Advanced Materials Research Vols. 756-759

Abstract: This paper adopts statistical learning theory and optimization theory to the analysis of the algorithm theory, probe into its theoretical foundation. The existing theoretical analysis on the basis of the establishment of clustering model algorithm design, code realization and finally a lot of different data set of test, choose soil data as a test database, will be in the database on a large number of data mining experiment to verify the performance of the proposed algorithm. The test result feedback back will further deepen the theoretical research or correct theory already mistakes, new theory and will continue to guide experiments, both mutual promoting common development.

Abstract: In the EMC interference environment of mine, we found the EMC is white noise and pulse interference. The UWB signal pulses is short and signal power spectral density distributed in wide band is very low. This article mainly proposes a de-noising method of Wavelet transform modulus maxima in signal band, focused on the traditional low-pass filter that can only remove the outside-band noise. Simulation results show that the scheme can effectively retain the signal singularity based on information to achieve obvious noise reduction

Abstract: At present, the telemetry data is stored and released in the form of binary file or text file, and the user cant find out the data structure from the file itself, this bring difficulties in query and the information sharing. To deal with the problem, a XML-based telemetry data management method was proposed by taking the advantages of extensible makeup language (XML). Large scale of telemetry data can be stored in sections using this method, index efficiency of telemetry data is increased obviously in the operation of practical system. The distribution ability of telemetry data is increased too.

Abstract: Artificial Bee Colony Algorithm (ABCA) is a novel swarm intelligence algorithm which a colony of artificial bees cooperate in finding good solutions for numerical optimization problems and combinatorial optimization problems. Traveling Salesman Problem (TSP) is a famous combinatorial optimization problem which has been used in many fields such as network communication, transportation, manufacturing and logistics. However, it requires a considerably large amount of computational time and resources for solving TSP. To dealing with this problem, we present a Parallel Artificial Bee Colony Algorithm (PABCA) in several computers which operation system is Linux based on the Message Passing Interface (MPI). The entire artificial bee colony is divided into several subgroups by PABCA equally. Each subgroup performs an ABCA for TSP on each processor node, respectively. Each sub-colony on every processor node communicates the current best fitness function and parameters of current best fitness function according to ring topological structure during calculation process. Some well-known benchmark problems in TSP are used to evaluate the performance of ABCA and PABCA. Meanwhile, the performance of PABCA is compared with Genetic Algorithm (GA) and Particle Swarm Optimization (PSO). Experimental results show that the PABCA can obtain solutions with equal precision and reduce the time of computation obviously in comparison with serial ABCA. And PABCA have much better performance in contrast with GA and PSO.

Abstract: The demand for individualized teaching from E-learning websites is rapidly increasing due to the huge differences existed among E-learning learners. A method for clustering E-learningers based on rough set is proposed. The basic idea of the method is to reduce the learning attributes prior to clustering, and therefore the clustering of E-learningers is carried out in a relative low-dimensional space. Using this method, the E-learning websites can arrange corresponding teaching content for different clusters of learners so that the learners individual requirements can be more satisfied.

Abstract: A parallel multilevel k-way partitioning algorithm is used in OpenFOAM to perform parallel mesh-decomposition. Before the parallel decomposition procedure, mesh has to be pre-decomposed by the Simple method in OpenFOAM. Match-computation for the algorithms parallel coarsening phase is based on a kind of global match scheme which introduces large amount of communication overhead. However, the domains of the mesh generated by Simple maintain good locality and continuity, which makes it unnecessary to adopt global match scheme in the parallel coarsening phase. In this paper, a novel adaptive match scheme AMS is brought forward for the parallel multilevel k-way partitioning algorithm. An adaptive critical x is calculated firstly according to the scale of the mesh and the parallel degree. For the first x stages of the coarsening phase, a local match scheme is adopted in which vertexes are only allowed to match with their adjacent unmatched vertexes with heaviest edge-weight on their local processors. For the rest stages, the traditional global match scheme is introduced and match of two vertexes on different processors is allowed. AMS can efficiently reduce the communication overhead introduced by simply adopting global match scheme. The experiment is performed on mesh of LinearPTT application on Tianhe-1A. The results show that the parallel multilevel k-way paritioning algorithm based on AMS has a better performance than that based on traditional global match scheme.

Abstract: In this paper,according to the special generating polynomial, a class of bivariate matrix Padé-type approximation (BMPTA) is given by introducing a bivariate matrix-valued linear functional on the scalar polynomial space.An application in state-space realization of the 2-D filters is also given in the end.

Abstract: This article gives an example of using Matlab to solve practical problems

Abstract: In order to get an image with every object in focus, an image fusion process is required to fuse the images under different focal settings. In this paper, a novel multifocus image fusion algorithm based on multiresolution transform and particle swarm optimization (PSO) is proposed. Firstly the source images are decomposed into lowpass subbands coefficients and highpass subbands coefficients by the nonsubsampled contourlet transform (NSCT). Then, different fusion rules are applied for low and high frequency NSCT coefficients. Finally the fused image is reconstructed by the inverse NSCT transform. The experiment results demonstrate that the proposed method is effective and can provide better performance than the method based on the wavelet transform and the nonsubsampled contourlet transform.

Abstract: Tensor voting framework is an approach for perceptual organization. A tensor can provide more information than scalar or vector in image processing. However, the structure of tensor also makes it not unique but orientation dependent. In this paper, to quantify properly the intrinsic orientation-independent voting process, we proposed a new description of the tensor fields, which consists of three rotationally invariant quantities. Instead of coordinate transformation, this approach does not require tensor diagonalization or eigenvalue calculation. Therefore, our approach is not susceptible to potential artifacts induced during these number manipulations, meanwhile simplified the voting process at the same time.