Applied Mechanics and Materials Vols. 556-562

Abstract: The basic framework of computational philosophy has been established in this paper: core, innovation, system and methodology. The core is to seek unified theory of computation (UTC), which is the “hard core” in the research program of computational philosophy; the main purpose of innovation is to provide new research methods of philosophy for various computational theories, innovation is the most characteristic in computational philosophy, and is also the key for computational philosophy to establish its status in philosophy; its system is based on innovation research, and explains, models and provides solutions for traditional and new problems via the concepts, methods, tools and techniques of computation; methodology explains concepts, principles and methods in the computational discipline as well as other related disciplines to build the framework of meta-theoretical analysis based on innovation research.

Abstract: Bilingual teaching is the inevitable reform and development trend of higher education, and it is a very important job for us to evaluate the bilingual teaching quality. Currently, we do not have an effective evaluation system for bilingual teaching quality. In this study, the factors which affect bilingual teaching quality are analyzed and the evaluation index system of bilingual teaching quality in universities is established firstly. Then, a knowledge rule mining method for the evaluation of bilingual teaching quality in universities based on an improved genetic algorithm is proposed. In the algorithm, selection operator, dual crossover operator and dual mutation operator are used to generate new knowledge rules. Knowledge rules are evaluated by their accuracy, coverage and reliability. Experimental results show that this knowledge rule mining method is feasible and valid.

Abstract: Collaborative filtering (CF) is the most widely used and successful personalized recommendation technology in web usage mining. The traditional collaborative filtering algorithm based on user static evaluation of the item's neighbour to predict changes of the users’ interests, however, the user’s interest will make a difference over time. Taking the dynamic changes the user’s interest into account in the process, this paper presents a dynamic collaborative filtering recommendation method based on improved ant colony algorithm (EACF). Improved ant colony algorithm takes into account the user access time and access frequency, which can be more representative of the true interests of users. When generating the recommendation, this method not only takes into account the item’s score, but also will take into account intensity of “interest pheromone” on each item. Experimental results show that the EACF can significantly improve the prediction accuracy of the recommendation system compared with traditional CF.

Abstract: The emergency of a new computing pattern must be supported by powerful mathematical theory, weak theoretical supported ideology does not have a strong vitality, and which will become vulnerable. As being a new theory, the DNA computing treated as a new computational pattern also need a lot of basic theoretical research system. This article describes the nature of DNA computing, given the equivalence of DNA computing pattern with traditional computer under the support of automata theory system, further analysis and understanding the DNA computing pattern.

Abstract: Locality sensitive hashing is the most popular algorithm for approximate nearest neighbor search. As LSH partitions vector space uniformly and the distribution of vectors is usually non-uniform, it poorly fits real dataset and has limited search performance. In this paper, we propose a new Bi-level locality sensitive hashing algorithm, which has two-level structures to perform approximate nearest neighbor search in high dimensional spaces. In the first level, we train a number of cluster centers, then use the cluster centers to divide the dataset into many clusters and the vectors in each cluster has near uniform distribution. In the second level, we construct locality sensitive hashing tables for each cluster. Given a query, we determine a few clusters that it belongs to with high probability, and then perform approximate nearest neighbor search in the corresponding locality sensitive hash tables. Experimental results on the dataset of 1,000,000 vectors show that the search speed can be increased by 48 times compared to Euclidean locality sensitive hashing, while keeping high search precision.

Abstract: This paper present a parallel solving method of large-scale power flow correction equations and the program implementation based on the shared memory programming model OpenMP, which Improve the utilization rate the computer CPU resources and the computing speed. The method uses factor tables for solving the power flow equations. Combined the sparsity of correction equations coefficient matrix, the rows which can be do normalized simultaneously are grouped for doing the normalization and elimination operations in parallel. Meanwhile, use the row grouping information which is obtained during the factor table generation process to do parallel previous generation computing. Finally, the simulation for the actual power grid verified the validity and rationality of the method.

Abstract: The Mean-Shift algorithm has very good tracking effect when the background is in a simple; but for a complex environment, tracking effect is not very ideal. Therefore, a new gray feature modeling method is proposed in this paper. Firstly, target in the tracking window is uniformly divided into even pieces. Then the pixel gray value of each block is calculated with subtraction of certain rules. Finally, the gray value of gray difference and the whole object value fusion are fused and established the object model. The object model that established not only contains the whole gray value information, but also contains the gray value differences between regions, has a more accurate description of the target, and then distinguish target from background better. The experiment results show that: the target model using the method in this paper to track based on the Mean-Shift algorithm, has good adaptability when the target is partially occluded and has better robustness for complex background.

Abstract: In order to improve the controllability and rationality of vehicle object contour repair process, this paper proposes a fusion sobel evaluation function of vehicle contour clearness algorithm. Using the compensation method of image sequence inter-frame motion to restore vehicle object contour, to establish evaluation function based on sobel edge detection function, statistical characterization of the edge information of the number of pixels to restore judge, by constantly sequence of inter-frame motion compensation and evaluation of the iterative calculation, realize vehicle object contour clear. The experimental results show that this method is simple, effective、feasible and has good real-time performance and robustness.

Abstract: Spectral clustering is an efficient clustering algorithm based the information propagation between neighborhood nodes. Its performance is largely dependent on the distance metrics, thus it is possible to boost its performance by adapting more reliable distance metric. Given the advantages of sparse representation in discriminative ability, robust to noisy and more faithfully to measure the similarity between two samples, we propose an sparse representation algorithm based on sparse representation. The experimental study on several datasets shows that, the proposed algorithm performs better than the sparse clustering algorithms based on other similarity metrics.

Abstract: Association analysis of the production process data (PPD) can be discoveried on the quality relevant parameters with great impact, however, it’s different from correlation analysis of other fields, Huge amount of data due to the production process, and the many parameters involved in the production process, the existing association analysis algorithms as they deal with inefficiency, can not meet the practical application. This paper proposes a new process for the industrial production of efficient data association algorithm-AprioriMask, after the actual production process of association analysis verification, AprioriMask algorithm has significant performance improvements to meet the industrial production process data for correlation analysis.