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dc.contributor.authorLi, Hen_NZ
dc.contributor.authorLuo, Den_NZ
dc.contributor.authorSun, Yen_NZ
dc.contributor.authorGholamhosseini, Hen_NZ
dc.date.accessioned2021-03-05T03:10:13Z
dc.date.available2021-03-05T03:10:13Z
dc.date.copyright2019en_NZ
dc.identifier.citationSensors 2019, 19, 5033; doi:10.3390/s19225033
dc.identifier.issn1424-8220en_NZ
dc.identifier.urihttp://hdl.handle.net/10292/14037
dc.description.abstractRapid 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.en_NZ
dc.publisherMDPI
dc.relation.urihttps://www.mdpi.com/1424-8220/19/22/5033en_NZ
dc.rights© 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/).
dc.subjectElectronic nose; Industrial gas; Classification and identification; Kernel discriminant analysis
dc.titleClassification and Identification of Industrial Gases Based on Electronic Nose Technologyen_NZ
dc.typeJournal Article
dc.rights.accessrightsOpenAccessen_NZ
dc.identifier.doi10.3390/s19225033en_NZ
aut.relation.issue22en_NZ
aut.relation.volume19en_NZ
pubs.elements-id366459
aut.relation.journalSensors (Switzerland)en_NZ


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