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 - 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 - 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 - 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 -