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Articles by Li-Ping Shao
Total Records ( 4 ) for Li-Ping Shao
  Li-Ping Shao , Zheng Qin , Hong-Jiang Gao and Xing-Chen Heng
  The conventional 2D matrix transform represented by 2D Arnold transform and 2D Fibonacci-Q transform is applied widely in the security of image information, because of its easily selected scrambling variables and its abilities in enduring erasing, cropping and compression attacks. However, this scrambling method can only be used to scramble square image. For any rectangle image with its width not equal to its height, it needs to be expanded into square image or divided into several square images before using 2D matrix transform to scramble it, which adds the extra space or increases the computation cost. To address this problem, this study proposes two kinds of 2D matrix transforms called 2D triangular mappings and also gives their corresponding inverse transforms. The proposed mappings can be used to scramble or recover rectangle image directly and their iterative cost is low. The cost to scramble or recover image for one time is only the numbers of pixels and our proposed mappings need not to compute the iterative period in advance. Experiments show the proposed mappings validity in scrambling rectangle image, low cost in scrambling and recovering rectangle image and robustness in enduring erasing, cropping and compressing attacks.
  Bo Liu , Zheng Qin , Rui Wang , You-Bing Gao and Li-Ping Shao
  The aim of this study is to solve the target assignment of coordinated distributed multi-agent systems. Earlier methods (e.g., neural network, genetic algorithm, ant colony algorithm, particle swarm optimization and auction algorithm) used to address this problem have proved to be either too slow or not stable as far as converging to the global optimum is concerned. To address this problem, a new algorithm is proposed which combines heuristic ant colony system and decentralized cooperative auction. Based on ant colony system, the decentralized cooperative auction is used to construct ants` original solutions which can reduce the numbers of blind search and then the original solutions are improved by heuristic approach to increase the system stability. The performance of the new algorithm is studied on air combat scenarios. Simulation experiment results show present method can converge to the global optimum more stably and faster by comparing the original methods.
  Hong-Jiang Gao , Zheng Qin , Lei Lu , Li-Ping Shao and Xing-Chen Heng
  With increasingly complexity in Multi-Agent Systems (MAS), the problem of their verification and validation is acquiring increasing importance and rigorous design practices are needed in case of critical applications. Event B, which provides an accessible and rigorous development method, is ideal for the formal modelling of reactive systems. In this study, a practical approach for developing flexible and reliable formal specifications of MAS using Event B is described, exemplified on Contract Net Protocol (CNP) in the interaction of MAS and B models generated with evt2b supported by Atelier B are then proven in consistency and correctness. All the concepts of this approach are illustrated by a case study concerning the use of Event B for the modelling and verifying a multi-modal platform associating an intellectualized design system of shape and style in automobile. Moreover, the results of proof and evaluation of present method are presented.
  Xing-Chen Heng , Zheng Qin , Xian-Hui Wang and Li-Ping Shao
  A new approach to learning Bayesian networks (Bns) was proposed in this study. This approach was based on Particle Swarm Optimization (PSO). We start by giving a fitness function to evaluate possible structure of BN. Next, the definition and encoding of the basic mathematical elements of PSO were given and the basic operations of PSO was designed which provides guarantee of convergence. Next, full samples for the training set and test set are generated from a known original Bayesian network with probabilistic logic sampling. After that, the structure of BN was learned from complete training set using improved PSO algorithm steps. Finally, the simulation experimental results also demonstrated sthis new approach’s efficiency.
 
 
 
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