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Real-time selective gas detection by gas sensor array and deep learning
Mingu Kang(강민구),Incheol Cho(조인철),Inkyu Park(박인규) 대한기계학회 2021 대한기계학회 춘추학술대회 Vol.2021 No.11
The demand for gas sensors is increasing because of the growing interest in monitoring indoor/outdoor air pollutions. In particular, semiconductor metal oxide (SMO) gas sensors are attracting attention as the next-generation gas sensors. However, there are limitations in the actual applications of SMO gas sensors due to their low selectivity. In this study, the selectivity problem could be solved by fabricating a gas sensor array and using the deep learning network. The fabricated gas sensor array used nanocolumnar films of metal oxides (SnO₂, In₂O₃, WO₃, and CuO) deposited through the glancing angle deposition (GLAD) as the sensing materials, and the convolutional neural network (CNN) was selected as the deep learning network for gas identification. Finally, a real-time selective gas detection for CO, NH₃, NO₂, Methane, and Acetone gas was achieved with an accuracy of 98% by applying preprocessed sensing data collected from the gas sensor arrays to the CNN.