DETERMINATION OF GAS MIXTURE COMPONENTS USING FLUCTUATION ENHANCED SENSING AND THE LS-SVM REGRESSION ALGORITHM Academic Article

abstract

• 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.

authors

• Kish, Laszlo

published proceedings

• METROLOGY AND MEASUREMENT SYSTEMS

author list (cited authors)

• Lentka, L., Smulko, J. M., Ionescu, R., Granqvist, C. G., & Kish, L. B.

citation count

• 42

complete list of authors

• Lentka, Lukasz||Smulko, Janusz M||Ionescu, Radu||Granqvist, Claes G||Kish, Laszlo B

publication date

• January 2015

publisher

• DE GRUYTER  Publisher

published in

• Metrology and Measurement Systems  Journal

keywords

• Fluctuation Enhanced Sensing
• Gas Detection
• Ls-svm Algorithm
• Resistance Gas Sensor

Digital Object Identifier (DOI)

• 10.1515/mms-2015-0039

start page

• 341

end page

• 350

volume

• 22

issue

• 3

URL

• http%3A%2F%2Fdx.doi.org%2F10.1515%2Fmms-2015-0039