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dc.contributor.authorAli, Sen_US
dc.contributor.authorAlam, Fen_US
dc.contributor.authorArif, Ken_US
dc.contributor.authorPotgieter, J-Gen_US
dc.date.accessioned2023-01-19T19:34:41Z
dc.date.available2023-01-11en_US
dc.date.available2023-01-19T19:34:41Z
dc.date.issued2023-01-11en_US
dc.identifier.citationSensors, 2023, 23 (2)en_US
dc.identifier.issn1424-8220en_US
dc.identifier.urihttp://hdl.handle.net/10179/17941
dc.description.abstractThe advent of cost-effective sensors and the rise of the Internet of Things (IoT) presents the opportunity to monitor urban pollution at a high spatio-temporal resolution. However, these sensors suffer from poor accuracy that can be improved through calibration. In this paper, we propose to use One Dimensional Convolutional Neural Network (1DCNN) based calibration for low-cost carbon monoxide sensors and benchmark its performance against several Machine Learning (ML) based calibration techniques. We make use of three large data sets collected by research groups around the world from field-deployed low-cost sensors co-located with accurate reference sensors. Our investigation shows that 1DCNN performs consistently across all datasets. Gradient boosting regression, another ML technique that has not been widely explored for gas sensor calibration, also performs reasonably well. For all datasets, the introduction of temperature and relative humidity data improves the calibration accuracy. Cross-sensitivity to other pollutants can be exploited to improve the accuracy further. This suggests that low-cost sensors should be deployed as a suite or an array to measure covariate factors.en_US
dc.publisherMDPI AGen_US
dc.relation.urihttps://www.mdpi.com/1424-8220/23/2/854en_US
dc.rightsCC BY 4.0en_US
dc.titleLow-Cost CO Sensor Calibration Using One Dimensional Convolutional Neural Networken_US
dc.typeJournal Article
dc.citation.volume23en_US
dc.identifier.doi10.3390/s23020854en_US
dc.description.confidentialfalseen_US
dc.identifier.elements-id458809
dc.relation.isPartOfSensorsen_US
dc.citation.issue2en_US
pubs.organisational-group/Massey University
pubs.organisational-group/Massey University/College of Sciences
pubs.organisational-group/Massey University/College of Sciences/School of Agriculture & Environment
pubs.organisational-group/Massey University/College of Sciences/School of Agriculture & Environment/Agritech
pubs.organisational-group/Massey University/College of Sciences/School of Food and Advanced Technology
dc.identifier.harvestedMassey_Dark
pubs.notesNot knownen_US
dc.subject.anzsrc0301 Analytical Chemistryen_US
dc.subject.anzsrc0805 Distributed Computingen_US
dc.subject.anzsrc0906 Electrical and Electronic Engineeringen_US
dc.subject.anzsrc0502 Environmental Science and Managementen_US
dc.subject.anzsrc0602 Ecologyen_US


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