Jiu-fen Zhao

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A novel approach of using clustering genetic algorithms is put forward to solve the computer network intrusion detection problem. This algorithm includes two steps which are clustering step and genetic optimizing step. The algorithm can not only cluster the cases automatically, but also detect the unknown intruded action. The results showed that this(More)
This paper adopts unsupervised on-line shape learning for image analysis tasks, removing the requirement for a pre-defined set of templates and allowing the system to handle novel objects. This learning approach was chosen for its simplicity and extensibility. The results show that the size and shape features are sufficient for accurate object(More)
Based on the complexity of the mobile missile electronic command system (MMECS), applying the single method in system fault diagnosis can hardly achieve satisfactory results. The fault diagnosis system combining the BP neural network (BPNN) method and the case-based reasoning (CBR) method was presented. The framework of the mixed neural network and the case(More)
In this study, we proposed a contextual filter edge detector and a multiscale edge tracker to extract edges for further analysis of computer vision. The contextual filter detected significant edges in the finest scale gradient image. Many noised or blurred cases were suppressed with higher decision levels. The edge tracker further improves the detected(More)
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