원문정보
Acoustic Emission Source Classification of Finite-width Plate with a Circular Hole Defect using k-Nearest Neighbor Algorithm
초록
영어
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 , D&B(Rij) & Tou are discussed.
목차
1. 서론
2. 관련 이론
2.1 K-평균 군집화
2.2 k-최근접 이웃 분류기
3. 실험 재료 및 방법
3.1 실험재료 및 시험편
3.2 실험방법
4. 선행연구 학습결과 및 해석
4.1 선행연구의 결과
4.2 패턴인식 학습결과
5. 결론
6. 참고문헌
