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TD Kernel DM+V: time-dependent statistical gas distribution modelling on simulated measurements
Örebro University, School of Science and Technology. (AASS Mobile Robot and Olfaction Lab)
Örebro University, School of Science and Technology. (AASS Mobile Robot and Olfaction Lab)
Örebro University, School of Science and Technology.ORCID iD: 0000-0002-3122-693X
Örebro University, School of Science and Technology. (AASS Mobile Robot and Olfaction Lab)ORCID iD: 0000-0003-0217-9326
2011 (English)In: Olfaction and Electronic Nose: proceedings of the 14th International Symposium on Olfaction and Electronic Nose (ISOEN) / [ed] Perena Gouma, Springer Science+Business Media B.V., 2011, 281-282 p.Conference paper, Oral presentation only (Refereed)
Abstract [en]

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. While a time-independent random process 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 for building 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 for obtaining 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 algorithm (TD 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.

Place, publisher, year, edition, pages
Springer Science+Business Media B.V., 2011. 281-282 p.
Series
AIP Conference Proceedings, 1362
National Category
Signal Processing Robotics
Research subject
Computer and Systems Science
Identifiers
URN: urn:nbn:se:oru:diva-24101DOI: 10.1063/1.3651651ISBN: 978-0-7354-0920-0 (print)OAI: oai:DiVA.org:oru-24101DiVA: diva2:540989
Conference
14th International Symposium on Olfaction and Electronic Nose (ISOEN), 2–5 May 2011, New York City, NY (USA)
Funder
EU, FP7, Seventh Framework Programme, FP7-224318-DIADEM
Available from: 2012-08-02 Created: 2012-07-13 Last updated: 2017-10-17Bibliographically approved

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Publisher's full texthttp://proceedings.aip.org/resource/2/apcpcs/1362/1/281_1?isAuthorized=no

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