Machine learning-based discrimination of indoor pollutants using an oxide gas sensor array: High endurance against ambient humidity and temperature
DC Field | Value | Language |
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dc.contributor.author | Oh, J. | - |
dc.contributor.author | Kim, S.H. | - |
dc.contributor.author | Lee, M.-J. | - |
dc.contributor.author | Hwang, H. | - |
dc.contributor.author | Ku, W. | - |
dc.contributor.author | Lim, J. | - |
dc.contributor.author | Hwang, I.-S. | - |
dc.contributor.author | Lee, J.-H. | - |
dc.contributor.author | Hwang, J.-H. | - |
dc.date.accessioned | 2022-05-23T05:48:18Z | - |
dc.date.available | 2022-05-23T05:48:18Z | - |
dc.date.created | 2022-05-23 | - |
dc.date.issued | 2022-08-01 | - |
dc.identifier.issn | 0925-4005 | - |
dc.identifier.uri | https://scholarworks.bwise.kr/hongik/handle/2020.sw.hongik/27830 | - |
dc.description.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. | - |
dc.language | 영어 | - |
dc.language.iso | en | - |
dc.publisher | Elsevier B.V. | - |
dc.title | Machine learning-based discrimination of indoor pollutants using an oxide gas sensor array: High endurance against ambient humidity and temperature | - |
dc.type | Article | - |
dc.contributor.affiliatedAuthor | Lim, J. | - |
dc.contributor.affiliatedAuthor | Hwang, J.-H. | - |
dc.identifier.doi | 10.1016/j.snb.2022.131894 | - |
dc.identifier.scopusid | 2-s2.0-85129115600 | - |
dc.identifier.wosid | 000798365000001 | - |
dc.identifier.bibliographicCitation | Sensors and Actuators B: Chemical, v.364 | - |
dc.relation.isPartOf | Sensors and Actuators B: Chemical | - |
dc.citation.title | Sensors and Actuators B: Chemical | - |
dc.citation.volume | 364 | - |
dc.type.rims | ART | - |
dc.type.docType | Article | - |
dc.description.journalClass | 1 | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
dc.relation.journalResearchArea | Chemistry | - |
dc.relation.journalResearchArea | Electrochemistry | - |
dc.relation.journalResearchArea | Instruments & Instrumentation | - |
dc.relation.journalWebOfScienceCategory | Chemistry, Analytical | - |
dc.relation.journalWebOfScienceCategory | Electrochemistry | - |
dc.relation.journalWebOfScienceCategory | Instruments & Instrumentation | - |
dc.subject.keywordPlus | WATER-VAPOR | - |
dc.subject.keywordPlus | E-NOSE | - |
dc.subject.keywordPlus | METAL | - |
dc.subject.keywordPlus | SNO2 | - |
dc.subject.keywordPlus | CHEMIRESISTORS | - |
dc.subject.keywordPlus | NANOPARTICLES | - |
dc.subject.keywordPlus | PERFORMANCE | - |
dc.subject.keywordPlus | IMPACT | - |
dc.subject.keywordAuthor | Detection/discrimination | - |
dc.subject.keywordAuthor | Machine learning | - |
dc.subject.keywordAuthor | Neural networks | - |
dc.subject.keywordAuthor | Principal component analysis | - |
dc.subject.keywordAuthor | Semiconducting oxide gas sensors | - |
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