts the PV power based on current weather parameters. A weather database containing data sampled every second is used in developing the ANFIS and ESN based PV power prediction systems. Results are compared under different Clemson, SC weather conditions with the two approaches.

关键词： adaptive neuro-fuzzy system big data echo state network photo-voltaic system power system short term prediction weather data.

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Stochastic Optimization for Combined Economic and Emission Dispatch with Renewables

Stochastic Optimization for Combined Economic and Emission D...

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IEEE Symposium Series on Computational Intelligence

作者： Mehdi Rahmani-andebili Ganesh K. Venayagamoorthy Real-Time Power and Intelligent Systems Laboratory The Holcombe Department of Electrical and Computer Engineering Clemson University

ISBN: (纸本)9781479975617

Environmental issues of thermal power plants and depletion of natural energy resources are the main motivations for applying renewable energy sources (RESs) in power systems. Therefore, it is important to consider RESs when performing combined economic and emission dispatch (CEED). In this study, the variability and uncertainties concerned with RESs and load demand are addressed with batteries installed in the power system as energy storage systems and stochastic optimization applied to solve the problem. A case study is presented to demonstrate the economic and environmental benefits achieved as a result.

关键词： Renewable energy power systems Dispatching Thermoelectric Power Plants Environmental issues Stochastic Environmental services Natural energy Batteries emission Emissions Energy storage systems energy storage

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Investigating Effects of Changes in Power Market Regulations on Demand-Side Resources Aggregators

Investigating Effects of Changes in Power Market Regulations...

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IEEE Power and Energy Society General Meeting

作者： Mehdi Rahmani-andebili Ganesh K. Venayagamoorthy Real-Time Power and Intelligent Systems Laboratory The Holcombe Department of Electrical and Computer Engineering Clemson University

ISBN: (纸本)9781467380416

A significant role of power market regulator is increasing market efficiency by considering constraints of power system. Thus, a power market regulator implements several regulations to achieve this target. However, the changes in regulations may have undesirable effects on some emerging market players. In this study, effects of changes in spinning reserve market regulations on behavior of demand-side resources aggregators such as responsive loads aggregator and plug-in electric vehicles aggregator are investigated. In this study, a hybrid method based on multi-agents and dynamic game theory considering supply function equilibrium model for bidding strategy is proposed to simulate behavior of the market players from regulator's point of view. The results demonstrate that smart aggregators can convert threats to opportunities and enhance their own profit, their customers' income and even the market efficiency.

关键词： Dynamic game theory Market regulations Multi-agents Plug-in electric vehicles (PEVs) aggregator Responsive loads aggregator power markets Regulators undesirable effects market regulations Players power systems

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Voltage Stability Index for Online Voltage Stability Assessment

Voltage Stability Index for Online Voltage Stability Assessm...

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North American Power Symposium

作者： Rabindra Maharjan Sukumar Kamalasadan Power Energy and Intelligent Systems Laboratory Department of Electrical and Computer Engineering University of North Carolina-Charlotte

ISBN: (纸本)9781467373906

This paper proposes a voltage stability index to predict voltage collapse in power system. The index is based on maximum loading capability of the bus combined with a thevenin equivalent method for the aggregated representation. The technique is applied to transmission test system. Using thevenin equivalent calculated by fast load flow or PMU data, the index can be used for online voltage analysis. Comparison of the index with techniques used in previous literatures are presented. New England 39 bus and IEEE 118 bus test system are used to analyse the effectiveness of the method.

关键词： Online voltage stability Maximum loading capability Voltage stability index Thevenin equivalent

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An Architecture for Voltage Stability Constrained Optimal Power Flow Using Convex Semi-definite Programming

An Architecture for Voltage Stability Constrained Optimal Po...

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North American Power Symposium

作者： Seyedmahdi Moghadasi Sukumar Kamalasadan Power Energy and Intelligent Systems Laboratory Department of Electrical and Computer Engineering University of North Carolina at Charlotte

ISBN: (纸本)9781467373906

In this paper, a new voltage stability constrained optimal power flow (VSC-OPF) architecture is developed using semi-definite programming (SDP) which optimally calculate the loading margin and the cost of increasing stability margins. The main advantages of the proposed method are: a) it incorporates voltage stability constraints in a convex OPF formulation, b) always finds the optimal solution in comparison with probable local optimum solutions while using other non-convex methods, and c) evaluates the cost of increasing the loading margin using convex relaxation. The effectiveness of this method is evaluated using IEEE 14-bus and IEEE 57-bus test systems, considering various network constraints. The results show the effectiveness of proposed convex formulation to find the optimal operating schedule and critical loading point. The effect of incorporating voltage stability margin on the operating cost is also examined using different convex VSC-OPF formulations.

关键词： OPF Convex Relaxation Voltage Stability Constrained Optimal Power Flow (VSC-OPF) Loading Margin Semidefinite Programming (SDP)

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Hierarchical CNN for traffic sign recognition

Hierarchical CNN for traffic sign recognition

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IEEE Symposium on intelligent Vehicle

作者： Xuehong Mao Samer Hijazi Raúl Casas Piyush Kaul Rishi Kumar Chris Rowen Faculty of Electrical and Computer Engineering United States Naval Academy Annapolis MD USA School of Electronic Engineering Xidian University Xi’an China School of Computer and Information Science Hubei Engineering University Xiaogan China Centre for Quantum Computation & Intelligent Systems and the Faculty of Engineering and Information Technology University of Technology Sydney 81 Broadway Street Ultimo NSW Australia Center for Optical Imagery Analysis and Learning (OPTIMAL) State Key Laboratory of Transient Optics and Photonics Xi’an Institute of Optics and Precision Mechanics Chinese Academy of Sciences Xi’an Shaanxi China

ISBN: (纸本)9781509018222

The Convolutional Neural Network (CNN) is a breakthrough technique in object classification and pattern recognition. It has enabled computers to achieve performance superior to humans in specialized image recognition tasks. Prior art CNNs learn object features by stacking multiple convolutional/non-linear layers in sequence on top of a classifier. In this work, we propose a Hierarchical CNN (HCNN) which is inspired by a coarse-to-fine human learning methodology. For a given dataset, we introduce a CNN-oriented clustering algorithm to separate classes into K subsets, which are referred to as families. Then, the HCNN algorithm trains K+1 classification CNNs: one CNN for family classification and K dedicated CNNs corresponding to each family for member classification. We evaluate this HCNN approach on the German Traffic Sign Recognition Benchmark (GTSRB), and achieve 99.67% correct detection rate (CDR), which is superior to the best reported results (99.46%) achieved by a single network.

关键词： Clustering algorithms Training Classification algorithms Measurement Partitioning algorithms Standards Neural networks

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Scalable cellular computational network based WLS state estimator for power systems

Scalable cellular computational network based WLS state esti...

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Clemson University Power systems Conference (PSC)

作者： Ashfaqur Rahman Ganesh Kumar Venayagamoorthy Department of Electrical and Computer Engineering Real Time Power and Intelligent Systems Laboratory Clemson University Clemson SC USA

Modern interconnected electric power systems are made up of a large number of buses to meet the demand of electricity across large geographical distances. The large number of buses and interconnections across multiple areas result in a complex network and cause an increase in computational requirements on the processor. In order to meet the requirements of this increased complexity for state estimation, distributed estimation is getting attention nowadays. A new approach based on Cellular Computational Network (CCN) for static state estimation is proposed to overcome the computational demand of large power networks in general. The CCN architecture requires a cell at every bus where the states need to be estimated. A cell uses locally available information to estimate voltage magnitude and angle of its bus. The cells exploit output information of other cells in some electrical proximity prior to computing the outputs for next time step. Beside the promise of scalability of the CCN architecture, a fully observable system for state estimation and other applications can be realized. As the traditional estimators take all the measurements at a time and executes the estimation, missing some of the measurements may cause it to loose observability. In this paper, CCN based architecture is implemented with the popular Weighted Least Square (WLS) estimator on nonlinear power flow equations to estimate off-line data. Through simulation, the scalability and observability of the CCN based framework is investigated.

关键词： computer architecture Estimation Microprocessors Voltage measurement Time measurement Power systems Transmission line measurements

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