Applied Mechanics and Materials Vols. 556-562

Abstract: An algorithm of B-spline curve approximate merging of two adjacent B-spline curves is presented in this paper. In this algorithm, the approximation error between two curves is computed using norm which is known as best least square approximation. We develop a method based on weighed and constrained least squares approximation, which adds a weight function in object function to reduce error of merging. The knot insertion algorithm is also developed to meet the error tolerance.

Abstract: Association rule mining is one of the most important and well researched techniques of data mining. The key procedure of the association rule mining is to find frequent itemsets. In this paper, a new mining frequent itemsets algorithm based on matrix is introduced. Frequent itemsets are obtained by compressing the transaction matrix efficiently by a new strategy. The new algorithm optimizes the known mining frequent itemsets algorithms based on matrix given by some researchers in recent years, which greatly reduces the temporal complexity and spatial complexity. It is more feasible especially when the degrees of the frequent itemsets are high.

Abstract: For the time-consuming disadvantage of Solr when indexing large data, this paper combines MapReduce parallel computing framework, conducts a Solr index optimization study. The experiment proves that this study improves Solr’s efficiency of dealing with large data, and provides a solution to large data index.

Abstract: Image segmentation technique was used widely for computer vision and image processing. A robust technique of image segmentation plays a crucial role in identification problem. In this paper, a nonparametric and unsupervised method of automatic threshold for segmenting image was proposed, i.e. the optimal threshold is approximated by global average gray and local average gray, and this method was compared with other methods by using standard image. The experimental results show that our method proposed in this paper is robust. In addition, an image database of road traffic marking (www.ananth.in/RoadMarkingdetection.html) is provided to do this experiment for testing our method, the results show that our method is excellent.

Abstract: In this paper, a novel heuristic algorithm named Multivariant Optimization Algorithm (MOA) is presented to solve the 0-1 Knapsack Problem (KP). In MOA, multivariant search groups (locate and global search groups) execute the global exploration and local exploitation iteratively to locate the optimal solution automatically. The presented algorithm has been compared with Genetic Algorithm (GA) and Particle swarm algorithm (PSO) based on five data sets, results show that the optimization of MOA is better than GA and PSO when the dimension of problem is high.

Abstract: This research is regarding the optimized leisure activity selection when women choose. Apply the method of Incomplete Linguistic Preference Relations (InLinPreRa) to process of comparison and selection of the leisure activity. The most popular leisure which women always participate are listed in order: (1) exercise related (2) handicaft related (3) entertain related (4) knowledge related (5) social related. The most welcomed leisure activity is mountain climbing.

Abstract: The definition for the constellation availability of the navigation system is given firstly, and then calculates the availability of a single satellite by using Markov processes, and establishes the model based on the state probability of the constellation, to achieve mapping model from component reliability of the single satellite to the constellation availability. Finally, we analysis the regional availability performance of the GNSS by the algorithm, which can fit and satisfy the requirements of contradiction between accuracy and rapidity for analysis of availability for constellation of GNSS.

Abstract: In this work, in order to improve the operation speed of groundwater flow numerical model, we studied the approach of solving linear equations and the relative acceleration problems based on CUDA platform. We developed the GPCG module base on GPU platform to replace PCG module of MODFLOW 2005 by using NVIDIA TESLA C2070. We obtained the effective acceleration results on GPU platform, by establishing series of ideal and instance models, which showed that the overall speedup of the models is around 2.5times and the calculation speedup is about 10times.

Abstract: With the rapid development of computer technology and the popularity of the network, database scale, scope and depth of the constantly expanding, which has accumulated vast amounts of different forms of stored data. The use of data mining technology can access valuable information from a lot of data. Privacy preserving has been one of the greater concerns in data mining. Privacy preserving data mining has a rapid development in a short year. But it still faces many challenges in the future. A number of methods and techniques have been developed for privacy preserving data mining. This paper analyzed the representative techniques for privacy preservation. Finally the present problems and directions for future research are discussed.

Abstract: One key step in text mining is the categorization of texts, i.e., to put texts of the same or similar contents into one group so as to distinguish texts of different contents. However, traditional word-frequency-based statistical approaches, such as VSM model, failed to reflect the complicated meaning in texts. This paper ushers in domain ontology and constructs new conceptual vector space model in the pre-processing stage of text clustering, substituting the initial matrix (lexicon-text matrix) in the latent semantic analysis with concept-text matrix. In the clustering analysis stage, this model adopts semantic similarity, partially overcoming the difficulty in accurately and effectively evaluating the degree of similarity of text due to simply taking into account the frequency of words and/or phrases in the text. Experimental results indicate that this method is helpful in improving the result of text clustering.