Machine learning-based discrimination of indoor pollutants using an oxide gas sensor array: High endurance against ambient humidity and temperature
- Authors
- Oh, J.; Kim, S.H.; Lee, M.-J.; Hwang, H.; Ku, W.; Lim, J.; Hwang, I.-S.; Lee, J.-H.; Hwang, J.-H.
- Issue Date
- 1-Aug-2022
- Publisher
- Elsevier B.V.
- Keywords
- Detection/discrimination; Machine learning; Neural networks; Principal component analysis; Semiconducting oxide gas sensors
- Citation
- Sensors and Actuators B: Chemical, v.364
- Journal Title
- Sensors and Actuators B: Chemical
- Volume
- 364
- URI
- https://scholarworks.bwise.kr/hongik/handle/2020.sw.hongik/27830
- DOI
- 10.1016/j.snb.2022.131894
- ISSN
- 0925-4005
- Abstract
- Machine learning (ML) methodologies were applied to detect and discriminate five indoor volatile organic compounds (VOCs) such as benzene, xylene, toluene, formaldehyde, and ethanol using a sensor array constructed of five In2O3-based semiconducting metal oxide (SMO) gas sensors. The sensor array was evaluated using principal component analysis (PCA) and neural network-based classification in terms of the gas sensor data type/amount, neural network algorithms, sensor combinations, and environmental factors. The PCA analyses indicated the limitations on the discrimination of VOCs under temperature- and/or humidity-interfered gas sensing environments. Gas detection/discrimination could be improved significantly by using three supervised algorithms, i.e., artificial neural networks (ANNs), deep neural networks (DNNs), and 1-dimensional convolutional neural networks (1D CNNs). The neural network algorithm prediction based on the entire gas sensing/purge transient data outperforms deep learning-assisted predictions based on partial gas sensing transients. Compared to 1D CNNs, DNNs are more appropriate in terms of training/validation/test datasets. The effects due to humidity variation are more significant than those due to temperature fluctuation. A 2-sensor mode combination can be exploited to replace the 5-sensor operation in ML-based applications. The indoor pollutants can be successfully discriminated even under the variation of ambient humidity and temperature by ML-based approaches. © 2022 Elsevier B.V.
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Collections - College of Engineering > Materials Science and Engineering Major > 1. Journal Articles
- College of Engineering > School of Electronic & Electrical Engineering > 1. Journal Articles
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