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arXiv 2018年

作者： Liu, Guodong Ollis, Thomas B. Xiao, Bailu Zhang, Xiaohu Tomsovic, Kevin Power & Energy Systems Group Oak Ridge National Laboratory Oak Ridge United States Dept. of Electrical Engineering and Computer Science University of Tennessee Knoxville United States

In this paper, we propose networked microgrids to facilitate the integration of variable renewable generation and improve the economics and resiliency of electricity supply in microgrids. A new concept, probability of successful islanding (PSI) is used to quantify the islanding capability of a microgrid considering the uncertainty of renewable energy resources and load as well as exchanged power at PCC. With the goal of minimizing the total operating cost while preserving user specified PSI, a chance-constrained optimization problem is formulated for the optimal scheduling of both individual microgrid and networked microgrids. Numerical simulation results show significant saving in electricity cost can be achieved by proposed networked microgrids without compromising the resiliency. The impact of correlation coefficients among the renewable generation and load of adjacent microgrids has been studied as well. Copyright © 2018, The Authors. All rights reserved.

关键词： Distributed power generation

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Distributed Dynamic State Estimation for Smart Grid Transmission System

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IFAC-PapersOnLine 2017年第2期50卷 98-103页

作者： Rahman, Md Ashfaqur Venayagamoorthy, Ganesh Kumar Real-Time Power and Intelligent Systems Laboratory ClemsonSC29634 United States Dept. of Electrical and Computer Engineering North South University Dhaka Bangladesh School of Engineering University of Kwazulu-Natal Durban4041 South Africa

The advancement of synchrophasor technologies validates the dynamic nature of the state variables of a power system. On the other hand, the need for privacy of data in the deregulated market and speed of computation for large power systems present the importance of a fast distributed state estimation method. To serve both purposes, a distributed dynamic state estimator is developed in this study. The advantages of a predictor, the constant Jacobian method and a combining unit are utilized. The system is distributed to the bus level that makes the distributed approach a parallel estimation. The accuracy and the smoothness of the proposed method are shown to be satisfactory through simulation on the IEEE 68-bus test power system. It is also shown that the proposed method can execute faster than the fastest data rate of the phasor measurement units currently deployed in the power system. © 2017

关键词： Distributed computer systems Data privacy Deregulation Dynamics Electric power transmission Electric power transmission networks Estimation Phasor measurement units Smart power grids State estimation Computational networks distributed Distributed approaches Distributed dynamics Distributed state estimation Large power systems Parallel estimation State prediction

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A Fuzzy-PSO System for Indoor Localization based on Visible Light Communications

arXiv

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arXiv 2018年

作者： Pau, Giovanni Collotta, Mario Maniscalco, Vincenzo Choo, Kim-Kwang Raymond Faculty of Engineering and Architecture Kore University of Enna Enna94100 Italy Dept. of Info. Systems and Cyber Security and Department of Electrical and Computer Engineering University of Texas at San Antonio San AntonioTX78249-0631 United States

Indoor positioning systems using Visible Light Communication (VLC) have potential applications in smart buildings, for instance, in developing economical, easy-touse, widely accessible positioning system based on Light Emitting Diodes (LEDs). Thus using VLCs, we introduce a new fuzzy-based system for indoor localization in this paper. The system processes data from transmitters (i.e., anchor nodes) and delivers the calculated position of a receiver. A Particle Swarm Optimization (PSO) technique is then employed to obtain the optimal configuration of the proposed Fuzzy Logic Controllers (FLCs). Specifically, the proposed PSO technique optimizes the membership functions of the FLCs by adjusting their range to achieve the best results regarding the localization reliability. We demonstrate the utility of the proposed approach using experiments. Copyright © 2018, The Authors. All rights reserved.

关键词： Particle swarm optimization (PSO)

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Dynamic Human Gait VGRF Reference Profile Generation via Extreme Learning Machine

Dynamic Human Gait VGRF Reference Profile Generation via Ext...

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International Joint Conference on Neural Networks (IJCNN)

作者： Alexandra Vieira Bernardete Ribeiro Heloísa Sobral A. Paulo Coimbra Manuel Crisóstomo João P. Ferreira Tao Liu Dept. of Informatics Engineering CISUC Univ. of Coimbra Coimbra Portugal Dept. of Electrical and Computer Eng. Institute of Systems and Robotics Univ. of Coimbra Coimbra Portugal Dept. of Electrical Eng. Superior Institute of Eng. of Coimbra Institute of Systems and RoboticsUniv. of Coimbra Coimbra Portugal State Key Laboratory of Fluid Power and Mechatronic Systems School of Mechanical Engineering Zhejiang University Hangzhou China

Vertical Ground Reaction Forces (VGRF) reference profiles of human gait are important in medicine, for the recognition of gait disorders and in the assessment of rehabilitation treatments. A walking human is a dynamic varying system and in spite of the VGRF reference dependence on several patient's variables, doctors traditionally use the same static reference for all patients. The purpose of this study is to find out if an Extreme Learning Machine (ELM) is adequate to generate the dynamic VGRF reference profiles of healthy people depending on the subject's age, weight, height and stride duration. The ELM is compared with two other baseline Computational Intelligence (CI) methods, the Backpropagation Neural Network (BNN) and Multioutput Support Vector Regression (MSVR). Data from 28 healthy males walking at five different stride durations, collected using instrumented shoes, were used to train and test the CI models. The results showed that ELM is a well suited method to generate the dynamic VGRF reference profile for both dominant and non-dominant limbs, showing the lowest root mean square errors for the test set, 0.0201 and 0.0243 (fraction of body weight) for the dominant and non-dominant limbs respectively. This study reveals a promising methodology that can be implemented in real time gait analysis, allowing doctors to find a specific reference gait pattern for the gait analysis of an unhealthy person by specifying the age, weight, height and stride duration.

关键词： Training Biological neural networks Support vector machines Neurons Backpropagation Legged locomotion Adaptation models

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Real-time faulted line localization and PMU placement in power systems through convolutional neural networks

arXiv

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arXiv 2018年

作者： Li, Wenting Deka, Deepjyoti Chertkov, Michael Wang, Meng Dept. of Electrical Computer and Systems Engineering Rensselaer Polytechnic Institute TroyNY United States Theory Division Center for Nonlinear Studies Los Alamos National Laboratory Los AlamosNM United States

Diverse fault types, fast re-closures, and complicated transient states after a fault event make real-time fault location in power grids challenging. Existing localization techniques in this area rely on simplistic assumptions, such as static loads, or require much higher sampling rates or total measurement availability. This paper proposes a faulted line localization method based on a Convolutional Neural Network (CNN) classifier using bus voltages. Unlike prior data-driven methods, the proposed classifier is based on features with physical interpretations that improve the robustness of the location performance. The accuracy of our CNN based localization tool is demonstrably superior to other machine learning classifiers in the literature. To further improve the location performance, a joint phasor measurement units (PMU) placement strategy is proposed and validated against other methods. A significant aspect of our methodology is that under very low observability (7% of buses), the algorithm is still able to localize the faulted line to a small neighborhood with high probability. The performance of our scheme is validated through simulations of faults of various types in the IEEE 39-bus and 68-bus power systems under varying uncertain conditions, system observability, and measurement quality. Copyright © 2018, The Authors. All rights reserved.

关键词： Phasor measurement units

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Unsupervised detection of epileptic seizures from EEG signals: A channel-specific analysis of long-term recordings

Unsupervised detection of epileptic seizures from EEG signal...

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2018 IEEE EMBS International Conference on Biomedical and Health Informatics, BHI 2018

作者： Tsiouris, Kostas M. Konitsiotis, Spiros Markoula, Sofia Rigas, Georgios Koutsouris, Dimitrios D. Fotiadis, Dimitrios I. Biomedical Engineering Laboratory School of Electrical and Computer Engineering National Technical University of Athens AthensGR15773 Greece Department of Neurology Medical School University of Ioannina IoanninaGR45110 Greece University Hospital of Ioannina IoanninaGR45110 Greece Unit of Medical Technology and Intelligent Information Systems Dept. of Material Science and Engineering University of Ioannina IoanninaGR45110 Greece Department of Biomedical Research Institute of Molecular Biology and Biotechnology FORTH University of Ioannina IoanninaGR45110 Greece

ISBN: (纸本)9781538624050

As long-term EEG recordings are becoming more and more frequent in clinical practice, the volume of EEG data that require annotation by clinical experts is growing exponentially, highlighting the need for automated epileptic seizure detection systems. Supervised methodologies based on machine learning techniques have been proposed as a solution, but they require annotated EEG data from multiple patients for training, while unsupervised methodologies, which do not have such limitations, are scarce in the literature. Thus, an unsupervised seizure detection methodology is developed to offer high seizure detection performance with substantial reduction in the time and effort required to inspect large volumes of EEG signals. The ictal rhythmical activity is detected by analyzing the spectral information of each EEG channel independently in order to find intensive accumulation of signal energy over the fundamental delta, theta and alpha frequency bands. The ictal rhythmical activity that is expressed during a seizure is detected using a set of four simple seizure detection conditions. The proposed methodology is validated using the public CHB-MIT EEG database, and the results suggest that an average sensitivity of 95.1% can be obtained with a false detection rate of 10.13 FP/h, while 95% of the each patient's EEG recordings is automatically rejected as non-ictal. © 2018 IEEE.

关键词： Neurophysiology

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Robust Convergence of Power Flow using Tx Stepping Method with Equivalent Circuit Formulation

Robust Convergence of Power Flow using Tx Stepping Method wi...

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Power systems Computation Conference

作者： Amritanshu Pandey Marko Jereminov Martin R. Wagner Gabriela Hug Larry Pileggi Dept. of Electrical and Computer Engineering Carnegie Mellon University Pittsburgh P A Power Systems Laboratory ETH Zurich Switzerland

Robust solving of critical large power flow cases (with 50k or greater buses) forms the backbone of planning and operation of any large connected power grid. At present, reliable convergence with applications of existing power flow tools to large power systems is contingent upon a good initial guess for the system state. To enable robust convergence for large scale systems starting with an arbitrary initial guess, we extend our equivalent circuit formulation for power flow analysis to include a novel continuation method based on transmission line ('Tx') stepping. While various continuation methods have been proposed for use with the traditional 'PQV' power flow formulation, these methods have either failed to completely solve the problem or have resulted in convergence to a low voltage solution. The proposed "Tx Stepping" method in this paper demonstrates robust convergence to the high voltage solution from an arbitrary initial guess. Example systems, including 75k+ bus test cases representing different loading and operating conditions for Eastern Interconnection of the U.S. power grid, are solved from arbitrary initial guesses.

关键词： Continuation methods/homotopy Equivalent circuit formulation Power flow Robust convergence Tx Stepping

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Learn2Reg: comprehensive multi-task medical image registration challenge, dataset and evaluation in the era of deep learning

arXiv

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arXiv 2021年

作者： Hering, Alessa Hansen, Lasse Mok, Tony C.W. Chung, Albert C.S. Siebert, Hanna Häger, Stephanie Lange, Annkristin Kuckertz, Sven Heldmann, Stefan Shao, Wei Vesal, Sulaiman Rusu, Mirabela Sonn, Geoffrey Estienne, Théo Vakalopoulou, Maria Han, Luyi Huang, Yunzhi Yap, Pew-Thian Brudfors, Mikael Balbastre, Yaël Joutard, Samuel Modat, Marc Lifshitz, Gal Raviv, Dan Lv, Jinxin Li, Qiang Jaouen, Vincent Visvikis, Dimitris Fourcade, Constance Rubeaux, Mathieu Pan, Wentao Xu, Zhe Jian, Bailiang De Benetti, Francesca Wodzinski, Marek Gunnarsson, Niklas Sjölund, Jens Grzech, Daniel Qiu, Huaqi Li, Zeju Thorley, Alexander Duan, Jinming Großbröhmer, Christoph Hoopes, Andrew Reinertsen, Ingerid Xiao, Yiming Landman, Bennett Huo, Yuankai Murphy, Keelin Lessmann, Nikolas van Ginneken, Bram Dalca, Adrian V. Heinrich, Mattias P. Institute of Medical Informatics Universität zu Lübeck Ratzeburger Allee 160 Lübeck23562 Germany Fraunhofer MEVIS Institute for Digital Medicine Lübeck23562 Germany Department of Radiology and Nuclear Medicine Radboud University Medical Center GA Nijmegen6525 Netherlands The Institute of Medical Informatics Universität zu Lübeck Lübeck23562 Germany The Department of Computer Science and Engineering The Hong Kong University of Science and Technology Hong Kong The Fraunhofer MEVIS Institute for Digital Medicine Lübeck23562 Germany The Department of Radiology Stanford University StanfordCA94305 United States The Department of Urology Stanford University StanfordCA94305 United States The Université Paris-Saclay CentraleSupélec Mathématiques et Informatique pour la Complexité et les Systèmes Inria Saclay Gif-sur-Yvette 91190 France Université Paris-Saclay Institut Gustave Roussy Inserm Radiothérapie Moléculaire et Innovation Thérapeutique Villejuif94800 France The Université Paris-Saclay CentraleSupélec Mathématiques et Informatique pour la Complexité et les Systèmes Inria Saclay Gif-sur-Yvette91190 France The Department of Radiology and Nuclear Medicine Radboud University Medical Center Nijmegen6525 GA Netherlands The School of Automation Nanjing University of Information Science and Technology Nanjing210044 China The School of Biomedical Engineering and Imaging Sciences King’s College London London United Kingdom The Athinoula A. Martinos Center for Biomedical Imaging Massachusetts General Hospital United States Harvard Medical School Boston United States The King’s College London United Kingdom The Tel Aviv University The Britton Chance Center for Biomedical Photonics Wuhan National Laboratory for Optoelectronics-Huazhong University of Science and Technology Hubei Wuhan430074 China MoE Key Laboratory for Biomedical Photonics Collaborative Innovation Center for Biomedical Engineering School of Engineering Sciences Huazhong University of Sci

Image registration is a fundamental medical image analysis task, and a wide variety of approaches have been proposed. However, only a few studies have comprehensively compared medical image registration approaches on a wide range of clinically relevant tasks. This limits the development of registration methods, the adoption of research advances into practice, and a fair benchmark across competing approaches. The Learn2Reg challenge addresses these limitations by providing a multi-task medical image registration data set for comprehensive characterisation of deformable registration algorithms. A continuous evaluation will be possible at https://***. Learn2Reg covers a wide range of anatomies (brain, abdomen, and thorax), modalities (ultrasound, CT, MR), availability of annotations, as well as intra- and inter-patient registration evaluation. We established an easily accessible framework for training and validation of 3D registration methods, which enabled the compilation of results of over 65 individual method submissions from more than 20 unique teams. We used a complementary set of metrics, including robustness, accuracy, plausibility, and runtime, enabling unique insight into the current state-of-the-art of medical image registration. This paper describes datasets, tasks, evaluation methods and results of the challenge, as well as results of further analysis of transferability to new datasets, the importance of label supervision, and resulting bias. While no single approach worked best across all tasks, many methodological aspects could be identified that push the performance of medical image registration to new state-of-the-art performance. Furthermore, we demystified the common belief that conventional registration methods have to be much slower than deep-learning-based methods. © 2021, CC BY.

关键词： Image registration

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A Cross-Stack QoS Routing Approach For Underwater Acoustic Sensor Networks

A Cross-Stack QoS Routing Approach For Underwater Acoustic S...

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Underwater Communications and Networking Conference

作者： Lloyd E. Emokpae Zhiqiang Liu Geoffrey F. Edelmann Mohamed Younis Acoustic Signal Processing and Systems Branch Naval Research Laboratory - Code 7160 Dept. of Computer Science and Electrical Engineering University of Maryland Baltimore County

ISBN: (纸本)9781538664438

In this paper, we utilize a novel cross-stack design that factors in the dynamics of the underwater channel to optimize the single-hop performance amongst multiple node pairs. This will result in a set of links that meet or exceed QoS requirements, which is further leveraged for network discovery and energy-efficient routing with minimum end-to-end packet delay. Thus, our proposed routing approach will provide means to guarantee application-specific QoS while also maximizing the network lifetime. Simulation experiments were conducted to validate the approach in a shallow water multipath environment.

关键词： Underwater acoustic sensor networks Routing Energy-aware protocol

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Optimal Sizing of Energy Storage for Community Microgrids Considering Building Thermal Dynamics

Optimal Sizing of Energy Storage for Community Microgrids Co...

引用

IEEE Power and Energy Society General Meeting

作者： Guodong Liu Zhi Li Michael Starke Thomas B. Ollis Kevin Tomsovic Power & Energy Systems Group Oak Ridge National Laboratory Dept. of Electrical Engineering and Computer Science The University of Tennessee

ISBN: (纸本)9781538622131;9781538622124

This paper proposes an optimization model for the sizing of energy storage for community microgrids considering the building thermal dynamics and customer comfort preference. The proposed model minimizes the annualized cost of the community microgrid, including energy storage investment, purchased energy cost, demand charge, energy storage degradation cost, voluntary load shedding cost, and the cost associated with customer discomfort due to room temperature deviation. The decision variables are the power and energy capacity of invested energy storage. In particular, heating, ventilation and air-conditioning (HVAC) systems are assumed to be scheduled intelligently by the microgrid central controller while maintaining the indoor temperature in the comfort range set by customers. For this purpose, the detailed thermal dynamic characteristics of buildings has been integrated into the optimization model. Numerical simulations show significant cost reduction with the proposed model. The impacts of various costs on the optimal solution are investigated through sensitivity analysis.

关键词： Microgrids Energy storage Buildings Temperature distribution Temperature control Temperature sensors

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