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7th International Conference on Rough Sets and Knowledge Technology, RSKT 2012

作者： Yao, Dengbao Wang, Cuicui Mao, Junjun Zhang, Yanping School of Mathematical Sciences Anhui University Hefei 230039 China Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education Anhui University Hefei 230039 China Department of Computer Science University of Houston Houston TX 77204-3010 United States

ISBN: (纸本)9783642318993

Fuzzy soft set (FSs) is a novel mathematical tool, in order to describe and measure uncertain information of FSs perfectly, granularity analysis based on covering about FSs is originally discussed in this paper. Firstly, the α-dominance relation between any two objects is built by constructing the possibility degree or the weighted possibility degree after standardization, thus α-dominance class and α-covering approximation space could be generated on this relation. Secondly, granular capacity is proposed to measure the granular information of variable precision through introducing concepts of the description set and the indistinguishability set, Finally, an illustrative example is analysed to reflect dynamic changes of uncertain information under different granular structure. © 2012 Springer-Verlag.

关键词： Set theory

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PPLSA: Parallel probabilistic latent semantic analysis based on MapReduce 1

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7th IFIP International Conference on Intelligent Information processing, IIP 2012

作者： Li, Ning Zhuang, Fuzhen He, Qing Shi, Zhongzhi Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing China Graduate University Chinese Academy of Sciences Beijing China Key Lab. of Machine Learning and Computational Intelligence College of Mathematics and Computer Science Hebei University Baoding China

ISBN: (数字)9783642328916

ISBN: (纸本)9783642328909

PLSA(Probabilistic Latent Semantic Analysis) is a popular topic modeling technique for exploring document collections. Due to the increasing prevalence of large datasets, there is a need to improve the scalability of computation in PLSA. In this paper, we propose a parallel PLSA algorithm called PPLSA to accommodate large corpus collections in the MapReduce framework. Our solution efficiently distributes computation and is relatively simple to implement. © 2012 IFIP International Federation for Information processing.

关键词： MapReduce

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Contracting community for computing maximum flow

Contracting community for computing maximum flow

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IEEE International Conference on Granular computing (GRC)

作者： Yanping Zhang Xiansheng Xu Bo Hua Shu Zhao School of Computer Science and Technology Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education Anhui University Hefei China

In this paper, we propose a novel method named Contracting Community Approach (CCA) to get the maximum flow of flow network. Firstly, we contract communities in the original network. Then, we apply classic algorithms on the contracted network to approximately solve the maximum flow problem. Experimental results show that the efficiency of the proposed algorithm. For sparse networks, the size of network is reduced to 58.38% averagely and the correctness of maximum flow is over 95%. For middle dense networks, the size of network is reduced to 65.77% averagely. For dense networks, the size of network is reduced to 64.84% averagely. And the correctness of maximum flow even reach 100% both in many middle dense and dense cases in our experiments.

关键词： Educational institutions Artificial neural networks Video recording

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A dual population parallel ant colony optimization algorithm for solving the traveling salesman problem

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Journal of Convergence Information Technology 2012年第5期7卷 66-74页

作者： Liu, Fang Software Institute Dalian Jiaotong University Dalian 116028 China Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education Anhui University Hefei 230039 China

In allusion to the phenomenon of stagnation and precocity during evolution in ant colony optimization (ACO) algorithm, this paper proposed a dual population parallel ant colony optimization (DPPACO) algorithm, which was applied to the traveling salesman problem. The DPPACO algorithm separated the ants into soldier ant population and worker ant population which evolve separately by parallel method and exchanges information timely. The dynamic equilibrium between solution diversity and convergence speed is achieved by using the effect of the soldier ant's distribution to worker ants' movement choice. The DPPACO algorithm can enlarge searching range and avoid local minimum, prevent local convergence caused by misbalance of the pheromone and can improve the searching performance of the algorithm effectively. The proposed algorithm is applied in the traveling salesman problem by using the 17 data sets obtained from the TSPLIB. We compare the experimental results of the proposed DPPACO method with the traditional methods. The experimental results demonstrate that the proposed algorithm has a better global searching ability, higher convergence speed and solution diversity.

关键词： Ant colony optimization

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A novel approach based on domain ontology for web information extraction

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International Journal of Advancements in computing Technology 2012年第7期4卷 185-194页

The effect of Web information extraction depends on the quality of extraction rules. But for most approaches for Web information extraction, independence of extraction rules is their common shortages. In this paper, we propose a novel approach based on ontology for Web information extraction. We sum up four features for information items and induce these features to a group of extraction rules. Then according to a group of mapping rules between elements of ontology and extraction rules, extraction rules are well organized in ontology. According to properties of concept in ontology, the initial result of information extraction is got and then the final result is obtained by simplifying the initial results. Experiments show that our approach has higher precision.

关键词： Ontology

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Parallel implementation of apriori algorithm based on MapReduce

Parallel implementation of apriori algorithm based on MapRed...

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13th ACIS International Conference on Software Engineering, Artificial intelligence, Networking, and Parallel/Distributed computing, SNPD 2012

作者： Li, Ning Zeng, Li He, Qing Shi, Zhongzhi Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing 100190 China Graduate University Chinese Academy of Sciences Beijing 100139 China Key Lab. of Machine Learning and Computational Intelligence College of Mathematics and Computer Science Hebei University Baoding 071002 Hebei China

ISBN: (纸本)9780769547619

Searching frequent patterns in transactional databases is considered as one of the most important data mining problems and Apriori is one of the typical algorithms for this task. Developing fast and efficient algorithms that can handle large volumes of data becomes a challenging task due to the large databases. In this paper, we implement a parallel Apriori algorithm based on MapReduce, which is a framework for processing huge datasets on certain kinds of distributable problems using a large number of computers (nodes). The experimental results demonstrate that the proposed algorithm can scale well and efficiently process large datasets on commodity hardware. © 2012 IEEE.

关键词： MapReduce

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The stability and numerical dispersion analyses of high-order symplectic FDTD scheme for solving time-dependent Schrödinger equation

The stability and numerical dispersion analyses of high-orde...

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2012 International Conference on Microwave and Millimeter Wave Technology, ICMMT 2012

作者： Shen, Jing Sha, Wei E. I. Huang, Zhixiang Chen, Mingsheng Wu, Xianliang Key Laboratory of Intelligent Computing and Signal Processing Anhui University Hefei 230039 China Department of Electrical and Electronic Engineering University of Hong Kong Pokfulam Road Hong Kong Hong Kong Department of Physics and Electronic Engineering Hefei Normal University Lianhua Road Hefei 230601 China

ISBN: (纸本)9781467321839

A high-order symplectic FDTD (SFDTD) framework for solving the time-dependent Schrödinger equation is established. The third-order symplectic integrators and fourth-order collocated differences are employed in the time and space domains, respectively. The stabilities and numerical dispersions of FDTD(2,2), higher-order FDTD(2,4), and SFDTD(3,4) schemes are analyzed. We found that the stability limit of the SFDTD(3,4) scheme can be larger than that of the traditional FDTD(2,2) method through careful optimization of symplectic integrators. Moreover, the SFDTD(3,4) scheme and the FDTD(2,4) approach show better numerical dispersions than the traditional FDTD(2,2) method. © 2012 IEEE.

关键词： Numerical methods

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A new compressed sensing-based matching pursuit algorithm for image reconstruction

A new compressed sensing-based matching pursuit algorithm fo...

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Congress on Image and signal processing, CISP

作者： Hong Fang Hairong Yang School of Science Shanghai Second Polytechnic University Shanghai China Key Laboratory of Intelligent Computing Signal Processing Anhui University Hefei China

Compressed Sensing (CS), a popular technique which seeks to capture a discrete signal with a small number of linear measurements, could be used to compress a signal during the process of sampling. As an iterative greedy reconstruction algorithm for practical CS, sparsity adaptive matching pursuit (SAMP) takes advantage of the capability of signal reconstruction without prior information of the sparsity in the process of resuming the original high-dimension-data from low-dimension measurement. This paper presents a backward and adaptive matching pursuit reconstruction algorithm with fixed step sizes to avoid the overestimation phenomena of SAMP by using a standard regularized approach. Firstly, a fixed and biggish step size is set to make sure the size of support set of the signal to be reconstructed increasing stably. The energy difference between adjacent reconstructed signals is then taken as the halting condition of iteration. A standard regularized approach is employed to post-dispose the final iteration results, which backward eliminates superfluous atoms to acquire exact reconstruction. Experimental results show that such an improvement of SAMP is feasible in technology and effective in acquiring quick and exact reconstruction with sufficient measurement.

关键词： Matching pursuit algorithms Image reconstruction Accuracy Reconstruction algorithms Compressed sensing Minimization Sensors

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Analytical optimization for collaborative double threshold energy detection in cognitive radio network

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Journal of Information and Computational Science 2012年第13期9卷 3875-3882页

作者： Zhu, Jun Bai, Yun Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education Anhui University Hefei 230039 China School of Electronic Information Engineering Anhui University Hefei 230601 China

The conventional 'OR' fusion rule is frequently applied in two pre-determined limits energy detection networks but its overall performance of the false alarm and miss detection probability is generally. The new method to optimize the total error probability in three combination schemes is proposed and the presented approach focuses on combining the k-out-of-N decision fusion with data fusion. General expressions for these probabilities are derived and an analytical solution for the optimal value of k is found minimizing the overall error probability. It provides performance analysis and comparison due to the different parameters such as SNR, the threshold coefficient, for the Additive White Gaussian Noise (AWGN) channel. Numerical experiment results show there are superior performances of above fusion strategies as opposed to traditional hard rule, by selecting the right system parameters. 1548-7741/Copyright © 2012 Binary Information Press.

关键词： Data fusion

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Clustering based on fuzzy tolerance quotient spaces

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Journal of Computational Information Systems 2012年第24期8卷 10105-10112页

作者： Zhao, Shu Zhang, Ling Liu, Qianqian Zhang, Yanping School of Computer Science and Technology Anhui University Hefei 230039 China Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education Anhui University Hefei 230039 China

The quotient space theory based on fuzzy tolerance relation is put forward to solve the problem of clustering in this paper. The similarity matrix does not always satisfy ultrametric inequality, theoretically and practically. We give the method to construct the hierarchical quotient space chain if the similarity matrix is only reflexive and symmetric. We consider not only the subset of data (cluster) but also the structure of the different granular clusters. The main contributions in this paper include four parts. (1) We introduce sufficient and necessary condition that normalized distance d(x;y) is an equicrural distance;(2) The relation between equicrural distance and ultrametric inequality is discussed;(3) We propose a method to get the proximate partition of a covering from a tolerance relation;(4) An example for using fuzzy tolerance quotient spaces to clustering is given. © 2011 by Binary Information Press.

关键词： Information systems

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