Bayesian Estimation Algorithm Applying in Gas Detection Modules

Abstract:

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.

• Periodical: Applied Mechanics and Materials (Volumes 284-287)
• Pages: 1764-1769
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.284-287.1764
• Online since: January 2013
• Authors: Kuo Lan Su, Sheng Wen Shiau, Yi Lin Liao, J.H. Guo
• Keywords: Bayesian Estimation Algorithm, Competitiveness Sensors, Complementation Sensors, HOLTEK Microchip, Intelligent Building

References

[1] J.W. Gardner and P.N. Bartlet, Electronic noses: Principles and Applications, Oxford University Press, UK, (1999).

[2] E. Scorsone, A.M. Pisanelli and K.C. Persaud, Development of an electronic nose for fire detection, Sens. Actuators B, Vol. 116 (2006), p.55.

DOI: 10.1016/j.snb.2005.12.059

[3] F. Sarry and M. Lumbreras, Gas discrimination in an air-conditioned system, IEEE Transaction on Instr. Meas., Vol. 49, No. 4 (2000), p.809.

DOI: 10.1109/19.863929

[4] H. Yu and J. Wang, Quality grade identification of green tea using E-nose by CA and ANN, LWT-Food Science and Technology, Vol. 41 (2008), p.1268.

DOI: 10.1016/j.lwt.2007.08.018

[5] A. Bermak and S.B. Belhouari, Bayesian learning using Gaussian process for gas identification, IEEE Transaction on Instr. Meas., Vol. 55, No. 3 (2006), p.787.

DOI: 10.1109/tim.2006.873804

[6] H. Zhang and M. Chang, Evaluation of peach quality indices using an electronic nose by MLR, QPST and BP network, Sens. Actuators B, Vol. 134 (2008), p.332.

DOI: 10.1016/j.snb.2008.05.008

[7] M.A. Abidi and R.C. Gonzales, Data Fusion in Robotics and Machine Intelligent, by Academic Press Inc., (1992), p.54.