TY - JOUR A1 - Kubinger, Wilfried A1 - Sommer, Roland T1 - Industrie 4.0 - Auswirkungen von Digitalisierung und Internet auf den Industriestandort JF - e&i Elektrotechnik und Informationstechnik KW - Industry 4.0 KW - Digitalisation KW - Industry Location Y1 - 2018 VL - 133 IS - 7 SP - 330 EP - 333 ER - TY - JOUR A1 - Otrebski, Richard A1 - Rauer, Johannes A1 - Engelhardt-Nowitzki, Corinna A1 - Kryvinska, Natalia A1 - Aburaia, Mohamed A1 - Pospisil, Dominik T1 - Flexibility Enhancements in Digital Manufacturing by means of Ontological Data Modeling JF - International Journal of Ubiquitous Systems and Pervasive Networks (JUSPN) KW - Communication Modeling KW - Data Modeling KW - Digital Manufacturing Y1 - 2020 IS - Volume 12, Issue 2 ER - TY - JOUR A1 - Hu, Qingxi A1 - Feng, Di A1 - Zhang, Haiguang A1 - Yao, Yuan A1 - Aburaia, Mohamed A1 - Lammer, Herfried T1 - Oriented to Multi-Branched Structure Unsupported 3D Printing Method Research JF - Materials, vol. 13, no. 9, p. 2023, Apr. 2020 KW - Structure KW - 3D KW - Printing Y1 - 2020 ER - TY - JOUR A1 - Engelhardt-Nowitzki, Corinna A1 - Aburaia, Mohamed A1 - Rauer, Johannes T1 - Research-based teaching in digital manufacturing and robotics - the Digital Factory at the UAS Technikum Wien as an exemplary case JF - CLF2020 TU Graz KW - Digital Manufacturing KW - Robotics Y1 - 2020 ER - TY - JOUR A1 - Aburaia, Mohamed A1 - Stuja, Kemajl A1 - Markl, Erich T1 - Design and control of 4 axis additive manufactured robot using software tools JF - Procedia Engineering 100 KW - Robotics Y1 - ER - TY - JOUR A1 - Aburaia, Mohamed T1 - Digitale Fabrik JF - So funktioniert Wirtschaft. Ein Sachbuch für Jugendliche KW - Digital Factory KW - Automation Y1 - 2018 ER - TY - JOUR A1 - Deluca, Marco A1 - Bermejo Moratinos, Raúl A1 - Grünbichler, Hannes A1 - Pressler, Volker A1 - Danzer, Robert A1 - Nickel, Klaus G. T1 - Raman spectroscopy for the investigation of indentation-induced domain texturing in lead zirconate titanate piezoceramics JF - Scripta materialia KW - Materials Y1 - 2019 IS - 63(2) SP - 343 EP - 346 ER - TY - JOUR A1 - Grünbichler, Hannes A1 - Kreith, Josef A1 - Bermejo Moratinos, Raúl A1 - Supancic, Peter A1 - Danzer, Robert T1 - Modelling of the ferroic material behaviour of piezoelectrics: Characterisation of temperature-sensitive functional properties JF - Journal of the European Ceramic Society KW - Materials Y1 - 2019 IS - 30 SP - 249 EP - 254 ER - TY - JOUR A1 - Bermejo Moratinos, Raúl A1 - Grünbichler, Hannes A1 - Kreith, Josef A1 - Auer, Christoph T1 - Fracture resistance of a doped PZT ceramic for multilayer piezoelectric actuators: Effect of mechanical load and temperature JF - Journal of the European Ceramic Society KW - Materials Y1 - 2019 IS - 30 SP - 705 EP - 712 ER - TY - JOUR A1 - Schwaab, Holger A1 - Grünbichler, Hannes A1 - Supancic, Peter A1 - Kamlah, Marc T1 - Macroscopical non-linear material model for ferroelectric materials inside a hybrid finite element formulation JF - International Journal of Solids and Structures KW - Ferroelectricity KW - Piezoceramics KW - Materials Y1 - 2019 IS - 49 SP - 457 EP - 469 ER - TY - JOUR A1 - Bermejo Moratinos, Raul A1 - Grünbichler, Hannes A1 - Lube, Tanja A1 - Supancic, Peter A1 - Danzer, Robert A1 - Sestakova, Lucie T1 - Fracture Mechanisms of Structural and Functional Multilayer Ceramic Structures JF - Key engineering materials KW - Materials KW - Multilayer Architecture Y1 - 2019 VL - 2011 IS - Vol. 465 SP - 41 EP - 46 ER - TY - JOUR A1 - Grünbichler, Hannes A1 - Kreith, Josef A1 - Bermejo Moratinos, Raúl A1 - Krautgasser, Clemens A1 - Supancic, Peter T1 - Influence of the Load Dependent Material Properties on the Performance of Multilayer Piezoelectric Actuators JF - IUTAM Bookseries (24), Springer KW - Materials Y1 - 2019 VL - 2011 IS - 24 SP - 243 EP - 253 ER - TY - JOUR A1 - Kubinger, Wilfried A1 - Peschak, Bernhard A1 - Wöber, Wilfried A1 - Sulz, Clemens T1 - Bildgebende Sensorsystems für robotische Systeme in der Agrar- und Landtechnik JF - e&i Elektrotechnik und Informationstechnik KW - Sensor KW - Robotics KW - Agriculture Y1 - 2018 VL - 134 IS - 6 SP - 316 EP - 322 ER - TY - JOUR A1 - Wöber, Wilfried A1 - Rauer, Johannes A1 - Papa, Maximilian A1 - Aburaia, Ali A1 - Schwaiger, Simon A1 - Novotny, Georg A1 - Aburaia, Mohamed A1 - Kubinger, Wilfried T1 - Evaluierung von Navigationsmethoden für mobile Roboter JF - e & i Elektrotechnik und Informationstechnik KW - Robotics KW - Machine Learning KW - Industry 4.0 Y1 - 2020 ER - TY - JOUR A1 - Stadler, Philipp A1 - Blöschl, Günter A1 - Vogl, Wolfgang A1 - Koschelnik, Juri A1 - Epp, Markus, A1 - Lackner, Maximilian A1 - Oismüller, Markus A1 - Kumpan, Monika A1 - Nemeth, Lukas, A1 - Strauss, Peter A1 - Sommer, Regina A1 - Ryzinska-Paier, Gabriela A1 - Farnleitner, Andras A1 - Zessner, Matthias T1 - Real-time monitoring of beta-D-glucuronidase activity in sediment laden streams: A comparison of prototypes JF - Real-time monitoring of beta-D-glucuronidase activity in sediment laden streams KW - Enzymes KW - Water quality Y1 - 2018 ER - TY - JOUR A1 - Engelhardt-Nowitzki, Corinna A1 - Aburaia, Mohamed A1 - Otrebski, Richard A1 - Rauer, Johannes A1 - Orsolits, Horst T1 - Research-based teaching in Digital Manufacturing and Robotics – the Digital Factory at the UAS Technikum Wien as a Case Example JF - Procedia Manufacturing KW - Digital Factory KW - Virtual Reality KW - Robotics KW - Machine Learning Y1 - 2020 IS - Volume 45 SP - 164 EP - 170 ER - TY - JOUR A1 - Rauer, Johannes A1 - Aburaia, Mohamed A1 - Wöber, Wilfried T1 - Semi-Automatic Generation of Training Data for Neural Networks for 6D Pose Estimation and Robotic Graspin JF - Proceedings of Joint Austrian Computer Vision and Robotics Workshop 2020 KW - Robotics KW - Neural Networks Y1 - 2020 SP - 2 EP - 3 ER - TY - JOUR A1 - Engelhardt-Nowitzki, Corinna A1 - Aburaia, Mohamed A1 - Otrebski, Richard A1 - Rauer, Johannes A1 - Orsolits, Horst T1 - Research-based teaching in Digital Manufacturing and Robotics – the Digital Factory at the UAS Technikum Wien as a Case Example JF - Procedia Manuf KW - Teaching KW - Manufacturing KW - Digital Factory KW - Robotic Y1 - 2020 ER - TY - JOUR A1 - Aburaia, Mohamed A1 - Lackner, Maximilian A1 - Bucher, Michael A1 - Gonzalez-Gutierrez, Joamin A1 - Zhang, Haiguang A1 - Lammer, Herfried T1 - A Production Method for Standardized Continuous Fiber Reinforced FFF Filament JF - A Production Method for Standardized Continuous Fiber Reinforced FFF Filament,” vol. 4, no. 1, p. 12, 2020 KW - Production KW - Fiber KW - FFF KW - Filament Y1 - 2020 ER - TY - JOUR A1 - Tomic, Dana Kathrin A1 - Drenjanac, Domagoj A1 - Lazendic, Goran A1 - Hörmann, Sandra A1 - Handler, Franz A1 - Wöber, Wilfried A1 - Aschauer, Christian A1 - Auer, Wolfgang T1 - Semantische Technologien für Produktionsprozessinnovationen in der Landwirtschaft JF - e&i Elektrotechnik und Informationstechnik KW - Semantics KW - Agriculture KW - Production Process Y1 - 2019 VL - 131 IS - 7 SP - 223 EP - 229 ER - TY - JOUR A1 - Tomic, Dana Kathrin A1 - Drenjanac, Domagoj A1 - Lazendic, Goran A1 - Hörmann, Sandra A1 - Handler, Franz A1 - Wöber, Wilfried A1 - Aschauer, Christian A1 - Auer, Wolfgang T1 - Ontologies and semantic services for process optimization in agricultural production JF - e&i Elektrotechnik und Informationstechnik KW - Semantics KW - Production Process KW - Innovation KW - Agriculture Y1 - 2019 ER - TY - JOUR A1 - Kamravamanesh, Donya A1 - Pflügl, Stefan A1 - Nischkauer, Winfried A1 - Limbeck, Andreas A1 - Lackner, Maximilian A1 - Herwig, Christoph T1 - Photosynthetic poly-β-hydroxybutyrate accumulation in unicellular cyanobacterium Synechocystis sp. PCC 6714 JF - AMB Express KW - Bacteria Y1 - 2018 VL - 143 IS - 7 ER - TY - JOUR A1 - Spitzer-Sonnleitner, Birgit A1 - Kempe, Andre A1 - Lackner, Maximilian T1 - Influence of halide solutions on collagen networks - measurements of physical properties by atomic force microscopy (AFM) JF - Influence of halide solutions on collagen networks KW - Collagen Networks KW - Microscopy Y1 - 2018 ER - TY - JOUR A1 - Wöber, Wilfried A1 - Mehnen, Lars A1 - Curto, Manuel A1 - Dias Tibihika, Papius A1 - Tesfaye, Genanaw A1 - Meimberg, Harald T1 - Investigating Shape Variation Using Generalized Procrustes Analysis and Machine Learning JF - Applied Sciences N2 - Abstract: The biological investigation of a population’s shape diversity using digital images is typi- cally reliant on geometrical morphometrics, which is an approach based on user-defined landmarks. In contrast to this traditional approach, the progress in deep learning has led to numerous applications ranging from specimen identification to object detection. Typically, these models tend to become black boxes, which limits the usage of recent deep learning models for biological applications. However, the progress in explainable artificial intelligence tries to overcome this limitation. This study compares the explanatory power of unsupervised machine learning models to traditional landmark-based approaches for population structure investigation. We apply convolutional autoencoders as well as Gaussian process latent variable models to two Nile tilapia datasets to investigate the latent structure using consensus clustering. The explanatory factors of the machine learning models were extracted and compared to generalized Procrustes analysis. Hypotheses based on the Bayes factor are formulated to test the unambiguity of population diversity unveiled by the machine learning models. The findings show that it is possible to obtain biologically meaningful results relying on unsupervised machine learning. Furthermore we show that the machine learning models unveil latent structures close to the true population clusters. We found that 80% of the true population clusters relying on the convolutional autoencoder are significantly different to the remaining clusters. Similarly, 60% of the true population clusters relying on the Gaussian process latent variable model are significantly different. We conclude that the machine learning models outperform generalized Procrustes analysis, where 16% of the population cluster was found to be significantly different. However, the applied machine learning models still have limited biological explainability. We recommend further in-depth investigations to unveil the explanatory factors in the used model. Keywords: generalized procrustes analysis; machine learning; convolutional autoencoder; Gaussian process latent variable models KW - generalized procrustes analysis KW - machine learning KW - convolutional autoencoder KW - Gaussian process latent variable models Y1 - VL - 2022 IS - 12(6), 3158 ER - TY - JOUR A1 - Wöber, Wilfried A1 - Curto, Manuel A1 - Tibihika, Papius D. A1 - Meulenboek, Paul A1 - Alemayehu, Esayas A1 - Mehnen, Lars A1 - Meimberg, Harald A1 - Sykacek, Peter T1 - Identifying geographically differentiated features of Ethopian Nile tilapia (Oreochromis niloticus) morphology with machine learning JF - PlosONE KW - Machine Learning Y1 - VL - 16 IS - 4 ER - TY - JOUR A1 - Sattinger, Vinzenz A1 - Papa, Maximilian A1 - Stuja, Kemajl A1 - Kubinger, Wilfried T1 - Methodik zur Entwicklung sicherer kollaborativer Produktionssysteme im Rahmen von Industrie 4.0 JF - e & i Elektrotechnik und Informationstechnik KW - Robotics KW - Industry 4.0 Y1 - ER - TY - JOUR A1 - Wöber, Wilfried A1 - Mehnen, Lars A1 - Sykacek, Peter A1 - Meimberg, Harald T1 - Investigating Explanatory Factors of Machine Learning Models for Plant Classification JF - Plants N2 - Recent progress in machine learning and deep learning has enabled the implementation of plant and crop detection using systematic inspection of the leaf shapes and other morphological characters for identification systems for precision farming. However, the models used for this approach tend to become black-box models, in the sense that it is difficult to trace characters that are the base for the classification. The interpretability is therefore limited and the explanatory factors may not be based on reasonable visible characters. We investigate the explanatory factors of recent machine learning and deep learning models for plant classification tasks. Based on a Daucus carota and a Beta vulgaris image data set, we implement plant classification models and compare those models by their predictive performance as well as explainability. For comparison we implemented a feed forward convolutional neuronal network as a default model. To evaluate the performance, we trained an unsupervised Bayesian Gaussian process latent variable model as well as a convolutional autoencoder for feature extraction and rely on a support vector machine for classification. The explanatory factors of all models were extracted and analyzed. The experiments show, that feed forward convolutional neuronal networks (98.24% and 96.10% mean accuracy) outperforms the Bayesian Gaussian process latent variable pipeline (92.08% and 94.31% mean accuracy) as well as the convolutional autoenceoder pipeline (92.38% and 93.28% mean accuracy) based approaches in terms of classification accuracy, even though not significant for Beta vulgaris images. Additionally, we found that the neuronal network used biological uninterpretable image regions for the plant classification task. In contrast to that, the unsupervised learning models rely on explainable visual characters. We conclude that supervised convolutional neuronal networks must be used carefully to ensure biological interpretability. We recommend unsupervised machine learning, careful feature investigation, and statistical feature analysis for biological applications. View Full-Text Keywords: deep learning; machine learning; plant leaf morphometrics; explainable AI KW - deep learning KW - machine learning KW - plant leaf morphometrics KW - explainable AI Y1 - VL - 2021 IS - 10(12):2674 ER -