Applied Mechanics and Materials Vols. 411-414

Abstract: Semantic link is a hyperlink whick contains rich semantics. Semantic Link Network (SLN) consists of semantic links. Compared with hyperlinks, semantic link contains richer semantic information. There are many kinds of relationships between two SLNs, which contain equality relationship, inclusion relationship and empty set relationship. This paper mainly introduces an algorithm that can determine inclusion relationship between two SLNs. Semantic link newwork may consist of atom nodes or complex node. The determination algorithm proposed in this paper applies only to atomic node.

Abstract: In this paper, we develop a novel method of 3D object classification based on a Two-Dimensional Hidden Markov Model (2D HMM). Hidden Markov Models are a widely used methodology for sequential data modeling, of growing importance in the last years. In the proposed approach, each object is decomposed by a spiderweb model and a shape function D2 is computed for each bin. These feature vectors are then arranged in a sequential fashion to compose a sequence vector, which is used to train HMMs. In 2D HMM, we assume that feature vectors are statistically dependent on an underlying state process which has transition probabilities conditioning the states of two neighboring bins. Thus the dependency of two dimensions is reflected simultaneously. To classify an object, the maximized posteriori probability is calculated by a given model and the observed sequence of an unknown object. Comparing with 1D HMM, the 2D HMM gets more information from the neighboring bins. Analysis and experimental results show that the proposed approach performs better than existing ones in database.

Abstract: The twisted cube TQ_n is an alternative to the popular hypercube network and some interesting properties of a TQ_n were investigated recently. The problem of how to embed paths into a host graph has attracted a great attention in recent years. However, there are few systematic methods proposed to generate the desired paths in a TQ_n. In this paper, we provide two kinds of systematic methods of embedding paths into a TQ_n.

Abstract: In numerous agricultural information technologies, the agricultural knowledge system has become an important part of the agricultural intelligent information technology (AIIT). This paper introduces the development process of the accomplishment of the expert system for the apple disease diagnosis by use of the binary tree-based knowledge editor.

Abstract: Artificial intelligence based on the genetic algorithm and DNA computing based on the biological intelligence is two kinds of important intelligent computing model, Graph theory and combinatorial optimization problem is a hotspot of research on intelligent computing. This paper designs a coding space optimized by using genetic algorithm, and by using DNA computing to solve Minimum Spanning Tree Problem calculation model. Because MSTP (Minimum Spanning Tree Problem) refer to Weight, IMCE (Incompletion-Molecule Commixed Encoding) is used in vertex, edges and weights encoding. The calculation process of the MSTP solution has been detailed described detailed.

Abstract: DNA computing has the support of automata theory completely, based on the equivalent for expressing problem by DNA computing model and the double-shift language in automata theory, using a DNA molecule may encode the instantaneous description of Turing machine, and the operation of continuous sequence can be realized by the DNA molecule s operation with enzymes. Insert - Remove System is a computing system in DNA computing, designed an Binary Tree DNA computing model based on the Insert - Remove System in this paper, which can realize the insert, delete and traversal operation, and has the completeness of the theory.

Abstract: Gene Expression Programming is a new and adaptive brand evolution algorithm which is developed on the basis of genetic algorithm. In recent years, Multi-Expression Programming which is proposed in the genetic programming is a linear structure coding scheme,its main feature is a chromosome contains multiple expressions. The idea of MEP is introduced into the GEP in this paper, so a single GEP gene contains multiple solutions to solve the problem.The new algorithm analyzes each gene in the GEP to extract relational subexpressions, then fitness evaluate certain subexpressions to choose the best fitness as individuals fitness, and carry on related genetic manipulation. Finally, the improved algorithm experiment with GEP and MEP, compare their mining the same functions ability,record average fitness value and success rate. The experiment results show that the improved algorithm has better evolutionary efficiency.

Abstract: The modeling objects of existing prediction models for interval grey number are limited to the interval grey number sequences with unknown or the same type of whitenization weight function. Therefore, the existing methods are useless when the types of whitenization weight function of interval grey number in the modeling sequence are heterogeneous. On the basis of the existing prediction models for interval grey number and according to the axiom of undecreased degree of greyness and grey number, the present paper build a prediction model for interval grey number based on different types of whitenization weight functions through expanding the calculation of "kernel and grey degree" of the interval grey number. At last, this model was applied in forecasting the demand for emergency materials in disaster. The research results are significant for enriching and perfecting the grey prediction model theory system, and extending the applied scope of grey models and promoting the effective association of the grey theory and the practical issues.

Abstract: The research on the theory and method of single machine scheduling is a difficult subject, but it is very important for the companies to improve the production efficiency and effectiveness. The study on single machine scheduling problem has the history of 50 years, but there is still a gap between the classical scheduling theories and practical scheduling problems. According to this characteristic, the problems in practical scheduling area and the various factors need to be concerned are mentioned, and the main methods to solving the single machine scheduling problem and their applications are presented in details. Finally, the directions and suggestions of future work in single machine scheduling problem are summarized.

Abstract: In this paper, the rough set theory is applied to reduce the complexity of data space and to induct decision rules. It proposes the generic label correcting (GLC) algorithm incorporated with the decision rules to solve supply chain modeling problems. This proposed approach is agile because by combining various operators and comparators, different types of paths in the reduced networks can be solved with one algorithm.