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

  • 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 andGas 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.zeige mehrzeige weniger

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Metadaten
Autor*innen:Nicolas P. WinklerORCiD
Koautor*innen:H. Matsukura, Patrick P. Neumann, E. Schaffernicht, H. Ishida, A. J. Lilienthal
Dokumenttyp:Vortrag
Veröffentlichungsform:Präsentation
Sprache:Englisch
Jahr der Erstveröffentlichung:2022
Organisationseinheit der BAM:8 Zerstörungsfreie Prüfung
8 Zerstörungsfreie Prüfung / 8.1 Sensorik, mess- und prüftechnische Verfahren
DDC-Klassifikation:Naturwissenschaften und Mathematik / Chemie / Analytische Chemie
Freie Schlagwörter:Deep Learning; Gas Distribution Mapping; Sensor Network; Spatial Interpolation; Super-Resolution
Themenfelder/Aktivitätsfelder der BAM:Umwelt
Umwelt / Sensorik
Veranstaltung:19th International Symposium on Olfaction and Electronic Nose
Veranstaltungsort:Aveiro, Portugal
Beginndatum der Veranstaltung:29.05.2022
Enddatum der Veranstaltung:01.06.2022
Verfügbarkeit des Dokuments:Datei im Netzwerk der BAM verfügbar ("Closed Access")
Datum der Freischaltung:08.06.2022
Referierte Publikation:Nein
Eingeladener Vortrag:Nein
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