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Algorithms for Automatic Data Validation and Performance Assessment of MOX Gas Sensor Data Using Time Series Analysis

  • The following work presents algorithms for semi-automatic validation, feature extraction and ranking of time series measurements acquired from MOX gas sensors. Semi-automatic measurement validation is accomplished by extending established curve similarity algorithms with a slope-based signature calculation. Furthermore, a feature-based ranking metric is introduced. It allows for individual prioritization of each feature and can be used to find the best performing sensors regarding multiple research questions. Finally, the functionality of the algorithms, as well as the developed software suite, are demonstrated with an exemplary scenario, illustrating how to find the most power-efficient MOX gas sensor in a data set collected during an extensive screening consisting of 16,320 measurements, all taken with different sensors at various temperatures and analytes.

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Document Type:Article
Author:Christof Hammer, Sebastian Sporrer, Johannes Warmer, Peter Kaul, Ronald Thoelen, Norbert Jung
Parent Title (English):Algorithms
Article Number:360
Number of pages:16
Place of publication:Basel
Publishing Institution:Hochschule Bonn-Rhein-Sieg
Date of first publication:2022/09/28
Copyright:© 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license.
Funding:This research received internal funding from the Institute of Safety and Security Research (ISF) at BRS-U.
Keyword:MOX gas sensors; automatic measurement validation; feature; prioritizable ranking; slope based signature; time series analysis
Departments, institutes and facilities:Fachbereich Informatik
Fachbereich Angewandte Naturwissenschaften
Institut für Sicherheitsforschung (ISF)
Dewey Decimal Classification (DDC):0 Informatik, Informationswissenschaft, allgemeine Werke / 00 Informatik, Wissen, Systeme / 004 Datenverarbeitung; Informatik
Open access funding:Hochschule Bonn-Rhein-Sieg / Publikationsfonds / 2022 ff
Entry in this database:2022/10/12
Licence (German):License LogoCreative Commons - CC BY - Namensnennung 4.0 International