가스 누출 농도측정을 위한 딥러닝 다중 분류 모델 적용 직접흡수분광법 연구
Ⓒ 2024 Korea Society for Naval Science & Technology
초록
본 연구는 친환경 선박의 운용을 위해 고려되는 대체 에너지원 암모니아 가스 사용에 앞서 가스 누출 농도측정에 관한 연구이다. 가스 측정에 광학식 측정법인 TDLAS를 이용하였으며 흡광도 계측의 정밀도를 평가하기 위해 딥러닝 다중 분류 모델을 적용하였다. 또한 흡광도 추출의 정밀도 향상을 위해 직접흡수분광법의 개선 가능성을 평가한다.
Abstract
This study investigates the measurement of gas leakage concentration preceding the use of ammonia gas, a potential alternative energy source for eco-friendly ships. Tunable Diode Laser Absorption Spectroscopy (TDLAS), an optical measurement technique, was employed for gas detection, and a deep learning multi-classification model was applied to evaluate the precision of absorbance measurements. Furthermore, the study explores the potential improvements in direct absorption spectroscopy to enhance the accuracy of absorbance extraction.
Keywords:
Gas Leakage, Ammonia, Concentration, TDLAS, Multi-classification키워드:
가스 누출, 암모니아, 농도, 가변 다이오드 레이저 흡수 분광법, 다중 분류Acknowledgments
이 논문은 2023년 정부(방위사업청)의 재원으로 국방기술진흥연구소의 지원을 받아 수행된 연구임 (KRIT-CT-23-030).
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