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  <doc>
    <id>60778</id>
    <completedYear/>
    <publishedYear>2024</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>1</pageFirst>
    <pageLast>12</pageLast>
    <pageNumber/>
    <edition/>
    <issue/>
    <volume>419</volume>
    <type>article</type>
    <publisherName>Elsevier B.V.</publisherName>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>1</belongsToBibliography>
    <completedDate>--</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Super-resolution for Gas Distribution Mapping</title>
    <abstract language="eng">Gas Distribution Mapping (GDM) is a valuable tool for monitoring the distribution of gases in a wide range of applications, including environmental monitoring, emergency response, and industrial safety. While GDM is actively researched in the scope of gas-sensitive mobile robots (Mobile Robot Olfaction), there is a potential for broader applications utilizing sensor networks. This study aims to address the lack of deep learning approaches in GDM and explore their potential for improved mapping of gas distributions. In this paper, we introduce Gas Distribution Decoder (GDD), a learning-based GDM method. GDD is a deep neural network for spatial interpolation between sparsely distributed sensor measurements that was trained on an extensive data set of realistic-shaped synthetic gas plumes based on actual airflow measurements. As access to ground truth representations of gas distributions remains a challenge in GDM research, we make our data sets, along with our models, publicly available. We test and compare GDD with state-of-the-art models on synthetic and real-world data. Our findings demonstrate that GDD significantly outperforms existing models, demonstrating a 35% improvement in accuracy on synthetic data when measured using the Root Mean Squared Error over the entire distribution map. Notably, GDD appears to have superior capabilities in reconstructing the edges and characteristic shapes of gas plumes compared to traditional models. These potentials offer new possibilities for more accurate and efficient environmental monitoring, and we hope to inspire other researchers to explore learning-based GDM.</abstract>
    <parentTitle language="eng">Sensors and Actuators B: Chemical</parentTitle>
    <identifier type="issn">0925-4005</identifier>
    <identifier type="doi">10.1016/j.snb.2024.136267</identifier>
    <identifier type="urn">urn:nbn:de:kobv:b43-607786</identifier>
    <enrichment key="opus.source">publish</enrichment>
    <enrichment key="date_peer_review">26.08.2024</enrichment>
    <licence>Creative Commons - CC BY - Namensnennung 4.0 International</licence>
    <author>Nicolas P. Winkler</author>
    <author>O. Kotlyar</author>
    <author>E. Schaffernicht</author>
    <author>H. Matsukura</author>
    <author>H. Ishida</author>
    <author>Patrick P. Neumann</author>
    <author>A. J. Lilienthal</author>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Gas distribution mapping</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Spatial interpolation</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Sensor networks</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Deep learning</value>
    </subject>
    <collection role="ddc" number="628">Sanitär- und Kommunaltechnik; Umwelttechnik</collection>
    <collection role="institutes" number="">8 Zerstörungsfreie Prüfung</collection>
    <collection role="institutes" number="">8.1 Sensorik, mess- und prüftechnische Verfahren</collection>
    <collection role="themenfelder" number="">Umwelt</collection>
    <collection role="literaturgattung" number="">Verlagsliteratur</collection>
    <collection role="fulltextaccess" number="">Datei für die Öffentlichkeit verfügbar ("Open Access")</collection>
    <collection role="unnumberedseries" number="">Wissenschaftliche Artikel der BAM</collection>
    <collection role="themenfelder" number="">Sensorik</collection>
    <thesisPublisher>Bundesanstalt für Materialforschung und -prüfung (BAM)</thesisPublisher>
    <file>https://opus4.kobv.de/opus4-bam/files/60778/Super-resolution for Gas Distribution Mapping.pdf</file>
  </doc>
  <doc>
    <id>60107</id>
    <completedYear/>
    <publishedYear>2024</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>1</pageFirst>
    <pageLast>3</pageLast>
    <pageNumber/>
    <edition/>
    <issue/>
    <volume/>
    <type>conferenceobject</type>
    <publisherName>IEEE</publisherName>
    <publisherPlace>USA</publisherPlace>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>1</belongsToBibliography>
    <completedDate>--</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Outdoor Gas Plume Reconstructions: A Field Study with Aerial Tomography</title>
    <abstract language="eng">This paper outlines significant advancements in our previously developed aerial gas tomography system, now optimized to reconstruct 2D tomographic slices of gas plumes with enhanced precision in outdoor environments. The core of our system is an aerial robot equipped with a custom-built 3-axis aerial gimbal, a Tunable Diode Laser Absorption Spectroscopy (TDLAS) sensor for CH4 measurements, a laser rangefinder, and a wide-angle camera, combined with a state-of-the-art gas tomography algorithm. In real-world experiments, we sent the aerial robot along gate-shaped flight patterns over a semi-controlled environment with a static-like gas plume, providing a welldefined ground truth for system evaluation. The reconstructed cross-sectional 2D images closely matched the known ground truth concentration, confirming the system’s high accuracy and reliability. The demonstrated system’s capabilities open doors for potential applications in environmental monitoring and industrial safety, though further testing is planned to ascertain the system’s operational boundaries fully.</abstract>
    <parentTitle language="eng">2024 IEEE International Symposium on Olfaction and Electronic Nose (ISOEN) Proceedings</parentTitle>
    <identifier type="isbn">979-8-3503-4865-1</identifier>
    <identifier type="doi">10.1109/isoen61239.2024.10556071</identifier>
    <enrichment key="eventName">20th International Symposium on Olfaction and Electronic Nose</enrichment>
    <enrichment key="eventPlace">Grapevine, Texas, USA</enrichment>
    <enrichment key="eventStart">12.05.2024</enrichment>
    <enrichment key="eventEnd">15.05.2024</enrichment>
    <enrichment key="opus.source">publish</enrichment>
    <enrichment key="date_peer_review">26.08.2024</enrichment>
    <author>Patrick P. Neumann</author>
    <author>Dino Hüllmann</author>
    <author>Nicolas P. Winkler</author>
    <author>Heiko Lohrke</author>
    <author>A. J. Lilienthal</author>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Aerial robot</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>TDLAS</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Gas Tomography</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Plume</value>
    </subject>
    <collection role="ddc" number="628">Sanitär- und Kommunaltechnik; Umwelttechnik</collection>
    <collection role="ddc" number="660">Chemische Verfahrenstechnik</collection>
    <collection role="institutes" number="">8 Zerstörungsfreie Prüfung</collection>
    <collection role="institutes" number="">8.1 Sensorik, mess- und prüftechnische Verfahren</collection>
    <collection role="themenfelder" number="">Umwelt</collection>
    <collection role="themenfelder" number="">Chemie und Prozesstechnik</collection>
    <collection role="fulltextaccess" number="">Datei im Netzwerk der BAM verfügbar ("Closed Access")</collection>
    <collection role="literaturgattung" number="">Graue Literatur</collection>
    <collection role="themenfelder" number="">Anlagensicherheit und Prozesssimulation</collection>
    <collection role="themenfelder" number="">Sensorik</collection>
  </doc>
</export-example>
