Applied Mechanics and Materials Vols. 568-570

Abstract: The satellite range scheduling problem is one of the most important problems in the field of the satellite operation. The purpose of this problem is finding the optimal feasible schedules, scheduling the communications between satellites and ground stations effectively, in another word. The problem is known for its high complexity and is an over-constrained problem. This paper present the resolution of the problem through a Station Coding Based Evolution Algorithm, particularly with the priority constraint, which adopting a new chromosome encoding method based on arranging the tasks in the ground station ID order. Computational results and analysis are presented for the case of the multi-ground stations scheduling.

Abstract: Cloud computing is tailored to the needs of the real-time and effectiveness of the grid witch can solve complicated problems. However, using the existing algorithms may always run into the problem of local optimal solution witch will effect the accuracy. This paper use the method of IHA to reduce probability of local optimal solution by change task decomposition to feasible operation, then prove the method by using Cloudsim.

Abstract: An improved ant colony algorithm based grid environment model for global path planning method for USV was introduced. The main idea of the improved ant colony algorithm was distributing each ant route dynamically. When the active ant was selecting the next route, this algorithm program determined the nearest direction to the end point. There were many possible route points which were distributed artificially. Thereby, the probability for each ant to choose the right direction was increased. The simulating results demonstrate that the improved ant colony algorithm in this paper is very suitable for solving the question of global path planning for USV system in the complex oceanic environment where there are a lot of obstacles. At the same time, this method costs less time, and the path is very smooth.

Abstract: In this paper, a iterative method for approximating equilibrium problem and a fixed point of nonexpansive mappings was introduced in Hilbert spaces. And a strong convergence theorems of the iteration scheme was established. The results improve and extend the corresponding results of many others.

Abstract: The Genetic Algorithm (GA) is a stochastic global search method that mimics the metaphor of natural biological evolution. GA operates on a population of potential solutions applying the principle of survival of the fittest to produce (hopefully) better and better approximations to a solution. Genetic algorithms are particularly suitable for solving complex optimization problems and for applications that require adaptive problem solving strategies. Here, in this paper genetic algorithm is introduced as an optimization technique.

Abstract: It occurs time and space performance bottlenecks when traditional association rules algorithms are used to big data mining. This paper proposes a parallel algorithm based on matrix under cloud computing to improve Apriori algorithm. The algorithm uses binary matrix to store transaction data, uses matrix "and" operation to replace the connection between itemsets and combines cloud computing technology to implement the parallel mining for frequent itemsets. Under different conditions, the simulation shows it improves the efficiency, solves the performance bottleneck problem and can be widely used in big data mining with strong scalability and stability.

Abstract: It is known that the Common Algorithmic Problem (CAP) has a nice property that several other NP-complete problems can be reduced to it in linear time. In the literature, the decision version of this problem can be efficiently solved with a family of recognizer P systems with active membranes with three electrical charges working in the maximally parallel way. We here work with a variant of P systems with active membranes that do not use polarizations and present a semi-uniform solution to CAP in the minimally parallel mode.

Abstract: The express traffic system could divide into highway traffic system and ordinary road traffic system, which have different linkage attributes and traffic attributes for segments and nodes. The time consumption space of traveling in the system is a non-Euclidean distance space. From the traffic condition of the express traffic system, the foundation data, principles and methods of NEDS algorithm are introduced. The steps and methods of optimum route planning in the express traffic system are deeply discussed. At the end, an example of optimum route planning in Henan express traffic system is given.

Abstract: A timed tissue P system is constructed by adding a time mapping to the rules of tissue P system to specify the execution time for each rule. It is a more realistic model from a biological point of view. In this study, we investigate the computational efficiency of timed tissue P systems. A uniform and time-free solution to QSAT problem, a famous PSPACE-complete problem, is proposed, where the execution time of the computational processes involved can vary arbitrarily and the output produced is always the same.

Abstract: RBF neural network and three kinds of preprocessing methods are introduced, and this paper used these preprocessing methods combined with RBF neural network and strict RBF neural network to perform elevation fitting. Comparing and analyzing the fitting results, the results show that preprocessing methods can affect elevation fitting results. Centralized preprocessing data maximum improves RBF neural network elevation fitting precision, and it also let RBF neural network have stronger generalization ability. Normalization preprocessing methods are not necessarily optimal. It is essential for us to choose preprocessing method to fit the elevation.