Huaqing Min

South China University of Technology, Guangzhou, Guangdong Sheng, China

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Publications (3)0 Total impact

  • HuaQing Min, XiJing Zheng, YanSheng Lu
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    ABSTRACT: The main attack strategy in an intense robotic soccer match is for an attack robot (AR) to avoid the competing side's threat and to avoid the most threatening defensive robot of the opponent to reach the objective and to effectively initiate an attack with the help of the cooperation robot (CR). This paper defines an attacking model and a cooperative model and their algorithms. Two co-evolution computation (CEC) populations of robot are also designed: one is denoted as AR subset, the other CR subset. Based on this definition, the paper proposes a new multiple robots avoidance based on CEC method (or MRACEC for short). A theoretical analysis indicates that the MRACEC method has better robustness and optimizing ability.
    Tools with Artificial Intelligence, 2005. ICTAI 05. 17th IEEE International Conference on; 12/2005
  • Xiaoyuan Xu, Guoqiang Han, Huaqing Min
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    ABSTRACT: Because of its accurate and robust performance, association rule-based approach is recently used for image classification. However, the existing algorithms for associative classification suffer from inefficiency. Addressing this problem, a novel algorithm based on atomic association rules is presented and successfully used in image block classification. Mining only the atomic association rules achieves fast image block classification. Using the strong atomic association rules, extracted under a high confidence threshold, can accurately differentiate instances from the image dataset. Furthermore, multi-passes of partial classifications can classify the whole dataset. This algorithm uses a self-adaptive confidence threshold and a dynamic support threshold, both of which are important for good classification performance. The experiments were performed on a standard dataset of image segmentation. The results show the proposed algorithm can classify the image blocks faster, more accurate and robust than the typical associative classification algorithm.
    Computer and Information Technology, 2004. CIT '04. The Fourth International Conference on; 10/2004
  • Xiaoyuan Xu, Guoqiang Han, Huaqing Min
    4th IEEE International Workshop on Source Code Analysis and Manipulation (SCAM 2004), 15-16 September 2004, Chicago, IL, USA; 01/2004

Publication Stats

8 Citations

Top Journals

Institutions

  • 2004–2005
    • South China University of Technology
      • School of Computer Science and Engineering
      Guangzhou, Guangdong Sheng, China