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arXiv 2018年

作者： Cristian, Vasar Iosif, Szeidert Ioan, Filip Gabriela, Prostean Department of Control System Engineering Politehnica' University from Timisoara Faculty of Automation and Computer Science Timisoara300223 Romania

This paper presents issues regarding short term electric load forecasting using feedforward and Elman recurrent neural networks. The study cases were developed using measured data representing electrical energy consume from Banat area. There were considered 35 different types of structure for both feedforward and recurrent network cases. For each type of neural network structure were performed many trainings and best solution was selected. The issue of forecasting the load on short term is essential in the effective energetic consume management in an open market environment. Copyright © 2007 IFAC Copyright © 2018, The Authors. All rights reserved.

关键词： Electric load forecasting

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Varying control intensity of synchronized chaotic system with time delay

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Journal of Physics: Conference Series 2021年第1期1828卷

作者： Babatunde O. Onasanya Shiping Wen Yuming Feng Wei Zhang Ning Tang Adeleke T. Ademola Key Laboratory of Intelligent Information Processing and Control Chongqing Three Gorges University Wanzhou Chongqing 404100 China Department of Mathematics University of Ibadan Ibadan Nigeria Australian AI Institute University of Technology Sydney Ultimo NSW 2007 Australia School of Computer Science and Engineering Chongqing Three Gorges University Wanzhou Chongqing 404100 China Chongqing Engineering Research Center of Internet of Things and Intelligent Control Technology Chongqing Three Gorges University Wanzhou Chongqing 404100 China School of Three Gorges Artificial Intelligence Chongqing Three Gorges University Wanzhou Chongqing 404100 China Department of Mathematics Obafemi Awolowo University Post Code 220005 Ile-Ife Nigeria

Hyperchaotic system is a very useful tool in secure and encrypted communications. But situations arise when engineers and scientists seek to synchronize two hyperchaotic systems. This gives another (error) system. The goal is to minimize the error as much as can be in order to make one system look like the other by synchronization. This is a particularly challenging situation. In this paper, two hyperchaotic systems are synchronized by impulsive control. Also, the condition for uniform asymptotic stability of the synchronized error system was given. Finally, the simulation results to justify the reliability of this method is also presented.

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A review and discussion of decomposition-based hybrid models for wind energy forecasting applications

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Applied Energy 2019年 235卷 939-953页

作者： Zheng Qian Yan Pei Hamidreza Zareipour Niya Chen School of Instrumentation Science and Opto-electronics Engineering Beihang University Beijing China Key Laboratory of Precision Opto-Mechatronics Technology Ministry of Education Beihang University Beijing China State Key Laboratory of Operation and Control of Renewable Energy & Storage Systems China Electric Power Research Institute Beijing 100192 China Department of Electrical and Computer Engineering University of Calgary Calgary Alberta Canada ABB Corporate Research Center Beijing China

With the continuous growth of wind power integration into the electrical grid, accurate wind power forecasting is an important component in management and operation of power systems. Given the challenging nature of wind power forecasting, various methods are presented in the literature to improve wind power forecasting accuracy. Among them, combining different techniques to construct hybrid models has been frequently reported in the literature. Decomposition-based models are a family of hybrid models that firstly decompose the wind speed/power time series into relatively more stationary subseries, and then build forecasting models for each subseries. In this paper, we present a comprehensive review of decomposition-based wind forecasting methods in order to explore their effectiveness. Decomposition-based hybrid forecasting models are classified into different groups based on the decomposition methods, such as, wavelet, empirical mode decomposition, seasonal adjust methods, variational mode decomposition, intrinsic time-scale decomposition, and bernaola galvan algorithm. We discuss decomposition methods in the context of alternative forecasting algorithms, and explore the challenges of each method. Comparative analysis of various decomposition-based models is also provided. We also explore current research activities and challenges, and identify potential directions for future research on this subject.

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The optimal artificial neural network model of time-voltage signal of the combination wave generator

The optimal artificial neural network model of time-voltage ...

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2018 International Symposium on Electrical Machines, SME 2018

作者： Dudzik, Marek Wrobel, Zofia Mielnik, Ryszard Department of Traction and Traffic Control Faculty of Electrical and Computer Engineering Cracow University of Technology Institute of Electrical Engineering and Computer Science ul. Warszawska 24 Kraków31-155 Poland Railway Lines Establishment in Rzeszów PKP Polish Railway Lines JSC ul. St. Batorego 26 Rzeszów35-005 Poland Faculty of Electrical and Computer Engineering Cracow University of Technology Institute of Electromechanical Energy Conversion ul. Warszawska 24 Kraków31-155 Poland

ISBN: (纸本)9781538652107

This article is the continuation (the next stage) of works related to modeling of the time waveform of voltage signal generated by combination wave generator. In the previous stage of work, a preliminary voltage signal model was developed. This model, that uses an artificial neural network, is built from one hidden layer and four neurons. This article presents the optimal structure of the model. This new, optimal structure of this model, had one hidden layer consisting of fourty seven neurons. The authors of this article propose the replacement of typical mathematical and numerical models describing the 1.250 s voltage surge of the combination wave generator, an elaborated optimal model of artificial neural network in the simulation of the voltage surge. This optimal model authors envisage using in simulation studies on surge protection systems. Such systems are also used in the surge protection of electric motors. © 2018 IEEE.

关键词： Surge protection

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A Robust State Estimator for Multi-Agent Systems Under Impulsive Noise and Missing Measurements

A Robust State Estimator for Multi-Agent Systems Under Impul...

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IEEE Annual Conference on Decision and control

作者： Junfei Xie Luis Rodolfo Garcia Carrillo Lei Jin Joao P. Hespanha Department of Electrical and Computer Engineering San Diego State University San Diego CA Unmanned Systems Laboratory (TAMUCC-USL) Texas A&M University - Corpus Christi Corpus Christi TX Department of Department of Mathematics & Statistics Texas A&M University-Corpus Christi Center for Control Dynamical Systems and Computation University of California Santa Barbara CA USA

ISBN: (数字)9781728113982

ISBN: (纸本)9781728113999

We address the problem of estimating the state of a Multi-Agent System (MAS) with dynamics subjected to impulsive disturbances, based on measurements that are corrupted with impulsive noise and are sometimes missing. To facilitate the online implementation of the proposed state estimator for MAS, a graph formulation is proposed first. Then, making use of the Huber Loss, the estimator adopts a general cost function that addresses missing measurements and is robust to impulsive noise and disturbances. The solution is validated under a synthetic scenario, where a team of UAVs equipped with onboard video cameras, inertial sensors, transceivers, and GPS, cooperatively geolocate and track a ground moving target agent. Comparison results with respect to three different state-of-the-art estimators are provided to show the superior performance and benefits of the proposed robust estimator.

关键词： Noise measurement Sensors Global Positioning System Robustness Loss measurement Time measurement Current measurement

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Stable Model-based control with Gaussian Process Regression for Robot Manipulators

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IFAC-PapersOnLine 2017年第1期50卷 3877-3884页

作者： Beckers T. Umlauft J. Hirche S. Chair of Information-oriented Control (ITR) Department of Electrical and Computer Engineering Technical University of Munich Germany

Computed-torque control requires a very precise dynamical model of the robot for compensating the manipulator dynamics. This allows reduction of the controller's feedback gains resulting in disturbance attenuation and other advantages. Finding precise models for manipulators is often difficult with parametric approaches, e.g. in the presence of complex friction or flexible links. Therefore, we propose a novel computed-torque control law which consists of a PD feedback and a dynamic feed forward compensation part with Gaussian Processes. For this purpose, the nonparametric Gaussian Process regression infers the difference between an estimated and the true dynamics. In contrast to other approaches, we can guarantee that the tracking error is stochastically bounded. Furthermore, if the number of training points tends to infinity, the tracking error is asymptotically stable in the large. In simulation and with an experiment, we demonstrate the applicability of the proposed control law and that it outperforms classical computed-torque approaches in terms of tracking precision. © 2017

关键词： Adaptive system control Data-based control Nonparametric methods robotic manipulators Stability of nonlinear systems Stochastic control

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Automatic student group generation for collaborative activities based on the zone of proximal development 1st

Automatic student group generation for collaborative activit...

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1st International Symposium on Emerging Technologies for Education, SETE 2016 Held in Conjunction with ICWL 2016

作者： De Marsico, Maria Giarlini, Emanuele Sterbini, Andrea Temperini, Marco Department of Computer Science Sapienza University Rome Italy Department of Computer Control and Management Engineering Sapienza University Rome Italy

ISBN: (纸本)9783319528359

The Zone of Proximal Development (ZPD) theorized by Lev Vygotskij can be considered as one of the most interesting insights on the value of collaborative learning: it specifies the cognitive distance reachable by a student in learning activities, given that some support by teachers, and peers, is available. Group handling is certainly one of the main aspects to be reproduced in an online learning system. This paper aims to offer a twofold contribution. First, with a proposal for the evaluation of the "pedagogical quality" of the partition of a class in groups, over a specific activity, in terms of internal homogeneity, and external balance of expected effort. Second, with policies to automatically, or semi-automatically, make groups according to the cognitive characteristics of single students. An evaluation of group composition is also provided according to a computation of the group ZPD, which is estimated basing on a notion of Daring Threshold, shaping the maximum viable effort. © Springer International Publishing AG 2017.

关键词： Students

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Significant Events Detection and Identification through Electrical Grid Load Profile

Significant Events Detection and Identification through Elec...

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Renewable Energies, Power Systems & Green Inclusive Economy (REPS-GIE)

作者： V. A. Shikhin A. E. Kochengin G.P. Pavliuk Department of Control and Informatics National Research University “Moscow Power Engineering Institute” Moscow Russia Department of Computer Engineering National Research University “Moscow Power Engineering Institute” Moscow Russia

ISBN: (纸本)9781538685167

Paper discusses the approach that supports data mining on significant events from electrical consumption load profile. The proposed approach leads to the detection and identification of significant events which do not violate the process of energy distribution, but are of significant importance for power utilities functioning. The data available from power meters and intelligent sensors is used to classify the events in power supply process. This information is of high importance and can be utilized to predict violations. It leads to rapid decision-making and allows to avoid the penalties on behalf of the power distribution utilities. For the certain types of significant events the distinctive features based on expert knowledge are introduced. Artificial neural networks (ANNs) of rather simple form application were tested through the numerical procedure of the proposed method. A profile of full load consumed power averaged in the time interval of two weeks, as well as data on the nominal current consumption of metallurgical enterprise were accepted for illustration of the method working capacity.

关键词： Meters Power demand Artificial neural networks Loss measurement Power measurement Training Smart meters

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Preface

Lecture Notes in Electrical Engineering

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Lecture Notes in Electrical engineering 2020年 664卷 v-vi页

作者： Sikander, Afzal Acharjee, Dulal Chanda, Chandan Kumar Mondal, Pranab Kumar Verma, Piyush Department of Instrumentation and Control Engineering Dr. B. R. Ambedkar National Institute of Technology JalandharPunjab India Applied Computer Technology KolkataWest Bengal India Department of Electrical Engineering Indian Institute of Engineering Science and Technology Shibpur HowrahWest Bengal India Department of Mechanical Engineering Indian Institute of Technology Guwahati GuwahatiAssam India International Energy Research Centre Cork Ireland

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Mobile Millimeter Wave Channel Tracking: A Bayesian Beamforming Framework Against DOA Uncertainty

Mobile Millimeter Wave Channel Tracking: A Bayesian Beamform...

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IEEE Global Communications Conference

作者： Yan Yang Shuping Dang Miaowen Wen Shahid Mumtaz Mohsen Guizani State Key Lab. of Rail Traffic Control and Safety Beijing Jiaotong University Beijing China Computer Electrical and Mathematical Science and Engineering Division King Abdullah University of Science and Technology Thuwal Kingdom of Saudi Arabia School of Electronic and Information Engineering South China University of Technology Guangzhou China Instituto de Telecomunicações Campus Universitário de Santiago Aveiro Portugal Department of Computer Science University of Idaho Moscow ID USA

ISBN: (数字)9781728109626

ISBN: (纸本)9781728109633

A Bayesian approach for joint beamforming and tracking is presented, which is robust to uncertain direction-of-arrival (DOA) estimation in millimeter wave (mmWave) multiple input multiple output (MIMO) systems. The uncertain or completely unknown DOA is modeled as a discrete random variable with a priori distribution defined over a set of candidate DOAs, which describes the level of uncertainty. The estimation problem of DOA is formulated as a weighted sum of previously observed DOA values, where the weights are chosen according to a posteriori probability density function (pdf) of the DOA. In particular, we present a motion trajectory-based a priori probability approximation method, which implies a high probability to perform a directional estimate within a specific spatial region. We demonstrate that the proposed approach is robust to DOA uncertainty, and the beam tracking problem can be addressed by incorporating the Bayesian approach with an expectation maximization (EM) algorithm. Simulation results validate the theoretical analysis and demonstrate the effectiveness of the proposed solution.

关键词： Direction-of-arrival estimation Radio frequency Bayes methods Array signal processing Uncertainty Estimation OFDM

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