For the laser-induced damage(LID) in large-aperture final optics, we present a novel approach of damage online inspection and its experimental system, which solves two problems: classification of true and false LID an...For the laser-induced damage(LID) in large-aperture final optics, we present a novel approach of damage online inspection and its experimental system, which solves two problems: classification of true and false LID and size measurement of the LID. We first analyze the imaging principle of the experimental system for the true and false damage sites, then use kernel-based extreme learning machine(K-ELM) to distinguish them, and finally propose hierarchical kernel extreme learning machine(HK-ELM) to predict the damage size. The experimental results show that the classification accuracy is higher than 95%, the mean relative error of the predicted LID size is within 10%. So the proposed method meets the technical requirements for the damage online inspection.展开更多
基金supported by the National Natural Science Foundation of China(Nos.51275120 and 61275096)the Fundamental Research Funds for the Central Universities(No.HIT.NSRIF.2013012)
文摘For the laser-induced damage(LID) in large-aperture final optics, we present a novel approach of damage online inspection and its experimental system, which solves two problems: classification of true and false LID and size measurement of the LID. We first analyze the imaging principle of the experimental system for the true and false damage sites, then use kernel-based extreme learning machine(K-ELM) to distinguish them, and finally propose hierarchical kernel extreme learning machine(HK-ELM) to predict the damage size. The experimental results show that the classification accuracy is higher than 95%, the mean relative error of the predicted LID size is within 10%. So the proposed method meets the technical requirements for the damage online inspection.
