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2012 3rd International Conference on Intelligent control and Information Processing, ICICIP 2012

作者： Li, Ronghui Li, Tieshan Zheng, Qinling Li, Qiang Navigaion College Dalian Maritime University Dalian China Center for Advanced Control Technologies Department of Electrical Computer Engineering Cleveland State University Cleveland OH United States

ISBN: (纸本)9781457721427

Owing to ship's high inertia, strong time lag, non-linearity and under-actuated characteristics, as well as sustaining external disturbances frequently, designing a high performance ship tracking controller is still one of the most important research topics of ship motion control. Active Disturbance Rejection control (ADRC) is proved to be very effective control method because it is independent of mathematical model of the plant and can compensate the internal and external disturbances dynamically. This paper investigates the applications of ADRC in ship motion tracking control and presents the design method of ADRC controller appropriate for ship tracking control. Firstly, ship motion mathematical model is introduced briefly and the linear ADRC (LADRC) is analyzed. Then considering the nonlinear ship track design model as a third order plant after utilizing variable substitution, a LADRC controller is designed by using fourth order linear extended state observer (LESO). At last, the simulation is performed. Simulation results show that the controller has a good tracking performance and disturbance rejection ability and is robust to the ship motion nonlinear characteristic and the external disturbances. The tracking process is fast, smooth and minor energy consumption. © 2012 IEEE.

关键词： controllers

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Short-term wind speed prediction method based on time series combined with LS-SVM

Short-term wind speed prediction method based on time series...

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31st Chinese control Conference, CCC 2012

作者： Xiaojuan, Han Xilin, Zhang Fang, Chen Zhihui, Song Chengmin, Wang College of Control and Computer Engineering North China Electric Power University Beijing 102202 China Dispatching Center Changchun Power Supply Company Changchun 130012 China Electrical and Information Engineering College Shanghai Jiao Tong University Shanghai 200240 China

ISBN: (纸本)9789881563811

A short-term wind speed prediction method based on time series combined with LS-SVM is proposed in this paper according to the characteristic of wind speed. The original wind speed signals are decomposed into high frequency part and low frequency part by wavelet decomposition and reconstruction. ARMA model is constructed to predict the wind speed values of high frequency part which can be regarded as smoothing and steady signals and LS-SVM model is used to predict the wind speed values of low frequency part. The final prediction result of original wind speed signal is the fusing of the respective predicting results. The effectiveness of the method is verified by a simulation example. The forecast precision is obviously improved by the combination forecast model provided in this paper. © 2012 Chinese Assoc of Automati.

关键词： Time series

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Interfaces of nested Web application framework as reusable software component

Interfaces of nested Web application framework as reusable s...

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Proceedings of the International Convention MIPRO

作者： S. Prstačić M. Žagar K. Kroflin Information Support Center Faculty of Electrical Engineering and Computing Zagreb Croatia Department of Control and Computer Engineering Faculty of Electrical Engineering and Computing Zagreb Croatia

The framework is designed to provide a simple and powerful way of Web application component development. Components of the framework can nest, and the framework is designed to be easy to reuse as a component, thus providing its components to be used by the host application. We explain the interfaces required to make this possible - what they provide, what issues they solve, and how their requirement was discovered. We also identify shortcomings of this approach, and issues that will be addressed by future work.

关键词： Blogs Abstracts Software Context Permission HTML Software engineering

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Noise reduction in intravascular ultrasound images using curvelet transform and adaptive complex diffusion filter: A comparative study

Noise reduction in intravascular ultrasound images using cur...

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Iranian Conference on electrical engineering (ICEE)

作者： F. Yousefi Rizi S. K. Setarehdan The Control and Intelligent Processing Center of Excellence School of Electrical and Computer Engineering College of Engineering University of Tehran Tehran Iran

Speckle noise reduction is the first and important step for intravascular ultrasound (IVUS) image analysis. A comparison between the performances of the curvelet transform and adaptive complex diffusion filter method for ultrasound despeckling is presented in this paper. In this study, the adaptive complex diffusion filter which is widely used for despeckling, is applied to real intravascular ultrasound images and results were quantitatively and visually compared to that of curvelet based despeckling. The results show that curvelet based method outperforms the adaptive complex diffusion filter and can effectively reduce most of the speckle noise content of an intravascular ultrasound image.

关键词： Medical diagnostic imaging Economic indicators Materials PSNR Wavelet domain

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Automatic segmentation of brain tumors in magnetic resonance images

Automatic segmentation of brain tumors in magnetic resonance...

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IEEE EMBS International Conference on Information Technology Applications in Biomedicine (ITAB)

作者： Neda Behzadfar Hamid Soltanian-Zadeh Control and Intelligent Processing Center of Excellence School of Electrical and computer Engineering College of Engineering University of Tehran Tehran Iran

Segmentation of tumors in magnetic resonance images (MRI) is an important task but is quite time consuming when performed manually by experts. Automating this process is challenging due to the high diversity in appearance of tumor tissue in different patients, and in many cases, similarity between tumor and normal tissues. This paper presents an automatic method for segmentation of brain tumors in MRI. We use images of patients with glioblastoma multiform tumors. After pre-processing and removal of the regions that do not have useful information (e.g., eyes and scalp), we create a projection image for determining the primary location of the tumor. This image provides an overall view of the tumor. Then, we grow the primary region to segment the entire tumor. This method is automatic and independent of the operator. It segments low contrast tumors without requiring their exacta tissue boundaries. The segmentation results obtained by the proposed approach are compared with those of an expert radiologist showing excellent correlations among them (R 2 =0.97).

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A method based on the granger causality and graph kernels for discriminating resting state from attentional task

A method based on the granger causality and graph kernels fo...

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International Conference on Biomedical engineering (ICoBE)

作者： Danesh Shahnazian Fatemeh Mokhtari Gholam-Ali Hossein-Zadeh Control and Intelligent Processing Center of Excellence School of Electrical and Computer Engineering College of Engineering University of Tehran Tehran Iran

Exploring the directional connections between brain regions is of great importance in understanding the brain function. As a method of this exploration, Granger causality is defined in terms of the amount of improvement in the estimation of a signal by past samples of another signal (cause). This method produced reliable results in various applications. In current study, we use connections of directed graphs as the features for discriminating two brain states, rest and attentional cueing task, in a block design fMRI dataset. We apply a support vector machine (SVM) which is enriched by graph kernels like random walk, graphlet and sub-tree kernels on directed graphs of different brain states. Graph kernel methods are a branch of graph matching methods and have recently been proposed as a theoretically sound and promising approach to the problem of graph comparison. They measure the inexact similarity between graphs. For the first time, we apply graph kernels on graphs of brain's effective connectivity. We achieved classification accuracy of 100% in discrimination of resting state from attentional task. We also obtain one graph for each brain state representing causal connections between brain regions. From the networks obtained for each state, we can infer that caudate is the source of information in both states and Left ventromedial prefrontal is the sink of information in the resting state.

关键词： Kernel Time series analysis Support vector machines Educational institutions Vectors Predictive models Mathematical model

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Distributed Network Size Estimation and Average Degree Estimation and control in Networks Isomorphic to Directed Graphs

Distributed Network Size Estimation and Average Degree Estim...

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Annual Allerton Conference on Communication, control, and Computing

作者： Iman Shames Themistoklis Charalambous Christoforos N. Hadjicostis Mikael Johansson Department of Electrical and Electronic Engineering University of Melbourne Automatic Control Lab Electrical Engineering Department and ACCESS Linnaeus Center Royal Institute of Technology (KTH) Department of Electrical and Computer Engineering at the University of Cyprus

ISBN: (纸本)9781467345378

Many properties of interest in graph structures are based on the nodes' average degree (i.e., the average number of edges incident to/from each node). In this work, we present asynchronous distributed algorithms, based on ratio consensus, that can be used to accurately estimate the number of nodes in a multi-component system whose communication topology is described by a directed graph. In addition, we describe an asynchronous distributed algorithm that allows each node to introduce or terminate links in order to reach a target average degree in the network. Such an approach can be useful in many realistic scenarios;for example, for the introduction and removal of renewable energy resources in a power network, while maintaining an average degree that fulfils some structural and dynamical properties and/or optimises some performance indicators of the network. The effectiveness of the proposed algorithms is demonstrated via illustrative examples.

关键词： distributed algorithms directed graphs Power grids performance indicators DISTRIBUTED NETWORKS Renewable energy resources Line of nodes Structural Elements (Construction) Ganglia Isomorphic Standing Orders Asynchronous

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The dynamic control during mode-switches for PHEV

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Journal of Information and Computational Science 2012年第15期9卷 4501-4508页

作者： Zang, Huaigang Han, Yanlong Niu, Yanxia Key Lab. of Industrial Computer Control Engineering of Hebei Province National Engineering Research Center for Equipment and Technology of Cold Strip Rolling Qinhuangdao 066004 China Key Lab. of Industrial Computer Control Engineering of Hebei Province Institute of Electrical Engineering Yanshan University Qinhuangdao 066004 China

The control system of a hybrid electric vehicle assigns powers between the engine and the motor according to the vehicle's running conditions, switching the optimum driving mode. So, how to keep the torque transition swift and steady during mode-switches becomes an important aspect of hybrid electric vehicles' control problem. To solve this kind of problems, basing on the estimation of the engine's torque, this paper uses the motor's output to compensate power shortages of the vehicle caused by the engine's time-relay, and verifies its efficiency by building simulation models. The results illustrate that torque dynamic compensation algorithms can effectively guarantee the desired torque during mode-switches, and make sure swiftness and stability of torque transition. © 2012 by Binary Information Press.

关键词： Torque

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Optimizing Grid Connected Renewable Energy Resources with Variability

Optimizing Grid Connected Renewable Energy Resources with Va...

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IEEE Power and Energy Society General Meeting

作者： James A. Momoh Keisha D'Arnaud School of Electrical and Computer Engineering Center for Energy Systems and Control (CESaC) Howard University Washington DC 20059

ISBN: (纸本)9781467327275

The randomness of photovoltaic (PV) and wind as renewable energy resources (RER) are best modeled as stochastic variable resources with given status and probability density function (pdf). The objective’s multiple criteria consist of cost and reliability. These are interlinked by the impact of RER and networks consisting of voltages, flows and available generation resources. To account for the variability and randomness, heuristic technique is developed so as to handle the resources in the implication of the optimal power flow. This will account for the stochastic nature of the problem. The scheme proposed is a framework towards building or designing a stochastic optimal power flow for handling variability of the load randomness. Case studies are proposed under different power output of PV and wind contributions are tested for both the performance load flow and optimization.

关键词： renewable resources variability probability density function optimal power flow Random Number Generators Power output Renewable energy resources optimal load flow Polydnaviridae Power flow Probability density function VOLTAGE Stochastic Buildings

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Feature-Based Approach to Fuse fMRI and DTI in Epilepsy Using Joint Independent Component Analysis

Feature-Based Approach to Fuse fMRI and DTI in Epilepsy Usin...

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Iranian Conference of Biomedical engineering

作者： Amir Hosein Riazi Hamid Soltanian-Zadeh Gholam-Ali Hossein-Zadeh Control and Intelligent Processing Center of Excellence (CIPCE) School of Electrical and Computer Engineering University College of Engineering University of Tehran Tehran Iran Control and Intelligent Processing Center of Excellence (CIPCE) School of Electrical and Computer Engineering University College of Engineering University of Tehran Tehran Iran & Image Analysis Laboratory Henry Ford Health System Detroit Michigan

ISBN: (纸本)9781467331289

Functional magnetic resonance imaging (fMRI) and structural MRI (sMRI) provide complementary information. Signal processing and statistical models may be used to fuse neuroimaging data across different imaging modalities. In this paper, we present a data driven method for fusing resting state fMRI and diffusion tensor imaging (DTI) data at feature level. The features are amplitude of low frequency fluctuations (ALFF) and fractional anisotropy (FA) extracted from fMRI and DTI datasets of epilepsy and healthy controls, respectively. We discuss main issues associated with group independent component analysis (ICA) as a fusion method. We address our proposed approach for combining two modalities across subjects and back reconstruction of independent components for each group and each subject. Our results indicate that connectivity of regions in default mode network depends on integrity of white matter that connects the two hemispheres (corpus callosum). The proposed signal processing and statistical methods facilitate evaluation of brain connectivity using different modalities. Separate analysis of data modalities does not reveal results of joint analysis.

关键词： FMRI DTI Joint ICA Group Analysis

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