@inproceedings{LeiterDietlmeierWohlschlaegeretal.2023, author = {Leiter, Nina and Dietlmeier, Maximilian and Wohlschl{\"a}ger, Maximilian and L{\"o}der, Martin G.J. and Versen, Martin and Laforsch, Christian}, title = {Development of a Neural Network for Automatic Classification of Post-Consumer Wood Using Rapid-FLIM}, series = {2023 IEEE Sensors Applications Symposium (SAS)}, booktitle = {2023 IEEE Sensors Applications Symposium (SAS)}, doi = {10.1109/SAS58821.2023.10254174}, pages = {1 -- 6}, year = {2023}, abstract = {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.}, language = {en} } @inproceedings{WohlschlaegerLeiterDietlmeieretal.2023, author = {Wohlschl{\"a}ger, Maximilian and Leiter, Nina and Dietlmeier, Maximilian and L{\"o}der, Martin G.J. and Versen, Martin and Laforsch, Christian}, title = {Comparison of Two Classification Methods Trained with FD-FLIM Data to Identify and Distinguish Plastics from Environmental Materials}, series = {2023 International Joint Conference on Neural Networks (IJCNN)}, booktitle = {2023 International Joint Conference on Neural Networks (IJCNN)}, doi = {10.1109/IJCNN54540.2023.10191054}, pages = {1 -- 9}, year = {2023}, abstract = {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.}, language = {en} }