Xiyu Liu

Shandong Normal University, Chi-nan-shih, Shandong Sheng, China

Are you Xiyu Liu?

Claim your profile

Publications (80)18.63 Total impact

  • Jie Xue, Xiyu Liu
    03/2014; 9(3). DOI:10.4304/jsw.9.3.716-725
  • Jie Xue, Xiyu Liu
    [Show abstract] [Hide abstract]
    ABSTRACT: Membrane computing is widely used in many areas, however, there are several limitations in its structures and rules. Although many researchers are engaged in the study of P systems, seldom focus on improving membrane structures. The purpose of this paper is to propose a new kind of communication P system on lattice (LTC-P systems). We describe membrane structures on lattice with communication rules. The computational completeness of the new P system is proved by simulation of register machine. The new P system is used in solving clustering problems. It is combined with the thought of density-based, partition-based and hierarchical clustering algorithm. Clustering is implemented by supremum and infimum rules. The result is obtained through output membrane. All the processes are conducted in membranes. Cluster result via a $20$ points data set verifies that the proposed new P systems cluster data set accurately and reduce time complexity. Wine data set are also used in testing the influence of parameters. More suitable $\varepsilon $ and ${ MinPts}$ are found to gain less missing data which are seen as noise. Comparative results in various aspects indicate LTC-P system based clustering algorithm consumes less time than traditional algorithms significantly. It also uses less rules and gives more simple membrane structures than conventional cell-like P system. The new P system provides an alternative for traditional membrane computing.
    Soft Computing 07/2013; 18(7). DOI:10.1007/s00500-013-1155-y · 1.30 Impact Factor
  • Jie Xue, Xiyu Liu
    06/2013; 5(11):219-228. DOI:10.4156/aiss.vol5.issue11.27
  • Xiyu Liu, Jie Xue
    [Show abstract] [Hide abstract]
    ABSTRACT: Spatial cluster analysis is an important data mining task. Typical techniques include CLARANS, density- and gravity-based clustering, and other algorithms based on traditional von Neumann's computing architecture. The purpose of this paper is to propose a technique for spatial cluster analysis based on sticker systems of DNA computing. We will adopt the Bin-Packing Problem idea and then design algorithms of sticker programming. The proposed technique has a better time complexity. In the case when only the intracluster dissimilarity is taken into account, this time complexity is polynomial in the amount of data points, which reduces the NP-completeness nature of spatial cluster analysis. The new technique provides an alternative method for traditional cluster analysis.
    Discrete Dynamics in Nature and Society 04/2013; 2013. DOI:10.1155/2013/891428 · 0.88 Impact Factor
  • [Show abstract] [Hide abstract]
    ABSTRACT: Trust is an important mechanism to promote cooperation among persons in social network. As a security mechanism, it is widely implemented computer networks. In P2P applications, trust among cooperating peers is an essential precondition. It is becoming increasing vital in open, distributed, dynamic and anonymous networks to protect against malicious peers. This paper proposed a trust-aware propagation model based on the social network and with the basis of analyzing aware-computing to attempt to discover the propagation characteristics and regularity of trust being as network stability factor, and to reveal the functional mechanism of trust in the network security, which can improve the stability and robustness of the network. Firstly, we proposed a trust-aware propagation framework that contains three layers: propagation layer, aware layer, and computation layer. And then, the trust-aware propagation algorithm is presented to implement the formation of trust networks as fast as in P2P. A fitness function for peers computes their fitness degree based on contextual information to provide an initial entity identification mechanism, and the process of trust-aware propagation based on Markov process is defined to apperceive of trust among peers to ulteriorly form trust alliance. Thirdly, we built the dynamics model of trust propagation in order to deeply analyze the stability of trust networks. Next, three propagation strategies is proposed for implementing trust propagation. Finally, simulation experiments are carried in the bio-network platform. The results show that the trust-aware propagation model can effectively enhance the security and stability of P2P network, and improve the availability of the peer's resources.
    Knowledge-Based Systems 03/2013; 41:8-15. DOI:10.1016/j.knosys.2012.12.005 · 3.06 Impact Factor
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: As a popular web innovation, Web service technology can provide a flexible solution to integrating diverse Web applications, taking advantage of existing Internet protocols and open standards. To provide on-request Web applications, Web services are evolving to be highly available, ubiquitous, self-managing, and adaptable to dynamic requests through distributed collaborations. However, the distributed collaborations unavoidably need security requirements, and traditional security mechanisms fail to address the challenge. On the other hand, trust is an important social concept and present in all human interactions. It has been proven as a promising way to resolving security problems raised by the distributed collaborations. This paper applies trust mechanisms into the development of a novel trust evaluation model of Web services. We first incorporate a trust management module into the standard Service Oriented Architecture (SOA). Then, regarding the trust relationships of service entities in Web service networks as a small-world network, we propose a trust evaluation model based on an amendatory subjective logic. The results of simulation experiments show the effectiveness of the proposed model, which outperforms other two models in terms of both detection capability and stability.
  • Xiyu Liu, Jie Xue, Xiaolin Yu
    [Show abstract] [Hide abstract]
    ABSTRACT: A novel multi-agent membrane computing technique is proposed. We provide a new membrane structure on graph to construct a multi-agent membrane system, which offers a new framework for collaboration and resources sharing among designers. The new design environment provides a tool to extend designer's ideas with the help of communication rules of membrane. Shapes were generated and changed in membrane systems by rules base on the idea of genetic computing with operators crossover, mutation and selection. A multi-agent membrane system with rules is constructed in detail. Finally, a pipe design example is adopted to illustrate the entire design process, which also shows the great parallelism, less time-consuming and synchronization of MAMC.
    Computer Supported Cooperative Work in Design (CSCWD), 2013 IEEE 17th International Conference on; 01/2013
  • Laisheng Xiang, Feng Qi, Xiyu Liu
    [Show abstract] [Hide abstract]
    ABSTRACT: A neural tree network model has been successfully applied to solving a variety of complex nonlinear problems. The optimization of the neural tree model is divided into two steps in general: first, structure optimization, and then parameter optimization. One major problem in the evolution of structure without parameter information is noisy fitness evaluation, so an improved breeder genetic programming algorithm is proposed to the synthesis of the optimization in neural tree network model. Simulation results on two time series prediction problems show that the proposed optimization strategy is a potential method with better performance and effectiveness.
    Kongzhi yu Juece/Control and Decision 01/2013; 28(1).
  • [Show abstract] [Hide abstract]
    ABSTRACT: A class of new cloud computing model DCaaS is proposed. This model combines traditional DNA computing with SaaS model. The main advantage of DCaaS model is to separate biological experiments with DNA computing, and obtain biological operations as a service via DNA programs. As application frame, approximate solution of a class of nonlinear problems is presented.
    Proceedings of the 2012 international conference on Pervasive Computing and the Networked World; 11/2012
  • Jie Xue, Xiyu Liu
    [Show abstract] [Hide abstract]
    ABSTRACT: The directed Hamiltonian path (DHP) problem is one of the hard computational problems for which there is no practical algorithm on conventional computer available. Many problems, including the traveling sales person problem and the longest path problem, can be translated into DHP problems. Inspired by the biological neurons, priority of rules in membrane computing, we introduce spiking neural P systems with priority and multiple output neurons into the application of DHP problems. In this paper, a new SN P System based algorithm is presented. We use neurons to stand for all the possible path and filter out the DHP we want automatically, all the processes will implement in the new SN P system. Instances indicate that the proposed SN P system based algorithm reduces the time complexity efficiently by huge parallelism.
    Proceedings of the 2012 international conference on Pervasive Computing and the Networked World; 11/2012
  • Yuzhen Zhao, Xiyu Liu, Jianhua Qu
    [Show abstract] [Hide abstract]
    ABSTRACT: In this paper a clustering algorithm based on a P System with active membranes is proposed which provides new ideas and methods for cluster analysis. The membrane system has great parallelism. It could reduce the computational time complexity. Firstly a clustering problem is transformed into a graph theory problem by transforming the objects into graph nodes and dissimilarities into edges with weights of complete undirected graph, and then a P system with all the rules to solve the problem is constructed. The specific P system with external output is designed for the dissimilarity matrix associated with n objects. First all combinations of all nodes are listed to show all possibilities of the paths (the solution space) by using division rules of P system. Then a shortest path with the minimum sum of weights is selected. At last the path is divided into k parts from the edges with the k-1 biggest weights according to the preset number of clusters k. That is to say, all nodes are divided into k clusters. The calculation of the P system can get all the clustering results. Through example test, the proposed algorithm is appropriate for cluster analysis. This is a new attempt in applications of membrane system.
    Proceedings of the 2012 international conference on Pervasive Computing and the Networked World; 11/2012
  • Source
    Hongyan Zhang, Xiyu Liu
    Biosystems 08/2012; DOI:10.1016/j.biosystems.2011.09.001 · 1.47 Impact Factor
  • [Show abstract] [Hide abstract]
    ABSTRACT: Logistics company alliance building problem studies that how to build alliance with a method of great rate and low cost. It presents an improved algorithm about graph clustering base on PSO to solve the problem. Optimize the clustering result through PSO, and expand the solution space through disturbance strategies in order to obtain the optimal solution. Experimental result shows that the algorithm can solve the problem in a high rate and low cost.
    Information Technology in Medicine and Education (ITME), 2012 International Symposium on; 01/2012
  • Feng Qi, Xiyu Liu, Yinghong Ma
    [Show abstract] [Hide abstract]
    ABSTRACT: Neural tree model has been successfully applied to solving a variety of interesting problems. In most previous studies, optimization of the neural tree model was divided into two steps: first structure optimization, then parameter optimization. One major problem in the evolution of structure without parameter information was noisy fitness evaluation. In this paper, an improved breeder genetic programming algorithm is proposed to the synthesis of neural tree model. The effectiveness and performance of the method are evaluated on time series prediction problems and compared with those of related methods. Simulation results show that the proposed algorithm is a potential method with better performance and effectiveness.
    Neural Computing and Applications 01/2012; 21(3). DOI:10.1007/s00521-010-0451-z · 1.76 Impact Factor
  • Xiyu Liu, Alice Xue
    [Show abstract] [Hide abstract]
    ABSTRACT: The purpose of this paper is to propose a new kind of P systems on simplicial complexes. We present the basic discrete Morse structure, membrane structures on complexes, and communication rules. A new grid-based clustering technique is described based on this kind of new P systems. Examples are given to show the effect of the algorithm. The new P systems provide an alternative for traditional membrane computing.
    Discrete Dynamics in Nature and Society 01/2012; 2012. DOI:10.1155/2012/415242 · 0.88 Impact Factor
  • [Show abstract] [Hide abstract]
    ABSTRACT: Spatial cluster analysis is an important data-mining task. Typical techniques include CLARANS, density- and gravity-based clustering, and other algorithms based on traditional von Neumann’s computing architecture. The purpose of this paper is to propose a technique for spatial cluster analysis based on DNA computing and a grid technique. We will adopt the Adleman-Lipton model and then design a flexible grid algorithm. Examples are given to show the effect of the algorithm. The new clustering technique provides an alternative for traditional cluster analysis.
    Discrete Dynamics in Nature and Society 01/2012; 2012. DOI:10.1155/2012/894207 · 0.88 Impact Factor
  • Huichuan Duan, Xiyu Liu
    [Show abstract] [Hide abstract]
    ABSTRACT: In this paper, a γ dependent lower C limits formula for the effective hyperparameter (C, γ) region for Support Vector Classification (SVC) with Radial Basis Function (RBF) kernel is derived, on the basis of a typical working set selection method for Sequential Minimal Optimization (SMO) algorithm along with the asymptotic behavior analysis of Support Vector Machines (SVM). The formula can delineate the tongue-shaped effective (C, γ) region in RBF SVC nearly perfectly as our experiments revealed. Our work may provide a basis for exploring the deep underpinnings that determine the shape of effective hyperparameter region in SVM, and may also invoke new ideas in hyperparameter tuning in SVM.
    Information Technology in Medicine and Education (ITME), 2012 International Symposium on; 01/2012
  • Jie Xue, Xiyu Liu
    [Show abstract] [Hide abstract]
    ABSTRACT: Using membrane computing to do some creative design is a new approach in this field. We use multi-sets of P system to assign customers' requirements and experienced models, constructing P system with rewriting rules which is suit for our design, getting the results that we want. All of the above steps are carried out in membranes. Because of the high parallelism of membrane computing, we can get the results rapidly.
  • Hongyan Zhang, Xiyu Liu
    [Show abstract] [Hide abstract]
    ABSTRACT: DNA computing has been applied in broad fields such as graph theory, finite state problems, and combinatorial problem. DNA computing approaches are more suitable used to solve many combinatorial problems because of the vast parallelism and high-density storage. The CLIQUE algorithm is one of the gird-based clustering techniques for spatial data. It is the combinatorial problem of the density cells. Therefore we utilize DNA computing using the closed-circle DNA sequences to execute the CLIQUE algorithm for the two-dimensional data. In our study, the process of clustering becomes a parallel bio-chemical reaction and the DNA sequences representing the marked cells can be combined to form a closed-circle DNA sequences. This strategy is a new application of DNA computing. Although the strategy is only for the two-dimensional data, it provides a new idea to consider the grids to be vertexes in a graph and transform the search problem into a combinatorial problem.
    Bio Systems 07/2011; 105(1):73-82. DOI:10.1016/j.biosystems.2011.03.004 · 1.47 Impact Factor
  • Enxiu Chen, Jianqing Li, Xiyu Liu
    [Show abstract] [Hide abstract]
    ABSTRACT: The particle swarm optimization algorithm is an innovative and competitive optimization technique in evolutionary computation. It has been found to be extremely effective in solving a wide range of problems with real-parameter representation; however, it is of low efficiency in dealing with the discrete problems. In this paper, the particle swarm algorithm is broken down into its essential components, and alternative interpretations of those components are proposed. It is simpler and more powerful than the algorithms available. Experimental results show that this algorithm is faster than the standard binary discrete PSO on two suites of test functions, and that accuracy is improved for most benchmark functions used. One suite concerns about binary encoding problems, the other is about continuous-valued functions. A queen informant is also introduced. It does not increase the number of function evaluations; however, it appears it greatly speeds up the convergence.
    Applied Soft Computing 04/2011; 11(3):3260-3269. DOI:10.1016/j.asoc.2011.01.002 · 2.68 Impact Factor