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  <doc>
    <id>2267</id>
    <completedYear>2023</completedYear>
    <publishedYear/>
    <thesisYearAccepted/>
    <language>deu</language>
    <pageFirst/>
    <pageLast/>
    <pageNumber/>
    <edition/>
    <issue/>
    <volume/>
    <type>conferenceobject</type>
    <publisherName/>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>--</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="deu">Klassifizierung von Altholz durch FD-FLIM Bilder mit neuronalen Netzen</title>
    <abstract language="deu">Holz ist eine der wichtigsten erneuerbaren Ressourcen und kann recycelt werden, wobei derzeit nur unbehandeltes Holz in mehreren Lebenszyklen verwendet werden kann. Allerdings werden in Altholzsortieranlagen meist behandeltes und unbehandeltes Altholz gemischt angeliefert und es gibt keine automatische Klassifizierung zur sortenreinen Sortierung. Frühere Studien zeigen, dass behandelte und unbehandelte Holzproben mit Hilfe von bildgebender Fluoreszenzabklingzeit Mikroskopie unterschieden werden können. Um den Prozess der Klassifizierung von Altholz anhand von Fluoreszenzabklingzeitbildern zu automatisieren, werden zwei neuronale Netzwerke (NN) evaluiert. Das erste NN ist ein MLP (Multi Layer Perceptron), das das Altholz anhand von Erwartungswert und Standardabweichung eines Fluoreszenzabklingzeitbildes klassifiziert. Das zweite untersuchte NN ist ein CNN (Convolutional Neural Network), das das Holz direkt anhand der Fluoreszenzabklingzeitbilder klassifiziert. Beide Netzwerke sind in Python implementiert, um die spätere Verwendung in einem automatischen Klassifizierungs- und Sortierprozess zu erleichtern. Die Evaluation der NN zeigt vielversprechende Ergebnisse zur automatisierten Klassifikation von Altholz.</abstract>
    <parentTitle language="deu">Tagungsband AALE 2023</parentTitle>
    <identifier type="doi">10.33968/2023.24</identifier>
    <enrichment key="opus.import.data">@inproceedingsdietlmeier_klassifizierung_2023, title = Klassifizierung von Altholz durch FD-FLIM Bilder mit neuronalen Netzen, doi = 10.33968/2023.24, abstract = Holz ist eine der wichtigsten erneuerbaren Ressourcen und kann recycelt werden, wobei derzeit nur unbehandeltes Holz in mehreren Lebenszyklen verwendet werden kann. Allerdings werden in Altholzsortieranlagen meist behandeltes und unbehandeltes Altholz gemischt angeliefert und es gibt keine automatische Klassifizierung zur sortenreinen Sortierung. Frühere Studien zeigen, dass behandelte und unbehandelte Holzproben mit Hilfe von bildgebender Fluoreszenzabklingzeit Mikroskopie unterschieden werden können. Um den Prozess der Klassifizierung von Altholz anhand von Fluoreszenzabklingzeitbildern zu automatisieren, werden zwei neuronale Netzwerke (NN) evaluiert. Das erste NN ist ein MLP (Multi Layer Perceptron), das das Altholz anhand von Erwartungswert und Standardabweichung eines Fluoreszenzabklingzeitbildes klassifiziert. Das zweite untersuchte NN ist ein CNN (Convolutional Neural Network), das das Holz direkt anhand der Fluoreszenzabklingzeitbilder klassifiziert. Beide Netzwerke sind in Python implementiert, um die spätere Verwendung in einem automatischen Klassifizierungs- und Sortierprozess zu erleichtern. Die Evaluation der NN zeigt vielversprechende Ergebnisse zur automatisierten Klassifikation von Altholz., language = ger, booktitle = Tagungsband AALE 2023, author = Dietlmeier, Maximilian and Rajan, Aromal Somarajan and Leiter, Nina and Wohlschläger, Maximilian and Versen, Martin, month = mar, year = 2023, note = ISBN: 9783910103016 Place: Luxemburg,</enrichment>
    <enrichment key="opus.import.dataHash">md5:1cd4b03d601770e66c7693df27327cf0</enrichment>
    <enrichment key="opus.import.date">2023-06-19T12:28:22+00:00</enrichment>
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    <enrichment key="opus.import.format">bibtex</enrichment>
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    <author>Maximilian Dietlmeier</author>
    <author>Aromal Somarajan Rajan</author>
    <author>Nina Leiter</author>
    <author>Maximilian Wohlschläger</author>
    <author>Martin Versen</author>
    <collection role="institutes" number="">Fakultät für Ingenieurwissenschaften</collection>
  </doc>
  <doc>
    <id>2434</id>
    <completedYear>2023</completedYear>
    <publishedYear/>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst/>
    <pageLast/>
    <pageNumber/>
    <edition/>
    <issue/>
    <volume/>
    <type>conferenceobject</type>
    <publisherName/>
    <publisherPlace/>
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    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>--</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Development of a Neural Network for an Automated Differentiation of Plastics using Rapid-FLIM</title>
    <abstract language="eng">The fast classification and identification of plastics presents a significant challenge. The study assesses the suitability of a Multilayer Perceptron to classify and identify commonly found plastic types using Rapid-FLIM, achieving an accuracy of 88.33%.</abstract>
    <parentTitle language="eng">Optica Sensing Congress 2023</parentTitle>
    <identifier type="doi">10.1364/ES.2023.EW4E.5</identifier>
    <enrichment key="opus.import.data">@inproceedingsamal_thomas_development_2023, title = Development of a Neural Network for an Automated Differentiation of Plastics using Rapid-FLIM, abstract = The fast classification and identification of plastics presents a significant challenge. The study assesses the suitability of a Multilayer Perceptron to classify and identify commonly found plastic types using Rapid-FLIM, achieving an accuracy of 88.33%., booktitle = Optica Sensing Congress 2023, author = Amal Thomas and Shaif Saleem and Nina Leiter and Maximilian Dietlmeier and Maximilian Wohlschläger and Martin Versen and Christian Laforsch, year = 2023, keywords = Fluorescence lifetime imaging, Phase shift, Laser sources, Neural networks, Positron emission tomography, Raman spectroscopy, pages = EW4E.5,</enrichment>
    <enrichment key="opus.import.dataHash">md5:9ebd3a369a146f94ee63f7840f2ee5a8</enrichment>
    <enrichment key="opus.import.date">2024-05-27T12:38:19+00:00</enrichment>
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    <enrichment key="opus.import.id">66547ebb787039.90003661</enrichment>
    <author>Amal Thomas</author>
    <author>Shaif Saleem</author>
    <author>Nina Leiter</author>
    <author>Maximilian Dietlmeier</author>
    <author>Maximilian Wohlschläger</author>
    <author>Martin Versen</author>
    <author>Christian Laforsch</author>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Fluorescence lifetime imaging</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Phase shift</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Laser sources</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Neural networks</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Positron emission tomography</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Raman spectroscopy</value>
    </subject>
  </doc>
  <doc>
    <id>2441</id>
    <completedYear>2023</completedYear>
    <publishedYear/>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>1</pageFirst>
    <pageLast>9</pageLast>
    <pageNumber/>
    <edition/>
    <issue/>
    <volume/>
    <type>conferenceobject</type>
    <publisherName/>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>--</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Comparison of Two Classification Methods Trained with FD-FLIM Data to Identify and Distinguish Plastics from Environmental Materials</title>
    <abstract language="eng">Previous research on identifying plastic types and differentiating plastics from environmental material is promising by utilizing the specific fluorescence lifetime, but the evaluation still has to be automated. Therefore, an automated Gaussian analysis is developed for evaluating frequency-domain fluorescence lifetime images of plastics and environmental materials. Furthermore, we applied a “Multilayer Perceptron” and “Random Forest Classifier” to the data resulting from the Gaussian analysis of the frequency domain fluorescence lifetime imaging microscopy data. The classification results show high F1-scores, whereby the best “Multilayer Perceptron” and “Random Forest Classifier” achieved an F1-score of 90%. Thus, identifying and differentiating plastics and environmental materials is possible by applying a “Multilayer Perceptron” or “Random Forest Classifier” to the Gaussian-analyzed imaged fluorescence lifetime data.</abstract>
    <parentTitle language="eng">2023 International Joint Conference on Neural Networks (IJCNN)</parentTitle>
    <identifier type="doi">10.1109/IJCNN54540.2023.10191054</identifier>
    <enrichment key="opus.import.data">@inproceedingswohlschlager_comparison_2023, title = Comparison of Two Classification Methods Trained with FD-FLIM Data to Identify and Distinguish Plastics from Environmental Materials, doi = 10.1109/IJCNN54540.2023.10191054, booktitle = 2023 International Joint Conference on Neural Networks (IJCNN), author = Wohlschläger, Maximilian and Leiter, Nina and Dietlmeier, Maximilian and Löder, Martin G.J. and Versen, Martin and Laforsch, Christian, year = 2023, keywords = Fluorescence, Microscopy, Plastics, FD-FLIM, Neural networks, Environment, Frequency-domain analysis, Gaussian analysis, MLP, Multilayer perceptrons, Random forests, RFC, pages = 1–9,</enrichment>
    <enrichment key="opus.import.dataHash">md5:e9b53ffef54730e6c949ed39d9e70201</enrichment>
    <enrichment key="opus.import.date">2024-05-27T12:38:19+00:00</enrichment>
    <enrichment key="opus.import.file">/tmp/php7IG1AD</enrichment>
    <enrichment key="opus.import.format">bibtex</enrichment>
    <enrichment key="opus.import.id">66547ebb787039.90003661</enrichment>
    <enrichment key="opus.doi.autoCreate">false</enrichment>
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    <author>Maximilian Wohlschläger</author>
    <author>Nina Leiter</author>
    <author>Maximilian Dietlmeier</author>
    <author>Martin G.J. Löder</author>
    <author>Martin Versen</author>
    <author>Christian Laforsch</author>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Fluorescence</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Microscopy</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Plastics</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>FD-FLIM</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Neural networks</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Environment</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Frequency-domain analysis</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Gaussian analysis</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>MLP</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Multilayer perceptrons</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Random forests</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>RFC</value>
    </subject>
  </doc>
  <doc>
    <id>2442</id>
    <completedYear>2023</completedYear>
    <publishedYear/>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>1</pageFirst>
    <pageLast>6</pageLast>
    <pageNumber/>
    <edition/>
    <issue/>
    <volume/>
    <type>conferenceobject</type>
    <publisherName/>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>--</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Development of a Neural Network for Automatic Classification of Post-Consumer Wood Using Rapid-FLIM</title>
    <abstract language="eng">The economic use of wood is a growing sector, not only because of the significant advantage of wood to retain CO 2 . It is crucial to increase the material recycling of wood in several lifecycles, but currently, there is no reliable post-consumer wood sorting technique in line. This research measures 365 post-consumer wood samples of classes A1-4 four times with the frequency domain fluorescence lifetime imaging microscopy (FD-FLIM) subset method rapid fluorescence lifetime imaging microscopy (Rapid-FLIM). The data is analyzed on their statistical features. Four neural networks based on Multilayer perceptron are then trained and tested with twelve statistical features extracted from the Rapid-FLIM images. The best model for this application contains the optimizer RMSprop, the activation function SELU and the loss function binary crossentropy. The best model of this structure could achieve a false positive ratio of 4.79 % over the ten folds.</abstract>
    <parentTitle language="eng">2023 IEEE Sensors Applications Symposium (SAS)</parentTitle>
    <identifier type="doi">10.1109/SAS58821.2023.10254174</identifier>
    <enrichment key="opus.import.data">@inproceedingsleiter_development_2023, title = Development of a Neural Network for Automatic Classification of Post-Consumer Wood Using Rapid-FLIM, doi = 10.1109/SAS58821.2023.10254174, booktitle = 2023 IEEE Sensors Applications Symposium (SAS), author = Leiter, Nina and Dietlmeier, Maximilian and Wohlschläger, Maximilian and Löder, Martin G.J. and Versen, Martin and Laforsch, Christian, year = 2023, keywords = fluorescence, Fluorescence, Microscopy, Neural networks, MLP, Current measurement, Feature extraction, Loss measurement, Post-consumer wood classification, Rapid-FLIM, Rapid-Fluorescence Lifetime Imaging Microscopy, Time measurement, pages = 01–06,</enrichment>
    <enrichment key="opus.import.dataHash">md5:32f666ffd4ce419dce9463823190d772</enrichment>
    <enrichment key="opus.import.date">2024-05-27T12:38:19+00:00</enrichment>
    <enrichment key="opus.import.file">/tmp/php7IG1AD</enrichment>
    <enrichment key="opus.import.format">bibtex</enrichment>
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    <author>Nina Leiter</author>
    <author>Maximilian Dietlmeier</author>
    <author>Maximilian Wohlschläger</author>
    <author>Martin G.J. Löder</author>
    <author>Martin Versen</author>
    <author>Christian Laforsch</author>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>fluorescence</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Fluorescence</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Microscopy</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Neural networks</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>MLP</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Current measurement</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Feature extraction</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Loss measurement</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Post-consumer wood classification</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Rapid-FLIM</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Rapid-Fluorescence Lifetime Imaging Microscopy</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Time measurement</value>
    </subject>
  </doc>
  <doc>
    <id>2443</id>
    <completedYear>2023</completedYear>
    <publishedYear/>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>1</pageFirst>
    <pageLast>6</pageLast>
    <pageNumber/>
    <edition/>
    <issue/>
    <volume/>
    <type>conferenceobject</type>
    <publisherName/>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>--</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Comparative Analysis of Fluorescence Properties of Post-Consumer Wood Using FD-FLIM</title>
    <abstract language="eng">The FD-FLIM technique has a high potential for automated post-consumer wood sorting. A problem of analysing post-consumer wood fluorescence properties is the uncertainty of the post-consumer wood category as the origin of the samples are unknown. In this study, the fluorescence properties of actual post-consumer wood is compared with prepared wood samples. The post-consumer wood samples display slightly different fluorescence intensities and lifetimes due to environmental influences and a higher sample diversity. For improved training of evaluation algorithms for post-consumer wood sorting, the prepared sample set should be extended or the post-consumer wood should be additionally analysed in the laboratory.</abstract>
    <parentTitle language="eng">2023 IEEE Sensors Applications Symposium (SAS)</parentTitle>
    <identifier type="doi">10.1109/SAS58821.2023.10254052</identifier>
    <enrichment key="opus.import.data">@inproceedingsleiter_comparative_2023, title = Comparative Analysis of Fluorescence Properties of Post-Consumer Wood Using FD-FLIM, doi = 10.1109/SAS58821.2023.10254052, booktitle = 2023 IEEE Sensors Applications Symposium (SAS), author = Leiter, Nina and Wohlschläger, Maximilian and Dietlmeier, Maximilian and Versen, Martin and Löder, Martin and Laforsch, Christian, year = 2023, keywords = fluorescence, Fluorescence, Neural networks, fluorescence properties, Moisture, post-consumer wood, Sensors, Training, Uncertainty, Visualization, waste wood, pages = 1–6,</enrichment>
    <enrichment key="opus.import.dataHash">md5:72865d2f2abde5cc0635ac4f1d2848f3</enrichment>
    <enrichment key="opus.import.date">2024-05-27T12:38:19+00:00</enrichment>
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    <enrichment key="opus.import.id">66547ebb787039.90003661</enrichment>
    <enrichment key="opus.doi.autoCreate">false</enrichment>
    <enrichment key="opus.urn.autoCreate">true</enrichment>
    <author>Nina Leiter</author>
    <author>Maximilian Wohlschläger</author>
    <author>Maximilian Dietlmeier</author>
    <author>Martin Versen</author>
    <author>Martin Löder</author>
    <author>Christian Laforsch</author>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>fluorescence</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Fluorescence</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Neural networks</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>fluorescence properties</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Moisture</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>post-consumer wood</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Sensors</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Training</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Uncertainty</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Visualization</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>waste wood</value>
    </subject>
  </doc>
  <doc>
    <id>1259</id>
    <completedYear>2019</completedYear>
    <publishedYear/>
    <thesisYearAccepted/>
    <language>deu</language>
    <pageFirst>179</pageFirst>
    <pageLast>187</pageLast>
    <pageNumber/>
    <edition/>
    <issue/>
    <volume/>
    <type>conferenceobject</type>
    <publisherName>VDE Verlag</publisherName>
    <publisherPlace>Berlin</publisherPlace>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>--</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="deu">Bestimmung einer Quanteneffizienz für das automatisierte Sortieren von Kunststoffen durch Fluoreszenz</title>
    <parentTitle language="deu">AALE 2019 - Autonome und intelligente Systeme in der Automatisierungstechnik 16. Fachkonferenz</parentTitle>
    <identifier type="isbn">978-3-8007-4860-0</identifier>
    <enrichment key="RS_Correlation">Ja</enrichment>
    <enrichment key="opus.doi.autoCreate">false</enrichment>
    <enrichment key="opus.urn.autoCreate">false</enrichment>
    <author>Maximilian Wohlschläger</author>
    <author>Martin Versen</author>
    <subject>
      <language>deu</language>
      <type>uncontrolled</type>
      <value>Quanteneffizienz</value>
    </subject>
    <subject>
      <language>deu</language>
      <type>uncontrolled</type>
      <value>Fluoreszenz</value>
    </subject>
    <collection role="institutes" number="">Fakultät für Ingenieurwissenschaften</collection>
    <thesisPublisher>Technische Hochschule Rosenheim</thesisPublisher>
  </doc>
  <doc>
    <id>1308</id>
    <completedYear>2019</completedYear>
    <publishedYear/>
    <thesisYearAccepted/>
    <language>deu</language>
    <pageFirst>687</pageFirst>
    <pageLast>694</pageLast>
    <pageNumber/>
    <edition/>
    <issue/>
    <volume/>
    <type>conferenceobject</type>
    <publisherName>AMA Service GmbH</publisherName>
    <publisherPlace>Wunstorf</publisherPlace>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>--</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="deu">Detektion von Kunststoffen in Wasser mithilfe von Fluoreszenz</title>
    <abstract language="deu">Die wohl größte aktuelle Umweltverschmutzung mit Kunststoffen findet in Ozeanen, Seen und Flüssen statt. Bis jetzt gibt es kein etabliertes Verfahren, mit dem Kunststoffe in Umwelt- bzw. Wassermatrizen detektiert werden können. Deshalb wird mit einem Verfahren, welches auf dem Prinzip der Fluoreszenz basiert, untersucht, ob unter Laborbedingungen Kunststoffe in Wasser detektiert und identifiziert werden können. Anhand eines mathematischen Modells, einer Simulation und mit Hilfe von optischen Experimenten wird gezeigt, dass der Nachweis und auch eine Identifikation von Kunststoffen in Abhängigkeit der Wassertiefe, mit diesem Verfahren möglich ist.</abstract>
    <parentTitle language="deu">20. GMA/ITG-Fachtagung Sensoren und Messsysteme 2019</parentTitle>
    <identifier type="isbn">978-3-9819376-0-2</identifier>
    <enrichment key="RS_Correlation">Ja</enrichment>
    <enrichment key="opus.doi.autoCreate">false</enrichment>
    <enrichment key="opus.urn.autoCreate">false</enrichment>
    <author>Maximilian Wohlschläger</author>
    <author>Martin Versen</author>
    <subject>
      <language>deu</language>
      <type>uncontrolled</type>
      <value>Kunststoffe</value>
    </subject>
    <subject>
      <language>deu</language>
      <type>uncontrolled</type>
      <value>Umwelt</value>
    </subject>
    <subject>
      <language>deu</language>
      <type>uncontrolled</type>
      <value>Fluoreszenzmesstechnik</value>
    </subject>
    <subject>
      <language>deu</language>
      <type>uncontrolled</type>
      <value>Fluoreszenz</value>
    </subject>
    <collection role="institutes" number="">Fakultät für Ingenieurwissenschaften</collection>
    <thesisPublisher>Technische Hochschule Rosenheim</thesisPublisher>
  </doc>
  <doc>
    <id>1324</id>
    <completedYear>2019</completedYear>
    <publishedYear/>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>186</pageFirst>
    <pageLast>189</pageLast>
    <pageNumber/>
    <edition/>
    <issue/>
    <volume/>
    <type>conferenceobject</type>
    <publisherName/>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>--</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">An Approach of Identifying Polymers With Fluorescence Lifetime Imaging</title>
    <abstract language="eng">Nowadays, fluorescence lifetime measurements and the method of fluorescence lifetime imaging are already state of the art in biomedical research. Also first investigations show that polymers could be identified due to their endogenous fluorescent lifetime. Thus an approach of identifying polymers with fluorescence lifetime imaging is done in this contribution. Therefore, four different polymers are examined and evaluated with statistical methods in order to determine their specific fluorescence lifetimes.</abstract>
    <parentTitle language="eng">2019 IEEE International Conference on Electrical Engineering and Photonics (EExPolytech)</parentTitle>
    <identifier type="old">DOI: 10.1109/EExPolytech.2019.8906800</identifier>
    <enrichment key="opus.doi.autoCreate">false</enrichment>
    <enrichment key="opus.urn.autoCreate">true</enrichment>
    <author>Maximilian Wohlschläger</author>
    <author>Martin Versen</author>
    <author>Gerhard Holst</author>
    <author>Robert Franke</author>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>polymer</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>fluorescence</value>
    </subject>
    <thesisPublisher>Technische Hochschule Rosenheim</thesisPublisher>
  </doc>
  <doc>
    <id>1300</id>
    <completedYear>2019</completedYear>
    <publishedYear/>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst/>
    <pageLast/>
    <pageNumber/>
    <edition/>
    <issue/>
    <volume/>
    <type>conferenceobject</type>
    <publisherName/>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>--</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">A method for sorting of plastics with an apparatus specific quantum efficiency approach</title>
    <abstract language="eng">Today, the automatic separation of polymers from each other in an industrial scale is an unsolved problem. In laboratory environments, two methods are known whereby plastic is sorted either by color or by fluorescence decay time measurements that require fast synchronization and thus expensive equipment. A simple and pragmatic process is proposed to separate plastics from each other: all fluorescent photons are counted in relation to the absorbed photons. A theoretical model and an experimental setup are built in order to determine an apparatus specific quantum efficiency.</abstract>
    <parentTitle language="eng">2019 IEEE Sensors Applications Symposium (SAS)</parentTitle>
    <identifier type="url">https://ieeexplore.ieee.org/document/8706034</identifier>
    <enrichment key="RS_Correlation">Ja</enrichment>
    <author>Maximilian Wohlschläger</author>
    <author>Martin Versen</author>
    <author>H. Langhals</author>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>polymer</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>plastic sorting</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>fluorescence</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>quantum efficiency</value>
    </subject>
    <collection role="institutes" number="">Fakultät für Ingenieurwissenschaften</collection>
    <thesisPublisher>Technische Hochschule Rosenheim</thesisPublisher>
  </doc>
  <doc>
    <id>1491</id>
    <completedYear>2020</completedYear>
    <publishedYear/>
    <thesisYearAccepted/>
    <language>deu</language>
    <pageFirst>223</pageFirst>
    <pageLast>231</pageLast>
    <pageNumber/>
    <edition/>
    <issue/>
    <volume/>
    <type>conferenceobject</type>
    <publisherName>VDE Verlag Berlin</publisherName>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>--</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="deu">Automatische Erkennung von Kunststoffen durch bildgebende Fluoreszenzabklingzeitmessung</title>
    <parentTitle language="deu">Tagungsband: AALE 2020 - Automatisierung und Mensch-Technik-Interaktion 17. Fachkonferenz</parentTitle>
    <identifier type="isbn">978-3-8007-5180-8</identifier>
    <enrichment key="RS_Correlation">Ja</enrichment>
    <author>Maximilian Wohlschläger</author>
    <author>Gerhard Holst</author>
    <author>Martin Versen</author>
    <subject>
      <language>deu</language>
      <type>uncontrolled</type>
      <value>Erkennung von Kunststoffen</value>
    </subject>
    <subject>
      <language>deu</language>
      <type>uncontrolled</type>
      <value>Fluoreszenzabklingzeitmessung</value>
    </subject>
    <collection role="ddc" number="620">Ingenieurwissenschaften und zugeordnete Tätigkeiten</collection>
    <collection role="institutes" number="">Fakultät für Ingenieurwissenschaften</collection>
    <thesisPublisher>Technische Hochschule Rosenheim</thesisPublisher>
  </doc>
</export-example>
