In this paper, we use the relations of quotient space theory and martingale therory to research the iterated function system that is fractal geometry images, and propose these conclusions: Given an irreducible iterate...
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(纸本)7900719229
In this paper, we use the relations of quotient space theory and martingale therory to research the iterated function system that is fractal geometry images, and propose these conclusions: Given an irreducible iterated function system {X,wipij;i,j = 1,2,}, then exists a corresponding chain of quotient space {Wk = (Xk, μk,Fk);&=1,2,:} and a martingale {(μk, Fk );k = 1,2,}on the chain, therefore there are: l)Assume Pk is a invariant subsets of Wk, P is a invariant subsets of W, then exists lim k&rarr∞ Pk =P and the convergence is according to Hausdorff distance. 2)Assume μk is a invariant measure of Fk, μ is a invariant measure of F, then exists limμk k&rarr∞=μ 3) Pk is a support set of μk, P is a support set of μ Namely we present the quotient approximation theorem about fractal geometry images, and build relations among chain of quotient space, martingale, fractal geometry images and Markovian process.
In order to represent a complex large concept hierarchy tree, this paper proposes a more general coding scheme which applies concept hierarchy into the mining of fuzzy association rules. As it is difficult to determin...
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In order to represent a complex large concept hierarchy tree, this paper proposes a more general coding scheme which applies concept hierarchy into the mining of fuzzy association rules. As it is difficult to determine the membership function subjectively, a self-organizing feature map (SOFM) network is introduced to determine the membership function of sample data. Based on the improved coding scheme and the SOFM network, fuzzy set is then introduced to design a new algorithm of mining multi-level fuzzy association rules. Experimental results show that the proposed algorithm is of high efficiency and scalability and can effectively mine multi-level fuzzy association rules that are meaningful and easily understandable.
Automatic keyword extraction is one of the most important techniques in natural language processing. In this paper, features of complex networks composed of Chinese are studied. A novel automatic keyword extraction al...
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Automatic keyword extraction is one of the most important techniques in natural language processing. In this paper, features of complex networks composed of Chinese are studied. A novel automatic keyword extraction algorithm for Chinese document is proposed which is based on the features of the complex networks according to the small world structure in language networks and the theoretical achievements in complex networks. It extracts keyword based on the feature values of the word nodes in a documental language network. Experimental results show the proposed algorithm obtains higher average precision compared with the keyword extraction algorithm based on TFIDF.
The purpose of this study is to present an application of a novel enhancement technique for enhancing medical images generated from X-rays. The method presented in this study is based on a nonlinear partial differenti...
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The purpose of this study is to
present an application of a novel enhancement technique for enhancing medical images generated from X-rays. The method presented in this study is based on a nonlinear partial differential equation （PDE） model, Kramer＇s PDE model. The usefulness of this method is investigated by experimental results. We apply this method to a medical X-ray image. For comparison, the X-ray image is also processed using classic Perona-Malik PDE model and Catte PDE model. Although the Perona-Malik model and Catte PDE model could also enhance the image, the quality of the enhanced images is considerably inferior compared with the enhanced image using Kramer＇s PDE model. The study suggests that the Kramer＇s PDE model is capable of enhancing medical X-ray images, which will make the X-ray images more reliable.
The purpose of this study is to present an application of a novel enhancement technique for enhancing medical images generated from X-rays. The method presented in this study is based on a nonlinear partial differenti...
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The purpose of this study is to present an application of a novel enhancement technique for enhancing medical images generated from X-rays. The method presented in this study is based on a nonlinear partial differential equation (PDE) model, Kramer’s PDE model. The usefulness of this method is investigated by experimental results. We apply this method to a medical X-ray image. For comparison, the X-ray image is also processed using classic Perona-Malik PDE model and Catte PDE model. Although the Perona-Malik model and Catte PDE model could also enhance the image, the quality of the enhanced images is considerably inferior compared with the enhanced image using Kramer’s PDE model. The study suggests that the Kramer’s PDE model is capable of enhancing medical X-ray images, which will make the X-ray images more reliable.
The DS-CDMA signal model and the noisy linear independent component analysis (ICA) model are analyzed in this paper. Comparing these models shows that they have the same form. The adaptive minimum mean-square error (M...
The DS-CDMA signal model and the noisy linear independent component analysis (ICA) model are analyzed in this paper. Comparing these models shows that they have the same form. The adaptive minimum mean-square error (MMSE) multiuse detection based on ICA is proposed. It uses the output of adaptive MMSE multi-user detection to initialize the ICA iterations, not only the known spread information of interesting user is used to overcome the uncertainness of ICA, but also the character of statistical independence is used. The simulation results show that the performance is improved obviously.
It is widely recognized that clustering ensemble is fit for any shape and any distribution dataset and that the boosting method provides superior results for classification problems. In the paper, a dual boosting is p...
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It is widely recognized that clustering ensemble is fit for any shape and any distribution dataset and that the boosting method provides superior results for classification problems. In the paper, a dual boosting is proposed for fuzzy clustering ensemble . At each boosting iteration, a new training set is created based on the original datasets' probability which is associated with the previous clustering. According to the dual boosting method, the new training subset contains not only the instances which is hard to cluster in previous stages , but also the instances which is easy to cluster. The final clustering solution is produced by using the clustering based on the co-association matrix. Experiments on both artificial and realworld datasets demonstrate the efficiency of the fuzzy clustering ensemble based on dual boosting in stability and accuracy.
The mechanism of the classical particle swarm optimization and the comparison criterion of different natural computing methods is investigated by introducing the discrepancy and good lattice points in number theory an...
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The mechanism of the classical particle swarm optimization and the comparison criterion of different natural computing methods is investigated by introducing the discrepancy and good lattice points in number theory and proposes a novel optimization method, called good lattice points-based particle swarm optimization algorithm, which intends to produce faster and more accurate convergence because it has a solid theoretical basis and better global search ability, meanwhile the global convergence of the presented algorithm with asymptotic probability one is proved by the property of the optimal lattice. Finally experiment results are very promising to illustrate the outstanding feature of the presented algorithm.
In this article, we propose a (t,n) threshold verifiable multi-secret sharing scheme, in which to reconstruct t secrets needs to solve t simultaneous equations. The analysis results show that our scheme is as easy as ...
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In this article, we propose a (t,n) threshold verifiable multi-secret sharing scheme, in which to reconstruct t secrets needs to solve t simultaneous equations. The analysis results show that our scheme is as easy as Yang's scheme [8] in the secret reconstruction and requires less public values than Chien's [7] and Yang's schemes. Furthermore, the shares in our scheme can be verified their validity with t public values based on ECDLP, and there are two verified forms: one is computationally secure as Feldman 's scheme [12] and other is unconditionally secure as Pedersen's scheme [13]. In addition, for the main computation: a i,1 P 1 + a i,2 P 2 + hellip + a i,t P t in our scheme, we present a new method based on the signed factorial expansion and implement it, the results show that it is more efficient than the current public methods. Thus our scheme is a secure and efficient (t,n) threshold verified multi-secret sharing scheme.
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