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Liu Y.-Z.,Hefei University of Technology | Liu Y.-Z.,The MOE Key Laboratory of Process Optimization and Intelligent Decision making | Zhou Y.-L.,Hefei University of Technology | Zhou Y.-L.,The MOE Key Laboratory of Process Optimization and Intelligent Decision making
Xitong Gongcheng Lilun yu Shijian/System Engineering Theory and Practice | Year: 2014

Studying the topology of scale-free networks mainly concentrates on computing clustering coefficient and average path length, and analyzing degree distribution. This paper argues that in real world, the three parameters are interrelated. A parameter can be replaced by the other two parameters. According to the viewpoint, this paper gives a formula to compute the average path length l>SF of large scale-free networks based on a tree structure model, and analyzes the impact of network scale and junction between nodes on l>SF. The results indicate that l>SF is related to average degreek, average clustering coefficient C and power exponent γ which are three parameters characterizing the topology of scale-free networks. Therefore, the complexity can be reduced by transferring computing average path length directly to indirectly. The experiments' results show that the formula is valid and the efficiency of studying the topology of large scale-free networks is greatly raised. Source


Xue Y.-J.,Hefei University of Technology | Xue Y.-J.,The MOE Key Laboratory of Process Optimization and Intelligent Decision making | Ni Z.-W.,Hefei University of Technology | Ni Z.-W.,The MOE Key Laboratory of Process Optimization and Intelligent Decision making
Xitong Gongcheng Lilun yu Shijian/System Engineering Theory and Practice | Year: 2014

With the rapid development of the information technology, it is challenging for the traditional machine learning and data mining algorithms to deal with large scale explosive growth data. Manifold learning is a dimensionality reduction algorithm which can overcome some shortages of traditional linear dimensionality reduction methods. However, it is not useful for large scale data because of high complexity. In order to deal with the dimensionality reduction of large scale data, a distributed manifold learning algorithm is proposed based on MapReduce. Local sensitive hash functions are used to map the similarity points to the same bucket, then the geodesic distance between points in the same bucket can be computed by Euclidean norm according to the local homeomorphisms of Euclidean spaces of manifold and the geodesic distance among points between buckets can be computed by the modified geodesic distance formula which takes use of central points and edge points. Experiments on large scale of manmade dataset and real dataset show that this distributed manifold learning algorithm can approximate the geodesic distance between points effectively and it is useful for large scale dimensionality reduction. Source


Zhou K.-L.,Hefei University of Technology | Zhou K.-L.,The MOE Key Laboratory of Process Optimization and Intelligent Decision making | Yang S.-L.,Hefei University of Technology | Yang S.-L.,The MOE Key Laboratory of Process Optimization and Intelligent Decision making | And 2 more authors.
Xitong Gongcheng Lilun yu Shijian/System Engineering Theory and Practice | Year: 2014

This paper proposes an adaptive fuzziness parameter selection method of fuzzy c-means (FCM) algorithm based on the establishment of five-stage load classification process model. The evaluation index of adaptive fuzziness parameter selection is the ratio of the sum of within-class distances and the sum of between-class distances. At the same time, simulated annealing algorithm and genetic algorithm are utilized to optimize the global search capability of FCM algorithm. Experimental results show that the widely used fuzziness parameter of FCM algorithm in load classification m=2 is not optimal, and we give the optimum range that is [2.6, 3.2]. The modified algorithm enhances the global search capability of traditional FCM algorithm, thus enhancing the accuracy and effectiveness of load classification. Source


Zhang Q.-P.,Hefei University of Technology | Zhang Q.-P.,The MOE Key Laboratory of Process Optimization and Intelligent Decision making | Liu Y.-Z.,Hefei University of Technology | Liu Y.-Z.,The MOE Key Laboratory of Process Optimization and Intelligent Decision making | Li Y.-J.,Hefei University of Technology
Xitong Gongcheng Lilun yu Shijian/System Engineering Theory and Practice | Year: 2014

This paper has studied the problem of allocating the fixed cost among decision making units. Suppose the production processes of decision making units during the two continuous periods which are before and after the fixed cost input are available, and the modeling premise is that combining the allocated cost with other input elements averagely. Firstly, the decision making units' super CCR effciency evaluation model considering allocated cost is given. Then the input-output variation and allocated fixed cost oriented decision making units' relative benefit recognition model is built. Based on the Nash bargaining cooperative game theory, the cost allocation model considering relative effciency and benefit is proposed. The approach is illustrated by a numerical example, which figures that the approach is available and acceptable. Source


Zhang C.,Hefei University of Technology | Zhang C.,The MOE Key Laboratory of Process Optimization and Intelligent Decision making | Ni Z.-W.,Hefei University of Technology | Ni Z.-W.,The MOE Key Laboratory of Process Optimization and Intelligent Decision making | And 3 more authors.
Xitong Gongcheng Lilun yu Shijian/System Engineering Theory and Practice | Year: 2015

PM2.5 is the main pollutant affecting the air quality, the concentration of PM2.5 is closed related to meteorological conditions, studying the influence of meteorological conditions on the concentration of PM2.5 has important significance for improving urban air quality. As fractal and wavelet have lots of advantages when dealing with complex nonlinear system, the calculating method of joint multifractal based on wavelet packet transform modulus maxima (WPTMM) has been proposed, first the variable sequences are decomposed by wavelet packet, this paper uses modulus maxima to denoise, then constructs the joint distribution function, finally calculates the joint multifractal spectrum, and analyzes the fractal correlation between two variables. This proposed method has extended single multifractal to the joint multifractal of two interacting variables, calculating joint multifractal spectra based on WPTMM can reduce computational complexity, meanwhile avoid the effects of noise. The paper has analyzed the relationship between the concentration of PM2.5 and the meteorological factors of Beijing and Hong Kong, experiment results show that this method can effectively analyze each meteorological factor on the impact of PM2.5 concentration in different seasons. ©, 2015, Xitong Gongcheng Lilun yu Shijian/System Engineering Theory and Practice. All right reserved. Source

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