As the graph-based query in SPARQL query processing becames more and more inefficient due to the increasing structure complexity of Resource Description Framework (RDF) in the graph, by analyzing the basic structure of RDF graphs and the selectivity of the RDF vertices, RDF Triple Patterns Selectivity (RTPS) was proposed to improve the efficienccy of subgraph matching for graph with RDF, which is a graph structure segmentation rule based on selectivity of RDF vertices. Firstly, according the commonality of the predicate structure in the data graph and the query graph, an RDF Adjacent Predicate Path (RAPP) index was built, and the data graph structure was transformed into incoming-outgoing predicate path structure to determine the search space of query vertices and speed up the filtering of RDF vertices. Secondly, the model of Integer Linear Programming (ILP) problem was built to divide a RDF query graph with complicated structure into several query subgraphs with simple structure. By analyzing the structure characteristics of the RDF vertices in the adjacent subgraphs, the selectivity of the query vertices was established and the optimal segmentation method was determined. Thirdly, with the searching space narrowed down by the RDF vertex selectivity and structure characteristics of adjacent subgraphs, the matchable RDF vertices in the data graph were found. Finally, the RDF data graph was traversed to find the subgraphs whose structure matched the structure of query subgraphs. Then, the result graph was output by joining the subgraphs together. The controlling variable method was used in the experiment to compare the query response time of RTPS, RDF Subgraph Matiching (RSM), RDF-3X, GraSS and R3F. The experimental results show that, compared with the other four methods, when the number of triple patterns in a query graph is more than 9, RTPS has shorter query response time and higher query efficiency.

Widespread attentions have been drawn to Recommendation Systems (RS) as rapid development of social networks. To solve the cold-start problem and neglect to user's social network information in current recommendation algorithms, a novel Personalized Recommend Algorithm based on Graph Entropy (PRAGE) in trust social network was proposed. Firstly, a weighted User-Item Graph (UIG) was built based on feedback information, and a trust mechanism was introduced to establish a User Trust Graph (UTG). Secondly, by using random walk algorithm on two graphs, the initial similarity of user and item and new user-item similarity based on trust mechanism were obtained; the above algorithm process was repeated until the similarity value reaches convergence value. Then, a method to weight two sets of similarity values with graph entropies of both UIG and UTG was proposed and final recommendation list was accordingly created. The experimental results on two real-world datasets named as Epinions and FilmTrust reveal that, compared with classical Random Walk algorithm, the accuracy of PRAGE is increased by about 34.7%and 19.4% respectively, and its recall is increased by 28.9% and 21.1% respectively, which can alleviate cold start problem effectively and has better performance in accuracy and coverage.

The hardware cost of fast computing of structural topology optimization based on traditional parallel computing method is high and the efficiency of coding development is low. In order to solve the problems, an entire parallel computing method of Bi-directional Evolutionary Structural Optimization (BESO) based on Matlab and Graphics Processor Unit (GPU) was proposed. Firstly, the advantages, disadvantages and application range of three kinds of GPU computing methods used for GPU parallel computing in Matlab were discussed. Secondly, the built-in function was introduced to directly realize parallel computing of the math operations between vector and dense matrix, the MEX function was introduced to realize the fast computing of sparse finite element equations by CUSOLVER library, and the Parallel Thread eXecution (PTX) code was introduced to realize the parallel computing of the optimization decisions of the element sensitivity analysis and other decisions in the topology optimization. The numerical examples show that the GPU parallel computing program based on Matlab has a high coding efficiency, and can avoid the precision difference between the different programming languages. The GPU parallel computing program can ultimately achieve a considerable speedup with the same results.

To overcome the limitations of traditional face recognition methods for single sample, an improved gradient face algorithm named Orthogonal Gradient Binary Pattern (OGBP), which is robust to variations of illumination, face expression and posture, was proposed. Firstly, the features of the image samples were extracted by orthogonal gradient binary pattern. Then the feature vectors of each direction were concatenated into the general feature vector for face recognition. Finally the Principle Component Analysis (PCA) method was used to reduce dimensions and the nearest neighbor classifier was used for face image classification and recognition. Experimental results on YALE and AR face database indicate that the proposed method is simple, effective and better than the original gradient face algorithm, and also has better performance in face description for single sample.

Concerning the low accuracy of face recognition systems, a face recognition algorithm based on Histograms of Nonsubsampled contourlet Oriented Gradient (HNOG) was proposed. Firstly, a face image was decomposed with Non-Subsampled Contourlet Transform (NSCT) and the coefficients were divided into several blocks. Then histograms of oriented gradient were calculated all over the blocks and used as face features. Finally, multi-channel nearest neighbor classifier was used to classify the faces. The experimental results on YALE , ORL and CAS-PEAL-R1 face databases show that the descriptor HNOG is discriminative, the feature dimension is small and the feature is robust to variations of illumination, face expression and position.

The advantages and disadvantages of SETBOC (SET based on one-way Certification) were analyzed. According to the characteristics of SET (Secure Electronic Transaction), such as security, atomicity, anonymity and non-repudiation, an improved scheme was proposed to fully possess the properties above. And it has been proved effective.