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2010 2nd International Conference on Signal Processing system(2010年信号处理系统国际会议 ICSPS 2010)

作者： Yixu Song Jing Hu Xiaokui Yang Xiufen Xie Jie Fu State Key Laboratory of Intelligent Technology and System Department of Computer Science and Technol Department of Computer Science and Technology University of Science and Technology Beijing Beijing Sany Intelligent Control Equipment CO.LTD Changsha Hunan China

The sampling storage method which used in the current data stream could not respond data tendency effectively. For the problem, this paper presents a new processing method based on curve fitting. A weighted least square principle is used to fit the cached stream data and better model description is obtained. Then the fitting results are analyzed by clustering algorithm, which serves as a classifier for polynomial fitting parameters According to the clustering result, the appropriate window size will be given to fit the periodic stream data. Comparing the function solutions with the actual data, the different methods are adopted to store data according to the comparison result. The experimental results indicate that the proposed method has better fitting accuracy and compression ratio, could meet the requirement of data stream processing. And the data tendency could be responded effectively by the fitting results.

关键词： curve fitting data stream clustering least-square principle

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Internal model control for hysteretic nonlinear systems based on support vector regression

Internal model control for hysteretic nonlinear systems base...

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World Congress on Intelligent control and Automation (WCICA)

作者： Wenguang Zhang Jianqin Mao Yanhua Ma Beijing Key Laboratory of Measurement and Control Technology and System for Industrial Process Control & Computer Engineering School North China Electric Power University Beijing China School of Automation Science and Electrical Engineering Beijing Aeronautics and Astronautics University Beijing China

Aim at the hysteretic nonlinearity characteristic of the giant magnetostrictive actuator in control, an internal model control method based on support vector regression is presented in this paper. Its models are built by support vector regression using the input and output data of the actuator and the internal model control is achieved. The simulation results show the method in this paper has better control precision.

关键词： Support vector machines Actuators Automation Data models Computational modeling Hysteresis Magnetostriction

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Comparison of voxel-based morphometry (VBM) and tractography of diffusion tensor MRI (DT-MRI) in temporal lobe epilepsy

Comparison of voxel-based morphometry (VBM) and tractography...

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Iranian Conference on Electrical Engineering (ICEE)

作者： Maryam Afzali Hamid Soltanian-Zadeh Control and Intelligent Processing Center of Excellence School of Electrical and Computer Engineering University of Tehran Tehran Iran Image Analysis Laboratory Radiology Department Henry Ford Health System Detroit MI USA

ISBN: (纸本)9781424467600

Temporal lobe epilepsy (TLE) is a neurological disease that affects millions of individuals in the world. Majority of TLE patients suffer from refractory seizures. Determining abnormal/damaged regions of the brain that harbor seizure focus is a critical step towards treatment of TLE patients. Diffusion tensor imaging (DTI) is a non-invasive method that we use in this work to determine damaged regions of white matter in the brain. To this end, we apply two methods, voxel based morphometry (VBM) and fiber tracking. We use these methods along with two anisotropy maps (EAR and FA) extracted from the DTI data. We use DTI data of 31 subjects (12 normal subjects and 19 TLE patients) for testing and evaluation studies. The VBM method shows that TLE is associated with reduction of both of FA and EAR values in several regions within the temporal lobes. The tractography method shows abnormal diffusion characteristics in three fiber bundles (fornix, uncinate fasciculus, and cingulum). The results show that the EAR maps are less sensitive to noise than the FA maps and the EAR values change more than the FA values in TLE.

关键词： Tensile stress Magnetic resonance imaging Diffusion tensor imaging Temporal lobe Epilepsy Ear Optical fiber testing Diseases Focusing Medical treatment

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Feature selection for semi-supervised multi-label learning with application to gene function analysis 10

Feature selection for semi-supervised multi-label learning w...

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Proceedings of the First ACM International Conference on Bioinformatics and Computational Biology

作者： Li, Guo-Zheng You, Mingyu Ge, Lei Yang, Jack Y. Yang, Mary Qu MOE Key Laboratory of Embedded System and Service Computing Tongji University Shanghai 201804 China Department of Control Science and Engineering Tongji University Shanghai 201804 China School of Computer Science and Engineering Shanghai University Shanghai 200072 China Purdue Univ. ECE West Lafayette IN 47907 United States Indiana Univ. Bioinformatics Center Indianapolis IN 46202 United States

ISBN: (纸本)9781450304382

This paper investigates gene function annotation of Yeast by using semi-supervised multi-label learning. Multi-label learning has been a hot topic in the bioinformatics field, but there are many samples unlabeled. Semi-supervised learning may be employed to utilize the unlabeled data. This paper proposes a novel semi-supervised multi-label learning algorithm COMN by combining Co-Training with ML-kNN to utilize the unlabeled yeast gene data to improve modeling accuracy of function annotation. Furthermore, an embedded feature selection algorithm PRECOMN is proposed to perform feature selection for COMN to remove the irrelevant and redundant features. Experimental results on one benchmark data set of Yeast show COMN and PRECOMN perform better than the original multi-label learning algorithm ML-kNN. Furthermore PRECOMN improves generalization performance of COMN. Copyright © 2010 ACM.

关键词： Feature Selection

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Spectral clustering approach with sparsifying technique for functional connectivity detection in the resting brain

Spectral clustering approach with sparsifying technique for ...

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International Conference on Intelligent and Advanced systems, ICIAS

作者： Mahdi Ramezani Amin Heidari Emad Fatemizadeh Hamid Soltanian-Zadeh Biomedical Signal and Image Processing Laboratory Sharif University of Technology Tehran Iran Radiology Image Analysis Laboratory Henry Ford Health System Detroit MI USA Control and Intelligent Processing Center of Excellence Electrical and Computer Engineering Department University of Tehran Iran

The aim of this study is to assess the functional connectivity from resting state functional magnetic resonance imaging (fMRI) data. Spectral clustering algorithm was applied to the realistic and real fMRI data acquired from a resting healthy subject to find functionally connected brain regions. In order to make computation of the spectral decompositions of the entire brain volume feasible, the similarity matrix has been sparsified with the t-nearest-neighbor approach. Realistic data were created to investigate the performance of the proposed algorithm and comparing it to the recently proposed spectral clustering algorithm with the Nystrom approximation and also with some well-known algorithms such as the Cross Correlation Analysis (CCA) and the spatial Independent Component Analysis (sICA). To enhance the performance of the methods, a variety of data pre and post processing steps, including data normalization, outlier removal, dimensionality reduction by using wavelet coefficients, estimation of number of clusters and optimal number of independent components (ICs). Results demonstrate the applicability of the proposed algorithm for functional connectivity analysis.

关键词： Clustering algorithms Algorithm design and analysis Correlation Approximation methods Independent component analysis Time series analysis Approximation algorithms

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Fuzzy dynamic characteristic modeling and intelligent adaptive control for complex spacecraft

Fuzzy dynamic characteristic modeling and intelligent adapti...

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World Congress on Intelligent control and Automation (WCICA)

作者： Xiong Luo Song Hu Zengqi Sun Xintao Qiu School of Information Engineering Key Laboratory of Advanced Control of Iron and Steel Process (Ministry of Education) University of Science and Technology Beijing Beijing China State Key Laboratory of Intelligent Technology and System Department of Computer Science and Technology Tsinghua National Laboratory for Information Science and Technology Tsinghua University Beijing China

Because of strong coupling, nonlinear, and time-varying characteristics, it is difficult to control complex spacecraft. By means of combining with controlled object dynamics and performance requirements, characteristic modeling and control approach is an effective way to solve this problem. Aimed at the flexible satellites described by using characteristic modeling approach, with the help of fuzzy rules, fuzzy dynamic characteristic modeling method and intelligent adaptive controller are designed to control this complex spacecraft. Meanwhile, based on the satellite system simulation platform, we validate the correctness and efficiency of the proposed modeling and control method by comparing with the simulation results of the other similar methods.

关键词： Adaptation model Laboratories Satellites Space vehicles Intelligent control Artificial intelligence Information science

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Multiple-atlas-based automatic sementation of hippocampus for lateralization in temporal lobe epilepsy

Multiple-atlas-based automatic sementation of hippocampus fo...

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IEEE International Symposium on Biomedical Imaging

作者： Alireza Akhondi-Asl Kourosh Jafari-Khouzani Hamid Soltanian-Zadeh Image Analysis Laboratory Department of Diagnostic Radiology Henry Ford Health System Detroit MI USA School of Cognitive Sciences Institute of Studies in Theoretical Physics and Mathematics Tehran Iran Control and Intelligent Processing Center of Excellence School of Electrical and Computer Engineering University of Tehran Tehran Iran

ISBN: (纸本)9781424441259

We introduce a 3D segmentation framework which uses principal shapes. The probabilistic energy function of the method is defined based on intensity, tissue type, and location information of the structures using a multiple atlas method. For intensity information, nonparametric probability density function is used which considers intensity relation of different structures. To find a local minimum of the energy function, a two-step optimization strategy is used. In the first step, shape parameters are optimized based on the analytic derivatives of the energy function. In the second step, shapes of the structures are fine-tuned using a level set method. The proposed method is shown to be superior to some popular methods in the literature using a dataset of 64 patients with mesial temporal lobe epilepsy. In addition, the method can be used for lateralization with accuracy close to that of manual segmentation.

关键词： Hippocampus Temporal lobe Epilepsy Image segmentation Principal component analysis Shape control Probability density function Level set Skull Algorithm design and analysis

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A methodology for power quality evaluation in distribution network with distributed generation

A methodology for power quality evaluation in distribution n...

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International Conference on Critical Infrastructures, CRIS

作者： Gengyin Li Zhe Zhang Xiang Li Song Wang Ming Zhou Key Laboratory of Power System Protection and Dynamic Security Monitoring and Control under Ministry of Education North China Electric Power University Beijing China Department of Electrical Engineering Tsinghua University Haidian Beijing China School of Electrical and Computer Engineering North China Electric Power University Beijing China

ISBN: (纸本)9781424480807

The issue of power quality in the distribution system is always in sense of the concern. With the high penetration of distributed generation (DG) currently, it tends to be more complicated. This paper presents a new approach to evaluate impacts of the DG on power quality of the distribution network. In the process an objective function will be modeled, which will be solved by the optimization toolbox, in order to calculate the result for the evaluating grade. Furthermore, the results of the overall evaluation will be given via the eigenvalue weighted method. The power quality ranking of each feeder with the distributed generators in the distribution network will be apparent with the given weight of influence caused by DGs divided in forms of energy resource.

关键词： Power quality Generators Indexes Distributed power generation Eigenvalues and eigenfunctions Optimization Voltage fluctuations

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A boundary method for outlier detection based on support vector domain description

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Pattern Recognition 2009年第1期42卷 77-83页

作者： Guo, S.M. Chen, L.C. Tsai, J.S.H. Department of Computer Science and Information Engineering National Cheng-Kung University Tainan 701 Taiwan Control System Laboratory Department of Electrical Engineering National Cheng-Kung University Tainan 701 Taiwan

The support vector domain description (SVDD) is a popular kernel method for outlier detection, which tries to fit a class of data with a sphere and uses a few target objects to support its decision boundary. The problem is that even with a flexible Gaussian kernel function, the SVDD could sometimes generate such a loose decision boundary that the discrimination ability becomes poor. Therefore, a computationally intensive procedure called kernel whitening is often required to improve the performance. In this paper, we propose a simple post-processing method which tries to modify the SVDD boundary in order to achieve a tight data description with no need of kernel whitening. With the derivation of the distance between an object and its nearest boundary point in input space, the proposed method can efficiently construct a new decision boundary based on the SVDD boundary. The improvement from the proposed method is demonstrated with synthetic and real-world datasets. The results show that the proposed decision boundary can fit the shape of synthetic data distribution closely and achieves better or comparable classification performance on real-world datasets. © 2008 Elsevier Ltd. All rights reserved.

关键词： Data description

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Discrete time minimax tracking control with disturbance estimation 10

Discrete time minimax tracking control with disturbance esti...

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2009 10th European control Conference, ECC 2009

作者： Bauer, Peter Kulcsar, Balazs Bokor, Jozsef Systems and Control Laboratory Computer and Automation Research Institute Hungarian Academy of Sciences BudapestH-1111 Hungary Delft Center for System and Control Faculty of Mechanical Maritime and Materials Engineering Technical University of Delft Mekelweg 2 Delft Netherlands

ISBN: (纸本)9783952417393

The paper proposes an alternative way to solve robust reference tracking problem. Instead of rejecting the effect of the disturbance directly, an intermediate step is built into the state estimation problem. The advantage of the methodology is to elaborate a modified optimal state estimation problem taking the unbiased estimate of the disturbance into account. Henceforward, the solution of the discrete time, optimal LQ minimax tracking problem is modified and subjected to attenuate the disturbance residual. The paper addresses the nominal case for constant reference and disturbance signal showing the asymptotical stability and the tracking performance of the discrete time, optimal min-max control. Suboptimal solution is given for time varying reference and disturbance signal. A linearized hovering quadrotor example demonstrates the importance of the suggested technique. © 2009 EUCA.

关键词： State estimation

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