TY - CHAP A1 - Wöber, Wilfried A1 - Novotny, Georg A1 - Aburaia, Mohamed A1 - Otrebski, Richard A1 - Kubinger, Wilfried T1 - Estimating a Sparse Representation of Gaussian Processes Using Global Optimization and the Bayesian Information Criterion T2 - Austrian Robotics Workshop 2018 KW - Mobile Robotics KW - Localizations KW - Gaussian process KW - Robotics Y1 - ER - TY - CHAP A1 - Dannereder, Florian A1 - Engelhardt-Nowitzki, Corinna A1 - Markl, Erich A1 - Lackner, Maximilian A1 - Kreith, Josef A1 - Pachschwöll, Paul Herwig A1 - Aburaia, Mohamed A1 - Shooman, Diane T1 - Development of a 3D-Printed Bionic Hand with Muscle- and Force Control T2 - Proceedings of the Austrian Robotics Workshop 2018 KW - Robotics Y1 - ER - TY - CHAP A1 - Wöber, Wilfried A1 - Schulmeister, Klemens A1 - Aschauer, Christian A1 - Gronauer, Andreas A1 - Tomic, Dana Kathrin A1 - Fensel, Anna A1 - Riegler, Thomas A1 - Handler, Franz A1 - Hörmann, Sandra A1 - Otte, Marcel A1 - Auer, Wolfgang T1 - Adaptive Agricultural Processes via Open Interfaces and Linked Services T2 - Referate der 34. GIL-Jahrestagung - IT-Standards in der Agrar- und Ernährungswissenschaft KW - Adaption KW - Agriculture Y1 - 2019 SP - 157 EP - 160 ER - TY - BOOK A1 - Orsolits, Horst A1 - Lackner, Maximilian ED - Lackner, Maximilian ED - Orsolits, Horst T1 - Virtual Reality und Augmented Reality in der Digitalen Produktion KW - Virtual Reality KW - Production KW - Augmented Reality Y1 - SN - 978-3-658-29009-2 PB - Springer Gabler ER - TY - CHAP A1 - Gonzalez-Gutierrez, Joamin A1 - Treitler, Manuel A1 - Spoerk, Martin A1 - Arbeiter, Florian A1 - Schuschnigg, Stephan A1 - Lammer, Herfried A1 - Lackner, Maximilian A1 - Aburaia, Mohamed A1 - Poszvek, Günther A1 - Zhang, Haiguang A1 - Sapkota, Janak A1 - Holzer, Clemes T1 - Carbon fiber reinforced thermoplastics for material extrusion additive manufacturing T2 - Conference proceedings of 35th International Conference of the Polymer Processing Society N2 - In an effort to broaden the engineering applications of material extrusion based additive manufacturing (MEAM), new materials are being developed. Adding carbon-fibers (CF) has been one strategy to increase the mechanical performance of different thermoplastics. One challenge is to determine the amount of CF needed to increase the mechanical performance without affecting the “printability” of the compounds. In this paper, different amounts (10, 15, and 20 vol.%) of CF were added to recycled polypropylene (rPP) and polyamide 12 (PA12). A compatibilizer was used for rPP, but not for PA12. Filaments for MEAM were extruded from the different compounds and the viscosity as well as the tensile properties were measured and compared to the processed polymeric matrices. It was observed that the viscosities at the angular frequencies relevant for MEAM (100 to 200 rad/s) were not significantly different for rPP+CF compounds, but it was higher for PA12+CF compounds. As expected, the elongation at break significantly decreased with the addition of CF for all compounds. For the composites with an rPP matrix, the Young’s modulus and the ultimate tensile strength (UTS) continuously increased as the CF content increased to 20 vol.%. For PA12-based materials, the Young’s modulus and the UTS increased with CF content, but adding more than 15 vol.% did not further improve these values. Therefore, it was concluded that for PA12 the maximum amount of CF that should be added was 15 vol.%. Using scanning electron microscopy, it was observed that the CF were homogeneously dispersed in the rPP matrix, but not so well in the PA12 matrix, with fibers being more concentrated towards the rim of the filament. Finally, filaments of rPP, rPP+20CF, PA12 and PA12+15CF were used to print complex geometries by means of MEAM, and it was observed that CF helped to reduce the warpage compared to the unfilled filaments. A potential application of this phenomenon could be the reduction of the bed temperature to develop a more energy efficient MEAM process for semi-crystalline polymers. KW - Additive Manufacturing KW - Materials Y1 - ER - TY - CHAP A1 - Rauer, Johannes A1 - Wöber, Wilfried A1 - Aburaia, Mohamed T1 - An Autonomous Mobile Handling Robot Using Object Recognition T2 - Proceedings of ARW & OAGM Workshop 2019 KW - Mobile Robotics Y1 - 2020 N1 - https://workshops.aapr.at/wp-content/uploads/2019/05/ARW-OAGM19_06.pdf ER - TY - CHAP A1 - Wöber, Wilfried A1 - Aburaia, Ali A1 - Aburaia, Mohamed A1 - Kubinger, Wilfried A1 - Otrebski, Richard A1 - Engelhardt-Nowitzki, Corinna A1 - Markl, Erich T1 - Digital Manufacturing & Robotics im Department Industrial Engineering T2 - Konferenz der Mechatronik Plattform Autonome mechatronische Systeme N2 - Digitalisierung und Automatisierung in produzierender Industrie sind Schlagwörter heutiger Forschungsbarbeiten am Department Industrial Engineering der Fachhochschule Technikum Wien. Künstliche Intelligenz, vernetzte Sensorsysteme oder übergreifende Prozessplanung ermöglichen Optimierung hinsichtlich Produktionszeit oder Fertigung von individualisierten Produkten, was zur Losgröße 1 führt. In diesem Dokument werden Forschungstätigkeiten des Departments Industrial Engineering im Kontext Vernetzung, Servicerobotik sowie Industrierobotik zusammengefasst. KW - Industrial Engineering KW - Digital Manufacturing KW - Networking KW - Robotics Y1 - 2018 SP - 20 EP - 21 ER - TY - GEN A1 - Wöber, Wilfried A1 - Aburaia, Ali A1 - Kubinger, Wilfried A1 - Otrebski, Richard A1 - Engelhardt-Nowitzki, Corinna A1 - Markl, Erich A1 - Aburaia, Mohamed T1 - Digital Manufacturing & Robotics im Department Industrial Engineering KW - Industrial Engineering KW - Digital Manufacturing KW - Networking KW - Robotics Y1 - ER - TY - CHAP A1 - Aburaia, Mohamed A1 - Lackner, Maximilian A1 - Grünbichler, Hannes A1 - Engelhardt-Nowitzki, Corinna A1 - Markl, Erich A1 - Lammer, H. A1 - Zhang, Haiguang A1 - Wang, J. A1 - Sapotka, J. A1 - Janics, T. A1 - Hailberger, M. T1 - Freeform-FDM process development using natural fibre reinforced biopolymers T2 - 2nd International Conference on 3D Prinitng Technology and Innovation March 19-20, 2018 London, UK KW - Freeform Printing KW - Additive Manufacturing Y1 - ER - TY - CHAP A1 - Dannereder, Florian A1 - Pachschwöll, Paul A1 - Aburaia, Mohamed A1 - Markl, Erich A1 - Lackner, Maximilian A1 - Engelhardt-Nowitzki, Corinna A1 - Shooman, Diane T1 - Development of a 3D-printed Bionic Hand with Muscle- and Force Control T2 - Austrian Robotics Workshop 2018 KW - Robotics KW - Prosthetics KW - 3D-Printing Y1 - ER - TY - CHAP A1 - Tomic, Dana Kathrin A1 - Drenjanac, Domagoj A1 - Lazendic, Goran A1 - Hörmann, Sandra A1 - Handler, Franz A1 - Wöber, Wilfried A1 - Schulmeister, Klemens A1 - Otte, Marcel A1 - Auer, Wolfgang T1 - Semantic Services for Adaptive Processes in Livestock Farming T2 - International Conference of Agricultural Engineering (AgEng 2014) KW - Agriculture KW - Semantic Services KW - Adaption Y1 - 2019 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 - GEN A1 - Kamravamanesh, Donya A1 - Pflügl, Stefan A1 - Lackner, Maximilian A1 - Herwig, Christoph T1 - Photosynthetic poly-β-hydroxybutyrate accumulation in unicellular cyanobacterium Synechocystis sp. PCC 6714 KW - Bacteria Y1 - 2018 ER - TY - BOOK A1 - Lackner, Maximilian A1 - Grabow, Wilhelm A1 - Stadler, Philipp T1 - Handbook of Online and Near-real-time Methods in Microbiology KW - Microbiology Y1 - 2018 ER - TY - GEN A1 - Lackner, Maximilian T1 - PBAT - A versatile bioplastics KW - Bioplastics Y1 - 2018 ER - TY - BOOK A1 - Orsolits, Horst A1 - Lackner, Maximilian T1 - Virtual Reality und Augmented Reality in der Digitalen Produktion KW - Virtual Reality Y1 - 2021 SN - 978-3-658-29008-5 PB - Gabler ER - TY - BOOK A1 - Lackner, Maximilian T1 - Chemical Engineering Vocabulary: Bilingual KW - Chemical Engineering KW - Language Y1 - 2018 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 - CHAP A1 - Wöber, Wilfried A1 - Tibihika, Papius D A1 - Olaverri-Monreal, Cristina A1 - Mehnen, Lars A1 - Sykacek, Peter A1 - Meimberg, Harald T1 - Comparison of Unsupervised Learning Methods for Natural Image Processing T2 - Biodiversity Information Science and Standards KW - Machine Learning KW - Deep Learning KW - Image Processing Y1 - IS - 3 ER - TY - CHAP A1 - Wöber, Wilfried A1 - Szuegyi, Daniel A1 - Kubinger, Wilfried A1 - Mehnen, Lars T1 - A principal component analysis based object detection for thermal infra-red images T2 - Proceedings of the 55th International Symposium ELMAR KW - Principal Component Analysis KW - Object Detection KW - Infra-Red Camera Y1 - 2019 ER - TY - GEN A1 - Spulak, David A1 - Otrebski, Richard A1 - Kubinger, Wilfried T1 - Object Tracking by Combining Supervised and Adaptive Online Learning through Sensor Fusion of Multiple Stereo Camera Systems KW - Object Tracking Y1 - ER - TY - CHAP A1 - Wöber, Wilfried A1 - Aburaia, Mohamed A1 - Olaverri-Monreal, Cristina T1 - Classification of Streetsigns Using Gaussian Process Latent Variable Models T2 - 2019 IEEE International Conference on Connected Vehicles and Expo (ICCVE), Graz, Austria KW - Streetsigns KW - Gaussian KW - Vehicles Y1 - 2020 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 - CHAP A1 - Papa, Maximilian A1 - Kaselautzke, David A1 - Radinger, Thomas A1 - Stuja, Kemajl T1 - Development of a safety industry 4.0 production environment T2 - 28th DAAAM International Symposium on Intelligent Manufacturing and Automation KW - Saftey KW - Robotics Y1 - 2020 ER - TY - BOOK A1 - Lackner, Maximilian A1 - Chen, Wei-Ying, A1 - Suzuki, Toshio T1 - Handbook of Climate Change Mitigation and Adaptation KW - Climate Change Y1 - 2018 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 -