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IEEE Workshop on Evolving and Adaptive Intelligent systems (EAIS)

作者： Guilherme G. Netto Alexandre C. Barbosa Mateus N. Coelho Arthur R. L. Miranda Vitor N. Coelho Marcone J. F. Souza Frederico G. Guimarães Agnaldo J. R. Reis Department of Control and Automation Engineering Universidade Federal de Ouro Preto Ouro Preto MG Brazil Graduate Program of Electrical Engineering Univesidade Federal de Minas Gerais Belo Horizonte MG Brazil Department of Computer Science Universidade Federal de Ouro Preto Ouro Preto MG Brazil Departament of Electrical Engineering Univesidade Federal de Minas Gerais Belo Horizonte MG Brazil

Several works in the literature so far have been focused on deterministic point forecasts, which, usually, indicates the conditional mean of future observations. An increasing need for generating the entire conditional distribution of future observations has been required for the new generation of soft sensors. This study aims the probabilistic forecasts, reporting the use of a hybrid fuzzy forecasting model applied in two different forecasting problems. Our adapted model is applied to predict the rain of the city of Vitoria, in the state of Espírito Santo, Brazil. Real data from a wind farm, provided by the Irish EirGrid institute, was used for analyzing the proposal over a real time series with high fluctuations. Due to the stochasticity of the the hybrid model, which is calibrated through the use of an evolutionary metaheuristic, we adapted it in order to generate future using quantile regression. Computational experiments indicated the ability of the model in finding useful probabilistic quantiles, which were flexible enough in order to limit the lower and upper bounds of the historical datasets. While the probabilistic quantiles suggested the probability of rain and its magnitude, they were also able to predict expected ranges of the amount of energy generated from the wind farm.

关键词： Forecasting Predictive models Wind forecasting Probabilistic logic Wind power generation Adaptation models Time series analysis

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Reconfigurable control of input affine nonlinear systems under actuator fault 9

Reconfigurable control of input affine nonlinear systems und...

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9th IFAC Symposium on Fault Detection, Supervision and Safety for Technical Processes, SAFEPROCESS 2015

作者： Tabatabaeipour, S. Mojtaba Galeazzi, Roberto System Operation Analysis Energinet.dk Tonne Kjærsvej 65 Fredericia Denmark Automation and Control Department of Electrical Engineering Technical University of Denmark Kgs. Lyngby Denmark

This paper proposes a fault tolerant control method for input-affine nonlinear systems using a nonlinear reconfiguration block (RB). The basic idea of the method is to insert the RB between the plant and the nominal controller such that fault tolerance is achieved without re-designing the nominal controller. The role of the RB is twofold: on one hand it transforms the output of the faulty system such that its behaviour is similar to that of the nominal one from the controller's viewpoint;on the other hand it modifies the control input to the faulty system such that the stability of the reconfigured loop is preserved. The RB is realized by a virtual actuator and a reference model. Using notions of incremental and input-to-state stability (ISS), it is shown that ISS of the closed-loop reconfigured system can be achieved by the separate design of the virtual actuator. The proposed method does not need any knowledge of the nominal controller and only assumes that the nominal closed-loop system is ISS. The method is demonstrated on a dynamic positioning system for an offshore supply vessel, where the virtual actuator is designed using backstepping. © 2015, IFAC (International Federation of Automatic control) Hosting by Elsevier Ltd. All rights reserved.

关键词： Fault tolerance

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A Hybrid Deep Learning Forecasting Model Using GPU Disaggregated Function Evaluations Applied for Household Electricity Demand Forecasting

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Energy Procedia 2016年 103卷 280-285页

作者： Vitor N. Coelho Igor M. Coelho Eyder Rios Alexandre S.T. Filho Agnaldo J.R. Reis Bruno N. Coelho Alysson Alves Guilherme G. Netto Marcone J.F. Souza Frederico G. Guimarães Graduate Program in Electrical Engineering Universidade Federal de Minas Gerais Belo Horizonte Brazil Instituto de Pesquisa e Desenvolvimento de Tecnologias Ouro Preto Brazil Department of Computer Science State University of Rio de Janeiro Rio de Janeiro Brazil Institute of Computer Science Fluminense Federal University Niteroi Brazil Department of Control Engineering and Automation Universidade Federal de Ouro Preto Ouro Preto Brazil Department of Computer Science Universidade Federal de Ouro Preto Ouro Preto Brazil Department of Electrical Engineering Universidade Federal de Minas Gerais BeloHorizonte Brazil

As the new generation of smart sensors is evolving towards high sampling acquisitions systems, the amount of information to be handled by learning algorithms has been increasing. The Graphics Processing Unit (GPU) architectures provide a greener alternative with low energy consumption for mining big-data, harnessing the power of thousands of processing cores in a single chip, opening a widely range of possible applications. Here, we design a novel evolutionary computing GPU parallel function evaluation mechanism, in which different parts of time series are evaluated by different processing threads. By applying a metaheuristics fuzzy model in a low-frequency data for household electricity demand forecasting, results suggested that the proposed GPU learning strategy is scalable as the number of training rounds increases.

关键词： Microgrid Household Electricity Demand Deep Learning Graphics Processing Unit Parallel forecasting model Big Time-series Data

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A generalized piecewise quadratic lyapunov function approach to estimating the domain of attraction of a saturated system

A generalized piecewise quadratic lyapunov function approach...

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1st IFAC Conference on Modelling, Identification and control of Nonlinear systems, MICNON 2015

作者： Li, Yuanlong Lin, Zongli Department of Automation Shanghai Jiao Tong University Key Laboratory of System Control and Information Processing of Ministry of Education Shanghai200240 China Charles L. Brown Department of Electrical and Computer Engineering University of Virginia CharlottesvilleVA229044743 United States

This paper revisits the problem of estimating the domain of attraction of a saturated system with an algebraic loop. A piecewise quadratic Lyapunov function of an augmented state vector composing of the system state and the saturated input has been proposed for use in estimating the domain of attraction for a saturated system. Considering the relationship between the system states and the saturation function, we propose in this paper a generalized piecewise quadratic Lyapunov function, which results from adding a term that characterizes the regional sector condition of the saturated input to the piecewise quadratic Lypunov function. The matrix associated with the generalized piecewise quadratic Lyapunov function is not required to be positive definite, and thus a set of less conservative stability conditions are established, from which a larger estimate of the domain of attraction can be obtained. Simulation results indicate that the effectiveness and superiority of the proposed method.

关键词： Lyapunov functions

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Photovoltaic power plant submitted to voltage sags: Model and performance evaluation 13

Photovoltaic power plant submitted to voltage sags: Model an...

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13th IEEE Brazilian Power Electronics Conference and 1st Southern Power Electronics Conference, COBEP/SPEC 2016

作者： Pires, Igor A. Machado, Alysson Foureaux, Nicole Silva, Sidelmo M. Filho, Braz J. Cardoso Brito, Jose Department of Electronic Engineering - UFMG Belo Horizonte - MG Brazil Control and Automation Engineering Undergraduate Course - UFMG City - Belo Horizonte - MG Brazil Electrical Engineering Graduate Program- UFMG Belo Horizonte - MG Brazil Department of Electrical Engineering - UFMG Belo Horizonte - MG Brazil Companhia de Eletricidade Do Estado da Bahia Salvador-BA Brazil

ISBN: (纸本)9781479987795

This paper brings the PV model of a real PV farm installation with a total power of 917 kW. All the elements of the PV farm (panels and inverter) are described. Simulation on PSCAD and experimental results at the installation are reported. The PV model is submitted to voltage sags originated in the grid as well as their impact on the photovoltaic power plant. © 2015 IEEE.

关键词： Solar panels

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Identifying the best machine learning algorithms for brain tumor segmentation, progression assessment, and overall survival prediction in the BRATS challenge

arXiv

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arXiv 2018年

作者： Bakas, Spyridon Reyes, Mauricio Jakab, Andras Bauer, Stefan Rempfler, Markus Crimi, Alessandro Shinohara, Russell Takeshi Berger, Christoph Ha, Sung Min Rozycki, Martin Prastawa, Marcel Alberts, Esther Lipkova, Jana Freymann, John Kirby, Justin Bilello, Michel Fathallah-Shaykh, Hassan M. Wiest, Roland Kirschke, Jan Wiestler, Benedikt Colen, Rivka Kotrotsou, Aikaterini Lamontagne, Pamela Marcus, Daniel Milchenko, Mikhail Nazeri, Arash Weber, Marc-Andr Mahajan, Abhishek Baid, Ujjwal Gerstner, Elizabeth Kwon, Dongjin Acharya, Gagan Agarwal, Manu Alam, Mahbubul Albiol, Alberto Albiol, Antonio Albiol, Francisco J. Alex, Varghese Allinson, Nigel Amorim, Pedro H.A. Amrutkar, Abhijit Anand, Ganesh Andermatt, Simon Arbel, Tal Arbelaez, Pablo Avery, Aaron Azmat, Muneeza Pranjal, B. Bai, Wenjia Banerjee, Subhashis Barth, Bill Batchelder, Thomas Batmanghelich, Kayhan Battistella, Enzo Beers, Andrew Belyaev, Mikhail Bendszus, Martin Benson, Eze Bernal, Jose Bharath, Halandur Nagaraja Biros, George Bisdas, Sotirios Brown, James Cabezas, Mariano Cao, Shilei Cardoso, Jorge M. Carver, Eric N. Casamitjana, Adri Castillo, Laura Silvana Cat, Marcel Cattin, Philippe Cérigues, Albert Chagas, Vinicius S. Chandra, Siddhartha Chang, Yi-Ju Chang, Shiyu Chang, Ken Chazalon, Joseph Chen, Shengcong Chen, Wei Chen, Jefferson W. Chen, Zhaolin Cheng, Kun Choudhury, Ahana Roy Chylla, Roger Clrigues, Albert Colleman, Steven Colmeiro, Ramiro German Rodriguez Combalia, Marc Costa, Anthony Cui, Xiaomeng Dai, Zhenzhen Dai, Lutao Daza, Laura Alexandra Deutsch, Eric Ding, Changxing Dong, Chao Dong, Shidu Dudzik, Wojciech Eaton-Rosen, Zach Egan, Gary Escudero, Guilherme Estienne, Tho Everson, Richard Fabrizio, Jonathan Fan, Yong Fang, Longwei Feng, Xue Ferrante, Enzo Fidon, Lucas Fischer, Martin French, Andrew P. Fridman, Naomi Fu, Huan Fuentes, David Gao, Yaozong Gates, Evan Gering, David Gholami, Amir Gierke, Willi Glocker, Ben Gong, Mingming Gonzlez-Vill, Sandra Grosges, T. Guan, Yuanfang Guo, Sheng Gupta, Sudeep Han, Woo-Sup Han, Il Song Harmuth, Ko Center for Biomedical Image Computing and Analytics University of Pennsylvania PhiladelphiaPA United States Department of Radiology Perelman School of Medicine University of Pennsylvania PhiladelphiaPA United States Department of Pathology and Laboratory Medicine Perelman School of Medicine University of Pennsylvania PhiladelphiaPA United States Institute for Surgical Technology and Biomechanics University of Bern Bern Switzerland Center for MR-Research University Children's Hospital Zurich Zurich Switzerland Support Centre for Advanced Neuroimaging Inselspital Institute for Diagnostic and Interventional Neuroradiology Bern University Hospital Bern Switzerland University Hospital of Zurich Zurich Switzerland Center for Clinical Epidemiology and Biostatistics University of Pennsylvania Philadelphia United States Image-Based Biomedical Modeling Group Technical University of Munich Munich Germany Icahn School of Medicine Mount Sinai Health System New YorkNY United States Leidos Biomedical Research Inc. Frederick National Laboratory for Cancer Research FrederickMD21701 United States Cancer Imaging Program National Cancer Institute National Institutes of Health BethesdaMD20814 United States Department of Neurology University of Alabama at Birmingham BirminghamAL United States Department of Diagnostic Radiology University of Texas MD Anderson Cancer Center HoustonTX United States Department of Psychology Washington University St. LouisMO United States Neuroimaging Informatics and Analysis Center Washington University St. LouisMO United States Department of Radiology Washington University St. LouisMO United States Institute of Diagnostic and Interventional Radiology Pediatric Radiology and Neuroradiology University Medical Center Rostock Ernst-Heydemann-Str. 6 Rostock18057 Germany Tata Memorial Centre Homi Bhabha National Institute Mumbai India Shri Guru Gobind Singhji Institute of Engineering and Technology Nanded India NVIDIA Santa Clara

Gliomas are the most common primary brain malignancies, with different degrees of aggressiveness, variable prognosis and various heterogeneous histologic sub-regions, i.e., peritumoral edematous/invaded tissue, necrotic core, active and non-enhancing core. This intrinsic heterogeneity is also portrayed in their radio-phenotype, as their sub-regions are depicted by varying intensity profiles disseminated across multi-parametric magnetic resonance imaging (mpMRI) scans, reflecting varying biological properties. Their heterogeneous shape, extent, and location are some of the factors that make these tumors difficult to resect, and in some cases inoperable. The amount of resected tumor is a factor also considered in longitudinal scans, when evaluating the apparent tumor for potential diagnosis of progression. Furthermore, there is mounting evidence that accurate segmentation of the various tumor sub-regions can offer the basis for quantitative image analysis towards prediction of patient overall survival. This study assesses the state-of-the-art machine learning (ML) methods used for brain tumor image analysis in mpMRI scans, during the last seven instances of the International Brain Tumor Segmentation (BraTS) challenge, i.e., 2012-2018. Specifically, we focus on i) evaluating segmentations of the various glioma sub-regions in pre-operative mpMRI scans, ii) assessing potential tumor progression by virtue of longitudinal growth of tumor sub-regions, beyond use of the RECIST/RANO criteria, and iii) predicting the overall survival from pre-operative mpMRI scans of patients that underwent gross total resection. Finally, we investigate the challenge of identifying the best ML algorithms for each of these tasks, considering that apart from being diverse on each instance of the challenge, the multi-institutional mpMRI BraTS dataset has also been a continuously evolving/growing dataset. Copyright © 2018, The Authors. All rights reserved.

关键词： Tumors

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Robust tracking control design for a flexible air-breathing hypersonic vehicle

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Journal of Central South University 2014年第1期21卷 130-139页

作者：张垚鲜斌刁琛赵勃郭建川 Institute of Robotics and Autonomous System Tianjin Key Laboratory of Process Measurement and ControlSchool of Electrical Engineering and AutomationTianjin University

A nonlinear robust controller was presented to improve the tracking control performance of a flexible air-breathing hypersonic vehicle(AHV) which is subjected to system parametric uncertainties and unknown additive time-varying *** longitudinal dynamic model for the flexible AHV was used for the control ***-gain observers were designed to compensate for the system uncertainties and additive *** gain theorem and Lyapunov based stability analysis were utilized to prove the stability of the closed loop *** uniformly ultimately bounded tracking of the vehicle's velocity,altitude and attack angle were achieved under aeroelastic effects,system parametric uncertainties and unknown additive ***/Simulink simulation results were provided to validate the robustness of the proposed control *** simulation results demonstrate that the tracking errors stay in a small region around zero.

关键词： hypersonic vehicles nonlinear robust control flexible mode small gain theorem Lyapunov

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A Simulation Platform for Characteristic Model-Based Adaptive control systems

A Simulation Platform for Characteristic Model-Based Adaptiv...

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第三十四届中国控制会议

作者： Liangren Shi Zongli Lin Yuanlong Li Department of Automation and Key Laboratory of System Control and Information ProcessingShanghai Jiao Tong University Charles L. Brown Department of Electrical and Computer Engineering University of Virginia

ISBN: (纸本)9781467374439

Characteristic modeling and the adaptive control design based on it have attracted increasing attention from the research community due to their many successful practical applications that have been reported. This paper presents a Matlab simulation platform that has been developed to facilitate the study of characteristic model based adaptive control systems. The simulation platform consists of two interweaving components: for a given continuous-time linear time-invariant plant, represented by a transfer function, and a specified sampling period, the simulation platform calculates a discretized model of the plant, and the corresponding characteristic model, and with a specified set of initial conditions of the characteristic model and its coefficients, along with the specified duration of simulation and reference input, the simulation platform will adaptively tune the coefficients of the characteristic model and calculate the outputs of the plant and the characteristic model. All the data needed for the simulation will be entered through a graphic user interface, which also plots the evolution of the outputs of the plant and the characteristic model as well as the evolution of the coefficients of the characteristic model. The simulation results can also be saved for off line plotting and analysis through the graphic user interface.

关键词： Characteristic model,adaptive control,simulation

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Repetitive controller with low-pass filter compensation applied to Uninterruptible Power Supplies (UPS) 41

Repetitive controller with low-pass filter compensation appl...

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41st Annual Conference of the IEEE Industrial Electronics Society, IECON 2015

作者： Lorenzini, Charles Flores, Jeferson Vieira Pereira, Luis Fernando Alves Salton, Aurelio Tergolina Castro, Rafael Silveira Department of Electrical Engineering Universidade Federal Do Rio Grande Do sul Av. Osvaldo Aranha 103 Porto Alegre-RS90035-190 Brazil Automation and System Control Group Pontifícia Universidade Católica Do Rio Grande Do sul Av. Ipiranga 6681 Porto Alegre-RS90619-900 Brazil

ISBN: (纸本)9781479917624

In this paper is proposed a method to improve the tracking performance of repetitive controllers in Uninterruptible Power Supplies (UPS). The main idea is to simultaneously compensate the gain reduction and frequency shift caused by the introduction of a low-pass filter in series with the delay element. Also, an optimization problem based on LMI constraints is presented to determine controller gains that guarantee stability and performance of the closed loop system. Simulation results based on the output stage of 3.5kVA UPS demonstrate the effectiveness of the proposed method. © 2015 IEEE.

关键词： Uninterruptible power systems

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A discrete-time current controller for permanent magnet synchronous motor drives

A discrete-time current controller for permanent magnet sync...

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Annual Conference of Industrial Electronics Society

作者： Türker Türker Ümit Büyükkeleş Hüseyin Meşe Faruk Bakan Dept. of Control & Automation Engineering Yildiz Technical University Istanbul Turkey Defence System Technologies ASELSAN A.S. Ankara Turkey Dept. of Electrical Engineering Yildiz Technical University Istanbul Turkey

A discrete-time current controller is presented for the current loop of the permanent magnet synchronous motor drives. Firstly, the difference equations of current errors are obtained for constant reference values. Then, a dead-beat controller is designed to drive the dq-axes currents to the desired values. Discrete-time integral action with an anti-windup scheme is added to the dead-beat structure to achieve a better steady-state behaviour. In order to improve the transient properties of the controller, a current prediction scheme based on dead-beat structure is also introduced to the controller. Finally, the proposed controller is implemented through numerical simulations to illustrate the effectiveness for both transient and steady-state responses.

关键词： Torque Pulse width modulation Delays Numerical simulation Uncertainty Current measurement Current control

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