DocumentCode :
1360602
Title :
Accurate Estimation of Gaseous Strength Using Transient Data
Author :
Kar, Swarnendu ; Varshney, Pramod K.
Author_Institution :
Dept. of Electr. Eng. & Com puter Sci., Syracuse Univ., Syracuse, NY, USA
Volume :
60
Issue :
4
fYear :
2011
fDate :
4/1/2011 12:00:00 AM
Firstpage :
1197
Lastpage :
1205
Abstract :
Information about the strength of gas sources in buildings has a number of applications in the area of building automation and control, including temperature and ventilation control, fire detection, and security systems. In this paper, we consider the problem of estimating the strength of a gas source in an enclosure when some of the parameters of the gas transport process are unknown. Traditionally, these problems are either solved by the maximum-likelihood method, which is accurate but computationally intensive, or by recursive least squares (also Kalman) filtering, which is simpler but less accurate. In this paper, we suggest a different statistical estimation procedure based on the concept of method of moments. We outline techniques that make this procedure computationally efficient and amenable for recursive implementation. We provide a comparative analysis of our proposed method based on experimental results, as well as Monte Carlo simulations. When used with the building control systems, these algorithms can estimate the gaseous strength in a room both quickly and accurately and can potentially provide improved indoor air quality in an efficient manner.
Keywords :
Kalman filters; Monte Carlo methods; building management systems; fires; security; statistical analysis; ventilation; Kalman filtering; Monte Carlo simulation; accurate estimation; building automation; building control systems; fire detection; gas sources; gas transport process; gaseous strength; indoor air quality; maximum likelihood method; recursive implementation; recursive least squares; security systems; statistical estimation; temperature; transient data; ventilation control; Method of moments (MME); monomolecular growth curve; nonlinear regression; occupancy estimation; parameter estimation;
fLanguage :
English
Journal_Title :
Instrumentation and Measurement, IEEE Transactions on
Publisher :
ieee
ISSN :
0018-9456
Type :
jour
DOI :
10.1109/TIM.2010.2084731
Filename :
5609200
Link To Document :
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