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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...
Autores principales: | , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2018
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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 |
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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 |
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