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Learning From the P...
Learning From the Past : Sequential Deep Learning for Gas Distribution Mapping
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- Winkler, Nicolas P., 1991- (author)
- Bundesanstalt für Materialforschung und -prüfung (BAM), Berlin, Germany
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- Kotlyar, Oleksandr, 1982- (author)
- Örebro universitet,Institutionen för naturvetenskap och teknik
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- Schaffernicht, Erik, 1980- (author)
- Örebro universitet,Institutionen för naturvetenskap och teknik
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- Fan, Han, 1989- (author)
- Örebro universitet,Institutionen för naturvetenskap och teknik
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- Matsukura, Haruka (author)
- University of Electro-Communications, 1-5-1 Chofugaoka, Chofu, Tokyo, Japan
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- Ishida, Hiroshi (author)
- Tokyo University of Agriculture and Technology, 2-24-16 Nakacho, Koganei, Tokyo, Japan
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- Neumann, Patrick P. (author)
- Bundesanstalt für Materialforschung und -prüfung (BAM), Berlin, Germany
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- Lilienthal, Achim, 1970- (author)
- Örebro universitet,Institutionen för naturvetenskap och teknik
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(creator_code:org_t)
- 2022-11-19
- 2022
- English.
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In: ROBOT2022. - Cham : Springer. - 9783031210617 - 9783031210624 ; , s. 178-188
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https://urn.kb.se/re...
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https://doi.org/10.1...
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Abstract
Subject headings
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- To better understand the dynamics in hazardous environments, gas distribution mapping aims to map the gas concentration levels of a specified area precisely. Sampling is typically carried out in a spatially sparse manner, either with a mobile robot or a sensor network and concentration values between known data points have to be interpolated. In this paper, we investigate sequential deep learning models that are able to map the gas distribution based on a multiple time step input from a sensor network. We propose a novel hybrid convolutional LSTM - transpose convolutional structure that we train with synthetic gas distribution data. Our results show that learning the spatial and temporal correlation of gas plume patterns outperforms a non-sequential neural network model.
Subject headings
- NATURVETENSKAP -- Data- och informationsvetenskap -- Datavetenskap (hsv//swe)
- NATURAL SCIENCES -- Computer and Information Sciences -- Computer Sciences (hsv//eng)
Keyword
- Convolutional LSTM
- Gas Distribution Mapping
- Sequential Learning
- Spatial Interpolation
Publication and Content Type
- ref (subject category)
- kon (subject category)
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