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Type of publication:Inbook
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TitleStatistical Gas Distribution Modelling Using Kernel Methods
Bibtex cite ID
Year published 2011
Pages 153-179
Chapter 6
Publisher IGI Global
Keywords statistical gas distribution modelling,Gaussian kernel,gas sensor,environmental monitoring
Abstract
Gas distribution models can provide comprehensive information about a large number of gas concentration measurements, highlighting, for example, areas of unusual gas accumulation. They can also help to locate gas sources and to plan where future measurements should be carried out. Current physical modeling methods, however, are computationally expensive and not applicable for real world scenarios with real-time and high resolution demands. This chapter reviews kernel methods that statistically model gas distribution. Gas measurements are treated as random variables, and the gas distribution is predicted at unseen locations either using a kernel density estimation or a kernel regression approach. The resulting statistical apmodels do not make strong assumptions about the functional form of the gas distribution, such as the number or locations of gas sources, for example. The major focus of this chapter is on two-dimensional models that provide estimates for the means and predictive variances of the distribution. Furthermore, three extensions to the presented kernel density estimation algorithm are described, which allow to include wind information, to extend the model to three dimensions, and to reflect time-dependent changes of the random process that generates the gas distribution measurements. All methods are discussed based on experimental validation using real sensor data.
Authors
Asadi, Sahar
Reggente, Matteo
Stachniss, Cyrill
Plagemann, Christian
Lilienthal, Achim J.
Editors
Hines, Evor
Leeson, Mark
Topics
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