Fang, Xinwei and Bate, Iain John orcid.org/0000-0003-2415-8219 (2020) An Improved Sensor Calibration with Anomaly Detection and Removal. SENSORS AND ACTUATORS B-CHEMICAL. 127428. ISSN 0925-4005
Abstract
Sensor calibration is a widely adopted process for improving data quality of low-cost sensors. However, such a process may not address data issues caused by anomalies. Anomalies are considered as data errors that are inconsistent to the actual physical phenomena. This paper presents an improved sensor calibration, which applies a process for detection and removal of anomalies before the sensor calibration process. A Bayesian-based method is used for anomaly detection that takes advantage of cross-sensitive parameters in a sensor array. The method utilises dependencies between cross-sensitive parameters, which allows underlying physical phenomena to be modelled and anomalies to be detected. The calibration approach is based on stepwise regression, which automatically and systematically selects suitable supporting parameters for a calibration function. The evaluation for anomaly detection shows that the results are better than the state-of-the-art methods, in terms of accuracy, precision and completeness. The overall evaluation confirms that data quality can be further enhanced when anomalies are removed before the calibration.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2019 Elsevier B.V. All rights reserved. This is an author-produced version of the published paper. Uploaded in accordance with the publisher’s self-archiving policy. |
Dates: |
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Institution: | The University of York |
Academic Units: | The University of York > Faculty of Sciences (York) > Computer Science (York) |
Funding Information: | Funder Grant number EUROPEAN COMMISSION UNSPECIFIED |
Depositing User: | Pure (York) |
Date Deposited: | 14 Jan 2020 12:10 |
Last Modified: | 26 Nov 2024 00:44 |
Published Version: | https://doi.org/10.1016/j.snb.2019.127428 |
Status: | Published |
Refereed: | Yes |
Identification Number: | 10.1016/j.snb.2019.127428 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:155644 |
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