Machine Learning for Enhanced Operation of Underperforming Sensors in Humid Conditions
Using a single sensor as a virtual electronic nose, we demonstrate the possibility of obtaining good results with underperforming sensors that, at first glance, would be discarded. For this aim, we characterized chemical gas sensors with low repeatability and random drift towards both dangerous and...
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| Format: | Article |
| Language: | English |
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MDPI AG
2024-03-01
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| Series: | Proceedings |
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| Online Access: | https://www.mdpi.com/2504-3900/97/1/87 |
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| author | Guillem Domènech-Gil Donatella Puglisi |
| author_facet | Guillem Domènech-Gil Donatella Puglisi |
| author_sort | Guillem Domènech-Gil |
| collection | DOAJ |
| description | Using a single sensor as a virtual electronic nose, we demonstrate the possibility of obtaining good results with underperforming sensors that, at first glance, would be discarded. For this aim, we characterized chemical gas sensors with low repeatability and random drift towards both dangerous and innocuous volatile organic compounds (VOCs) under different levels of relative humidity. Our results show classification accuracies higher than 90% when differentiating harmful from harmless VOCs and coefficients of determination, R<sup>2</sup>, higher than 80% when determining their concentration in the parts per billion to parts per million range. |
| format | Article |
| id | doaj-art-043299d2a53d4843ab1b6fcbc2097801 |
| institution | DOAJ |
| issn | 2504-3900 |
| language | English |
| publishDate | 2024-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Proceedings |
| spelling | doaj-art-043299d2a53d4843ab1b6fcbc20978012025-08-20T02:56:54ZengMDPI AGProceedings2504-39002024-03-019718710.3390/proceedings2024097087Machine Learning for Enhanced Operation of Underperforming Sensors in Humid ConditionsGuillem Domènech-Gil0Donatella Puglisi1Department of Thematic Studies and Environmental Change (TEMA M), Linköping University, 58183 Linköping, SwedenDepartment of Physics, Chemistry and Biology (IFM), Linköping University, 58183 Linköping, SwedenUsing a single sensor as a virtual electronic nose, we demonstrate the possibility of obtaining good results with underperforming sensors that, at first glance, would be discarded. For this aim, we characterized chemical gas sensors with low repeatability and random drift towards both dangerous and innocuous volatile organic compounds (VOCs) under different levels of relative humidity. Our results show classification accuracies higher than 90% when differentiating harmful from harmless VOCs and coefficients of determination, R<sup>2</sup>, higher than 80% when determining their concentration in the parts per billion to parts per million range.https://www.mdpi.com/2504-3900/97/1/87air quality monitoringindoorelectronic nosevirtual sensormachine learning |
| spellingShingle | Guillem Domènech-Gil Donatella Puglisi Machine Learning for Enhanced Operation of Underperforming Sensors in Humid Conditions Proceedings air quality monitoring indoor electronic nose virtual sensor machine learning |
| title | Machine Learning for Enhanced Operation of Underperforming Sensors in Humid Conditions |
| title_full | Machine Learning for Enhanced Operation of Underperforming Sensors in Humid Conditions |
| title_fullStr | Machine Learning for Enhanced Operation of Underperforming Sensors in Humid Conditions |
| title_full_unstemmed | Machine Learning for Enhanced Operation of Underperforming Sensors in Humid Conditions |
| title_short | Machine Learning for Enhanced Operation of Underperforming Sensors in Humid Conditions |
| title_sort | machine learning for enhanced operation of underperforming sensors in humid conditions |
| topic | air quality monitoring indoor electronic nose virtual sensor machine learning |
| url | https://www.mdpi.com/2504-3900/97/1/87 |
| work_keys_str_mv | AT guillemdomenechgil machinelearningforenhancedoperationofunderperformingsensorsinhumidconditions AT donatellapuglisi machinelearningforenhancedoperationofunderperformingsensorsinhumidconditions |