D-CIS Publication Database


Type of publication:Inproceedings
Entered by:
TitleTD Kernel DM+V: Time-Dependent Statistical Gas Distribution Modelling on Simulated Measurements
Bibtex cite ID
Booktitle Olfaction and Electronic Nose - Proceedings of the 14th International Symposium on Olfaction and Electronic Nose (ISOEN)
Year published 2011
Month May
Location New York, USA
Keywords time-dependent modelling,statistical gas distribution models,gas dispersial simulation
To study gas dispersion, several statistical gas distribution modelling approaches have been proposed recently. A crucial assumption in these approaches is that gas distribution models are learned from measurements that are generated by a time-invariant random process which can capture certain fluctuations in the gas distribution. More accurate models can be obtained by modelling changes in the random process over time. In this work we propose a time-scale parameter that relates the age of measurements to their validity to build the gas distribution model in a recency function. The parameters of the recency function define a time-scale and can be learned. The time-scale represents a compromise between two conflicting requirements to obtain accurate gas distribution models: using as many measurements as possible and using only very recent measurements. We have studied several recency functions in a time-dependent extension of the Kernel DM+V. Based on real-world experiments and simulations of gas dispersal (presented in this paper) we demonstrate that TD Kernel DM+V improves the obtained gas distribution models in dynamic situations. This represents an important step towards statistical modelling of evolving gas distributions.
Asadi, Sahar
Pashami, Sepideh
Loutfi, Amy
Lilienthal, Achim J.
Total mark: 5