잡음과 스펙트럼 이동에 강인한 CNN 기반 라만 분광 알고리즘CNN based Raman Spectroscopy Algorithm That is Robust to Noise and Spectral Shift

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Raman spectroscopy is an equipment that is widely used for classifying chemicals in chemical defense operations. However, the classification performance of Raman spectrum may deteriorate due to dark current noise, background noise, spectral shift by vibration of equipment, spectral shift by pressure change, etc. In this paper, we compare the classification accuracy of various machine learning algorithms including k-nearest neighbor, decision tree, linear discriminant analysis, linear support vector machine, nonlinear support vector machine, and convolutional neural network under noisy and spectral shifted conditions. Experimental results show that convolutional neural network maintains a high classification accuracy of over 95 % despite noise and spectral shift. This implies that convolutional neural network can be an ideal classification algorithm in a real combat situation where there is a lot of noise and spectral shift.
Publisher
한국군사과학기술학회
Issue Date
2021-06
Language
Korean
Citation

한국군사과학기술학회지, v.24, no.3, pp.264 - 271

ISSN
1598-9127
URI
http://hdl.handle.net/10203/290486
Appears in Collection
EE-Journal Papers(저널논문)
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