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dc.contributor.authorJørstad, Per Martin
dc.contributor.authorWojcikowski, Marek
dc.contributor.authorCao, Tuan-Vu
dc.contributor.authorLepioufle, Jean-Marie
dc.contributor.authorWojtkiewicz, Krystian
dc.contributor.authorHa, Hoai Phuong
dc.date.accessioned2024-02-14T10:54:00Z
dc.date.available2024-02-14T10:54:00Z
dc.date.issued2023-09-05
dc.description.abstract<p>Monitoring air pollution is a critical step towards improving public health, particularly when it comes to identifying the primary air pollutants that can have an impact on human health. Among these pollutants, particulate matter (PM) with a diameter of up to 2.5 μm (or PM2.5) is of particular concern, making it important to continuously and accurately monitor pollution related to PM. The emergence of mobile low-cost PM sensors has made it possible to monitor PM levels continuously in a greater number of locations. However, the accuracy of mobile low-cost PM sensors is often questionable as it depends on geographical factors such as local atmospheric conditions. <p>This paper presents new calibration methods for mobile low-cost PM sensors that can correct inaccurate measurements from the sensors in real-time. Our new methods leverage Neural Architecture Search (NAS) to improve the accuracy and efficiency of calibration models for mobile low-cost PM sensors. The experimental evaluation shows that the new methods reduce accuracy error by more than 26% compared with the state-of-the-art methods. Moreover, the new methods are lightweight, taking less than 2.5 ms to correct each PM measurement on Intel Neural Compute Stick 2, an AI-accelerator for edge devices deployed in air pollution monitoring platforms.en_US
dc.identifier.citationJørstad PM, Wojcikowski M, Cao TV, Lepioufle J, Wojtkiewicz K, Ha HP: Accurate Lightweight Calibration Methods for Mobile Low-Cost Particulate Matter Sensors. In: Nguyen NT, Boonsang, Fujita H, Hnatkowska, Hong T, Pasupa, Selamat A. Intelligent Information and Database Systems. 15th Asian Conference, ACIIDS 2023, Phuket, Thailand, July 24–26, 2023, Proceedings, Part I., 2023. Springeren_US
dc.identifier.cristinIDFRIDAID 2175918
dc.identifier.doi10.1007/978-981-99-5834-4_20
dc.identifier.isbn978-981-99-5833-7
dc.identifier.issn0302-9743
dc.identifier.issn1611-3349
dc.identifier.urihttps://hdl.handle.net/10037/32928
dc.language.isoengen_US
dc.publisherSpringer Natureen_US
dc.relation.projectIDNILU: 122114en_US
dc.relation.projectIDNorges forskningsråd: 270053en_US
dc.relation.projectIDSigma2: NN9342Ken_US
dc.relation.projectIDinfo:eu-repo/grantAgreement/EC/H2020/101086541/EU/Autonomous Multi-Format In-Situ Observation Platform for Atmospheric Carbon Dioxide and Methane Monitoring in Permafrost & Wetlands/MISO/en_US
dc.rights.accessRightsopenAccessen_US
dc.rights.holderCopyright 2023 The Author(s)en_US
dc.rights.urihttps://creativecommons.org/licenses/by/4.0en_US
dc.rightsAttribution 4.0 International (CC BY 4.0)en_US
dc.titleAccurate Lightweight Calibration Methods for Mobile Low-Cost Particulate Matter Sensorsen_US
dc.type.versionacceptedVersionen_US
dc.typeChapteren_US
dc.typeBokkapittelen_US


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Attribution 4.0 International (CC BY 4.0)
Except where otherwise noted, this item's license is described as Attribution 4.0 International (CC BY 4.0)