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DC Field | Value | Language |
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dc.citation.endPage | 15032 | - |
dc.citation.number | 9 | - |
dc.citation.startPage | 15023 | - |
dc.citation.title | IEEE SENSORS JOURNAL | - |
dc.citation.volume | 24 | - |
dc.contributor.author | Cho, Jeonghoon | - |
dc.contributor.author | Pyeon, You Jang | - |
dc.contributor.author | Kwon, Yeong Min | - |
dc.contributor.author | Kim, Yonggi | - |
dc.contributor.author | Yeom, Junyeong | - |
dc.contributor.author | Kim, Myeong Woo | - |
dc.contributor.author | Park, Chan Sam | - |
dc.contributor.author | Kim, In-Ho | - |
dc.contributor.author | Lee, Yun-Sik | - |
dc.contributor.author | Kim, Jae Joon | - |
dc.date.accessioned | 2024-03-25T14:35:09Z | - |
dc.date.available | 2024-03-25T14:35:09Z | - |
dc.date.created | 2024-03-21 | - |
dc.date.issued | 2024-05 | - |
dc.description.abstract | This paper presents a mixture-gas detectable edge-computing device with a generative learning framework for selectivity and accuracy. Mixture-gas detection capability is enabled through two proposed schemes of temperature modulation and cross-iterative-tuning artificial neural network (CIT-ANN). Their related computations are facilitated inside the edge device level, applying analog normalization concepts in the readout integrated circuit (ROIC). This proposed edge platform provides generative training data for mixture-gas detection, allowing much less empirical data for its learning process, especially under mixture gas environment. An edge-computing IoT device prototype was manufactured based on a fabricated ROIC and in-house metal-oxide-semiconductor sensor arrays embedding heater modulation function. Under mixture-gas experiments of NO2 and CO gases, the proposed CIT-ANN together with the heater modulation demonstrated 44% higher recognition performance than in the conventional ANN. The proposed generative learning method showed higher relative label coincidence, achieving 17% higher correlation with real training data than in the conventional mathematical interpolation method | - |
dc.identifier.bibliographicCitation | IEEE SENSORS JOURNAL, v.24, no.9, pp.15023 - 15032 | - |
dc.identifier.doi | 10.1109/JSEN.2024.3374358 | - |
dc.identifier.issn | 1530-437X | - |
dc.identifier.scopusid | 2-s2.0-85188534457 | - |
dc.identifier.uri | https://scholarworks.unist.ac.kr/handle/201301/81808 | - |
dc.identifier.wosid | 001219652600130 | - |
dc.language | 영어 | - |
dc.publisher | Institute of Electrical and Electronics Engineers | - |
dc.title | A Mixture-Gas Edge-Computing Multi-Sensor Device with Generative Learning Framework | - |
dc.type | Article | - |
dc.description.isOpenAccess | FALSE | - |
dc.relation.journalWebOfScienceCategory | Engineering, Electrical & Electronic; Instruments & Instrumentation; Physics, Applied | - |
dc.relation.journalResearchArea | Engineering; Instruments & Instrumentation; Physics | - |
dc.type.docType | Article | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
dc.subject.keywordAuthor | Cross-Iterative-Tuning Artificial Neural Network | - |
dc.subject.keywordAuthor | Edge Computing | - |
dc.subject.keywordAuthor | Gas detectors | - |
dc.subject.keywordAuthor | Generative Adversarial Networks | - |
dc.subject.keywordAuthor | Heating systems | - |
dc.subject.keywordAuthor | Image edge detection | - |
dc.subject.keywordAuthor | Metal-Oxide Semiconductor | - |
dc.subject.keywordAuthor | Mixture Gas Sensor | - |
dc.subject.keywordAuthor | Modulation | - |
dc.subject.keywordAuthor | Readout Integrated Circuit | - |
dc.subject.keywordAuthor | Resistance | - |
dc.subject.keywordAuthor | Sensors | - |
dc.subject.keywordAuthor | Temperature sensors | - |
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