基于KPCA与MRVM的二元混合气体成分识别算法研究

Yinsheng Chen; Zhongming Luo; Kun Sun; Yonghui Xu; Qi Wang

doi:10.3969/j.issn.1004-1699.2019.02.003

基于KPCA与MRVM的二元混合气体成分识别算法研究

Translated title of the contribution: A Binary Mixed Gas Component Identification Algorithm Based on KPCA and MRVM

Yinsheng Chen^*
, Zhongming Luo
, Kun Sun
, Yonghui Xu
, Qi Wang

^*Corresponding author for this work

Harbin University of Science and Technology
School of Electrical Engineering and Automation, Harbin Institute of Technology

Research output: Contribution to journal › Article › peer-review

3 Scopus citations

Abstract

Gas component identification is a key technology for gas detection and analysis of electronic nose system. In order to improve the identification accuracy of binary mixed gas components,a binary mixed gas component identification algorithm based on KPCA coupled with MRVM is proposed in this paper. This algorithm uses the nonlinear feature extraction capability of KPCA to extract the features of the response signal of sensor array. The multiple classification correlation vector machines(MRVM)classifier is used to identify the binary mixed gas component. The availability of the proposed algorithm is verified by the gas sample set obtained by the self-designed experiment system. The experimental results show that the identification accuracy of binary mixed gas component is 99.83%.

Translated title of the contribution	A Binary Mixed Gas Component Identification Algorithm Based on KPCA and MRVM
Original language	Chinese (Traditional)
Pages (from-to)	172-176
Number of pages	5
Journal	Chinese Journal of Sensors and Actuators
Volume	32
Issue number	2
DOIs	https://doi.org/10.3969/j.issn.1004-1699.2019.02.003
State	Published - 1 Feb 2019
Externally published	Yes

Access to Document

10.3969/j.issn.1004-1699.2019.02.003

Cite this

@article{c24542c85c664672aa780d695db4bb9a,

title = "基于KPCA与MRVM的二元混合气体成分识别算法研究",

abstract = "Gas component identification is a key technology for gas detection and analysis of electronic nose system. In order to improve the identification accuracy of binary mixed gas components,a binary mixed gas component identification algorithm based on KPCA coupled with MRVM is proposed in this paper. This algorithm uses the nonlinear feature extraction capability of KPCA to extract the features of the response signal of sensor array. The multiple classification correlation vector machines(MRVM)classifier is used to identify the binary mixed gas component. The availability of the proposed algorithm is verified by the gas sample set obtained by the self-designed experiment system. The experimental results show that the identification accuracy of binary mixed gas component is 99.83\%.",

keywords = "Electronic nose, Gas identification, KPCA, MRVM",

author = "Yinsheng Chen and Zhongming Luo and Kun Sun and Yonghui Xu and Qi Wang",

year = "2019",

month = feb,

day = "1",

doi = "10.3969/j.issn.1004-1699.2019.02.003",

language = "繁体中文",

volume = "32",

pages = "172--176",

journal = "Chinese Journal of Sensors and Actuators",

issn = "1004-1699",

publisher = "Guojia Jiaowei Quanguo Gaoxiao Chuangan Jishu Yanjiuhui",

number = "2",

}

TY - JOUR

T1 - 基于KPCA与MRVM的二元混合气体成分识别算法研究

AU - Chen, Yinsheng

AU - Luo, Zhongming

AU - Sun, Kun

AU - Xu, Yonghui

AU - Wang, Qi

PY - 2019/2/1

Y1 - 2019/2/1

N2 - Gas component identification is a key technology for gas detection and analysis of electronic nose system. In order to improve the identification accuracy of binary mixed gas components,a binary mixed gas component identification algorithm based on KPCA coupled with MRVM is proposed in this paper. This algorithm uses the nonlinear feature extraction capability of KPCA to extract the features of the response signal of sensor array. The multiple classification correlation vector machines(MRVM)classifier is used to identify the binary mixed gas component. The availability of the proposed algorithm is verified by the gas sample set obtained by the self-designed experiment system. The experimental results show that the identification accuracy of binary mixed gas component is 99.83%.

AB - Gas component identification is a key technology for gas detection and analysis of electronic nose system. In order to improve the identification accuracy of binary mixed gas components,a binary mixed gas component identification algorithm based on KPCA coupled with MRVM is proposed in this paper. This algorithm uses the nonlinear feature extraction capability of KPCA to extract the features of the response signal of sensor array. The multiple classification correlation vector machines(MRVM)classifier is used to identify the binary mixed gas component. The availability of the proposed algorithm is verified by the gas sample set obtained by the self-designed experiment system. The experimental results show that the identification accuracy of binary mixed gas component is 99.83%.

KW - Electronic nose

KW - Gas identification

KW - KPCA

KW - MRVM

UR - https://www.scopus.com/pages/publications/85067095475

U2 - 10.3969/j.issn.1004-1699.2019.02.003

DO - 10.3969/j.issn.1004-1699.2019.02.003

M3 - 文章

AN - SCOPUS:85067095475

SN - 1004-1699

VL - 32

SP - 172

EP - 176

JO - Chinese Journal of Sensors and Actuators

JF - Chinese Journal of Sensors and Actuators

IS - 2

ER -

基于KPCA与MRVM的二元混合气体成分识别算法研究

Abstract

Access to Document

Other files and links

Fingerprint

Cite this