[Title]
Deep Learning Algorithm with Residual Blocks for Chemical Gas Concentration Estimation
[Authors]
Hee-Deok Jang, Jae-Hyeon Park, Dong Eui Chang, Hyun-Soo Seo, Hyunwoo Nam
[Abstract]
Chemical warfare agents (CWA) are highly toxic and hazardous substances that cause serious harm to humans, even when used in small quantities. The accurate estimation of the concentration of CWA is crucial to allow effective responses to these types of attacks. In this paper, we propose a deep learning algorithm for chemical gas concentration estimation, referred to as MLP-res, and compare its estimation performance with those of other machine learning algorithms. MLP-res utilizes a structure with residual blocks and demonstrates comparable or even superior performance compared with those of existing machine learning algorithms. Additionally, MLP-res exhibits high-generalization performance even with the use of experimental condition data that were not used for training. These results indicate that MLP-res can accurately estimate the concentration of chemical gases in actual environments.
