TY - GEN
T1 - Fast automatic classification of input and exit surface laser-induced damage in large-aperture final optics
AU - Liu, Guodong
AU - Wei, Fupeng
AU - Chen, Fengdong
AU - Tang, Jun
AU - Peng, Zhitao
AU - Liu, Bingguo
AU - Zhu, Qihua
AU - Hu, Dongxia
AU - Feng, Bin
AU - Xiang, Yong
AU - Wang, Chengcheng
N1 - Publisher Copyright:
© 2018 IEEE.
PY - 2018/12/14
Y1 - 2018/12/14
N2 - Under the condition of inhomogeneous total internal reflection illumination, a fast automatic classification method based on machine learning is proposed to solve the problem of determining the location of surface damage sites. First, the far-field light intensity distributions of damage sites on the input and exit surfaces are calculated by using numerical calculations. After analyzing the optical properties, we present a feature vector for characterizing all damage sites in the image captured by the final optics damage inspection system. Finally, an autoencoder-based extreme learning machine is used to identify the surface on which a damage site resides. The experimental results show that the maximum testing accuracy of this method is 97.66%. Compared to the method used at Lawrence Livermore National Laboratory, the method proposed in this paper shows improved accuracy and faster speed of classification in practical applications. In particular, the proposed algorithm for damage classification outperforms several state-of-the-art methods on our experimental dataset.
AB - Under the condition of inhomogeneous total internal reflection illumination, a fast automatic classification method based on machine learning is proposed to solve the problem of determining the location of surface damage sites. First, the far-field light intensity distributions of damage sites on the input and exit surfaces are calculated by using numerical calculations. After analyzing the optical properties, we present a feature vector for characterizing all damage sites in the image captured by the final optics damage inspection system. Finally, an autoencoder-based extreme learning machine is used to identify the surface on which a damage site resides. The experimental results show that the maximum testing accuracy of this method is 97.66%. Compared to the method used at Lawrence Livermore National Laboratory, the method proposed in this paper shows improved accuracy and faster speed of classification in practical applications. In particular, the proposed algorithm for damage classification outperforms several state-of-the-art methods on our experimental dataset.
KW - extreme learning machine
KW - features
KW - input and exit surface
KW - laser-induced damage
UR - https://www.scopus.com/pages/publications/85060369611
U2 - 10.1109/IAEAC.2018.8577501
DO - 10.1109/IAEAC.2018.8577501
M3 - 会议稿件
AN - SCOPUS:85060369611
T3 - Proceedings of 2018 IEEE 3rd Advanced Information Technology, Electronic and Automation Control Conference, IAEAC 2018
SP - 782
EP - 790
BT - Proceedings of 2018 IEEE 3rd Advanced Information Technology, Electronic and Automation Control Conference, IAEAC 2018
A2 - Xu, Bing
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 3rd IEEE Advanced Information Technology, Electronic and Automation Control Conference, IAEAC 2018
Y2 - 12 October 2018 through 14 October 2018
ER -