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Search Results: 1 - 10 of 120172 matches for " Guoyin Wang "
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The Uncertainty Measure of Hierarchical Quotient Space Structure
Qinghua Zhang,Guoyin Wang
Mathematical Problems in Engineering , 2011, DOI: 10.1155/2011/513195
Abstract: In the application of fuzzy reasoning, researchers usually choose the membership function optionally in some degree. Even though the membership functions may be different for the same concept, they can generally get the same (or approximate) results. The robustness of the membership function optionally chosen has brought many researchers' attention. At present, many researchers pay attention to the structural interpretation (definition) of a fuzzy concept, and find that a hierarchical quotient space structure may be a better tool than a fuzzy set for characterizing the essential of fuzzy concept in some degree. In this paper, first the uncertainty of a hierarchical quotient space structure is defined, the information entropy sequence of a hierarchical quotient space structure is proposed, the concept of isomorphism between two hierarchical quotient space structures is defined, and the sufficient condition of isomorphism between two hierarchical quotient space structures is discovered and proved also. Then, the relationships among information entropy sequence, hierarchical quotient space structure, fuzzy equivalence relation, and fuzzy similarity relation are analyzed. Finally, a fast method for constructing a hierarchical quotient space structure is presented.
Knowledge Reduction Based on Divide and Conquer Method in Rough Set Theory
Feng Hu,Guoyin Wang
Mathematical Problems in Engineering , 2012, DOI: 10.1155/2012/864652
Abstract: The divide and conquer method is a typical granular computing method using multiple levels of abstraction and granulations. So far, although some achievements based on divided and conquer method in the rough set theory have been acquired, the systematic methods for knowledge reduction based on divide and conquer method are still absent. In this paper, the knowledge reduction approaches based on divide and conquer method, under equivalence relation and under tolerance relation, are presented, respectively. After that, a systematic approach, named as the abstract process for knowledge reduction based on divide and conquer method in rough set theory, is proposed. Based on the presented approach, two algorithms for knowledge reduction, including an algorithm for attribute reduction and an algorithm for attribute value reduction, are presented. Some experimental evaluations are done to test the methods on uci data sets and KDDCUP99 data sets. The experimental results illustrate that the proposed approaches are efficient to process large data sets with good recognition rate, compared with KNN, SVM, C4.5, Naive Bayes, and CART.
Leading Tree in DPCLUS and Its Impact on Building Hierarchies
Ji Xu,Guoyin Wang
Computer Science , 2015,
Abstract: This paper reveals the tree structure as an intermediate result of clustering by fast search and find of density peaks (DPCLUS), and explores the power of using this tree to perform hierarchical clustering. The array used to hold the index of the nearest higher-densitied object for each object can be transformed into a Leading Tree (LT), in which each parent node P leads its child nodes to join the same cluster as P itself, and the child nodes are sorted by their gamma values in descendant order to accelerate the disconnecting of root in each subtree. There are two major advantages with the LT: One is dramatically reducing the running time of assigning noncenter data points to their cluster ID, because the assigning process is turned into just disconnecting the links from each center to its parent. The other is that the tree model for representing clusters is more informative. Because we can check which objects are more likely to be selected as centers in finer grained clustering, or which objects reach to its center via less jumps. Experiment results and analysis show the effectiveness and efficiency of the assigning process with an LT.
Self-Learning Facial Emotional Feature Selection Based on Rough Set Theory
Yong Yang,Guoyin Wang,Hao Kong
Mathematical Problems in Engineering , 2009, DOI: 10.1155/2009/802932
Abstract: Emotion recognition is very important for human-computer intelligent interaction. It is generally performed on facial or audio information by artificial neural network, fuzzy set, support vector machine, hidden Markov model, and so forth. Although some progress has already been made in emotion recognition, several unsolved issues still exist. For example, it is still an open problem which features are the most important for emotion recognition. It is a subject that was seldom studied in computer science. However, related research works have been conducted in cognitive psychology. In this paper, feature selection for facial emotion recognition is studied based on rough set theory. A self-learning attribute reduction algorithm is proposed based on rough set and domain oriented data-driven data mining theory. Experimental results show that important and useful features for emotion recognition can be identified by the proposed method with a high recognition rate. It is found that the features concerning mouth are the most important ones in geometrical features for facial emotion recognition.
Are There Sixth Order Three Dimensional PNS Hankel Tensors?
Guoyin Li,Liqun Qi,Qun Wang
Mathematics , 2014,
Abstract: Are there positive semi-definite (PSD) but not sums of squares (SOS) Hankel tensors? If the answer to this question is "no", then the problem for determining an even order Hankel tensor is PSD or not can be solved in polynomial-time. By Hilbert, one of the cases of low order (degree) and dimension (number of variables), in which PSD non-SOS (PNS) symmetric tensors (homogeneous polynomials) exists, is of order six and dimension three. The famous Motzkin polynomial falls into this case. In this paper, we study the existence problem of sixth order three dimensional PNS Hankel tensors. We examine various important classes of sixth order three dimensional Hankel tensors. No PNS Hankel tensors are found in these cases. We then randomly generate several thousands of sixth order three dimensional Hankel tensors and make them PSD by adding adequate multiple of a fixed sixth order three dimensional positive definite Hankel tensor. Again, still no PNS Hankel tensors are found. Thus, we make a conjecture that there are no sixth order three dimensional PNS Hankel tensors. If this conjecture turns out to be true, this implies that the problem for determining a given sixth order three dimensional Hankel tensor is PSD or not can be solved by a semi-definite linear programming problem.
Positive Semi-Definiteness of Generalized Anti-Circulant Tensors
Guoyin Li,Liqun Qi,Qun Wang
Mathematics , 2014,
Abstract: Anti-circulant tensors have applications in exponential data fitting. They are special Hankel tensors. In this paper, we extend the definition of anti-circulant tensors to generalized anti-circulant tensors by introducing a circulant index $r$ such that the entries of the generating vector of a Hankel tensor are circulant with module $r$. In the special case when $r =n$, where $n$ is the dimension of the Hankel tensor, the generalized anticirculant tensor reduces to the anti-circulant tensor. Hence, generalized anti-circulant tensors are still special Hankel tensors. For the cases that $GCD(m, r) =1$, $GCD(m, r) = 2$ and some other cases, including the matrix case that $m=2$, we give necessary and sufficient conditions for positive semi-definiteness of even order generalized anti-circulant tensors, and show that in these cases, they are SOS tensors. This shows that, in these cases, there are no PNS (positive semidefinite tensors which are not sum of squares) Hankel tensors.
Heuristic algorithms for finding distribution reducts in probabilistic rough set model
Xi'ao Ma,Guoyin Wang,Hong Yu
Computer Science , 2015,
Abstract: Attribute reduction is one of the most important topics in rough set theory. Heuristic attribute reduction algorithms have been presented to solve the attribute reduction problem. It is generally known that fitness functions play a key role in developing heuristic attribute reduction algorithms. The monotonicity of fitness functions can guarantee the validity of heuristic attribute reduction algorithms. In probabilistic rough set model, distribution reducts can ensure the decision rules derived from the reducts are compatible with those derived from the original decision table. However, there are few studies on developing heuristic attribute reduction algorithms for finding distribution reducts. This is partly due to the fact that there are no monotonic fitness functions that are used to design heuristic attribute reduction algorithms in probabilistic rough set model. The main objective of this paper is to develop heuristic attribute reduction algorithms for finding distribution reducts in probabilistic rough set model. For one thing, two monotonic fitness functions are constructed, from which equivalence definitions of distribution reducts can be obtained. For another, two modified monotonic fitness functions are proposed to evaluate the significance of attributes more effectively. On this basis, two heuristic attribute reduction algorithms for finding distribution reducts are developed based on addition-deletion method and deletion method. In particular, the monotonicity of fitness functions guarantees the rationality of the proposed heuristic attribute reduction algorithms. Results of experimental analysis are included to quantify the effectiveness of the proposed fitness functions and distribution reducts.
Molecular Cloning and Characterization of cDNA Encoding Taxane 2α-O-benzoyltransferase, Catalyzing Taxol Biosynthesis from Taxus media
Dongli Zhao,Qian Wang,Guoyin Kai,Xinglong Wang
Journal of Biological Sciences , 2006,
Abstract: By RACE-PCR cloning, a cDNA encoding taxane 2α-O-benzoyltransferase (designated as TmTBT) was isolated from Taxus media, which catalyzes the conversion of 2-debenzoyl-7,13-diacetylbaccatin III, a semisynthetic substrate, to 7,13-diacetylbaccatin III. The full-length cDNA of TmTBT was 1478 bp and contained a 1320 bp Open Reading Frame (ORF) encoding a protein of 440 amino acid residues with molecular weight of 50,090 Da and an isoelectric point (pI) of 6.25, similar to taxane 2α-O-benzoyltransferase from Taxus cuspidata. Sequence comparison analysis revealed that TmTBT had high similarity with other members of plant transferase family. Phylogenetic tree analysis showed that TmTBT had close relationship with taxane 2α-O-benzoyltransferase from T. cuspidata. Tissue expression pattern analysis revealed that TmTBT expressed only in leaves and no expression could be detected in fruits and stems, indicating that TmTBT was a tissue-specific gene.
Two-step gene feature selection based on permutation testing

WANG Guoyin,LUO Chuanjiang,

重庆邮电大学学报(自然科学版) , 2012,
Abstract: In order to filter noisy and redundancy genes, this paper presents a two-step gene feature selection algorithm based on Permutation Testing. Due to the permutation testing technique, the proposed algorithm can select gene efficiently and process large dataset quickly. Twelve datasets of RSCTC 2010 Discovery Challenge and PAM(prediction analysis for microarrays) classifiers are adopted to evaluate the performance of the proposed algorithm. The experiment results show that the small gene subset with high discriminant and low redundancy can be selected by the proposed algorithm, and improve the classifiers compared to other algorithm.
A Method for Real Time Screen Sharing Based on Message Mechanism

Du JunYong,Wang GuoYin,

计算机科学 , 2005,
Abstract: Real time screen sharing technology is used widely in remote controls. In most systems, screen information is transferred as static image. There is some delay for the image transmission. In this paper, a method using message mechanism of Windows operation system to share screen information dynamically is developed. Its performance is better than old methods.
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