• Proceedings of the Korean Society of Tribologists and Lubrication Engineers Conference
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• 2002.10b
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• pp.455-456
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• 2002

Wear is one of the main factors causing breakdown and fault of machine, so ferrography technique analyzing wear particles can be an effective way for condition monitoring and fault diagnosis. On the base of the forward multilayer neural network, a nodes self-deleting neural network model is provided in this paper. This network can itself deletes the nodes to optimize its construction. On the basis of the nodes self-deleting neural network, an intelligent ferrography diagnosis expert system (IFDES) for wear particles recognition and wear diagnosis is described. This intelligent expert system can automatically slim lip knowledge by learning from samples and realize basically the entirely automatic processing from wear particles recognition to wear diagnosis.

• Journal of KIISE:Software and Applications
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• v.31 no.3
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• pp.353-360
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• 2004

Ontology defines the terms used in a specific domain and the relationships between them and represents them as hierarchical taxonomy. The present paper proposes a semi-automatic domain-specific ontology construction method based on terminology Processing. For this purpose, it presents an algorithm to extract terminology according to the noun/suffix pattern of terminology in domain texts and find their hierarchical structure. The experiment was carried out using pharmacy-related documents. As singleton terminology with noun/suffix were identified, the average accuracy was 92.57%. In case of multi-word terminology, the average accuracy was 66.64%. The constructed ontology forms natural semantic clusters with based on suffices and semantic information, so can be utilized in approaches to specific knowledge such as information look-up or as the base of inference to improve searching abilities.