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Machine Learning for Enhanced Operation of UnderperformingSensors in Humid Conditions
Linköping University, Department of Thematic Studies, Tema Environmental Change. Linköping University, Faculty of Arts and Sciences.ORCID iD: 0000-0002-9036-0856
Linköping University, Department of Physics, Chemistry and Biology, Sensor and Actuator Systems. Linköping University, Faculty of Science & Engineering.ORCID iD: 0000-0003-0646-5266
2024 (English)In: Proceedings, MDPI, 2024, Vol. 97, p. 87-89Conference paper, Published paper (Refereed)
Abstract [en]

Using a single sensor as a virtual electronic nose, we demonstrate the possibility of obtaininggood results with underperforming sensors that, at first glance, would be discarded. For this aim, wecharacterized chemical gas sensors with low repeatability and random drift towards both dangerousand innocuous volatile organic compounds (VOCs) under different levels of relative humidity. Ourresults show classification accuracies higher than 90% when differentiating harmful from harmlessVOCs and coefficients of determination, R2, higher than 80% when determining their concentrationin the parts per billion to parts per million range.

Place, publisher, year, edition, pages
MDPI, 2024. Vol. 97, p. 87-89
National Category
Engineering and Technology Natural Sciences
Identifiers
URN: urn:nbn:se:liu:diva-202214DOI: 10.3390/proceedings2024097087OAI: oai:DiVA.org:liu-202214DiVA, id: diva2:1849407
Conference
EUROSENSORS XXXV, Lecce, Italy, 10–13 September, 2023
Available from: 2024-04-07 Created: 2024-04-07 Last updated: 2025-02-17Bibliographically approved

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Domènech-Gil, GuillemPuglisi, Donatella

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Domènech-Gil, GuillemPuglisi, Donatella
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CiteExportLink to record
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Citation style
  • apa
  • ieee
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  • de-DE
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