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International Conference on Computational Intelligence and Security

作者： J.Q. Feng C. Xu W.X. Xie Institute of Intelligent Computing Science Shenzhen University Shenzhen China Key Laboratory of Intelligent Information Processing Shenzhen University Shenzhen Guangdong China

This paper presents a method for monitoring the particle swarm optimization process that accounts for the random nature of the system's external environment and the fuzzy character of the particles' decision-making process by regarding the fitness function as a fuzzy random variable. The belief level value and the Borel set of chance measures are also used to monitor the particle swarm optimization process and two simulation experiments show the congregate scenes of the particle swarm optimization.

关键词： Particle swarm optimization Random variables Convergence Monitoring Fuzzy sets Algorithm design and analysis Optimization

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An intelligent load balancing algorithm towards efficient cloud computing

An intelligent load balancing algorithm towards efficient cl...

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2011 AAAI Workshop

作者： Xu, Yang Wu, Lei Guo, Liying Chen, Zheng Yang, Lai Shi, Zhongzhi Agent-based Cooperative System Lab School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu SC 611731 China Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing 100190 China

ISBN: (纸本)9781577355243

MapReduce provided a novel computing model for complex job decomposition and sub-tasks management to support cloud computing with large distributed data sets. However, its performance is significantly influenced by the working data distributions over those data sets. In this paper, we put forward a novel model to balance data distribution to improve cloud computing performance in data-intensive applications, such as distributed data mining. By extending the classic MapReduce model with an agent-aid layer and abstracting working load requests for data blocks as tokens, the agents can reason from previously received tokens about where to send other tokens in order to balance the working tasks and improve system performance. Our key contribution lies in building an efficient token routing algorithm in spite of agents' unknowing to the global state of data distribution in cloud. We also built a prototype of our system, and the experimental results show that our approach can significantly improve the efficiency of cloud computing. Copyright © 2011, Association for the Advancement of Artificial Intelligence. All rights reserved.

关键词： MapReduce

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Fast intra mode selection for stereo video coding using epipolar constraint

Fast intra mode selection for stereo video coding using epip...

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IEEE Visual Communications and Image processing (VCIP)

作者： Guolei Yang Luhong Liang Wen Gao Peking University Beijing China Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences China

Application of stereo video in TV industry and consumer electronics becomes very popular recently. Thus, fast algorithm for stereo video coding is highly desired because of its huge inter-view computational redundancy. In our previous work we proposed an epipolar constraint based fast inter mode selection for stereo video using motion vector of blocks as a indicator of similarity, nevertheless, intra mode selection is also highly complex in standards like H.264/AVC. In this paper, a practical fast intra mode selection is proposed to eliminate the computational redundancy by exploiting inter-view dependency based on epipolar constraint. The proposed method does not rely on disparity estimation. Instead, a sliding window is employed to generate an intra mode candidate pool from macro-blocks on the epipolar line. The candidate pool is then rectified to remove invalid modes and improve accuracy. Finally, optimal prediction mode is selected from the candidate pool. The proposed method can significantly reduce the number of mode candidates/prediction directions compared to exhaustive mode selection by 79%. Experiments on 5 HD video coded in 1-frame demonstrate the overall coding time of one view is saved by 56% on average, with slightly video quality loss less than 0.1 dB.

关键词： Encoding Prediction algorithms Redundancy Signal processing algorithms Video coding Estimation Complexity theory

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The Estimation of Winter Wheat Yield Based on MODIS Remote Sensing Data

The Estimation of Winter Wheat Yield Based on MODIS Remote S...

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Computer and computing Technologies in Agriculture V——5th IFIP TC 5/SIG 5.1 Conference(CCTA2011) Part II

作者： Linsheng Huang Qinying Yang Dong Liang Yansheng Dong Xingang Xu Wenjiang Huang Key Laboratory of Intelligent Computing & Signal Processing Ministry of EducationAnhui University Beijing Research Center for Information Technology in Agriculture

ISBN: (纸本)9783642272776

A yield estimation method by remote sensing was used to estimate the yield of winter wheat in Jiangsu province,*** first step of this study was to extract the planting area of winter wheat from environmental satellite images and land-use map of Jiangsu province,meanwhile,correlation analyses were performed by using 8-day of composite Leaf Area Index(LAI)data from Moderate Resolution Imaging Spectroradiometer(MODIS)and statistical yield of corresponding ***,the average LAI was calculated at the optimal growth period,and the statistical yields of wheat for all counties were collected,in which the former was chosen as the independent variable and the latter was the dependent variable,and the regression model was ***,the accuracy and stability of the regression model were validated using the data of another *** results indicated that the yield estimation model at provincial level was reliable,the Root Mean Square Error(RMSE)and the Mean Absolute Error(MAE)of the model was 12.1%and 9.7%,*** addition,the yield estimation system of winter wheat in Jiangsu province was constructed and published based on ArcMap and ArcGIS Server.

关键词： LAI remote sensing yield estimation regression model ArcGIS Server

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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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IEEE International Conference on Cloud computing Technology and Science (CloudCom)

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

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 scale up, speedup and size up.

关键词： Smoothing methods Parallel algorithms Computers Arrays Detection algorithms Algorithm design and analysis Runtime

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Visual saliency detection by spatially weighted dissimilarity

Visual saliency detection by spatially weighted dissimilarit...

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作者： Duan, Lijuan Wu, Chunpeng Miao, Jun Qing, Laiyun Fu, Yu College of Computer Science and Technology Beijing University of Technology Beijing 100124 China Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing 100190 China School of Information Science and Engineering Graduate University Chinese Academy of Sciences Beijing 100049 China Department of Computing University of Surrey Guildford Surrey GU2 7XH United Kingdom

ISBN: (纸本)9781457703942

In this paper, a new visual saliency detection method is proposed based on the spatially weighted dissimilarity. We measured the saliency by integrating three elements as follows: the dissimilarities between image patches, which were evaluated in the reduced dimensional space, the spatial distance between image patches and the central bias. The dissimilarities were inversely weighted based on the corresponding spatial distance. A weighting mechanism, indicating a bias for human fixations to the center of the image, was employed. The principal component analysis (PCA) was the dimension reducing method used in our system. We extracted the principal components (PCs) by sampling the patches from the current image. Our method was compared with four saliency detection approaches using three image datasets. Experimental results show that our method outperforms current state-of-the-art methods on predicting human fixations. © 2011 IEEE.

关键词： Principal component analysis

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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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IEEE International Conference on Granular computing (GRC)

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

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.

关键词： Upper bound Computational modeling information systems Approximation methods Computers Educational institutions Knowledge acquisition

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Local Regression Model for Automatic Face Sketch Generation

Local Regression Model for Automatic Face Sketch Generation

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International Conference on Image and Graphics (ICIG)

作者： Naye Ji Xiujuan Chai Shiguang Shan Xilin Chen Chinese Academy and Sciences Beijing China Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy and Sciences Beijing China

As one of the important artistic styles of portrait, sketch portrait has wide applications for both digital entertainment and law enforcement. In this paper, an automatic face sketch generation approach is presented by learning from photo-sketch pair examples. Specifically, the relationship between a face photo and its corresponding face sketch is learned on image patch level. By applying this relationship to the input face photo patch, we can infer the output face sketch patch by exploiting some regression techniques such as kNN, the Lasso and so on. Via our local regression model, we can synthesize an appealing sketch portrait from a given face photo in a few minutes. Experiments conducted on CUHK database have shown that our results are more compelling than previous methods especially in two respects: (1) our synthesized sketches preserve more identity information of the original face photo, (2) our synthesized sketches presents more pencil sketch texture.

关键词： Face Training Databases Hair Rendering (computer graphics) Measurement Law enforcement

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Study on the dynamic monitoring of urban expansion of Nanning City using Remote Sensing

Study on the dynamic monitoring of urban expansion of Nannin...

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International Conference on Remote Sensing, Environment and Transportation Engineering (RSETE)

作者： Chao-kui Wu Chang-an Yuan Li-qiong Zhang Juan Lao Guangxi University Nanning China Key Laboratory of Scientific Computing & Intelligent Information processing in Universities of Guangxi Guangxi Teachers Education University Nanning China

Take multi-time-phase Landstat TM/ETM+ Remote Sensing images (1990, 2002, 2007) of Nanning City as data source and use RS and GIS intergration technology to extract the information of city. The paper analyses the prop... 详细信息

ISBN: (纸本)9781424491728

关键词： Cities and towns Remote sensing Monitoring Data mining Geographic information Systems Economics Satellites

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Particle swarm optimization for automatic parameters determination of pulse coupled neural network

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Journal of Computers 2011年第8期6卷 1546-1553页

作者： Xinzheng, Xu Shifei, Ding Zhongzhi, Shi Zuopeng, Zhao Hong, Zhu School of Computer Science and Technology China University of Mining and Technology Xuzhou China Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing China

Pulse coupled neural network (PCNN), a wellknown class of neural networks, has original advantage when applied to image processing because of its biological background. However, when PCNN is used, the main problem is that its parameters aren't self-adapting according to different image which limits the application range of PCNN. Considering that, this paper proposed a new method based on particle swarm optimization (PSO) to determine automatically the parameters of PCNN. In this method, the algorithm of PSO is applied to search automatically optimum in the solution space of PCNN's parameters until finding global optimal solution. Experimental results demonstrate that the proposed method is accurate and robust for image segmentation, and its performance is better than the methods of Otsu, manual adjustment of parameters when mutual information is adopted as evaluation criteria. © 2011 ACADEMY PUBLISHER.

关键词： Image segmentation

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