Advances in Computing, Control and Industrial Engineering

Paper Title Page

Authors: Xiao Ying Chen, Min Wang, Shu Dao Zhou
Abstract: This paper proposes a new algorithm to classify the cloud of all-sky ground-based based on transparency and texture features. First, we uses the transparency to separate the single sky background and cloud foreground image, which based on the natural matting of perceptual color space method, then analysis the texture features of cloud foreground image with second moment, contrast, correlation and entropy, finally, uses BP neural network to identify the type of the cloud. The experimental results show that the algorithm can separate the sky and cloud effectively, and the cloud classification recognition rate is higher.
Authors: Chun Hua Ju, Li Li Mao
Abstract: Data stream mining has been applied in many domains, but the concept drifts of data streams bring great obstacles to data mining. Current researches about classification algorithm for streaming data with concept drift have achieved many successes, while they pay little attention to the iterancy of data streams, namely, the situation of the historical concept reappears. For this characteristic, this paper puts forward that it utilizes the classifier model of the historical concepts or high similarity concepts through calculating the concept similarity to classify and predict. In this way, we don’t need training any more. Meanwhile, it reduces the cost of update model, speeds up the classification of the rate and improves the prediction efficiency.
Authors: Li Min Liu, Xiao Ping Fan, Yue Shan Xie
Abstract: Clustering ensemble has been known as an effective method to improve the robustness and stability of clustering analysis. Clustering ensemble solves the problem in two steps:firstly,generating a large set of clustering partitions based on the clustering algorithms;secondly,combining them using a consensus function to get the final clustering result. The key technology of clustering ensemble is the proper consensus function. Recent research proposed using the matrix factorization to solve clustering ensemble. In this paper, we firstly analyze some traditional matrix factorization algorithms; secondly, we propose a new consensus function using binary nonnegative matrix factorization (BMF) and give the optimization algorithm of BMF; lastly, we propose the new framework of clustering ensemble algorithm and give some experiments on UCI Machine Learning Repository. The experiments show that the new algorithm is effective and clustering performance could be significantly improved.
Authors: Yu Guang Yang, Hai Ping Chai
Abstract: In most existing authentication schemes users are authenticated by the server one by one which results in lower efficiency of authentication when the number of users is large. Aiming at the drawback, this paper propose an efficient trusted multi-party authentication scheme based on threshold secret sharing, the discrete logarithm problem and ElGamal cryptosystem. Using (t,n) threshold secret sharing idea, the paper propose a (t,n) threshold authentication scheme which can not only simultaneously authenticate t users satisfying some specific conditions, but also authenticate new users dynamically by distributing a new authentication key for the new user which was produced by t old users and the new user together. Finally, the security and efficiency of the proposed scheme are analyzed.
Authors: Ming Li, Wen Cheng Tang
Abstract: The volumetric penalization approach is an important method for gray elements suppression in topology optimization. With the volumetric penalization approach, the topology optimization problem is consistent and regularized, and topology description is unambiguous. Considering the fact that an unreasonable topological form of structure is sometimes resulted with the traditional volumetric penalization function, a modified function is proposed. By utilizing the modified function, the gray elements are compelled to be 0 or 1, and the efficiency of optimization solving is also improved. To verify the validity of the modified method, it is compared with the famous volumetric penalization method, SINH method, through a typical example. Simultaneously, its numerical instabilities are also analyzed.
Authors: Zhan Jun Cai, Wei Min Kang, Bo Wen Cheng, Ya Bin Li
Abstract: This paper studies the different porosity of porous medium how to affect the flow pressure field under the conditions of same inlet velocity and fiber diameter by CFDmethod. Geometric model of the catalytic converter has been established and meshed by the pre-processing tool of FLUENT. The flow pressure simulation filled contours and the curve of center line static pressure distribution of the fiber porous material show that in the case of other conditions remain unchanged, the less the porosity of the fiber porous material, the higher the inlet pressure and the more the pressure loss of the porous material. The more porosity of fiber is beneficial to exhaust catalytic reaction.
Authors: Jie Zhu, Wei Dong, Jing Zhang, Xu Ning Liu
Abstract: In order to improve the efficiency of prediction and diagnose of crop diseases and pests, and solve the problems during the course of crop production and management, then an neural networks with weight adjustment of prediction model is proposed. The crop disasters are regarded as example, after the symptoms of disasters and features are classified, abstracted and coded, the adjustment of weight, optimization of network structure and reasonable adjustment of parameters of BP neural network are discussed, then a model is constructed to forecast the disasters of crop, the weight is used as knowledge to predict disasters of crop through studying training samples. Results have shown that the optimization expert system of crop disasters based on neural network has enhanced the ability of decision making of expert system, then greatly improved the accuracy and reliability of crop diagnosis.
Authors: Lin Chen, Fu Ke Wu
Abstract: This paper deals with analytical and numerical stability properties of highly nonlinear stochastic differential equations (SDEs) with unbounded delay. Sufficient conditions for almost sure decay stability of previous system, almost sure decay stability of the backward Euler-Maruyama (BEM) methods are investigated. In \cite{Wu2010} and \cite{Mao2011}, the authors consider one-side linear growth condition and sufficient small step size. In this paper, we consider the monotone condition, which is weaker than one-side linear growth condition. And we only need a very weak restriction of the step size. Different from \cite{Szpruch2010}, Szpruch and Mao consider the asymptotic stability of the numerical approximate. In this paper we consider the almost sure decay stability of the numerical solution. This improves the existing results greatly.
Authors: Hai Tao Liu, Yin Long Wang, Hui Fen Yao
Abstract: In this paper an improved image segmentation algorithm based on watershed transform is presented. Firstly the normalized cut method and watershed transform are explained and analyzed. Secondly the idea of the improved algorithm and the main formula are explained. We consider the area and perimeter when we merge adjacent regions. We define a new weight value and discuss the value of the parameterαandβ. Finally the experiment result is presented. The new algorithm reduces the nodes and the computational demand of the common normalized cut technique.

Showing 1 to 10 of 81 Paper Titles