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Determination Of Gas Mixture Components Using Fluctuation Enhanced Sensing And The LS-SVM Regression Algorithm
Abstract
This paper analyses the effectiveness of determining gas concentrations by using a prototype WO3 resistive gas sensor together with fluctuation enhanced sensing. We have earlier demonstrated that this method can determine the composition of a gas mixture by using only a single sensor. In the present study, we apply Least-Squares Support-Vector-Machine-based (LS-SVM-based) nonlinear regression to determine the gas concentration of each constituent in a mixture. We confirmed that the accuracy of the estimated gas concentration could be significantly improved by applying temperature change and ultraviolet irradiation of the WO3 layer. Fluctuation-enhanced sensing allowed us to predict the concentration of both component gases.
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Authors (5)
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Radu Ionescu
- Rovira i Virgili University Department of Electronics
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Claes Goran Granqvist
- Uppsala University Department of Engineering Sciences
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Laszlo B. Kish
- Texas A&M University Department of Electrical and Computer Engineering
Cite as
Full text
- Publication version
- Accepted or Published Version
- DOI:
- Digital Object Identifier (open in new tab) 10.1515/mms-2015-0039
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Details
- Category:
- Articles
- Type:
- artykuł w czasopiśmie wyróżnionym w JCR
- Published in:
-
Metrology and Measurement Systems
no. XXII,
edition 3,
pages 341 - 350,
ISSN: 0860-8229 - Language:
- English
- Publication year:
- 2015
- Bibliographic description:
- Lentka Ł., Smulko J., Ionescu R., Granqvist C., Kish L.: Determination Of Gas Mixture Components Using Fluctuation Enhanced Sensing And The LS-SVM Regression Algorithm// Metrology and Measurement Systems. -Vol. XXII, iss. 3 (2015), s.341-350
- DOI:
- Digital Object Identifier (open in new tab) 10.1515/mms-2015-0039
- Verified by:
- Gdańsk University of Technology
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