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Driven by machine learning to intelligent damage recognition of terminal optical components
Neural Computing and Applications ( IF 6 ) Pub Date : 2020-06-15 , DOI: 10.1007/s00521-020-05051-x
Xiangbao Yin

In order to realize the terminal optical element online detection system in the Shenguang III system, each optical element in each terminal optical component in the target room is detected. The research on the optical damage of terminal optical components focuses on the search for damage points, the extraction of damage information, and the classification of damage types. In addition, damage classification and identification of terminal optical components are performed through machine learning, and infrared nondestructive testing is used as technical support to improve the identification model and reduce the complexity of the spectral model. After studying the preprocessing and dimensionality reduction methods of near-infrared spectroscopy, this paper compares the effects of different preprocessing methods and screening feature methods and combines different modeling methods to conduct experiments. The research results show that the method proposed in this paper has certain effects.



中文翻译:

通过机器学习驱动到终端光学组件的智能损伤识别

为了实现神光三号系统中的终端光学元件在线检测系统,需要对目标房间内每个终端光学组件中的每个光学元件进行检测。终端光学组件的光学损伤研究集中在损伤点的搜索,损伤信息的提取以及损伤类型的分类上。此外,通过机器学习对终端光学组件进行损伤分类和识别,并使用红外无损检测作为技术支持,以改进识别模型并降低光谱模型的复杂性。在研究了近红外光谱的预处理和降维方法之后,本文比较了不同预处理方法和筛选特征方法的效果,并结合了不同的建模方法进行实验。研究结果表明,本文提出的方法具有一定的效果。

更新日期:2020-06-15
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