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Stabilization of linear systems with distributed input delay and input saturation

Stabilization of linear systems with distributed input delay...

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作者： Zhou, Bin Gao, Huijun Lin, Zongli Duan, Guang-Ren Center for Control Theory and Guidance Technology Harbin Institute of Technology Harbin 150001 China Research Institute of Intelligent Control and Systems Harbin Institute of Technology Harbin 150001 China Charles L. Brown Department of Electrical and Computer Engineering University of Virginia Charlottesville VA 22904-4743 United States

This paper is concerned with stabilization of a linear system with distributed input delay and input saturation. Both constant and time-varying delays are considered. In the case that the input delay is constant, under the stabilizability assumption on an auxiliary system, it is shown that the system can be stabilized by state feedback for an arbitrarily large delay as long as the open-loop system is not exponentially unstable. In the case that the input delay is time-varying, but bounded, it is shown that the system can be stabilized by state feedback if the non-asymptotically stable poles of the open-loop system are all located at the origin. In both cases, stabilizing controllers are explicitly constructed by utilizing the parametric Lyapunov equation based low gain design approach we recently developed. It is also shown that in the presence of actuator saturation and under the same assumptions on the system, these controllers achieve semi-global stabilization. Some discussions on the assumptions we impose on the system are given. A numerical example illustrates the effectiveness of the proposed stabilization approach. © 2012 Elsevier Ltd. All rights reserved.

关键词： Stabilization

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Polyp Detection in Wireless Capsule Endoscopy Images by Using Region-Based Active Contour Model

Polyp Detection in Wireless Capsule Endoscopy Images by Usin...

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Iranian Conference of Biomedical engineering

作者： Hoda Eskandari Mahdi Alizadeh Alireza Talebpour Hamid Soltanian-Zadeh Department of Radiation Medicine Engineering Shahid Beheshti University Control and Intelligent Processing Center of Excellence (CIPE) School of Electrical and Computer Engineering University of Tehran

ISBN: (纸本)9781467331289

Wireless Capsule Endoscopy (WCE) is an almost new technology to capture images from the whole of Gastrointestinal (GI) tract, noninvasively. WCE is a very useful technology to detect various abnormalities like blood based abnormalities, ulcers and polyps. We note that polyps are growing tissues occur on the surface of tissue instead of inside an organ. Most polyps are not cancerous but if one becomes larger than a centimeter, it can turn into cancer by great chance. So, one of the most important advantages of WCE can be the early detection of polyps and cancers. In this paper we proposed using region-based Active Contour Method (ACM) and geometric feature for automatic detection of polyps. The results on a set of images show that the proposed method can achieve %90.91 accuracy, %100 sensitivity and %-83.33 specificity on our data set.

关键词： Polyp Wireless Capsule Endoscopy Active Countor Method

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A recursive local linear estimator for identification of nonlinear ARX systems

A recursive local linear estimator for identification of non...

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Universite Libre de Bruxelles

作者： Zhao, Wen-Xiao Zheng, Wei Xing Bai, Er-Wei Key Laboratory of Systems and Control Academy of Mathematics and Systems Science CAS Beijing China National Center for Mathematics and Interdisciplinary Sciences CAS Beijing China School of Computing Engineering and Mathematics University of Western Sydney Penrith NSW 2751 Australia Department of Electrical and Computer Engineering University of Iowa Iowa City IA 52242 United States

In this work, we propose a recursive local linear estimator (RLLE) for identification of nonlinear autoregressive systems with exogenous inputs, along with an analysis of its strong consistency and asymptotical mean s... 详细信息

ISBN: (纸本)9783902823069

关键词： Mean square error

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Fast on-chip quad-trees on GPU

Fast on-chip quad-trees on GPU

引用

IET International Conference on Information Science and control engineering 2012, ICISCE 2012

作者： Jian, Liheng Yi, Weidong Liu, Ying School of Electronic Electrical and Communication Engineering University of Chinese Academy of Sciences 100049 Beijing China School of Computer and Control University of Chinese Academy of Sciences 100049 Beijing China Research Center on Fictitious Economy and Data Science Chinese Academy of Sciences Beijing 100080 China

ISBN: (纸本)9781849196413

A quad-tree is a desirable data structure for neighbour search in many applications. Due to the irregular nature of trees, however, there is not an efficient implementation of a quad-tree on GPU. In this paper, we propose and set up CUDA-quad-trees on NVIDIA's GPU+CUDA parallel computing architecture, which takes advantage of the fast on-chip memory of GPU. This data structure for acceleration is used to organize sensor nodes so as to facilitate detection of possible transmitters in simulation of radio propagation in WSNs. Experimental results show that our tree is very efficient and greatly outperforms another CUDA-based tree with one or two order of magnitude speedup in different stages.

关键词： Graphics processing unit

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A Fast Foreground Object Detection Algorithm Using Kernel Density Estimation

A Fast Foreground Object Detection Algorithm Using Kernel De...

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2012 IEEE 11th International Conference on Signal Processing(ICSP 2012)

作者： Dawei Li Lihong Xu Erik Goodman Dept.of Control Science and Engineering Tongji University Dept.of Electrical and Computer Engineering and Beacon Center Michigan State University East Lansing USA 48824

a real-time foreground moving object detection algorithm based on Kernel Density Estimation in srgb color space is proposed in this paper, followed by an iterative noise reduction procedure, which removes dispersed noise and enhances the foreground contour in a binary image mask within a Bayesian framework. A simulated annealing strategy is applied in MRF-type decision making at the texture level to accelerate the convergence of the iterative noise reduction procedure. Experiments show that the proposed algorithm can resist undesirable effects of changing environmental illumination and shadow. Compared to several classical methods, better detection results are achieved on various datasets including both indoor and outdoor cases.

关键词： foreground detection, kernel density estimation, markov random field, noise reduction, simulated annealing

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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 Biomedical and Health Informatics, BHI 2012. In Conj. with the 8th Int. Symp.on Medical Devices and Biosensors and the 7th Int. Symp. on Biomedical and Health engineering

作者： Behzadfar, Neda Soltanian-Zadeh, Hamid Control and Intelligent Processing Center of Excellence School of Electrical and Computer Engineering University of Tehran Tehran 14395-515 Iran Pooyandegan Rah Saadat Company Tehran 16756-965 Iran Image Analysis Laboratory Radiology Department HenryFord Healh System Detroit MI 48202 United States

ISBN: (纸本)9781457721779

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 (R2=0.97). © 2012 IEEE.

关键词： Tumors

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Sparse online LS-SVM based on modified particle swarm optimization algorithm and application

Sparse online LS-SVM based on modified particle swarm optimi...

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International Conference on Intelligent control and Information Processing (ICICIP)

作者： Weiping Zhang Peifeng Niu Guoqiang Li Institute of Electrical Engineering Qinhuangdao Institute of Technology Yanshan University China key Laboratory of Industrial Computer Control Engineering of Hebei Province National Engineering Research Center for Equipment and Technology of Cold Strip Rolling Rolling China Institute of Electrical Engineering Yanshan University China

In this paper, A simple and effective mechanism is proposed to realize the parsimoniousness of the online least squares support vector machine. Hence, the response time is curtailed. Besides, a modified Particle Swarm Optimization (PSO) algorithm is proposed to ascertain the optimal model parameters. Simulation results show that it outperforms GA and common PSO algorithms and LS-SVM model based on the modified PSO algorithm has the best regression accuracy and generalization ability. The sparse online LS-SVM algorithm is applied to build a turbine heat rate forecasting model, which possesses dynamic prediction functions.

关键词： Prediction algorithms Heating Mathematical model Training Computational modeling Support vector machines Optimization

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A novel system for driver's sleepiness detection using SSVEP

A novel system for driver's sleepiness detection using SSVEP

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International Symposium on Artificial Intelligence and Signal Processing (AISP)

作者： Seyed Navid Resalat Valiallah Saba Fardin Afdideh Control and Intelligent Processing Center of Excellence School of Electrical and Computer Engineering College of Engineering University of Tehran Tehran Iran AJA University of Medical sciences Tehran Iran Tehran Iran

A novel EEG-based system for driver's sleepiness detection is proposed. Driver's sleepiness is an important factor in many accidents. Therefore, real-time sleepiness detection can restrain accidents effectively. In this study, SSVEPs are used for running the proposed system. In order to generate SSVEPs in the brain activities, two experimental setups consisting four single and paired LEDs are proposed. In addition, the effect of two different FFT-based feature extraction methods, and two different classifiers of the LDA and the SVM on the accuracy of the system are studied. Related features are extracted from three different segments (sweep lengths) of 0.5, 1, and 2 seconds. The experimental results show that higher sweep lengths have higher accuracies and the SVM classifier, experimental setup of 4-paired LEDs and sweep length of 1 second has the highest ITR value of 24 bits/min. Therefore, this study demonstrates the feasibility of the proposed system in a practical driving application.

关键词： Light emitting diodes Accuracy Feature extraction Vehicles Sleep Support vector machines Electroencephalography

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Takagi-Sugeno control of the Elevation Channel of A Twin-Rotor System Using Closed-Loop Empirical Data

Takagi-Sugeno Control of the Elevation Channel of A Twin-Rot...

引用

American control Conference

作者： Hamidreza Azimian Alireza Fatehi Babak N. Araabi Department of Mechanical and Materials Engineering The University of Western Ontario (UWO) London ON Canada Advanced Process Automation and Control (APAC) Research Group Industrial Control Center of Excellence Faculty of Electrical and Computer engineering K. N. Toosi University of Technology Tehran Iran School of Electrical and Computer Engineering University of Tehran Tehran Iran

ISBN: (纸本)9781457710957

In this paper, design of Takagi-Sugeno controllers for a twin-rotor system from empirical data is addressed. First, identification of linear models using Prediction Error Method in the presence of nonlinear distortions and in feedback systems is revisited, and it is demonstrated that feedback nonlinear systems with an integral action can be locally represented by linear models rather than affine models. Moreover, it is demonstrated that how the consistency and unbiasedness of the identified models can be improved by proper design of the experiments. In order to ensure that the unmodeled contributions in the resulting piecewise linear model are sufficiently small, a criterion is proposed based on the small-gain theorem that can be used for unfalsification of the piecewise linear model. Finally, by taking advantage of the separation principle, design of fuzzy Takag-Sugeno state feedback and observer is formulated in terms of Linear Matrix Inequalities (LMIs). The implementation results on the actual system demonstrate significant improvements with respect to the initial controller and previous work on similar systems.

关键词： Nonlinear systems Closed loop systems Observers Uncertainty Data models Predictive models

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A Hierarchal Classifier for Identifying Independent Components

A Hierarchal Classifier for Identifying Independent Componen...

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International Joint Conference on Neural Networks

作者： Chin-Teng Lin Yu-Kai Wang Shi-An Chen Department of Electrical and Control Engineering and Brain Research Center National Chiao-Tung University Department of Computer Science and Brain Research Center National Chiao-Tung University

ISBN: (纸本)9781467314886

Brain-computer interface (BCI) has shown explosive growth for multiple applications in the recently years. Removing artifacts and selecting useful brain sources are essential in BCI research. Independent Component Analysis (ICA) has been proven as an effective technique to remove artifacts and many brain related researches are based on ICA. However, the useful independent components with brain sources are usually selected manually according to the scalp-plots. This is great inconvenience and a barrier for real-time BCI applications of EEG. In this investigation, a two-layer automatic identification model is proposed to select useful brain sources. It is based on neural network including support vector machine with radial basis function (SVMRBF) and self-organizing map (SOM). In the first layer, SVM discriminates useful independent components from the artifact effectively. In the second layer, these selected useful components are automatically classified to different spatial brain sources according to SOM. This study suggests this model to one general application for EEG study. It can reduce the effect of subjective judgment and improve the performance of EEG analysis.

关键词： Component Brain-computer interface Independent component analysis Electroencephalogram Neural network independent components Electroencephalography Independent component analysis self-organising feature maps Support Vector Network Artifacts Cerebrum islet cell antibody Scalp Classifiers

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