k-최근접 이웃 알고리즘을 이용한 원공결함을 갖는 유한 폭 판재의 음향방출 음원분류에 대한 연구 (Acoustic Emission Source Classification of Finite-width Plate with a Circular Hole Defect using k-Nearest Neighbor Algorithm)

서지정보

· 발행기관 : 대한안전경영과학회
· 수록지 정보 : 대한안전경영과학회지 / 11권 / 1호 / 27 ~ 33페이지
· 저자명 : 이장규, 오진수

초록

A study of fracture to material is getting interest in nuclear and aerospace industry as a viewpoint of safety. Acoustic emission (AE) is a non-destructive testing and new technology to evaluate safety on structures. In previous research continuously, all tensile tests on the pre-defected coupons were performed using the universal testing machine, which machine crosshead was move at a constant speed of 5mm/min. This study is to evaluate an AE source characterization of SM45C steel by using k-nearest neighbor classifier, k-NNC. For this, we used K-means clustering as an unsupervised learning method for obtained multi-variate AE main data sets, and we applied k-NNC as a supervised learning pattern recognition algorithm for obtained multi-variate AE working data sets. As a result, the criteria of Wilk's λ, D&B(Rij) & Tou are discussed.

영어초록

A study of fracture to material is getting interest in nuclear and aerospace industry as a viewpoint of safety. Acoustic emission (AE) is a non-destructive testing and new technology to evaluate safety on structures. In previous research continuously, all tensile tests on the pre-defected coupons were performed using the universal testing machine, which machine crosshead was move at a constant speed of 5mm/min. This study is to evaluate an AE source characterization of SM45C steel by using k-nearest neighbor classifier, k-NNC. For this, we used K-means clustering as an unsupervised learning method for obtained multi-variate AE main data sets, and we applied k-NNC as a supervised learning pattern recognition algorithm for obtained multi-variate AE working data sets. As a result, the criteria of Wilk's λ, D&B(Rij) & Tou are discussed.

참고자료

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