Cargando…

Feature Extraction of Electronic Nose Signals Using QPSO-Based Multiple KFDA Signal Processing

The aim of this research was to enhance the classification accuracy of an electronic nose (E-nose) in different detecting applications. During the learning process of the E-nose to predict the types of different odors, the prediction accuracy was not quite satisfying because the raw features extract...

Descripción completa

Detalles Bibliográficos
Autores principales: Wen, Tailai, Yan, Jia, Huang, Daoyu, Lu, Kun, Deng, Changjian, Zeng, Tanyue, Yu, Song, He, Zhiyi
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5855868/
https://www.ncbi.nlm.nih.gov/pubmed/29382146
http://dx.doi.org/10.3390/s18020388
_version_ 1783307198356520960
author Wen, Tailai
Yan, Jia
Huang, Daoyu
Lu, Kun
Deng, Changjian
Zeng, Tanyue
Yu, Song
He, Zhiyi
author_facet Wen, Tailai
Yan, Jia
Huang, Daoyu
Lu, Kun
Deng, Changjian
Zeng, Tanyue
Yu, Song
He, Zhiyi
author_sort Wen, Tailai
collection PubMed
description The aim of this research was to enhance the classification accuracy of an electronic nose (E-nose) in different detecting applications. During the learning process of the E-nose to predict the types of different odors, the prediction accuracy was not quite satisfying because the raw features extracted from sensors’ responses were regarded as the input of a classifier without any feature extraction processing. Therefore, in order to obtain more useful information and improve the E-nose’s classification accuracy, in this paper, a Weighted Kernels Fisher Discriminant Analysis (WKFDA) combined with Quantum-behaved Particle Swarm Optimization (QPSO), i.e., QWKFDA, was presented to reprocess the original feature matrix. In addition, we have also compared the proposed method with quite a few previously existing ones including Principal Component Analysis (PCA), Locality Preserving Projections (LPP), Fisher Discriminant Analysis (FDA) and Kernels Fisher Discriminant Analysis (KFDA). Experimental results proved that QWKFDA is an effective feature extraction method for E-nose in predicting the types of wound infection and inflammable gases, which shared much higher classification accuracy than those of the contrast methods.
format Online
Article
Text
id pubmed-5855868
institution National Center for Biotechnology Information
language English
publishDate 2018
publisher MDPI
record_format MEDLINE/PubMed
spelling pubmed-58558682018-03-20 Feature Extraction of Electronic Nose Signals Using QPSO-Based Multiple KFDA Signal Processing Wen, Tailai Yan, Jia Huang, Daoyu Lu, Kun Deng, Changjian Zeng, Tanyue Yu, Song He, Zhiyi Sensors (Basel) Article The aim of this research was to enhance the classification accuracy of an electronic nose (E-nose) in different detecting applications. During the learning process of the E-nose to predict the types of different odors, the prediction accuracy was not quite satisfying because the raw features extracted from sensors’ responses were regarded as the input of a classifier without any feature extraction processing. Therefore, in order to obtain more useful information and improve the E-nose’s classification accuracy, in this paper, a Weighted Kernels Fisher Discriminant Analysis (WKFDA) combined with Quantum-behaved Particle Swarm Optimization (QPSO), i.e., QWKFDA, was presented to reprocess the original feature matrix. In addition, we have also compared the proposed method with quite a few previously existing ones including Principal Component Analysis (PCA), Locality Preserving Projections (LPP), Fisher Discriminant Analysis (FDA) and Kernels Fisher Discriminant Analysis (KFDA). Experimental results proved that QWKFDA is an effective feature extraction method for E-nose in predicting the types of wound infection and inflammable gases, which shared much higher classification accuracy than those of the contrast methods. MDPI 2018-01-29 /pmc/articles/PMC5855868/ /pubmed/29382146 http://dx.doi.org/10.3390/s18020388 Text en © 2018 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 (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Wen, Tailai
Yan, Jia
Huang, Daoyu
Lu, Kun
Deng, Changjian
Zeng, Tanyue
Yu, Song
He, Zhiyi
Feature Extraction of Electronic Nose Signals Using QPSO-Based Multiple KFDA Signal Processing
title Feature Extraction of Electronic Nose Signals Using QPSO-Based Multiple KFDA Signal Processing
title_full Feature Extraction of Electronic Nose Signals Using QPSO-Based Multiple KFDA Signal Processing
title_fullStr Feature Extraction of Electronic Nose Signals Using QPSO-Based Multiple KFDA Signal Processing
title_full_unstemmed Feature Extraction of Electronic Nose Signals Using QPSO-Based Multiple KFDA Signal Processing
title_short Feature Extraction of Electronic Nose Signals Using QPSO-Based Multiple KFDA Signal Processing
title_sort feature extraction of electronic nose signals using qpso-based multiple kfda signal processing
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5855868/
https://www.ncbi.nlm.nih.gov/pubmed/29382146
http://dx.doi.org/10.3390/s18020388
work_keys_str_mv AT wentailai featureextractionofelectronicnosesignalsusingqpsobasedmultiplekfdasignalprocessing
AT yanjia featureextractionofelectronicnosesignalsusingqpsobasedmultiplekfdasignalprocessing
AT huangdaoyu featureextractionofelectronicnosesignalsusingqpsobasedmultiplekfdasignalprocessing
AT lukun featureextractionofelectronicnosesignalsusingqpsobasedmultiplekfdasignalprocessing
AT dengchangjian featureextractionofelectronicnosesignalsusingqpsobasedmultiplekfdasignalprocessing
AT zengtanyue featureextractionofelectronicnosesignalsusingqpsobasedmultiplekfdasignalprocessing
AT yusong featureextractionofelectronicnosesignalsusingqpsobasedmultiplekfdasignalprocessing
AT hezhiyi featureextractionofelectronicnosesignalsusingqpsobasedmultiplekfdasignalprocessing