Volume 7 Number 2 (Feb. 2012)
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JSW 2012 Vol.7(2): 303-307 ISSN: 1796-217X
doi: 10.4304/jsw.7.2.303-307

Knowledge-based Genetic Algorithms Data Fusion and its Application in Mine Mixed-gas Detection

Haigang Li1, Deming Wang1, and Yong Zhang2
1School of Safety Engineering,China University of Mining &Technology,Xuzhou,China
2China University of Mining & Technology Xuzhou, 221116, China

Abstract—In Considering that the high concentration of mine gas and hydrogen will disturb the output of electrochemical carbon monoxide sensor, this paper integrates gas sensor array with data fusion Algorithm. The output signals of three sensors are trained by BP neural network to get the mathematical model of information fusion for the analysis of mixed gas of methane, hydrogen and carbon monoxide. The experiments show that the information fusion could correct the crossed sensitivity error, and improve the accuracy of carbon monoxide, therefore achieve quantitative analysis mixed gas of coal mine.

Index Terms—Gas Sensor, Information Fusion, Neural Networks, Genetic Algorithm

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Cite: Haigang Li, Deming Wang, and Yong Zhang, "Knowledge-based Genetic Algorithms Data Fusion and its Application in Mine Mixed-gas Detection," Journal of Software vol. 7, no.2, pp. 303-307, 2012.

General Information

ISSN: 1796-217X (Online)
Frequency: Monthly (2006-2019); Bimonthly (Since 2020)
Editor-in-Chief: Prof. Antanas Verikas
Executive Editor: Ms. Yoyo Y. Zhou
Abstracting/ Indexing: DBLP, EBSCO, Google Scholar, ProQuest, INSPEC, ULRICH's Periodicals Directory, WorldCat, etc
E-mail: jsw@iap.org
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