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Robust low-rank tensor factorization by cyclic weighted median

Science China(information Sciences) 2015年第5期58卷 145-155页

作者： MENG DeYu ZHANG Biao XU ZongBen ZHANG Lei GAO ChenQiang Institute for Information and System Sciences and Ministry of Education Key Lab for Intelligent Networks and Network Security Xi'an Jiaotong University Department of Computing Hong Kong Polytechnic University Chongqing Key Laboratory of Signal and Information Processing Chongqing University of Posts and Telecommunications

Low-rank tensor factorization(LRTF) provides a useful mathematical tool to reveal and analyze multi-factor structures underlying data in a wide range of practical applications. One challenging issue in LRTF is how to recover a low-rank higher-order representation of the given high dimensional data in the presence of outliers and missing entries, i.e., the so-called robust LRTF problem. The L1-norm LRTF is a popular strategy for robust LRTF due to its intrinsic robustness to heavy-tailed noises and outliers. However, few L1-norm LRTF algorithms have been developed due to its non-convexity and non-smoothness, as well as the high order structure of data. In this paper we propose a novel cyclic weighted median(CWM) method to solve the L1-norm LRTF problem. The main idea is to recursively optimize each coordinate involved in the L1-norm LRTF problem with all the others fixed. Each of these single-scalar-parameter sub-problems is convex and can be easily solved by weighted median filter, and thus an effective algorithm can be readily constructed to tackle the original complex problem. Our extensive experiments on synthetic data and real face data demonstrate that the proposed method performs more robust than previous methods in the presence of outliers and/or missing entries.

关键词： tensor factorization low-rank weighted median filter tensorface

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Algebraic Solution to Distributed H2 control of Multi-agent systems with Time Delays 34

Algebraic Solution to Distributed H2 Control of Multi-agent ...

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第三十四届中国控制会议

作者： YE Fei ZHANG Weidong Department of Automation Shanghai Jiao Tong University Shanghai 200240 China Key Laboratory of System Control and Information Processing Shanghai Jiao Tong University

ISBN: (纸本)9781467374439

This work deals with the problem of designing distributed controllers to improve consensus performance for identical dynamically coupled agents with time delays. Firstly, an H2 optimization problem of the whole multi-agent systems is formulated. The decomposition approach is utilized to simplify the minimization of H2 performance index to a set of independent optimization problems. For each decomposed subsystem, it is accomplished by an effective parametrization of all stabilizing controllers, which can be used to compute the H2 optimal controller. Finally, the controllers with the same structure are obtained to achieve the performance improvement of the whole systems. The advantages of the proposed approach are that the design procedure is conducted analytically and a simple quantitative tuning way is developed to trade off the nominal performance and robustness. The simulation results show the effectiveness of this method.

关键词： Multi-agent systems,analytical solution,H2 performance index,consensus,robustness

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Real-time cloud detection in high resolution images using Maximum Response Filter and Principle Component Analysis

Real-time cloud detection in high resolution images using Ma...

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IEEE International Symposium on Geoscience and Remote Sensing (IGARSS)

作者： Yihua Tan Ji Qi Feifei Ren National Key Laboratory of Science and Technology on Multispectral Information Processing Huazhong University of Science and Technology Wuhan China Science and Technology on Electro-optic Control Laboratory Luoyang China

ISBN: (纸本)9781509033331

In order to maximally make use of the limited capacities of storage and transmission, it's necessary for onboard computer to adaptively compress the cloud regions with lower quality compared with the regions without cloud cover. Therefore, the information processing unit needs to recognize the cloud regions before compress the image. To meet this requirement of satellite imaging payload, a novel approach for real time cloud detection is proposed. First, the visual dictionary is learnt from the training features extracted using Maximum Response (MR) Filter. Second, Principle Component Analysis (PCA) is utilized to reduce the dimensions of the visual words for the quick word search. Third, the MR feature of an image patch is converted into the histogram of visual word, in which the MR feature of a pixel is replaced by the index of the most similar visual word. Finally, the histogram is fed into the trained SVM classifier to detect cloud patch. The experimental results verify that the proposed approach can highly precisely detect the cloud region in patch unit.

关键词： Clouds Visualization Gabor filters Feature extraction IIR filters Dictionaries Principal component analysis

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Object detection based on ensemble of exemplars

Object detection based on ensemble of exemplars

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International Conference on Estimation, Detection and information Fusion, ICEDIF 2015

作者： Chen, Luyan Hu, Fuqiao Key Laboratory of System Control and Information Processing Ministry of Education Department of Automation Shanghai Jiao Tong University Shanghai China

ISBN: (纸本)9781479964178

This paper proposes a detection approach for localizing the object of specific category in images. Based on the ensemble of exemplars, a per-exemplar classifier for each exemplar is learnt, which is simple but powerful to perform well in detecting visually similar objects. Meanwhile, considering the fact that the number of negatives is always considerably larger than that of positives, the method of hard-negatives mining is employed. In addition, using multiple instance learning, we also learn the per-category classifier with inputting the detections produced by per-exemplar classifier in the validation set. The better performance on object detection is boosted further with the contribution of co-occurrence matrices that encodes the relationship of each per-exemplar classifier. In the experiment section, we evaluate the performance of our approach on PASCAL VOC 2007 dataset and compare it to Tomasz's Exemplar-SVM model directly. The experimental result demonstrates that our approach outperforms the Exemplar-SVM model in object detection with higher average precision. © 2015 IEEE.

关键词： Object detection

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An efficient sparse representation based classification for undersampled face recognition

An efficient sparse representation based classification for ...

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Congress on Evolutionary Computation

作者： Zizhu Fan Lipan Kang Zuoyong Li School of Basic Science East China Jiaotong University Nanchang China Fujian Provincial Key Laboratory of Information Processing and Intelligent Control Fuzhou China

ISBN: (纸本)9781509006243

The typical sparse representation for classification (SRC) can obtain desirable recognition result when the training samples in each class are sufficient. Nevertheless, if the training sample set is small scale, i.e., each class has a few training samples, even single sample, the traditional SRC cannot perform well. Although one of the variants of the traditional SRC, the extended SRC(ESRC), can effectively address the above small-scale training set (SSTS) problem, its computational efficiency is very low and consequently constrains the application of the ESRC algorithm. In order to improve the computational efficiency of the ESRC algorithm, we propose a new algorithm based on coordinate descent scheme in this work. Our proposed algorithm is referred as to the fast extended SRC (FESRC) algorithm. Experiments on popular face datasets show that the FESRC algorithm can obtain the high computational efficiency without significantly degrading the recognition results.

关键词： Algorithm design and analysis Training Minimization Computational efficiency Classification algorithms Face Face recognition

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Prediction and Analysis of Hot Region in Protein-Protein Interactions

Prediction and Analysis of Hot Region in Protein-Protein Int...

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IEEE International Conference on Bioinformatics and Biomedicine

作者： Xiaoli Lin Xiaolong Zhang Hubei Key Laboratory of Intelligent Information Processing and Real-time Industrial System School of Computer Science and Technology Wuhan University of Science and Technology Wuhan China 430065

ISBN: (纸本)9781509016129

Proteins play a crucial role in every organism, which perform a vast amount of functions. The hot regions in protein-protein interactions consist of hot spot residues in protein-protein binding sites which are called interfaces, can help proteins to perform their biological function. Residue based computational prediction of hot regions might be useful to understand the molecular mechanism and is crucial in drug design and protein design. However, it is very challenging to identify the hot regions in protein-proteins. In this paper, we have proposed a support vector machine based on ensemble learning system for predicting hot spot residues, and predicted hot regions in protein-protein interactions. The efficiency of our method is analyzed in identifying hot spots and hot regions in protein-protein interactions and the results obtained are compared with the existing techniques. The results demonstrate that the proposed method is superior to identify the hot spots and hot regions in the protein interfaces.

关键词： ensemble learning protein-protein interactions hot region hot spot binding sites

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Automatic Detection of Follicle in Ultrasound Images of Cattle Ovarian using MCL Method

Automatic Detection of Follicle in Ultrasound Images of Catt...

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IEEE International Conference on systems, Man, and Cybernetics

作者： Jun Liu Hao Chen Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System College of Computer Science and Technology Wuhan University of Science and Technology Wuhan China

ISBN: (纸本)9781509018987

This paper proposed a new full automated detection algorithm for ultrasound follicle images. The proposed algorithm uses multiple concentric layers (MCL) technology, which is based on the presence of concentric layers surrounding a focal area in the follicle region. The algorithm experiment is based on three processes, which include image preprocessing, detection of focal areas and multiple concentric layers criterion. The results are compared with the edge based method and demonstrate that the proposed algorithm is more effective in follicle detection.

关键词： ROI Ultrasound Image Follicle detection MCL

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A robust terrain-based road vehicle localization algorithm

A robust terrain-based road vehicle localization algorithm

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IEEE Symposium on Intelligent Vehicle

作者： Tianyi Li Ming Yang Xujin Zhou Chunxiang Wang Department of Automation Key Laboratory of System Control and Information Processing Shanghai China Department of Instrument Science and Engineering Shanghai Jiao Tong University Shanghai China Research Institute of Robotics Shanghai Jiao Tong University Shanghai China

ISBN: (纸本)9781509018222

Terrain-based localization is an alternate to the global positioning system (GPS) in signal blocked areas. However, terrain-based localization technique may suffer from low accuracy or even fail when brake vibration occurs. This paper presents a real-time algorithm for vehicle localization which is robust against brake vibration. The input includes a reference map of pitch difference and measurements from rear wheel encoders and inertial measurement units (IMU). This method consists of two steps. In the first step, terrain map is generated using pitch difference at equidistant intervals. After that, the Bayesian inference and particle filters are adopted in the second step to identify the vehicle location during travel. To enhance system stability, we propose dynamic distributions of filter variances according to acceleration input. Experimental results demonstrate that the localization method with dynamic distributions can localize the vehicle quickly with high accuracy even when a quite severe shuddering happens.

关键词： Vehicles Roads Brakes Acceleration Heuristic algorithms Vibrations Standards

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Magnetic Anisotropy: Ionic Liquid Gating control of Spin Reorientation Transition and Switching of Perpendicular Magnetic Anisotropy (Adv. Mater. 30/2018)

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Advanced Materials 2018年第30期30卷

作者： Shishun Zhao Lei Wang Ziyao Zhou Chunlei Li Guohua Dong Le Zhang Bin Peng Tai Min Zhongqiang Hu Jing Ma Wei Ren Zuo‐Guang Ye Wei Chen Pu Yu Ce‐Wen Nan Ming Liu Electronic Materials Research Laboratory Key Laboratory of the Ministry of Education and International Center for Dielectric Research School of Electronic and Information Engineering Xi'an Jiaotong University Xi'an 710049 China Center for Spintronics and Quantum System State Key Laboratory for Mechanical Behavior of Materials School of Materials Science and Engineering Xi'an Jiaotong University Xi'an 710049 China State Key Laboratory of New Ceramics and Fine Processing and School of Materials Science and Engineering Tsinghua University Beijing 100084 China Department of Chemistry and 4D LABS Simon Fraser University Burnaby British Columbia V5A 1S5 Canada Materials Science Division Argonne National Laboratory 9700 Cass Avenue Lemont IL 60439 USA Institute for Molecular Engineering The University of Chicago 5640 South Ellis Avenue Chicago IL 60637 USA State Key Laboratory of Low Dimensional Quantum Physics and Department of Physics Tsinghua University Beijing 100084 China

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State estimation with quantized innovations in wireless sensor networks: Gaussian mixture estimator and posterior Cramér–Rao lower bound

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Chinese Journal of Aeronautics 2015年第6期28卷 1735-1746页

作者： Zhang Zhi Li Jianxun Liu Liu Liu Zhaolei Han Shan Department of Automation Shanghai Jiao Tong University Key Laboratory of System Control and Information Processing Ministry of Education of China Nanjing Research Institute of Electronics Technology Engineering Research Center of Wideband Wireless Communication Technology Ministry of EducationNanjing University of Posts and Telecommunications

Since the features of low energy consumption and limited power supply are very impor- tant for wireless sensor networks （WSNs）, the problems of distributed state estimation with quan- tized innovations are investigated in this paper. In the first place, the assumptions of prior and posterior probability density function （PDF） with quantized innovations in the previous papers are analyzed. After that, an innovative Gaussian mixture estimator is proposed. On this basis, this paper presents a Gaussian mixture state estimation algorithm based on quantized innovations for WSNs. In order to evaluate and compare the performance of this kind of state estimation algo- rithms for WSNs, the posterior Cram6r-Rao lower bound （CRLB） with quantized innovations is put forward. Performance analysis and simulations show that the proposed Gaussian mixture state estimation algorithm is efficient than the others under the same number of quantization levels and the performance of these algorithms can be benchmarked by the theoretical lower bound.

关键词： Posterior Cramer-Rao lower bounds Quantiation State estimation Target tracking Wireless sensor network

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