딥러닝을 이용한 함정 내부 가스누출 정밀 측정 기술에 관한 연구

전민규^*

해군사관학교 기계시스템공학과 부교수

Deep Learning-based Precision Measurement of Gas Leakage Inside Naval Vessels

Min-Gyu Jeon^*

Associate Professor, Dept. of Mechanical System Engineering, Republic of Korea Naval Academy

Correspondence to: ^*Min-Gyu Jeon Dept. of Mechanical System Engineering, Republic of Korea Naval Academy 1 Jungwon-ro, Jinhae-gu, Changwon-si, Gyungsangnam-do, 51704, Republic of Korea Tel: +82-55-907-5335 E-mail: mgjeon@navy.ac.kr

초록

본 연구에서는 해군 함정 운용에 있어 승조원의 안전을 위해 가스 누출에 대비한 가스 농도 측정 기술에 관한 연구이다. 흡수선 영역 선정에 표준편차를 적용하고 선형보간을 통해 흡광도를 재구성하는 DAS의 성능을 개선한 SD-LBC의 성능을 DAS와 비교 평가하고자 한다. 또한 딥러닝 다중 분류 모델을 적용하여 시계열 데이터의 농도 평가를 하고자 한다.

Abstract

This study develops gas concentration measurement technology to improve crew safety on naval vessels by detecting gas leaks. It evaluates an enhanced absorbance extraction method, SD-LBC (standard deviation-linear baseline correction), against conventional DAS (direct absorption spectroscopy). A deep learning-based multi-class classification model is also applied to time-series data to enhance the reliability of gas leak monitoring.

Keywords:

DAS, SD-LBC, Gas leakage detection, Deep learning, TDLAS

키워드:

직접흡수분광법, 표준편차-선형기준수정, 가스 누출 감지, 딥러닝, 가변 다이오드 레이저 흡수 분광법

Acknowledgments

본 논문은 2025년 해군사관학교 해양연구소 학술연구과제 연구비의 지원으로 수행된 연구임.

References

A. Korent, K. Z. Soderznik, S. Sturm, K. Z. Rozman, N. Redon, J. L. Wojkiewicz, C. Duc, “Facile Fabrication of an Ammonia-gas Sensor Using Electrochemically Synthesised Polyaniline on Commercial Screen-printed Three-electrode Systems”, Sensors, Vol. 21, No.1, 169, 2021. [https://doi.org/10.3390/s21010169]
M. Serafini, F. Mariani, I. Gualandi, F. Decataldo, L. Possanzini, M. Tessarolo, B. Fraboni, D. Tonelli, E. Scavetta, “A Wearable Electrochemical Gas Sensor for Ammonia Detection”, Sensors, Vol.21, No.23, 7905, 2021.
K. G. Krishna, S. Parne, N. Pothukanuri, V. Kathirvelu, S. Gandi and D. Joshi, “Nanostructured metal oxide semiconductor-based gas sensors: A comprehensive review”, Sensors and Actuators A, Vol.341, 11357, 2022. [https://doi.org/10.1016/j.sna.2022.113578]
A. Nasriddinov, R. Zairov, M. Rumyantseva, “Light-activated semiconductor gas sensors: pathways to improve sensitivity and reduce energy consumption”, Frontiers in Chemistry, Vol.13, 1538217, 2025.
N. Dossi, R. Toniolo, A. Pizzariello, E. Carrilho, E. Piccin, S. Battiston, G. Bontempelli, “An Electrochemical Gas Sensor based on Paper Supported Room Temperature Ionic Liquids, Lab on Chip, Vol.12, No.1, pp.153‒158, 2012. [https://doi.org/10.1039/c1lc20663j]
J. K. Magnuson, T. N. Anderson, R. P. Lucht, “Application of a diode-laser-based ultraviolet absorption sensor for in situ measurements of atomic mercury in coal-combustion exhaust”, Energy and Fuels, Vol.22, No.5, pp. 3029-3036, 2008. [https://doi.org/10.1021/ef800372k]
T. Kamimoto, Y. Deguchi, Y. Kiyota, “High temperature field application of two dimensional temperature measurement technology using CT tunable diode laser absorption spectroscopy”, Flow Measurement and Instrumentation, Vol.46, No.A, pp. 51-57, 2015. [https://doi.org/10.1016/j.flowmeasinst.2015.09.006]
D. W. Choi, M. G. Jeon, G. R. Cho, T. Kamimoto, Y. Deguchi, D. H. Doh, “Performance Improvements in Temperature Reconstructions of 2-D Tunable Diode Laser Absorption Spectroscopy (TDLAS)”, Journal of Thermal Science, Vol.25, No.1, pp. 84-89, 2016. [https://doi.org/10.1007/s11630-016-0837-z]
M. G. Jeon, Y. Deguchi, T. Kamimoto, D. H. Doh, G. R. Cho, “Performances of new reconstruction algorithms for CT-TDLAS (computer tomography-tunable diode laser absorption spectroscopy)”, Applied Thermal Engineering, Vol.115, pp. 1148~1160, 2017. [https://doi.org/10.1016/j.applthermaleng.2016.12.060]
M. G. Jeon, D. H. Doh, Y. Deguchi, “Measurement enhancement on two-dimensional temperature distribution of Methane-Air premixed flame using SMART algorithm in CT-TDLAS”, Applied Sciences, Vol.9, No.22, 4955, 2019. [https://doi.org/10.3390/app9224955]
M. G. Jeon, J. W. Hong, D. H. Doh, Y. Deguchi, “A study on two-dimensional temperature and concentration distribution of Propane-Air premixed flame using CT-TDLAS”, Modern Physics Letters B, Vol.34, No.7n9, 2040020, 2020. [https://doi.org/10.1142/s0217984920400205]
G. Aceto, D. Ciuonzo, A. Montieri, A. Pescape, “Multi-classification approaches for classifying mobile app traffic”, Journal of Network and Computer Applications, Vol.103, No.1, pp.131-145, 2018. [https://doi.org/10.1016/j.jnca.2017.11.007]
E. Irmak, “Multi-Classification of Brain Tumor MRI Images Using Deep Convolutional Neural Network with Fully Optimized Framework”, Iranian Journal of Science and Technology, Transactions of Electrical Engineering, Vol. 45, pp. 1015-1036, 2021. [https://doi.org/10.1007/s40998-021-00426-9]