Bayesian Estimation Algorithm Applying in Gas Detection Modules

• Published in: Applied Mechanics and Materials Vol. 284-287; pp. 1764 - 1769
• Main Authors: Liao, Yi Lin, Su, Kuo Lan, Shiau, Sheng Wen, Guo, J.H.
• Format: Journal Article
• Language: English
• Published: Zurich Trans Tech Publications Ltd 2013
• Subjects: HOLTEK Microchip; Intelligent Building; Competitiveness Sensors; Bayesian Estimation Algorithm; Complementation Sensors
• ISBN: 3037856122; 9783037856123
• ISSN: 1660-9336; 1662-7482; 1662-7482
• DOI: 10.4028/www.scientific.net/AMM.284-287.1764

The paper develops gas detection modules for the intelligent building. The modules use many gas sensors to detect environment of the home and building. The gas sensors of the detection modules are classified two types. One is competitiveness gas detection module, and uses the same sensors to detect gas leakage. The other is complementation gas detection module, and uses variety sensors to classify multiple gases. The paper uses Bayesian estimation algorithm to be applied in competitiveness gas detection module and complementation gas detection module, and implement the proposed algorithm to be nice for variety gas sensor combination method. In the competitiveness gas detection module, we use two gas sensors to improve the proposed algorithm to be right. In the complementation gas detection module, we use a NH3 sensor, an air pollution sensor, an alcohol sensor, a HS sensor, a smoke sensor, a CO sensor, a LPG sensor and a nature gas sensor, and can classify variety gases using Bayesian estimation algorithm. The controller of the two gas detection modules is HOLTEK microchip. The modules can communicate with the supervised computer via wire series interface or wireless RF interface, and cautions the user by the voice module. Finally, we present some experimental results to measure know and unknown gas using the two gas detection modules on the security system of the intelligent building.