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IEEE International Conference on DC Microgrids (ICDCM)

作者： Xingsi Zhong Lu Yu Richard Brooks Ganesh Kumar Venayagamoorthy Real-Time Power and Intelligent Systems Laboratory The Holcombe Department of Electrical and Computer Engineering Clemson University Clemson SC USA

Microgrids are low voltage electric distribution grids with modular distributed energy sources and controllable loads. The DC microgrids avoid DC to AC and AC to DC conversion and minimize transmission and distribution losses. Dynamic energy management systems enhance utilization of renewable energy sources and ensure uninterrupted supply of power to critical loads. Like the traditional transmission and distribution grid, AC and DC microgrids are vulnerable to cyber attacks. Further research is required on the cyber security to leverage the promises and potentials of DC microgrids. This paper discusses security vulnerabilities and some solutions for DC microgrids.

关键词： Microgrids computer crime Protocols Sensors Timing Generators

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An LMI-SSI model based PSS design approach for a multi-machine power system

An LMI-SSI model based PSS design approach for a multi-machi...

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Clemson University Power systems Conference (PSC)

作者： Ke Tang Ganesh K Venayagamoorthy The Holcombe Dept. of Electrical and Computer Engineering Real-Time Power and Intelligent Systems (RTPIS) Laboratory Clemson University SC USA

In recent days, as power systems are operated close to the stability margin, oscillation caused by lack of damping torque is frequently observed. Power System Stabilizer (PSS) enhances generator stability by providing a supplementary signal to the automatic voltage regulator which enhances the damping torque. Stochastic Subspace Identification (SSI) algorithm is applied to obtain a linearized single-input-multiple-output model of a power system and the Linear Matrix Inequality (LMI) approach is then used to design several PSSs. Typical results are presented for the LMI-SSI model based PSS design on the IEEE 68 16- machine power system.

关键词： Power system stability Generators Oscillators Mathematical model Damping Transfer functions

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Multiobjective Unfolding of Shared Power Consumption Pattern using Genetic Algorithm for Estimating Individual Usage in Smart Cities

Multiobjective Unfolding of Shared Power Consumption Pattern...

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International Conference on Tools with Artificial Intelligence

作者： M. Alamaniotis L.H. Tsoukalas A. Fevgas P. Tsompanopoulou P. Bozanis Applied Intelligent Systems Laboratory School of Nuclear Engineering Purdue University West Lafayette Indiana USA Electrical and Computer Engineering University of Thessaly Volos Greece

ISBN: (纸本)9781509001644

In smart cities residential homes are fully equipped with information networking and computing technologies and are connected to the power grid via intelligent meters. Connectivity of meters allows formation of groups of residents, which are physically close, and as a result individual consumptions can be aggregated into a shared consumption. In this paper an approach of unfolding shared consumption and making inferences about resident personal usage is presented. The proposed approach tackles the problem of unfolding as a multiobjective problem in which a set of residential profiles is fitted to the measured consumption. A solution to the multiobjective problem is sought by using the Non-dominated Sorting Genetic Algorithm-II (NSGA-II) that utilizes the Pareto optimality theory to identify an optimal solution. The approach is applied to a set electricity consumption signals for making inferences about the personal energy usage of residential participants in the shared consumption pattern.

关键词： genetic algorithms power consumption smart cities consumer inferences

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[email protected] : Analysis and results of evolving competitions for domestic and service robots

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Artificial Intelligence 2015年 229卷 258-281页

作者： Luca Iocchi Dirk Holz Javier Ruiz-del-Solar Komei Sugiura Tijn van der Zant Department of Computer Control and Management Engineering Sapienza University of Rome Italy Autonomous Intelligent Systems Group University of Bonn Germany Department of Electrical Engineering & Advanced Mining Technology Center Universidad de Chile Chile Information Services Platform Laboratory National Institute of Information and Communications Technology Japan Cognitive Robotics Laboratory University of Groningen The Netherlands

Scientific competitions are becoming more common in many research areas of artificial intelligence and robotics, since they provide a shared testbed for comparing different solutions and enable the exchange of research results. Moreover, they are interesting for general audiences and industries. Currently, many major research areas in artificial intelligence and robotics are organizing multiple-year competitions that are typically associated with scientific conferences. One important aspect of such competitions is that they are organized for many years. This introduces a temporal evolution that is interesting to analyze. However, the problem of evaluating a competition over many years remains unaddressed. We believe that this issue is critical to properly fuel changes over the years and measure the results of these decisions. Therefore, this article focuses on the analysis and the results of evolving competitions. In this article, we present the [email protected] competition, which is the largest worldwide competition for domestic service robots, and evaluate its progress over the past seven years. We show how the definition of a proper scoring system allows for desired functionalities to be related to tasks and how the resulting analysis fuels subsequent changes to achieve general and robust solutions implemented by the teams. Our results show not only the steadily increasing complexity of the tasks that [email protected] robots can solve but also the increased performance for all of the functionalities addressed in the competition. We believe that the methodology used in [email protected] for evaluating competition advances and for stimulating changes can be applied and extended to other robotic competitions as well as to multi-year research projects involving Artificial Intelligence and robotics.

关键词： Robotic competitions Artificial intelligence and robotics Benchmarking

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On Finite-Horizon Optimal Control of First-Order Plus Time Delay systems

On Finite-Horizon Optimal Control of First-Order Plus Time D...

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第27届中国控制与决策会议

作者： Dawei Shi Tongwen Chen State Key Laboratory of Intelligent Control and Decision of Complex Systems School of AutomationBeijing Institute of Technology Department of Electrical and Computer Engineering University of Alberta

In this work,a finite-horizon optimal control problem for first-order plus time delay(FOPTD) processes is *** show that if the control horizon is greater than three and the prediction horizon is great than the control horizon plus the time delay in discrete time,the optimal controller is not affected by either of the two ***,under these conditions,the controller parameters are explicitly calculated,the closed-loop system is shown to be stable,and the controller is *** problem considered is related to the results on linear quadratic regulation of linear systems with time delays;however,the detailed parameterization of the state-space model introduced by the FOPTD process provides an additional opportunity to investigate the exact controller structure and properties(e.g.,the locations of the closed-loop poles),which are also the major difficulties encountered and overcome in this *** problem is motivated from phenomena experienced in designing industrial model predictive control(MPC) tuning algorithms,and extensive numerical examples indicate that the proposed results speed up the MPC autotuning algorithms by 70%.

关键词： Optimal control First-order plus time delay process Performance visualization MPC tuning

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A Novel Performance Evaluation Method for Visual Tracking Methods

A Novel Performance Evaluation Method for Visual Tracking Me...

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2015年中国智能自动化学术会议

作者： Hui Teng Lianzhi Yu Huaping Liu Fuchun Sun Xiaojuan Liu Department of Computer Science and Technology State Key Laboratory of Intelligent Technology and Systems TNLIST Tsinghua University School of Optical Electrical and Computer Engineering University of Shanghai for Science and Technology

The evaluation for visual tracking is of utmost importance in computer vision. During the last two decades, several evaluation frameworks have been proposed and applied by a large amount of researchers, such as average central error, success rate, or Pascal score. It turns out that it is not easy to compare trackers fairly, precisely, and comprehensively with just only one evaluation index. Given the fact, a novel method for the evaluation of visual tracking algorithms is proposed in this paper, which combines both central error and Pascal score. Using this metric,an experimental validation was conducted finally and may serve as a reference for related research.

关键词： Visual tracking Evaluation Pascal score Central error

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An unsupervised methodology for the detection of epileptic seizures in long-term EEG signals 15

An unsupervised methodology for the detection of epileptic s...

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15th IEEE International Conference on Bioinformatics and Bioengineering, BIBE 2015

作者： Tsiouris, Kostas M. Konitsiotis, Spiros Markoula, Sofia Koutsouris, Dimitrios D. Sakellarios, Antonis I. 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 Institute of Communication and Computer Systems AthensGR10682 Greece Unit of Medical Technology and Intelligent Information Systems Department of Materials Science and Engineering FORTH-IMBB University of Ioannina IoanninaGR 45110 Greece

ISBN: (纸本)9781467379830

An unsupervised methodology for the detection of Epileptic seizures in EEG recordings is proposed. The time-frequency content of the EEG signals is extracted using the Short Time Fourier Transform. The analysis focuses on the EEG energy distribution among the well-established delta, theta and alpha rhythms (2-13 Hz), as energy variations in these frequency bands are widely associated with seizure activity. Relying on seizure rhythmicity, the classification is performed by isolating the segments where each rhythm is more clearly and dominantly expressed over the others. For the first time, an unsupervised methodology is evaluated using more than 978 hours of EEG recordings from a public database. The results show that the proposed methodology achieves high seizure detection sensitivity with significantly reduced human intervention. © 2015 IEEE.

关键词： Neurophysiology

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Hybrid orthogonal projection and estimation (HOPE): a new framework to learn neural networks

The Journal of Machine Learning Research

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The Journal of Machine Learning Research 2016年第1期17卷

作者： Kevin Murphy Bernhard Schölkopf Shiliang Zhang Hui Jiang Lirong Dai Google MPI for Intelligent Systems National Engineering Laboratory for Speech and Language Information Processing University of Science and Technology of China Hefei Anhui China Department of Electrical Engineering and Computer Science York UniversityToronto OntarioCanada

In this paper, we propose a novel model for high-dimensional data, called the Hybrid Orthogonal Projection and Estimation (HOPE) model, which combines a linear orthogonal projection and a finite mixture model under a unified generative modeling framework. The HOPE model itself can be learned unsupervised from unlabelled data based on the maximum likelihood estimation as well as discriminatively from labelled data. More interestingly, we have shown the proposed HOPE models are closely related to neural networks (NNs) in a sense that each hidden layer can be reformulated as a HOPE model. As a result, the HOPE framework can be used as a novel tool to probe why and how NNs work, more importantly, to learn NNs in either supervised or unsupervised ways. In this work, we have investigated the HOPE framework to learn NNs for several standard tasks, including image recognition on MNIST and speech recognition on TIMIT. Experimental results have shown that the HOPE framework yields significant performance gains over the current state-of-the-art methods in various types of NN learning problems, including unsupervised feature learning, supervised or semi-supervised learning.

关键词： PCA mixture models neural networks orthogonal projection unsupervised learning von Mises-Fisher (vMF) model

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Blending learning and inference in conditional random fields

The Journal of Machine Learning Research

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The Journal of Machine Learning Research 2016年第1期17卷

作者： Kevin Murphy Bernhard Schölkopf Tamir Hazan Alexander G. Schwing Raquel Urtasun Google MPI for Intelligent Systems Technion-Israel Institute of Technology Technion City Haifa Israel Electrical and Computer Engineering and Coordinated Science Laboratory University of Illinois at Urbana-Champaign Urbana IL University of Toronto Toronto ON

Conditional random fields maximize the log-likelihood of training labels given the training data, e.g., objects given images. In many cases the training labels are structures that consist of a set of variables and the computational complexity for estimating their likelihood is exponential in the number of the variables. Learning algorithms relax this computational burden using approximate inference that is nested as a sub-procedure. In this paper we describe the objective function for nested learning and inference in conditional random fields. The devised objective maximizes the log-beliefs -- probability distributions over subsets of training variables that agree on their marginal probabilities. This objective is concave and consists of two types of variables that are related to the learning and inference tasks respectively. Importantly, we afterwards show how to blend the learning and inference procedure and effectively get to the identical optimum much faster. The proposed algorithm currently achieves the state-of-the-art in various computer vision applications.

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Infeasibility-free inverse kinematics method

Infeasibility-free inverse kinematics method

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IEEE/SICE International Symposium on System Integration

作者： Wael Suleiman Fumio Kanehiro Eiichi Yoshida Electrical and Computer Engineering Department Université de Sherbrooke Sherbrooke Quebec Canada Intelligent Systems Research Institute National Institute of Advanced Industrial Science and Technology (AIST) Tsukuba Ibaraki Japan CNRS-AIST JRL (Joint Robotics Laboratory) Tsukuba Ibaraki Japan

ISBN: (纸本)9781467372435

The problem of inverse kinematics is revisited in the present paper. The paper is focusing on the problem of solving the inverse kinematics problem while respecting velocity limits on both the robot's joints and the end-effector. Even-though the conventional inverse kinematics algorithms have been proven to be efficient in many applications, defining an admissible trajectory for the end-effector is still a burdensome task for the user, and the problem can easily become unsolvable. The main idea behind the proposed algorithms is to consider the sampling time as a free variable, hence adding more flexibility to the optimization problem associated with the inverse kinematics. We prove that the reformulated problem has always a solution if the end-effector path is in the reachable space of the robot, thus solving the problem of infeasibility of conventional inverse kinematics methods. To validate the proposed approach, we have conducted three simulations scenarios. The simulation results point that while the conventional inverse kinematics methods fail to track precisely a desired end-effector trajectory, the proposed algorithms always succeed.

关键词： Kinematics Optimization Robots Trajectory Algorithm design and analysis Jacobian matrices Angular velocity

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