Advanced Materials Research Vols. 694-697

Abstract: In the belief function theory, the combination of highly conflicting evidences is a research focus,and the key lies in both the rationality and timeliness of combination method.This paper analyzes deeply the existing strategy of model modification, and putsforward a new rapid evidence synthesis method based on model modification. The average evidence ofweighted combination on mean evidence isfirstly given, then fast combination on pignistic transformation can be realized. Thus the BBM accordancewith the principle of proportion redistribution isgot. This method has the advantage of Murphy’s method, and overcomes the large calculation problems at the same time. Comparedwith other methods, the new method is more effective to solve the combinationproblem of highly conflicting evidences, and has fast convergence speed, small computationalcomplexity, and higher practical application value.

Abstract: The spread of forest fire is a complex adaptive system. The spread could be seen as the result of fire agents continuous learning, adaptation and co-ordination. This paper founded an Agent-based model for forest fire spread, modeled the generating of fire spread rules based on Genetic Algorithms. Created the spread rules with effect of wind and topography independently for forest fire, designed the fitness function, and took the genetic operation on the rules, which created new rules. Implemented the adaptive algorithm on Repast S, and used it in the Agent-based model of forest fire spread. The result of models running indicated the adaptive algorithm could improve the adaptive ability of fire agent.

Abstract: Prediction of vehicular operating speed is critical to evaluate the design consistency of road alignment. Elman neural networks are proposed to predict the truck’s 85th percentile operating speed. A total of 190 samples are collected from the two-lane rural roads and two factors are considered as input variables to the model including the curve radius and longitudinal slope. 100 samples are applied for training the networks to get the prediction model and the other 90 samples are used for the model validation. Additionally, the Elman neural networks are compared with back-propagation neural networks and linear regression, and the results show that the Elman neural networks are prior to the other two approaches and can be regarded as an alternative to predict truck’s operating speed.

Abstract: This paper discussed application of modified non-dominated sorting genetic algorithm-II (MNSGA-II) to multi-objective optimization of a coal-fired boiler combustion, the two objectives considered are minimization of overall heat loss and NOx emissions from coal-fired boiler. In the first step, BP neural network was proposed to establish a mathematical model predicting the NOx emissions & overall heat loss from the boiler. Then, BP model and the non-dominated sorting genetic algorithm II (NSGA-II) were combined to gain the optimal operating parameters. According to the problems such as premature convergence and uneven distribution of Pareto solutions exist in the application of NSGA-II, corresponding improvements in the crowded-comparison operator and crossover operator were performed. The optimal results show that MNSGA-II can be a good tool to solve the problem of multi-objective optimization of a coal-fired combustion, which can reduce NOx emissions and overall heat loss effectively for the coal-fired boiler. Compared with NSGA-II, the Pareto set obtained by the MNSGA-II shows a better distribution and better quality.

Abstract: The variable precision probabilistic rough set model is based on equivalent relation and probabilistic measure. However, the requirements of equivalent relation and probabilistic measure are too strict to satisfy in some practical applications. In order to solve the above problem, a variable precision rough set model based on covering relation and uncertainty measure is proposed. Moreover, the upper and lower approximation operators of the proposed model are given, while the properties of the operators are discussed.

Abstract: Based on the fuzzy clustering analysis, this article has obtained the classification of the known ore samples, obtained the dynamic cluster chart, and has given the samples best classification using variance analysis's principle.

Abstract: The identification of the echo signals is one of the key technologies of the underwater sound equipment. This paper presents the method that the active sonar signals can be extracted through time-frequency analysis and recognized through adaptive resonance theory (ART) according to the characteristics of the echo. The method has been proved to be effective in practice.

Abstract: Based on the traditional demodulation method of four phase shift keying (QPSK), a QPSK demodulation model was proposed. The FPGA-based QPSK modulation and demodulation system and circuit had been achieved. In Xilinx ISE12.3 development environment, using the SPARTAN-3E development board, the simulation results demonstrate the feasibility of this design.

Abstract: One of the famous mathematical inequalities is Carlemans inequality. It is an important inequality from both mathematical and application points of view. In this paper, a Carleman type inequality for Sugeno integrals is studied.

Abstract: It is difficult to directly compress the raw data of synthetic aperture radar for its low relativity. In this paper, a new algorithm is put forward. Firstly range focusing is imposed to SAR raw data, which makes it have comparative high relativity, secondly a linear prediction is performed along the azimuth, lastly block adaptive quantization is used to the prediction difference series. The experiments manifest that with same bit rate, SQNR and SDNR of the algorithm proposed in this paper surpass that of BAQ algorithm. The calculation in this paper is far less than that of compression method after range focusing advised in corresponding reference.