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A Benchmark System for Hardware-in-The-Loop Testing of Distributed Energy Resources

IEEE Power and Energy Technology systems Journal 2018年第3期5卷 94-103页

作者： Kotsampopoulos, Panos Lagos, Dimitris Hatziargyriou, Nikos Faruque, M. Omar Lauss, Georg Nzimako, Onyi Forsyth, Paul Steurer, Michael Ponci, F. Monti, A. Dinavahi, V. Strunz, Kai Smart Grids Research Unit School of Electrical and Computer Engineering National Technical University of Athens Zografou15780 Greece Department of Electrical and Computer Engineering Center for Advanced Power Systems Florida State University TallahasseeFL32310 United States Energy Department Austrian Institute of Technology Vienna1210 Austria RTDS Inc. WinnipegMBR3T 2E1 Canada Institute for Automation of Complex Power Systems E.ON Energy Research Center RWTH Aachen University Aachen52074 Germany Department of Electrical and Computer Engineering University of Alberta EdmontonABT6G 2V4 Canada SENSE Lab Berlin University of Technology Berlin10587 Germany

In order to overcome challenges associated with the integration of distributed energy resources (DER) into state-of-The-art and future power grids, a common basis for testing using appropriate benchmark systems is required. Real-time hardware-in-The-loop (HIL) simulation has proven to be an advanced and efficient tool for the analysis and validation of electric power systems and DER components. However, a common methodology for HIL testing of DER along with the required set of reference systems has not yet been developed. This task-force paper proposes a benchmark system for HIL testing incorporating DER into the real-time simulation environment. A low-voltage benchmark system with detailed HIL setup is proposed for the testing of DER performance. The modeling of DER for real-time applications is discussed, and the detailed laboratory procedures and setups for both controller HIL (CHIL) and power HIL (PHIL) are provided. Results from CHIL simulation related to the centralized controls and experimental results of PHIL simulation related to local control on the benchmark system substantiate the suitability of the proposed real-time simulation approach. © 2014 IEEE.

关键词： Hardware-in-the-loop simulation

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A statistical learning approach to reactive power control in distribution systems

arXiv

引用

arXiv 2019年

作者： Yang, Qiuling Sadeghi, Alireza Wang, Gang Giannakis, Georgios B. Sun, Jian State Key Lab of Intelligent Control and Decision of Complex Systems School of Automation Beijing Institute of Technology Beijing100081 China Department of Electrical and Computer Engineering University of Minnesota MinneapolisMN55455 United States

Pronounced variability due to the growth of renewable energy sources, flexible loads, and distributed generation is challenging residential distribution systems. This context, motivates well fast, efficient, and robust reactive power control. Real-time optimal reactive power control is possible in theory by solving a non-convex optimization problem based on the exact model of distribution flow. However, lack of high-precision instrumentation and reliable communications, as well as the heavy computational burden of non-convex optimization solvers render computing and implementing the optimal control challenging in practice. Taking a statistical learning viewpoint, the input-output relationship between each grid state and the corresponding optimal reactive power control is parameterized in the present work by a deep neural network, whose unknown weights are learned offline by minimizing the power loss over a number of historical and simulated training pairs. In the inference phase, one just feeds the real-time state vector into the learned neural network to obtain the 'optimal' reactive power control with only several matrix-vector multiplications. The merits of this novel statistical learning approach are computational efficiency as well as robustness to random input perturbations. Numerical tests on a 47-bus distribution network using real data corroborate these practical merits. Copyright © 2019, The Authors. All rights reserved.

关键词： Deep neural networks

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Negative terahertz conductivity and amplification of surface plasmons in graphene–black phosphorus injection laser heterostructures

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Physical Review B 2019年第11期100卷 115436-115436页

作者： V. Ryzhii T. Otsuji M. Ryzhii A. A. Dubinov V. Ya. Aleshkin V. E. Karasik M. S. Shur Research Institute of Electrical Communication Tohoku University Sendai 980-8577 Japan Institute of Ultra High Frequency Semiconductor Electronics of RAS Moscow 117105 Russia Center for Photonics and Two-Dimensional Materials Moscow Institute of Physics Technology Dolgoprudny 141700 Russia Department of Computer Science and Engineering University of Aizu Aizu-Wakamatsu 965-8580 Japan Institute for Physics of Microstructures of RAS and Lobachevsky University of Nizhny Novgorod Nizhny Novgorod 60395 Russia Center for Photonics and Infrared Technology Bauman Moscow State Technical University Moscow 111005 Russia Department of Electrical Computer and Systems Engineering Rensselaer Polytechnic Institute Troy New York 12180 USA

We propose and evaluate the heterostructure based on the graphene layer (GL) with the lateral electron injection from the side contacts and the hole vertical injection via the black phosphorus layer (BL) (P+−PL-PL-GL heterostructure). Due to a relatively small energy of the holes injected from the PL into the GL (about 100 meV, smaller than the energy of optical phonons in the GL which is about 200 meV), the hole injection can effectively cool down the two-dimensional electron-hole plasma in the GL. This simplifies the realization of the interband population inversion and the achievement of the negative dynamic conductivity in the terahertz (THz) frequency range enabling the amplification of the surface plasmon modes. The latter can lead to the plasmon lasing. The conversion of the plasmons into the output radiation can be used for new types of the THz sources.

关键词： Optical & microwave phenomena Plasmonics Plasmons Graphene Heterostructures

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Text summarization in the biomedical domain

arXiv

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arXiv 2019年

作者： Moradi, Milad Ghadiri, Nasser Department of Electrical and Computer Engineering Isfahan University of Technology Isfahan84156-83111 Iran Institute for Artificial Intelligence and Decision Support Center for Medical Statistics Informatics and Intelligent Systems Medical University of Vienna Vienna Austria

This chapter gives an overview of recent advances in the field of biomedical text summarization. Different types of challenges are introduced, and methods are discussed concerning the type of challenge that they address. Biomedical literature summarization is explored as a leading trend in the field, and some future lines of work are pointed out. Underlying methods of recent summarization systems are briefly explained and the most significant evaluation results are mentioned. The primary purpose of this chapter is to review the most significant research efforts made in the current decade toward new methods of biomedical text summarization. As the main parts of this chapter, current trends are discussed and new challenges are introduced. Copyright © 2019, The Authors. All rights reserved.

关键词： Text processing

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Pothole Detection Based on Disparity Transformation and Road Surface Modeling

arXiv

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arXiv 2019年

作者： Fan, Rui Ozgunalp, Umar Hosking, Brett Liu, Ming Pitas, Ioannis The Robotics Institute Department of Electronic and Computer Engineering The Hong Kong University of Science and Technology Hong Kong Hong Kong The Electrical and Electronic Engineering Near East University Nicosia Cyprus The National Oceanography Centre Southampton United Kingdom The School of Informatics Aristotle University of Thessaloniki Thessaloniki Greece

Pothole detection is one of the most important tasks for road maintenance. computer vision approaches are generally based on either 2D road image analysis or 3D road surface modeling. However, these two categories are always used independently. Furthermore, the pothole detection accuracy is still far from satisfactory. Therefore, in this paper, we present a robust pothole detection algorithm that is both accurate and computationally efficient. A dense disparity map is first transformed to better distinguish between damaged and undamaged road areas. To achieve greater disparity transformation efficiency, golden section search and dynamic programming are utilized to estimate the transformation parameters. Otsu’s thresholding method is then used to extract potential undamaged road areas from the transformed disparity map. The disparities in the extracted areas are modeled by a quadratic surface using least squares fitting. To improve disparity map modeling robustness, the surface normal is also integrated into the surface modeling process. Furthermore, random sample consensus is utilized to reduce the effects caused by outliers. By comparing the difference between the actual and modeled disparity maps, the potholes can be detected accurately. Finally, the point clouds of the detected potholes are extracted from the reconstructed 3D road surface. The experimental results show that the successful detection accuracy of the proposed system is around 98.7% and the overall pixel-level accuracy is approximately 99.6%. Copyright © 2019, The Authors. All rights reserved.

关键词： Dynamic programming

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Impact of Intention to Use to a Document Management System (Case Study Approach) 3

Impact of Intention to Use to a Document Management System (...

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3rd International Conference on Information Management and Technology, ICIMTech 2018

作者： Hartono, Sugiarto Kosala, Raymondus Raymond Supangkat, Suhono Harso Ranti, Benny Information Systems Department Computer Science Department BINUS Graduate Program-Doctor of Computer Science Jakarta11480 Indonesia Computer Science Department Bina Nusantara University BINUS Graduate Program-Doctor of Computer Science Jakarta11480 Indonesia Bandung Institute of Technology School of Electrical Engineering and Informatics Bandung Indonesia

ISBN: (纸本)9781538658215

Document management system makes user to access information anytime and anywhere. The purpose of this research is to analyze what variables have impact on the intention to use of document management system. To achieve the objectives conducted by identification problem on the research model developed from the model of Technology Acceptance Model and Unified Theory of Acceptance and Use of Technology to the use of document management system. Furthermore, the data obtained from the respondents tested outer model, inner model test, hypothesis test and to produce a comprehensive analysis will be conducted Importance Performance Analysis. This research will result in a comprehensive analysis and able to provide recommendations and solutions for improvement from the implementation DMS has been done earlier. © 2018 IEEE.

关键词： Information services

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Cooperative identification of advection–diffusion processes with spatially varying coefficients based on a multi-model structure

引用

Cyber-Physical systems 2017年第1-4期3卷 103-120页

作者： You, Jie Wu, Wencen Department of Electrical Computer and Systems Engineering Rensselaer Polytechnic Institute TroyNY United States

This work develops a practical real-time identification strategy for advection–diffusion processes with spatially varying coefficients. A novel multi-model structure that represents the processes in practical applications is proposed. The multi-model structure is parameterised as blended linear partial differential equation models. Both the offline centralised and the online distributed identification algorithms using mobile sensor networks are proposed. Trajectories for mobile sensor networks in the advection–diffusion field with spatially varying parameters are found, and distributed control laws to guide the mobile sensor networks to follow the trajectories while keeping desired formations are designed. The effectiveness of this proposed method is demonstrated in simulations. © 2017, © 2017 Informa UK Limited, trading as Taylor & Francis Group.

关键词： Sensor networks

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Forecasting intracranial hypertension using multi-scale waveform metrics

arXiv

引用

arXiv 2019年

作者： Hüser, Matthias Kündig, Adrian Karlen, Walter De Luca, Valeria Jaggi, Martin Biomedical Informatics Group Department of Computer Science ETH Zürich Universitätstrasse 6 Zürich8092 Switzerland Mobile Health Systems Lab Institute of Robotics and Intelligent Systems Department of Health Sciences and Technology ETH Zürich Zürich8008 Switzerland Department of Information Technology and Electrical Engineering ETH Zürich Zürich8092 Switzerland Machine Learning & Optimization Lab EPFL Lausanne1015 Switzerland

Objective: Acute intracranial hypertension is an important risk factor of secondary brain damage after traumatic brain injury. Hypertensive episodes are often diagnosed reactively, leading to late detection and lost time for intervention planning. A pro-active approach that predicts critical events several hours ahead of time could assist in directing attention to patients at risk. Approach: We developed a prediction framework that forecasts onsets of acute intracranial hypertension in the next 8 hours. It jointly uses cerebral auto-regulation indices, spectral energies and morphological pulse metrics to describe the neurological state of the patient. One-minute base windows were compressed by computing signal metrics, and then stored in a multi-scale history, from which physiological features were derived. Main results: Our model predicted events up to 8 hours in advance with alarm recall rates of 90% at a precision of 30.3% in the MIMIC-III waveform database, improving upon two baselines from the literature. We found that features derived from high-frequency waveforms substantially improved the prediction performance over simple statistical summaries of low-frequency time serie1s, and each of the three feature classes contributed to the performance gain. The inclusion of long-term history up to 8 hours was especially important. Significance: Our results highlight the importance of information contained in high-frequency waveforms in the neurological intensive care unit. They could motivate future studies on pre-hypertensive patterns and the design of new alarm algorithms for critical events in the injured brain. Copyright © 2019, The Authors. All rights reserved.

关键词： Morphology

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Truly aliasing-free digital RF-PWM power coding scheme for switched-mode power amplifiers

Truly aliasing-free digital RF-PWM power coding scheme for s...

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2018 IEEE Radio and Wireless Symposium, RWS 2018

作者： Tanovic, Omer Ma, Rui Mitsubishi Electric Research Laboratories CambridgeMA United States Laboratory for Information and Decision Systems Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology CambridgeMA United States

ISBN: (纸本)9781538607091

This paper presents a novel method for multilevel power coding and upconversion to RF frequency, by the means of a digital radio-frequency pulse width modulation (RF-PWM). Delta-sigma modulation (ΔΣM) and memoryless predistortion are used to mitigate spectral aliasing effects, inherent to digital RF-PWM operation, and achieve high linearity of the transmitter. In this method, time-domain characterization of a multilevel digital RF-PWM was fully exploited in order to provide optimal parameter selection for the ΔΣM, which in turn enables aliasing-free digital RF-PWM that runs at reasonable clock speeds. Experiments with an arbitrary waveform generator are used to demonstrate effectiveness of the proposed novel power encoding scheme. The novel algorithm offers in-band SNR of - 45dB, while maintaining coding efficiency of about 55%, for a 20MHz-bandwidth 64QAM signal, while mere RF-PWM, running at the same clock speed, can achieve SNR of only - 10dB. © 2018 IEEE.

关键词： Power amplifiers

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CHAOS Challenge - Combined (CT-MR) healthy abdominal organ segmentation

arXiv

引用

arXiv 2020年

作者： Kavur, A. Emre Gezer, N. Sinem Barış, Mustafa Conze, Pierre-Henri Groza, Vladimir Pham, Duc Duy Chatterjee, Soumick Ernst, Philipp Özkan, Savaş Baydar, Bora Lachinov, Dmitry Han, Shuo Pauli, Josef Isensee, Fabian Perkonigg, Matthias Sathish, Rachana Rajan, Ronnie Aslan, Sinem Sheet, Debdoot Dovletov, Gurbandurdy Speck, Oliver Nürnberger, Andreas Maier-Hein, Klaus H. Akar, Gözde Bozdagı Ünal, Gözde Dicle, Oguz Selver, M. Alper Graduate School of Natural and Applied Sciences Dokuz Eylul University Izmir Turkey Department of Radiology Faculty Of Medicine Dokuz Eylul University Izmir Turkey Dept. of Ophthalmology and Optometry Medical Uni. of Vienna Austria Intelligent Systems Faculty of Engineering University of Duisburg-Essen Germany Division of Medical Image Computing German Cancer Research Center Heidelberg Germany CIR Lab Dept of Biomedical Imaging and Image-guided Therapy Medical Uni. of Vienna Austria IMT Atlantique LaTIM UMR 1101 Brest France Department of Electrical and Electronics Engineering Middle East Technical University Ankara Turkey Department of Electrical Engineering Indian Institute of Technology Kharagpur India School of Medical Science and Technology Indian Institute of Technology Kharagpur India Ca’ Foscari University of Venice ECLT DAIS Venice Italy Ege University International Computer Institute Izmir Turkey Johns Hopkins University Baltimore United States Data and Knowledge Engineering Group Otto von Guericke University Magdeburg Germany Magnetic Resonance Otto von Guericke University Magdeburg Germany Median Technologies Valbonne France Department of Computer Engineering Istanbul Technical University Istanbul Turkey Department of Electrical and Electronics Engineering Dokuz Eylul University Izmir Turkey

—Segmentation of abdominal organs has been a comprehensive, yet unresolved, research field for many years. In the last decade, intensive developments in deep learning (DL) have introduced new state-of-the-art segmentation systems. Despite outperforming the overall accuracy of existing systems, the effects of DL model properties and parameters on the performance is hard to interpret. This makes comparative analysis a necessary tool to achieve explainable studies and systems. Moreover, the performance of DL for emerging learning approaches such as cross-modality and multi-modal tasks have been rarely discussed. In order to expand the knowledge in these topics, CHAOS – Combined (CT-MR) Healthy Abdominal Organ Segmentation challenge has been organized in the IEEE International Symposium on Biomedical Imaging (ISBI), 2019, in Venice, Italy. Despite a large number of the previous abdomen related challenges, the majority of which are focused on tumor/lesion detection and/or classification with a single modality, CHAOS providesboth abdominal CT and MR data from healthy subjects. Five different and complementary tasks have been designed to analyze the capabilities of the current approaches from multiple perspectives. The results are investigated thoroughly, compared with manual annotations and interactive methods. The outcomes are reported in detail to reflect the latest advancements in the field. CHAOS challenge and data will be available online to provide a continuous benchmark resource for segmentation. Copyright © 2020, The Authors. All rights reserved.

关键词： Medical imaging

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