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학술지 Classifying Gas Data Measured Under Multiple Conditions Using Deep Learning
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저자
이호중, 황재희, 박흰돌, 최재훈, 이종석
발행일
202206
출처
IEEE Access, v.10, pp.68138-68150
ISSN
2169-3536
출판사
IEEE
DOI
https://dx.doi.org/10.1109/ACCESS.2022.3185613
협약과제
22HR3800, Sub-ppb급 가스성분 감지를 위한 후각지능 기술 개발, 김승환
초록
Gas classification is a machine learning problem that is important for various applications including monitoring systems, health care, public security, etc. Since measuring the characteristic of gas molecules is greatly affected by external factors such as wind speed and the internal setting of detecting sensors, classification should be done by taking into account the combination of these individual factors, which we call a condition in this paper. In particular, when classifying gas data measured under multiple conditions, the data from each condition need to be integrated, which we call multi-conditioned gas classification. While there have been some studies on gas classification for a single condition, no previous approach deals with the multi-conditioned gas classification problem to the best of our knowledge. In this paper, we propose a novel multi-conditioned gas classification method for the first time. We present a new deep learning network structure that can efficiently extract features from the data of multiple conditions and effectively integrate them, which is referred to as a multi-conditioned gas classification network (MCGCN). We also propose a new training loss function to guarantee good performance reliably for the varying number of given conditions. Experimental results demonstrate the superiority of the proposed method, which achieves accuracies of 99.15% 짹 0.41 regardless of the number of conditions with 15 times fewer model parameters in comparison to the existing method.
KSP 제안 키워드
Classification method, Classification problems, Deep learning network, Gas classification, Gas molecules, Model parameter, Monitoring system, Public Security, Wind Speed, deep learning(DL), external factors
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