Network


Latest external collaboration on country level. Dive into details by clicking on the dots.

Hotspot


Dive into the research topics where Wen-Yuan Liu is active.

Publication


Featured researches published by Wen-Yuan Liu.


Journal of Materials Science | 1999

Niobium segregation in Inconel 718

Wen-Yuan Liu; M. Yao; Z. L. Chen; S. G. Wang

The segregation of niobium in Inconel 718 was investigated by means of X-ray diffraction. It was found that the very weak and diffuse profiles of sidebands on the lower angle side of γ phase (2 0 0), (2 2 0), (3 1 1), (2 2 2) diffraction peaks were observed in the X-ray diffraction patterns of Inconel 718 cold rolled to 25% reduction, and then solution treated at 1040 °C, 970 °C and aged (DA). The formation of sidebands was contributed to the Nb segregation in the γ matrix. The composition of the Nb rich region was estimated according to the lattice parameter of the Nb rich region. The results showed that the degree of Nb segregation in the matrix is less than that at the grain boundries.


software engineering, artificial intelligence, networking and parallel/distributed computing | 2007

Multidimensional Fuzzy Interpolative Reasoning Method Based on \lambda-Width Similarity

Bao-Wen Wang; Qingda Zhang; Wen-Yuan Liu; Yan Shi

Fuzzy reasoning is the very important process in the intelligent systems. Very few papers address the research for interpolative reasoning under multidimensional sparse rules. Moreover, these methods sometimes can not guarantee the convexity of result. Nowadays, multidimensional sparse rules base focus on the premises composed of many fuzzy sets, but do not consider the consequences composed of multidimensional fuzzy sets. Thus the fuzzy production rule can not express the complicate problems in the real world. It needs to be extended. This paper proposes a similarity relation between fuzzy sets. Based on the similarity relation, then we propose an improved fuzzy interpolative reasoning method. Moreover, we extend the method to the case of complex multidimensional consequences.


international conference hybrid intelligent systems | 2004

A new sparse rule-based fuzzy reasoning method

Bao-Wen Wang; Xia Li; Wen-Yuan Liu; Yan Shi

This paper presents a new fuzzy interpolative reasoning method in the sparse fuzzy rule bases based on so-called similarity relations of fuzzy sets. By this reasoning method, an inference consequence can be simply obtained, and is a normal and convex fuzzy set without any limitation, which shows the potential ability of the proposed method in real-world fuzzy applications.


international conference on machine learning and cybernetics | 2008

Shared signature based on elliptic curve and its application in electronic cash

Yong-An Luo; Ya-Li Si; Wen-Yuan Liu; Feng Li

An efficient shared signature based on elliptic curve cryptography is presented in this paper. The mainly steps of this scheme is given firstly, and then the detailed protocol in the environment of electronic transaction based on smart card is researched. Analysis shows the scheme has better security and efficiency. Lastly, the application of our scheme on electronic cash protocol with smart card is introduced.


international conference on machine learning and cybernetics | 2005

A kind of improved method of fuzzy clustering

Wen-Yuan Liu; Zhi-Wang Chen; Peng Bai; Shu-Fen Fang; Yan Shi

In this paper, the improvement of fuzzy c-means clustering method is discussed. In the improvement of fuzzy c-means clustering method, the distance definition in Euclidean space is replaced by fuzzy weighting distance, a new definition of distance. As a result, using the method of data simulation, we prove the improved method of fuzzy clustering has a better result and clearer classification than the original method.


international conference on machine learning and cybernetics | 2008

Visualization classification method of multi-dimensional data based on radar chart mapping

Wen-Yuan Liu; Bao-Wen Wang; Jia-Xin Yu; Fang Li; Shui-Xing Wang; Wen-Xue Hong

Fourier descriptor is an important method used in shape analysis and recognition. A novel method for designing the classifier of multi-dimensional data was proposed, which used radar chart of multi-statistics to show multidimensional data and applied Fourier descriptors to recognize the radar chart. Different multi-dimensional data formed different radar chart and distinguished different category. Then a new Fourier descriptor based on polar radius is defined, which describes curve of radar chart shape. The method of probabilistic neural network combined with Fourier descriptors is used to implement automatic classification. Experimental results show this method has the good classification precision, and may compare with the traditional classifier.


international conference on machine learning and cybernetics | 2007

A Security Multi-Bank E-cash Protocol Based on Smart Card

Wen-Yuan Liu; Yong-An Luo; Ya-Li Si

The security of electronic transaction protocol is an important research towards pushing electronic commerce into practice, but the double-spending problem of off-line system will cause a great loss to the bank. In order to enhance the security of the e-cash system, this paper presents a multi-bank and off-line e-cash protocol based on smart card and describes the protocol in detail. The scheme solves double-spending effectively using two protective methods: the prior restraint of smart card in payment phase and the check of bank in deposit phase. Furthermore, the protocols efficiency and security are analyzed.


international conference on machine learning and cybernetics | 2007

A Kind of Polymorphic Ant Colony Algorithm with Weight

Bao-Wen Wang; Hai-Ping Mu; Hong-Mei Fan; Wen-Yuan Liu

A new polymorphic ant colony algorithm with weight is presented in order to make balance between accelerating convergence and averting precocity stagnation as well. We add weight to the initialization of pheromone and the choice of transition probability. The pheromone has a max-value and we choose the traditional method to update the pheromone. The simulation result from TSP problem shows the validity of this algorithm.


international conference on innovative computing, information and control | 2007

A Fuzzy Lagrange_s Interpolative Reasoning Method Based on Geometric Parameter

Wen-Yuan Liu; Yong-an Luo; Qingda Zhang; Bao-Wen Wang; Yan Shi

When rule bases are sparse, we cannot get any reasoning result by traditional fuzzy reasoning method because an observation is in the gap between two neighboring antecedents. But authors have proved that fuzzy reasoning is really equal to interpolation. Hence Koczy and Hirota first proposed KH linear interpolative reasoning method. But its consequence does not always keep convexity and normality under many conditions. In order to get better result, this paper presents a fuzzy Lagranges interpolative method based geometric parameter. Reasoning is simple by the method; moreover it can keep the convexity of the reasoning consequence.


international conference on machine learning and cybernetics | 2003

The study of association algorithm BGL based on binary system and oriented graph

Wen-Yuan Liu; Yong-Shan Liu; Lina Liu; Shu-Fen Fang

The actuality of current algorithms in mining association rule is analyzed in this paper. A new algorithm, based on binary and graph, is put forward. The main idea and realization project of the algorithm are introduced detailed here. Then, comparing the performance of all kinds of algorithms proves the new algorithm having improved the efficiency of mining rules.

Collaboration


Dive into the Wen-Yuan Liu's collaboration.

Top Co-Authors

Avatar
Top Co-Authors

Avatar

Yan Shi

Kyushu Tokai University

View shared research outputs
Top Co-Authors

Avatar

Shu-Fen Fang

Harbin Institute of Technology

View shared research outputs
Top Co-Authors

Avatar
Top Co-Authors

Avatar
Top Co-Authors

Avatar

Xia Li

Shijiazhuang University of Economics

View shared research outputs
Top Co-Authors

Avatar
Top Co-Authors

Avatar
Top Co-Authors

Avatar
Top Co-Authors

Avatar
Researchain Logo
Decentralizing Knowledge