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Concentration estimation of formaldehyde using metal oxide semiconductor gas sensor array‐based e‐noses

Concentration estimation of formaldehyde using metal oxide semiconductor gas sensor array‐based... Purpose – The purpose of this paper is to present a novel concentration estimation model for improving the accuracy and robustness of low‐cost electronic noses (e‐noses) with metal oxide semiconductor sensors in indoor air contaminant monitoring and overcome the potential sensor drift. Design/methodology/approach – In the quantification model, a piecewise linearly weighted artificial neural network ensemble model (PLWE‐ANN) with an embedded self‐calibration module based on a threshold network is studied. Findings – The nonlinear estimation problem of sensor array‐based e‐noses can be effectively transformed into a piecewise linear estimation through linear weighted neural networks ensemble activated by a threshold network. Originality/value – In this paper, a number of experimental results have been presented, and it also demonstrates that the proposed model has very good accuracy and robustness in real‐time indoor monitoring of formaldehyde. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Sensor Review Emerald Publishing

Concentration estimation of formaldehyde using metal oxide semiconductor gas sensor array‐based e‐noses

Sensor Review , Volume 34 (3): 7 – Jun 10, 2014

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References (29)

Publisher
Emerald Publishing
Copyright
Copyright © 2014 Emerald Group Publishing Limited. All rights reserved.
ISSN
0260-2288
DOI
10.1108/SR-05-2013-673
Publisher site
See Article on Publisher Site

Abstract

Purpose – The purpose of this paper is to present a novel concentration estimation model for improving the accuracy and robustness of low‐cost electronic noses (e‐noses) with metal oxide semiconductor sensors in indoor air contaminant monitoring and overcome the potential sensor drift. Design/methodology/approach – In the quantification model, a piecewise linearly weighted artificial neural network ensemble model (PLWE‐ANN) with an embedded self‐calibration module based on a threshold network is studied. Findings – The nonlinear estimation problem of sensor array‐based e‐noses can be effectively transformed into a piecewise linear estimation through linear weighted neural networks ensemble activated by a threshold network. Originality/value – In this paper, a number of experimental results have been presented, and it also demonstrates that the proposed model has very good accuracy and robustness in real‐time indoor monitoring of formaldehyde.

Journal

Sensor ReviewEmerald Publishing

Published: Jun 10, 2014

Keywords: Sensors; Gas; Neural networks; Arrays; Multi‐sensor systems

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