1차원 컨볼루션 신경망을 이용한 TDLAS 기반 온도 예측

전민규^*

해군사관학교 기계시스템공학과 부교수

Temperature Prediction Using a One-Dimensional Convolutional Neural Network Based on TDLAS

Min-Gyu Jeon^*

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

Correspondence to: *Min-Gyu Jeon 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

환경 모니터링, 연소 진단 및 공정 제어를 위한 비접촉식 온도 측정의 정확도 향상을 위해 TDLAS 기반 1D-CNN 모델을 제안하였다. H2O 흡수 스펙트럼의 대표 특징점 5개와 Gaussian noise(5%) 기반 데이터 증강을 적용하였으며, 기존 다항식 피팅 기법보다 잡음 환경에서 우수한 온도 예측 정확도와 강건성을 확인하였다.

Abstract

To improve non-contact temperature measurement accuracy, a one-dimensional convolutional neural network (1D-CNN) based on tunable diode laser absorption spectroscopy (TDLAS) is proposed. Five representative features extracted from H2O absorption spectra were employed as the model inputs, and Gaussian noise (5%)-based data augmentation was applied to improve generalization. Experimental results demonstrate that the proposed method achieves higher temperature prediction accuracy and robustness under noisy conditions compared with the conventional polynomial spectrum-fitting method.

Keywords:

TDLAS, Temperature Prediction, 1D-CNN, Data Augmentation

키워드:

가변파장 반도체 레이저 흡수분광법, 온도 예측, 1차원 컨볼루션 신경망, 데이터 증강

Acknowledgments

이 논문은 2026년 해군사관학교 해양연구소 학술연구과제 연구비의 지원으로 수행된 연구임.

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