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Hee-Deok Jang,Jae-Hyeon Park,Hyunwoo Nam,Dong Eui Chang 제어로봇시스템학회 2022 제어로봇시스템학회 국제학술대회 논문집 Vol.2022 No.11
Many studies propose gas concentration estimators using machine learning algorithms owing to their high performance. Recently, estimation models using deep neural network have been studied due to their higher performance than conventional machine learning algorithms. The performance of deep neural network can be increased by hyperparameter optimization. In this paper, we propose two deep neural networks for gas concentration estimation and analyze how hyperparameter optimization affects the performance of the proposed deep neural networks. We optimize the hyperparameters of the proposed neural networks and compare the performance with conventional machine learning models. We train the proposed neural networks and evaluate the performance of the models with an open dataset. We confirm that the optimized neural network models show the high performance in gas concentration estimation, and that models using unoptimized parameters may show worse performance than conventional machine learning model.
화학 가스 농도 추정을 위한 잔차 블록 기반 딥 러닝 알고리즘
장희덕,박재현,장동의,서현수,남현우 제어·로봇·시스템학회 2023 제어·로봇·시스템학회 논문지 Vol.29 No.7
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.