DC Field | Value | Language |
---|---|---|
dc.contributor.author | Cho, Incheol | ko |
dc.contributor.author | Lee, Kichul | ko |
dc.contributor.author | Sim, Young Chul | ko |
dc.contributor.author | Jeong, Jaeseok | ko |
dc.contributor.author | Cho, Minkyu | ko |
dc.contributor.author | Jung, Heechan | ko |
dc.contributor.author | Kang, Mingu | ko |
dc.contributor.author | Cho, Yong-Hoon | ko |
dc.contributor.author | Ha, Seung Chul | ko |
dc.contributor.author | Yoon, Kuk-Jin | ko |
dc.contributor.author | Park, Inkyu | ko |
dc.date.accessioned | 2023-05-03T01:00:21Z | - |
dc.date.available | 2023-05-03T01:00:21Z | - |
dc.date.created | 2023-03-19 | - |
dc.date.created | 2023-03-19 | - |
dc.date.created | 2023-03-19 | - |
dc.date.created | 2023-03-19 | - |
dc.date.created | 2023-03-19 | - |
dc.date.issued | 2023-04 | - |
dc.identifier.citation | LIGHT-SCIENCE & APPLICATIONS, v.12, no.1 | - |
dc.identifier.issn | 2095-5545 | - |
dc.identifier.uri | http://hdl.handle.net/10203/306440 | - |
dc.description.abstract | Electronic nose (e-nose) technology for selectively identifying a target gas through chemoresistive sensors has gained much attention for various applications, such as smart factory and personal health monitoring. To overcome the cross-reactivity problem of chemoresistive sensors to various gas species, herein, we propose a novel sensing strategy based on a single micro-LED (μLED)-embedded photoactivated (μLP) gas sensor, utilizing the time-variant illumination for identifying the species and concentrations of various target gases. A fast-changing pseudorandom voltage input is applied to the μLED to generate forced transient sensor responses. A deep neural network is employed to analyze the obtained complex transient signals for gas detection and concentration estimation. The proposed sensor system achieves high classification (~96.99%) and quantification (mean absolute percentage error ~ 31.99%) accuracies for various toxic gases (methanol, ethanol, acetone, and nitrogen dioxide) with a single gas sensor consuming 0.53 mW. The proposed method may significantly improve the efficiency of e-nose technology in terms of cost, space, and power consumption. © 2023, The Author(s). | - |
dc.language | English | - |
dc.publisher | SPRINGERNATURE | - |
dc.title | Deep-learning-based gas identification by time-variant illumination of a single micro-LED-embedded gas sensor | - |
dc.type | Article | - |
dc.identifier.wosid | 000974854000001 | - |
dc.identifier.scopusid | 2-s2.0-85153238160 | - |
dc.type.rims | ART | - |
dc.citation.volume | 12 | - |
dc.citation.issue | 1 | - |
dc.citation.publicationname | LIGHT-SCIENCE & APPLICATIONS | - |
dc.identifier.doi | 10.1038/s41377-023-01120-7 | - |
dc.contributor.localauthor | Cho, Yong-Hoon | - |
dc.contributor.localauthor | Yoon, Kuk-Jin | - |
dc.contributor.localauthor | Park, Inkyu | - |
dc.contributor.nonIdAuthor | Cho, Minkyu | - |
dc.contributor.nonIdAuthor | Jung, Heechan | - |
dc.contributor.nonIdAuthor | Ha, Seung Chul | - |
dc.description.isOpenAccess | N | - |
dc.type.journalArticle | Article | - |
dc.subject.keywordPlus | ARTIFICIAL NEURAL-NETWORK | - |
dc.subject.keywordPlus | ARRAY | - |
dc.subject.keywordPlus | POWER | - |
dc.subject.keywordPlus | NOSE | - |
dc.subject.keywordPlus | DISCRIMINATION | - |
dc.subject.keywordPlus | PLATFORM | - |
dc.subject.keywordPlus | MIXTURE | - |
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