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19th ACM International Conference on Multimedia ACM Multimedia 2011, MM'11

作者： Zhang, Liu Wang, Chaokun Ba, Yiyuan Wang, Hao Wang, Jianmin Department of Computer Science and Technology Tsinghua University China School of Software Tsinghua University Beijing 100084 China Tsinghua National Laboratory for Information Science and Technology China Key Laboratory for Information System Security Ministry of Education China Key Laboratory of Intelligent Information Processing Institute of Computing Technology CAS China

ISBN: (纸本)9781450306164

Content-aware music adaption, i.e. music resizing, in temporal constraints starts drawing attention from multimedia communities because of the need of real-world scenarios, e.g. animation production and radio advertisement production. The goal of music resizing is to change the length of a music track to a user preferred length using a series of basic operations, e.g. compression, prolonging, cropping and repeating. The only existing music resizing approach so far, called LyDAR, suffers from some limitations. For example, it cannot support prolonging a music track and cannot compress music pieces with very small stretch rates. In this paper, we propose MUSIZ, a generic framework for MUsic reSIZing. Observing the diversity of quality degradation for different segments, we propose the concept of stretch-resistance to measure the degree of quality degradation after a segment is stretched. MUSIZ stretches high stretch-resistance segments intensively and relieves low stretch-resistance segments to reduce the negative impact on the stretched music piece. For short length resizing requests, we develop a contiguity-preservative cropping algorithm to remove segments before stretching, while smoothing the abrupt change at the joint between two segments. Comprehensive experimental results show that MUSIZ is superior to the existing approaches. © 2011 ACM.

关键词： stretch-resistance music retargeting music resizing

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A general framework for rule acquisition based on tolerance granular computing in IIDSs

A general framework for rule acquisition based on tolerance ...

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2011 IEEE International Conference on Granular Computing, GrC 2011

作者： Zuqiang, Meng Zhongzhi, Shi Ke, Xu College of Computer Electronics and Information Guangxi University Nanning 530004 China Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing 100190 China

ISBN: (纸本)9781457703713

This paper systematically studies the problem of decision rule acquisition in inconsistent incomplete decision systems (IIDSs). First, a tolerance granular framework model based on tolerance granular computing is presented, which is suitable for variety types of decision rules in IIDSs;secondly, with the proposed model, a framework for acquiring all minimum decision rule sets for each type is given, which solves the problem of decision rule acquisition in IIDSs to a certain degree;finally, an example is given to show the efficiency of our framework. © 2011 IEEE.

关键词： Granular computing

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Nondominated individual selection strategy based on adaptive partition for evolutionary multi-objective optimization

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Jisuanji Yanjiu yu Fazhan/Computer Research and Development 2011年第4期48卷 545-557页

作者： Gong, Maoguo Cheng, Gang Jiao, Licheng Liu, Chao Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education of China Xidian University Xi'an 710071 China Institute of Intelligent Information Processing Xidian University Xi'an 710071 China

Many real world problems involve the simultaneous optimization of various and often conflicting objectives. These optimization problems are known as multi-objective optimization problems. Evolutionary multi-objective optimization, whose main task is to deal with multi-objective optimization problems by evolutionary computation techniques, has become a hot topic in evolutionary computation community. The solution diversity of multi-objective optimization problems mainly focuses on two aspects, breadth and uniformity. After analyzing the traditional methods which were used to maintain the diversity of individual in multi-objective evolutionary algorithms, a novel nondominated individual selection strategy based on adaptive partition is proposed. The new strategy partitions the current trade-off front adaptively according to the individual's similarity. Then one representative individual will be selected in each partitioned regions for pruning nondominated individuals. For maintaining the diversity of the solutions, the adaptive partition selection strategy can be incorporated in multi-objective evolutionary algorithms without the need of any parameter setting, and can be applied in either the parameter or objective domain depending on the nature of the problem involved. In order to evaluate the validity of the new strategy, we apply it into two state-of-the-art multi-objective evolutionary algorithms. The experimental results based on thirteen benchmark problems show that the new strategy improves the performance obviously in terms of breadth and uniformity of nondominated solutions.

关键词： Multiobjective optimization

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Parallel outlier detection using KD-tree based on MapReduce

Parallel outlier detection using KD-tree based on MapReduce

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2011 3rd IEEE International Conference on Cloud Computing technology and Science, CloudCom 2011

作者： He, Qing Ma, Yunlong Wang, Qun Zhuang, Fuzhen Shi, Zhongzhi Key Laboratory of Intelligent Information Processing Institute of Computing Technology CAS Beijing 100190 China Graduate University of Chinese Academy of Sciences Beijing 100190 China

ISBN: (纸本)9780769546223

Distributed and Parallel algorithms have attracted a vast amount of interest and research in recent decades, to handle large-scale data set in real-world applications. In this paper, we focus on a parallel implementation of KD-Tree based outlier detection method to deal with large-scale data set. As one of the state-of-the-art outlier detection methods, KD-Tree based has been approved to be an effective algorithm. However, it still cannot process large-scale data set efficiently due to its serial implementation. Based on the current and powerful parallel programming framework - MapReduce, we propose to implement the parallel KD-Tree based outlier detection algorithm (e.g., PKDTree for short). Experimental results demonstrate the efficiency of PKDTree according to the evaluation criterions of scaleup, speedup and sizeup. © 2011 IEEE.

关键词： MapReduce

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Knowledge discovery from noisy datasets

Knowledge discovery from noisy datasets

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2011 International Conference on Automation and Robotics, ICAR 2011

作者： Li, Hong Zong, Yu Chen, Enhong Key Laboratory of Network and Intelligent Information Processing Department of Computer Science and Technology Hefei University 230001 China School of Computer Science and Technology University of Science and Technology of China 230001 China

ISBN: (纸本)9783642256455

It is a significant challenges to deal with the noise data in data mining and knowledge discovery applications. Most of previous works on data cleansing and correction have been focused on addressing class noise or attribute noise for the benefit of the subsequent mining process. In this paper, we propose an error-sensitive(ES) data mining framework, which makes use of noise knowledge to restore original data distributions and accommodates noise knowledge to enhance data classification accuracy. We materialize our main idea by constructing Attribute-Decision tree and measureing correlation among attributes. Experimental results show that ES data mining procedure has ability to significantly improve the quality of data mining results. © 2011 Springer-Verlag.

关键词： Classification (of information)

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Concatenated edge and co-occurrence feature extracted from Curvelet Transform for human detection

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Journal of information and Computational Science 2011年第4期8卷 662-670页

作者： Han, Hong Fan, Youjian Jiao, L.C. Chen, Zhichao Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education of China Institute of Intelligent Information Processing Xidian University Xi'an 710071 China

An efficient feature extraction method based on the Curvelet Transform for detecting human in static images is proposed in this paper. The edge features can be extracted with the block-based statistical information of each sub-band coefficients, and then the texture feature can be extracted from the co-occurrence matrixes of the lowest-frequency sub-band coefficients, all the extracted features are concatenated as the final feature vector of the images. All the training and test data are from the INRIA and MIT human dataset. The classification results with test data show that the proposed feature extraction method is suitable for human detection. From the detection results, it can be seen that, the detection accuracy of the proposed method is higher, meanwhile the false positive rate is lower than the Histograms of Oriented Gradients (HOG) method. Copyright © 2011 Binary information Press.

关键词： Feature extraction

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Inverse system internal model control of DIFG wind power generator system based on matrix converter

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Taiyangneng Xuebao/Acta Energiae Solaris Sinica 2012年第11期33卷 1983-1991页

作者： Chen, Caixue Xie, Yunxiang Ma, Xueliang Key Laboratory of Intelligent Computing and Information Processing of Ministry of Education Xiangtan University Xiangtan 411105 China School of Electric Power South China University of Technology Guangzhou 510641 China Guangdong MingYang Wind Power Industry Group Co. Ltd Zhongshan 528437 China

Aim to solve the poor dynamic performance and the low disturbance resistance of PI power decoupling controller for DFIG power generator system based on matrix converter, this paper propose a nonlinear control algorithm for the AC-excitation DFIG system. First, we establish the mathematical model of the DFIG, based on the model, use the inverse system theory and deduce the inverse model, add the inverse model in series to the front of the original system model, develop the whole system to a pseudo linear system. Consider the system performance influenced by the parameters of the linearized model, we use the predictive control to improve the robustness. The simulation results show that the control strategy has fast response and good dynamic performance. The off-grid and cut-grid experimental data validate that under sub-synchronous or super-synchronous state, the stator voltage and frequency can maintain stability and achieve variable speed constant frequency operation.

关键词： Model predictive control

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Modeling for MIMO wireless channels in mine tunnels

Modeling for MIMO wireless channels in mine tunnels

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2011 International Conference on Electric information and Control Engineering, ICEICE 2011

作者： Yao, Shan-Hua Wu, Xian-Liang School of Electric and Information Engineering Anhui University of Science and Technology Huainan China Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education Anhui University Hefei China

ISBN: (纸本)9781424480395

The electromagnetic waves propagation situation is very complex in mine tunnels, so it is important to establish an efficient MIMO channel model for applying wireless communication technology to coal mine underground. A stochastic MIMO channel model is proposed firstly based on wireless propagation environment in mine tunnels, and then two spatial correlative channel models are established through correcting channel matrices based on abundant scatter environment. In this paper, the singular values and the channel capacity obtained with a Rayleigh channel model are compared, and system performance of different channel models are simulated. The simulation results indicate that established channel models are feasible by comparing with measurements results to mine tunnels. © 2011 IEEE.

关键词： MIMO systems

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A research of heart rate prediction model based on evolutionary neural network

A research of heart rate prediction model based on evolution...

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2011 IEEE International conference on intelligent Computation and Bio-Medical Instrumentation, ICBMI 2011

作者： Xiao, Feng Yuchi, Ming Ding, Mingyue Jo, Jun Key Laboratory of Image Processing and Intelligent Control of Education Ministry of China Life College of Science and Technology Huazhong University of Science and Technology Wuhan China School of Information and Communication Technology Griffith University QLD Australia

ISBN: (纸本)9780769546230

Heart rate (HR) signal analysis is widely used in the medicine and medical research area. Physical activities (PA) are commonly recognized to greatly affect the changes of heart rate. A method of Evolutionary Neural Network - Neuro-evolution of Augmenting Topologies (NEAT) is used to build a PA-based HR predictor model. Through special coding, crossover and mutation operator, NEAT can implement network topology and connectivity weights evolution simultaneously. The common problem in evolutionary neural network, like competing conventions, how to protect the new innovation are effectively solved. The experimental results demonstrated the application potential of the approach. © 2011 IEEE.

关键词： Neural networks

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The improved non-negative matrix factorization algorithm for document clustering

The improved non-negative matrix factorization algorithm for...

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2011 8th International Conference on Fuzzy Systems and Knowledge Discovery, FSKD 2011, Jointly with the 2011 7th International Conference on Natural Computation, ICNC'11

作者： Zhao, Weizhong Ma, Huifang He, Qing Shi, Zhongzhi College of Information Engineering Xiangtan University Hunan 411105 China Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing China College of Mathematics and Information Northwest Normal University Gansu China

ISBN: (纸本)9781612841816

Non-negative Matrix Factorization (NMF) is one latest presented approach for obtaining document clusters, which aimed to provide a minimum error non-negative representation of the term-document matrix. In this paper, we have extended the classical NMF approach by imposing sparseness constraints explicitly. The new model can learn much sparser matrix factorization. Also, an objective function is defined to impose the sparseness constraint, in addition to the non-negative constraint. Experimental results on real-world document datasets show that the proposed method can treat document clustering effectively and efficiently. © 2011 IEEE.

关键词： Non-negative matrix factorization

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