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연관관계를 이용한 지능형 추천 프로세스 시뮬레이션

Intelligent Recommendation Processor Simulation using Association Relationship

  • 한정수 (백석대학교 정보통신학부)
  • Han, Jung-Soo (Division of Information & Communication, Baekseok Univ.)
  • 투고 : 2013.12.01
  • 심사 : 2013.12.20
  • 발행 : 2013.12.28

초록

본 논문은 자동차 부품 점검과정에서 발생할 수 있는 고장유형별 점검해야 할 부품을 연관관계로 나타내고 이를 온톨로지로 구현한 지능형 추천 프로세서를 제안하였다. 이를 위해 10가지 고장유형과 이에 연관된 부품을 설정하였고 5가지 뷰를 가진 추천 프로세스를 설계하고 시뮬레이션 하였다. 또한, 고장유형에 따라 점검해야 할 컴포넌트들에 대한 각 부품별 연관성에 따른 가중치 값을 조절함으로써 지능형으로 확장 추천이 가능하도록 하였다.

In this paper we proposed a intelligent recommendation processor that the type of auto parts failures that may occur in the checkout process is represented by association relationship and the relationship was implemented with ontology. For this purpose, we defined 10 kinds of failure types and their associated parts, and we designed to simulate the recommendation process of five views. For components to be checked with the type of fault, it was possible to be expansion recommendation to the intelligent by controlling the weight value according to the relationship on the components.

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참고문헌

  1. Jung-Soo Han, Gui-Jung Kim ,A method of intelligent recommendation using task ontology, Cluster Computing, 2013. doi:10.1007/s10586-013-0288-1,
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