TY - CHAP A1 - Wohlschläger, Maximilian A1 - Leiter, Nina A1 - Dietlmeier, Maximilian A1 - Löder, Martin G.J. A1 - Versen, Martin A1 - Laforsch, Christian T1 - Comparison of Two Classification Methods Trained with FD-FLIM Data to Identify and Distinguish Plastics from Environmental Materials T2 - 2023 International Joint Conference on Neural Networks (IJCNN) N2 - 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. KW - Fluorescence KW - Microscopy KW - Plastics KW - FD-FLIM KW - Neural networks KW - Environment KW - Frequency-domain analysis KW - Gaussian analysis KW - MLP KW - Multilayer perceptrons KW - Random forests KW - RFC Y1 - 2023 U6 - https://doi.org/10.1109/IJCNN54540.2023.10191054 SP - 1 EP - 9 ER - TY - CHAP A1 - Leiter, Nina A1 - Dietlmeier, Maximilian A1 - Wohlschläger, Maximilian A1 - Löder, Martin G.J. A1 - Versen, Martin A1 - Laforsch, Christian T1 - Development of a Neural Network for Automatic Classification of Post-Consumer Wood Using Rapid-FLIM T2 - 2023 IEEE Sensors Applications Symposium (SAS) N2 - 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. KW - fluorescence KW - Fluorescence KW - Microscopy KW - Neural networks KW - MLP KW - Current measurement KW - Feature extraction KW - Loss measurement KW - Post-consumer wood classification KW - Rapid-FLIM KW - Rapid-Fluorescence Lifetime Imaging Microscopy KW - Time measurement Y1 - 2023 U6 - https://doi.org/10.1109/SAS58821.2023.10254174 SP - 1 EP - 6 ER - TY - CHAP A1 - Leiter, Nina A1 - Wohlschläger, Maximilian A1 - Dietlmeier, Maximilian A1 - Versen, Martin A1 - Löder, Martin A1 - Laforsch, Christian T1 - Comparative Analysis of Fluorescence Properties of Post-Consumer Wood Using FD-FLIM T2 - 2023 IEEE Sensors Applications Symposium (SAS) N2 - 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. KW - fluorescence KW - Fluorescence KW - Neural networks KW - fluorescence properties KW - Moisture KW - post-consumer wood KW - Sensors KW - Training KW - Uncertainty KW - Visualization KW - waste wood Y1 - 2023 U6 - https://doi.org/10.1109/SAS58821.2023.10254052 SP - 1 EP - 6 ER - TY - CHAP A1 - Dietlmeier, Maximilian A1 - Rajan, Aromal Somarajan A1 - Leiter, Nina A1 - Wohlschläger, Maximilian A1 - Versen, Martin T1 - Klassifizierung von Altholz durch FD-FLIM Bilder mit neuronalen Netzen T2 - Tagungsband AALE 2023 N2 - 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. Y1 - 2023 U6 - https://doi.org/10.33968/2023.24 ER - TY - CHAP A1 - Thomas, Amal A1 - Saleem, Shaif A1 - Leiter, Nina A1 - Dietlmeier, Maximilian A1 - Wohlschläger, Maximilian A1 - Versen, Martin A1 - Laforsch, Christian T1 - Development of a Neural Network for an Automated Differentiation of Plastics using Rapid-FLIM T2 - Optica Sensing Congress 2023 N2 - 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%. KW - Fluorescence lifetime imaging KW - Phase shift KW - Laser sources KW - Neural networks KW - Positron emission tomography KW - Raman spectroscopy Y1 - 2023 U6 - https://doi.org/10.1364/ES.2023.EW4E.5 ER -