Super-Resolution for Gas Distribution Mapping: Convolutional Encoder-Decoder Network

Winkler, Nicolas P.,1991-Bundesanstalt für Materialforschung und -prüfung (BAM), Berlin, Germany (författare)

Super-Resolution for Gas Distribution Mapping : Convolutional Encoder-Decoder Network

Funding agency:Ministry of Education, Culture, Sports, Science and Technology, Japan (MEXT) Japan Society for the Promotion of Science 19H02103
Gas distribution mapping is important to have an accurate understanding of gas concentration levels in hazardous environments. A major problem is that in-situ gas sensors are only able to measure concentrations at their specific location. The gas distribution in-between the sampling locations must therefore be modeled. In this research, we interpret the task of spatial interpolation between sparsely distributed sensors as a task of enhancing an image's resolution, namely super-resolution. Because autoencoders are proven to perform well for this super-resolution task, we trained a convolutional encoder-decoder neural network to map the gas distribution over a spatially sparse sensor network. Due to the difficulty to collect real-world gas distribution data and missing ground truth, we used synthetic data generated with a gas distribution simulator for training and evaluation of the model. Our results show that the neural network was able to learn the behavior of gas plumes and outperforms simpler interpolation techniques.

Matsukura, HarukaUniversity of Electro-Communications, Tokyo, Japan (författare)
Neumann, Patrick P.Bundesanstalt für Materialforschung und -prüfung (BAM), Berlin, Germany (författare)
Schaffernicht, Erik,1980-Örebro universitet,Institutionen för naturvetenskap och teknik(Swepub:oru)est (författare)
Ishida, HiroshiTokyo University of Agriculture and Technology, Tokyo, Japan (författare)
Lilienthal, Achim J.,1970-Örebro universitet,Institutionen för naturvetenskap och teknik(Swepub:oru)amll (författare)
Bundesanstalt für Materialforschung und -prüfung (BAM), Berlin, GermanyUniversity of Electro-Communications, Tokyo, Japan (creator_code:org_t)

Ingår i:2022 IEEE International Symposium on Olfaction and Electronic Nose (ISOEN): IEEE97816654586039781665458610

Av författaren/redakt...: Winkler, Nicolas ...; Matsukura, Haruk ...; Neumann, Patrick ...; Schaffernicht, E ...; Ishida, Hiroshi; Lilienthal, Achi ...

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