Papers by Author: Hua Wang Shi

Abstract: Quantitative security risk evaluation of information systems is increasingly drawing more and more attention. The purpose of this paper is to propose a novel method integrated extension theory and unascertained method to classification for information systems (IS) security. The risks of information system are established on the basis of analyzing the factors affecting the risks of information system by applying the unascertained measure theory. Using matter-element theory, the extensibility of IS security is analyzed, and then the framework of matter-element models for IS security is formed. The matter element model of IS security risk evaluation is established using matter element model theory based on extension engineering method. Theoretical analysis and the design principle of the proposed method are described in detail. Some simulations are performed to demonstrate the effectiveness of the proposed extension and unascertained method. The result is believed to provide new means and ideas for the evaluation of IS security. The method is suitable for evaluating the risks of IS. Its evaluating results are reasonable. An example of practical application is given to show the effectiveness of this method. The model is more efficient than former models and can be easily realized in practice.

Abstract: By use of the properties of ant colony algorithm and particle swarm optimization, this paper presents an application of an Ant Colony Optimization (ACO) algorithm and artificial neural network (ANN) to fault diagnosis. The ACO algorithm is a novel heuristic bionic algorithm, which is based on the behavior of real ants in nature searching for food. Neural network is used to express the nonlinear function between the input and output of the fault diagnosis of the rolling bearing. And ant colony optimization (ACO) algorithm is used to learn NN. The new algorithm has the merits of both ACO algorithm and neural network. It also provides a new way for the fault diagnosis through constructing the intelligent model by ant colony-neural network and overcomes the shortcomings of traditional algorithm.