A blind source separation based micro gas sensor array modeling method

Guangfen Wei; Zhenan Tang; Philip Ching Ho Chan; Jun Yu

A blind source separation based micro gas sensor array modeling method

Guangfen Wei, Zhenan Tang, Philip Ching Ho Chan, Jun Yu

Research output: Journal article publication › Journal article › Academic research › peer-review

Abstract

Blind Source Separation (BSS) has been a strong method to extract the unknown independent source signals from sensor measurements which are unknown combinations of the source signals. In this paper, a BSS based modeling method is proposed and analyzed for a micro gas sensor array, which is fabricated with surface micromachining technology and is applied to detect the gas mixture of CO and CH4. Two widely used BSS methods-Independent Component Analysis (ICA) and Nonlinear Principal Component Analysis (NLPCA) are applied to obtain the gas concentration signals. The analyzing results demonstrate that BSS is an efficient way to extract the components which corresponding to the gas concentration signals.

Original language	English
Pages (from-to)	696-701
Number of pages	6
Journal	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	3173
Publication status	Published - 1 Dec 2004
Externally published	Yes

ASJC Scopus subject areas

Theoretical Computer Science
Computer Science(all)

Cite this

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title = "A blind source separation based micro gas sensor array modeling method",

abstract = "Blind Source Separation (BSS) has been a strong method to extract the unknown independent source signals from sensor measurements which are unknown combinations of the source signals. In this paper, a BSS based modeling method is proposed and analyzed for a micro gas sensor array, which is fabricated with surface micromachining technology and is applied to detect the gas mixture of CO and CH4. Two widely used BSS methods-Independent Component Analysis (ICA) and Nonlinear Principal Component Analysis (NLPCA) are applied to obtain the gas concentration signals. The analyzing results demonstrate that BSS is an efficient way to extract the components which corresponding to the gas concentration signals.",

author = "Guangfen Wei and Zhenan Tang and Chan, {Philip Ching Ho} and Jun Yu",

year = "2004",

month = dec,

day = "1",

language = "English",

volume = "3173",

pages = "696--701",

journal = "Lecture Notes in Computer Science",

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publisher = "Springer Verlag",

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A blind source separation based micro gas sensor array modeling method. / Wei, Guangfen; Tang, Zhenan; Chan, Philip Ching Ho; Yu, Jun.

In: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Vol. 3173, 01.12.2004, p. 696-701.

Research output: Journal article publication › Journal article › Academic research › peer-review

TY - JOUR

T1 - A blind source separation based micro gas sensor array modeling method

AU - Wei, Guangfen

AU - Tang, Zhenan

AU - Chan, Philip Ching Ho

AU - Yu, Jun

PY - 2004/12/1

Y1 - 2004/12/1

N2 - Blind Source Separation (BSS) has been a strong method to extract the unknown independent source signals from sensor measurements which are unknown combinations of the source signals. In this paper, a BSS based modeling method is proposed and analyzed for a micro gas sensor array, which is fabricated with surface micromachining technology and is applied to detect the gas mixture of CO and CH4. Two widely used BSS methods-Independent Component Analysis (ICA) and Nonlinear Principal Component Analysis (NLPCA) are applied to obtain the gas concentration signals. The analyzing results demonstrate that BSS is an efficient way to extract the components which corresponding to the gas concentration signals.

AB - Blind Source Separation (BSS) has been a strong method to extract the unknown independent source signals from sensor measurements which are unknown combinations of the source signals. In this paper, a BSS based modeling method is proposed and analyzed for a micro gas sensor array, which is fabricated with surface micromachining technology and is applied to detect the gas mixture of CO and CH4. Two widely used BSS methods-Independent Component Analysis (ICA) and Nonlinear Principal Component Analysis (NLPCA) are applied to obtain the gas concentration signals. The analyzing results demonstrate that BSS is an efficient way to extract the components which corresponding to the gas concentration signals.

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M3 - Journal article

VL - 3173

SP - 696

EP - 701

JO - Lecture Notes in Computer Science

JF - Lecture Notes in Computer Science

SN - 0302-9743

ER -

A blind source separation based micro gas sensor array modeling method

Abstract

ASJC Scopus subject areas

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