TY - CONF 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 UR - https://opus4.kobv.de/opus4-rosenheim/frontdoor/index/index/docId/2442 SP - 1 EP - 6 ER -