A algorithm benchmark data suite for chemical and biological (chem/bio) defense applications

Slamani, M.; Fisk, Brian; Chyba, Thomas H.; Emge, Darren K.; Waugh, Steve

doi:10.1117/12.784560

Cited by 5 publications

(1 citation statement)

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“…다 라만 전이의 산란광 세기가 다르다 [1] . 라만 분광기 (Raman spectroscopy)는 레이저를 표적 물질에 입사하 여 나오는 산란광을 필터링하여 라만 전이를 포함하 는 파수 대역의 라만 산란광만을 검출기로 보내서 빛 의 세기를 검출한다 [2] . 라만 전이에 따른 라만 산란광 의 세기를 라만 스펙트럼(Raman spectrum)이라고 하며 물질의 고유 특징이기에 라만 스펙트럼을 분석해서 물질을 탐지하고 분류할 수 있다 [3] .…”

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CNN based Raman Spectroscopy Algorithm That is Robust to Noise and Spectral Shift

Park¹,

Yu²,

Lee³

et al. 2021

J. KIMS Technol

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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.

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