Cargando…
Classification and Identification of Industrial Gases Based on Electronic Nose Technology
Rapid detection and identification of industrial gases is a challenging problem. They have a complex composition and different specifications. This paper presents a method based on the kernel discriminant analysis (KDA) algorithm to identify industrial gases. The smell prints of four typical industr...
Autores principales: | , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2019
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6891334/ https://www.ncbi.nlm.nih.gov/pubmed/31752238 http://dx.doi.org/10.3390/s19225033 |
_version_ | 1783475789139804160 |
---|---|
author | Li, Hui Luo, Dehan Sun, Yunlong GholamHosseini, Hamid |
author_facet | Li, Hui Luo, Dehan Sun, Yunlong GholamHosseini, Hamid |
author_sort | Li, Hui |
collection | PubMed |
description | Rapid detection and identification of industrial gases is a challenging problem. They have a complex composition and different specifications. This paper presents a method based on the kernel discriminant analysis (KDA) algorithm to identify industrial gases. The smell prints of four typical industrial gases were collected by an electronic nose. The extracted features of the collected gases were employed for gas identification using different classification algorithms, including principal component analysis (PCA), linear discriminant analysis (LDA), PCA + LDA, and KDA. In order to obtain better classification results, we reduced the dimensions of the original high-dimensional data, and chose a good classifier. The KDA algorithm provided a high classification accuracy of 100% by selecting the offset of the kernel function c = 10 and the degree of freedom d = 5. It was found that this accuracy was 4.17% higher than the one obtained using PCA. In the case of standard deviation, the KDA algorithm has the highest recognition rate and the least time consumption. |
format | Online Article Text |
id | pubmed-6891334 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-68913342019-12-12 Classification and Identification of Industrial Gases Based on Electronic Nose Technology Li, Hui Luo, Dehan Sun, Yunlong GholamHosseini, Hamid Sensors (Basel) Article Rapid detection and identification of industrial gases is a challenging problem. They have a complex composition and different specifications. This paper presents a method based on the kernel discriminant analysis (KDA) algorithm to identify industrial gases. The smell prints of four typical industrial gases were collected by an electronic nose. The extracted features of the collected gases were employed for gas identification using different classification algorithms, including principal component analysis (PCA), linear discriminant analysis (LDA), PCA + LDA, and KDA. In order to obtain better classification results, we reduced the dimensions of the original high-dimensional data, and chose a good classifier. The KDA algorithm provided a high classification accuracy of 100% by selecting the offset of the kernel function c = 10 and the degree of freedom d = 5. It was found that this accuracy was 4.17% higher than the one obtained using PCA. In the case of standard deviation, the KDA algorithm has the highest recognition rate and the least time consumption. MDPI 2019-11-18 /pmc/articles/PMC6891334/ /pubmed/31752238 http://dx.doi.org/10.3390/s19225033 Text en © 2019 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 Li, Hui Luo, Dehan Sun, Yunlong GholamHosseini, Hamid Classification and Identification of Industrial Gases Based on Electronic Nose Technology |
title | Classification and Identification of Industrial Gases Based on Electronic Nose Technology |
title_full | Classification and Identification of Industrial Gases Based on Electronic Nose Technology |
title_fullStr | Classification and Identification of Industrial Gases Based on Electronic Nose Technology |
title_full_unstemmed | Classification and Identification of Industrial Gases Based on Electronic Nose Technology |
title_short | Classification and Identification of Industrial Gases Based on Electronic Nose Technology |
title_sort | classification and identification of industrial gases based on electronic nose technology |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6891334/ https://www.ncbi.nlm.nih.gov/pubmed/31752238 http://dx.doi.org/10.3390/s19225033 |
work_keys_str_mv | AT lihui classificationandidentificationofindustrialgasesbasedonelectronicnosetechnology AT luodehan classificationandidentificationofindustrialgasesbasedonelectronicnosetechnology AT sunyunlong classificationandidentificationofindustrialgasesbasedonelectronicnosetechnology AT gholamhosseinihamid classificationandidentificationofindustrialgasesbasedonelectronicnosetechnology |