<?xml version="1.0" encoding="utf-8"?>
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  <doc>
    <id>51108</id>
    <completedYear/>
    <publishedYear>2020</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>250</pageFirst>
    <pageLast>253</pageLast>
    <pageNumber/>
    <edition/>
    <issue/>
    <volume>32</volume>
    <type>article</type>
    <publisherName>Elsevier Ltd.</publisherName>
    <publisherPlace>Amsterdam</publisherPlace>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>1</belongsToBibliography>
    <completedDate>--</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">High-quality meets low-cost: Approaches for hybrid-mobility sensor networks</title>
    <abstract language="eng">Air pollution within industrial scenarios is a major risk for workers, which is why detailed knowledge about the dispersion of dusts and gases is necessary. This paper introduces a system combining stationary low-cost and high-quality sensors, carried by ground robots and unmanned aerial vehicles. Based on these dense sampling capabilities, detailed distribution maps of dusts and gases will be created. This system enables various research opportunities, especially on the fields of distribution mapping and sensor planning. Standard approaches for distribution mapping can be enhanced with knowledge about the environment’s characteristics, while the effectiveness of new approaches, utilizing neural networks, can be further investigated. The influence of different sensor network setups on the predictive quality of distribution algorithms will be researched and metrics for the quantification of a sensor network’s quality will be investigated.</abstract>
    <parentTitle language="eng">Materials Today: Proceedings</parentTitle>
    <identifier type="doi">10.1016/j.matpr.2020.05.799</identifier>
    <enrichment key="eventName">36th Danubia-Adria Symposium on Advances in Experimental Mechanics</enrichment>
    <enrichment key="eventPlace">Pilsen, Czech Republic</enrichment>
    <enrichment key="eventStart">24.09.2019</enrichment>
    <enrichment key="eventEnd">27.09.2019</enrichment>
    <enrichment key="date_peer_review">03.12.2020</enrichment>
    <author>Nicolas P. Winkler</author>
    <author>Patrick P. Neumann</author>
    <author>A. Säämänen</author>
    <author>E. Schaffernicht</author>
    <author>A. J. Lilienthal</author>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Mobile robot olfaction</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Air quality monitoring</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Wireless sensor network</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Gas distribution mapping</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Occupational health</value>
    </subject>
    <collection role="ddc" number="543">Analytische Chemie</collection>
    <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 im Netzwerk der BAM verfügbar ("Closed Access")</collection>
    <collection role="themenfelder" number="">Sensorik</collection>
  </doc>
  <doc>
    <id>53939</id>
    <completedYear/>
    <publishedYear>2021</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>1</pageFirst>
    <pageLast>4</pageLast>
    <pageNumber/>
    <edition/>
    <issue/>
    <volume/>
    <type>conferenceobject</type>
    <publisherName>IEEE</publisherName>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>1</belongsToBibliography>
    <completedDate>--</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Using Redundancy in a Sensor Network to Compensate Sensor Failures</title>
    <abstract language="eng">Wireless sensor networks provide occupational health experts with valuable information about the distribution of air pollutants in an environment. However, especially low-cost sensors may produce faulty measurements or fail completely. Consequently, not only spatial coverage but also redundancy should be a design criterion for the deployment of a sensor network. For a sensor network deployed in a steel factory, we analyze the correlations between sensors and build machine learning forecasting models, to investigate how well the sensor network can compensate for the outage of sensors. While our results show promising prediction quality of the models, they also indicate the presence of spatially very limited events. We, therefore, conclude that initial measurements with, e.g., mobile units, could help to identify important locations to design redundant sensor networks.</abstract>
    <parentTitle language="eng">2021 IEEE SENSORS</parentTitle>
    <identifier type="doi">10.1109/sensors47087.2021.9639479</identifier>
    <identifier type="isbn">978-1-7281-9501-8</identifier>
    <enrichment key="eventName">IEEE SENSORS 2021</enrichment>
    <enrichment key="eventPlace">Online meeting</enrichment>
    <enrichment key="eventStart">31.10.2021</enrichment>
    <enrichment key="eventEnd">04.11.2021</enrichment>
    <enrichment key="opus.source">publish</enrichment>
    <enrichment key="date_peer_review">28.02.2022</enrichment>
    <author>Nicolas P. Winkler</author>
    <author>Patrick P. Neumann</author>
    <author>E. Schaffernicht</author>
    <author>A. J. Lilienthal</author>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Environmental monitoring</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Wireless sensor network</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Sensor placement</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Machine learning</value>
    </subject>
    <collection role="ddc" number="543">Analytische Chemie</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 im Netzwerk der BAM verfügbar ("Closed Access")</collection>
    <collection role="themenfelder" number="">Sensorik</collection>
  </doc>
  <doc>
    <id>54955</id>
    <completedYear/>
    <publishedYear>2022</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">Super-Resolution for Gas Distribution Mapping: Convolutional Encoder-Decoder Network</title>
    <abstract language="eng">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.</abstract>
    <parentTitle language="eng">2022 IEEE International Symposium on Olfaction and Electronic Nose (ISOEN) Proceedings</parentTitle>
    <identifier type="isbn">978-1-6654-5860-3</identifier>
    <identifier type="doi">10.1109/isoen54820.2022.9789555</identifier>
    <enrichment key="eventName">19th International Symposium on Olfaction and Electronic Nose</enrichment>
    <enrichment key="eventPlace">Aveiro, Portugal</enrichment>
    <enrichment key="eventStart">29.05.2022</enrichment>
    <enrichment key="eventEnd">01.06.2022</enrichment>
    <enrichment key="opus.source">publish</enrichment>
    <enrichment key="date_peer_review">26.09.2022</enrichment>
    <author>Nicolas P. Winkler</author>
    <author>H. Matsukura</author>
    <author>Patrick P. Neumann</author>
    <author>E. Schaffernicht</author>
    <author>H. Ishida</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>Deep Learning</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Super-Resolution</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Sensor Network</value>
    </subject>
    <collection role="ddc" number="543">Analytische Chemie</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="fulltextaccess" number="">Datei im Netzwerk der BAM verfügbar ("Closed Access")</collection>
    <collection role="literaturgattung" number="">Graue Literatur</collection>
    <collection role="themenfelder" number="">Sensorik</collection>
  </doc>
  <doc>
    <id>55909</id>
    <completedYear/>
    <publishedYear>2022</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>1</pageFirst>
    <pageLast>2</pageLast>
    <pageNumber/>
    <edition/>
    <issue/>
    <volume/>
    <type>conferenceobject</type>
    <publisherName/>
    <publisherPlace>Athens, Greece</publisherPlace>
    <creatingCorporation>Greek Society of Experimental Mechanics of Materials</creatingCorporation>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>--</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Gather Dust and Get Dusted: Long-Term Drift and Cleaning of Sharp GP2Y1010AU0F Dust Sensor in a Steel Factory</title>
    <abstract language="eng">The Sharp GP2Y1010AU0F is a widely used low-cost dust sensor, but despite its popularity, the manufacturer provides little information on the sensor. We installed 16 sensing nodes with Sharp dust sensors in a hot rolling mill of a steel factory. Our analysis shows a clear correlation between sensor drift and accumulated production of the steel factory. An eye should be kept on the long-term drift of the sensors to prevent early saturation. Two of 16 sensors experienced full saturation, each after around eight and ten months of operation.</abstract>
    <parentTitle language="eng">38th Danubia-Adria Symposium on Advances in Experimental Mechanics - Extended abstracts</parentTitle>
    <enrichment key="eventName">38th Danubia-Adria Symposium on Advances in Experimental Mechanics</enrichment>
    <enrichment key="eventPlace">Poros, Greece</enrichment>
    <enrichment key="eventStart">20.09.2022</enrichment>
    <enrichment key="eventEnd">23.09.2022</enrichment>
    <enrichment key="opus.source">publish</enrichment>
    <author>Nicolas P. Winkler</author>
    <author>Patrick P. Neumann</author>
    <author>E. Schaffernicht</author>
    <author>A. J. Lilienthal</author>
    <author>M. Poikkimäki</author>
    <author>A. Kangas</author>
    <author>A. Säämänen</author>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Low-cost</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Dust sensor</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Sensor network</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Sensor drift</value>
    </subject>
    <collection role="ddc" number="543">Analytische Chemie</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="fulltextaccess" number="">Datei im Netzwerk der BAM verfügbar ("Closed Access")</collection>
    <collection role="literaturgattung" number="">Graue Literatur</collection>
    <collection role="themenfelder" number="">Sensorik</collection>
  </doc>
  <doc>
    <id>56414</id>
    <completedYear/>
    <publishedYear>2023</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>178</pageFirst>
    <pageLast>188</pageLast>
    <pageNumber/>
    <edition/>
    <issue/>
    <volume/>
    <type>conferenceobject</type>
    <publisherName>Springer International Publishing</publisherName>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>1</belongsToBibliography>
    <completedDate>--</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Learning From the Past: Sequential Deep Learning for Gas Distribution Mapping</title>
    <abstract language="eng">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.</abstract>
    <parentTitle language="eng">ROBOT2022: Fifth Iberian Robotics Conference</parentTitle>
    <identifier type="isbn">978-3-031-21061-7</identifier>
    <identifier type="doi">10.1007/978-3-031-21062-4_15</identifier>
    <enrichment key="eventName">ROBOT2022: Fifth Iberian Robotics Conference</enrichment>
    <enrichment key="eventPlace">Zaragoza, Spain</enrichment>
    <enrichment key="eventStart">22.11.2022</enrichment>
    <enrichment key="eventEnd">25.11.2022</enrichment>
    <enrichment key="opus.source">publish</enrichment>
    <enrichment key="date_peer_review">27.02.2023</enrichment>
    <author>Nicolas P. Winkler</author>
    <author>O. Kotlyar</author>
    <author>E. Schaffernicht</author>
    <author>H. Fan</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>Sequential Learning</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Convolutional LSTM</value>
    </subject>
    <collection role="ddc" number="543">Analytische Chemie</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 im Netzwerk der BAM verfügbar ("Closed Access")</collection>
    <collection role="themenfelder" number="">Sensorik</collection>
  </doc>
  <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>64501</id>
    <completedYear/>
    <publishedYear>2025</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>1</pageFirst>
    <pageLast>4</pageLast>
    <pageNumber/>
    <edition/>
    <issue/>
    <volume/>
    <type>conferenceobject</type>
    <publisherName>IEEE</publisherName>
    <publisherPlace>Piscataway, NJ, USA</publisherPlace>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>--</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Heterogeneous Sensor Networks: Challenges and Insights from an Industrial Scenario</title>
    <abstract language="eng">Monitoring airborne pollutants is critical for occupational health, particularly in industrial environments where workers are exposed to hazardous emissions. Traditional measurements are typically limited to single-day campaigns, resulting in extremely sparse temporal data. Low-cost sensor networks offer a way to increase spatial and temporal resolution but are limited by issues of accuracy and reliability. To address this, we present a wireless heterogeneous sensor network that integrates low-cost stationary nodes with high-quality sensors on mobile platforms, including ground and aerial robots. We deploy this system in a hot rolling mill facility and evaluate its performance under real-world conditions. Field experiments reveal dynamic pollutant patterns, such as altitude-dependent PM2.5 gradients and temperature fluctuations. By introducing synchronized “rendezvous” events between mobile and stationary nodes, we demonstrate correlation capabilities of sensors. Our spatiotemporal analysis shows that, despite limitations of mobile sensing, strategically combining heterogeneous data sources enables capturing pollutant dynamics in complex industrial settings.</abstract>
    <parentTitle language="eng">2025 IEEE SENSORS Proceedings</parentTitle>
    <identifier type="isbn">979-8-3315-4467-6</identifier>
    <enrichment key="eventName">IEEE SENSORS 2025</enrichment>
    <enrichment key="eventPlace">Vancouver, BC, Kanada</enrichment>
    <enrichment key="eventStart">19.10.2025</enrichment>
    <enrichment key="eventEnd">22.10.2025</enrichment>
    <enrichment key="opus.source">publish</enrichment>
    <author>Nicolas P. Winkler</author>
    <author>Patrick P. Neumann</author>
    <author>E. Schaffernicht</author>
    <author>A. J. Lilienthal</author>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Environmental monitoring</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Sensor data fusion</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Sensor system networks</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Mobile robotics</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 im Netzwerk der BAM verfügbar ("Closed Access")</collection>
    <collection role="themenfelder" number="">Sensorik</collection>
  </doc>
</export-example>
