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 - CHAP A1 - Wöber, Wilfried A1 - Kefer, Martin A1 - Kubinger, Wilfried A1 - Szuegyi, Daniel T1 - Evaluation of Daylight and Thermal Infra-Red based Detection for Platooning Vehicles T2 - Annals of DAAM for 2012 and Proceedings of the 23rd International DAAM Symposium KW - Vehicle KW - Thermal Detection Y1 - 2019 SP - 719 EP - 722 ER - TY - CHAP A1 - Wöber, Wilfried A1 - Peschak, Bernhard A1 - Otrebski, Richard T1 - ASK: Entwicklung eines modularen Systems zur Automatisierung landwirtschaftlicher Maschinen T2 - Intelligente Systeme - Stand der Technik und neue Möglichkeiten. Lecture Notes in Informatics KW - Automation KW - Agriculture Y1 - 2018 SP - 221 EP - 224 CY - Bonn ER - TY - CHAP A1 - Steigl, D A1 - Aburaia, Mohamed A1 - Wöber, Wilfried T1 - Autonomous Grasping of Known Objects Using Depth Data and the PCA T2 - Austrian Robotics Workshop 2020 KW - Robotic KW - Autonomous KW - Grasping Y1 - 2020 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 - CHAP A1 - Schwaiger, Simon A1 - Aburaia, Mohamed A1 - Aburaia, Ali A1 - Wöber, Wilfried T1 - Explainable Artificial Intelligence for Robot Arm Control T2 - Proceedings of the 32nd International DAAAM Virtual Symposium `Intelligent Manufacturing & Automation`, 28-29th October 2021, Vienna KW - Artificial Intelligence KW - Machine Learning Y1 - VL - 32 IS - 1 SP - 0640 EP - 0647 ER - TY - CHAP A1 - Peschak, Bernhard A1 - Wöber, Wilfried A1 - Otrebski, Richard A1 - Sulz, Clemens A1 - Thalhammer, J. T1 - Sensorfusion für landwirtschaftliche Applikationen T2 - Referate der 37. GIL-Jahrestagung: Digitale Transofmration - Wege in eine zukunftsfähige Landwirtschaft KW - Sensor KW - Agriculture Y1 - 2019 ER - TY - CHAP A1 - Kriegler, Andreas A1 - Wöber, Wilfried A1 - Aburaia, Mohamed T1 - Artificial Neural Networks Based Place Categorization T2 - Digital Conversion on the Way to Industry 4.0 KW - Artificial Intelligence Y1 - SP - 201 EP - 209 PB - Springer Verlag ER - TY - CHAP A1 - Felber, Stefan Otto A1 - Aburaia, Mohamed A1 - Wöber, Wilfried A1 - Lackner, Maximilian T1 - Parameter Optimization for the 3D Print of Thermo-Plastic Pellets with an Industrial Robot T2 - Digital Conversion on the Way to Industry 4.0 KW - Thermo Plastics KW - Industrial Robot Y1 - SP - 236 EP - 247 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 - CHAP A1 - Abdank, Moritz A1 - Aburaia, Mohamed A1 - Wöber, Wilfried T1 - Using-Colour-Based Object Detection for Pick and Place Applications T2 - Proceedings of the 32nd International DAAAM Virtual Symposium 'Intelligent Manufacturing & Automation', 28-29th October 2021, Vienna KW - Computer Vision KW - Object Detection KW - ROS Y1 - VL - 32 IS - 1 SP - 0536 EP - 0541 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 - Konferenz der Mechatronik-Plattform: Autonome mechatronische Systeme T2 - FH CAMPUS 02, 22. November 2018 Digital Manufacturing & Robotics im Department Industrial Engineering KW - Autonom KW - Mechatronic Y1 - 2020 ER - TY - CHAP 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 Grasping T2 - Austrian Robotics Workshop 2020 KW - Semi-Automatic KW - Neural Network KW - Robotic KW - Grasping KW - 6D Y1 - 2020 ER - TY - CHAP A1 - Kefer, Martin A1 - Wöber, Wilfried A1 - Szuegyi, Daniel A1 - Kubinger, Wilfried T1 - A Novel and Automated Circle Pattern Recognition Technique for Infra-Red Stero Camera Calibration T2 - Proceedings of the 10th IASTED International Conference on Signal Processing, Pattern Recognition and Applications KW - Pattern Recognition KW - Infra-Red Camera Y1 - 2019 SP - 404 EP - 410 ER - TY - CHAP A1 - Kriegler, Andreas A1 - Wöber, Wilfried T1 - Vision-based Docking of a Mobile Robot T2 - Proceedings of the Joint Austrian Computer Vision and Robotics Workshop 2020 KW - Automation KW - Robotics Y1 - SP - 6 EP - 12 ER - TY - CHAP A1 - Wöber, Wilfried A1 - Supper, Georg A1 - Aschauer, Christian A1 - Gronauer, Andreas A1 - Tomic, Dana Kathrin A1 - Hörmann, Sandra T1 - Entwicklung eines auf semantischer Technologie basierenden Analysesystems zur Überwachung der Wasserversorgung von landwirtschaftlichen Nutzflächen T2 - Referate der 35. GIL-Jahrestagung in Geisenheim - Komplexität versus Bedienbarkeit/Mensch-Maschine-Schnittstellen N2 - Eine ressourcenschonende Bewässerung von Nutzpflanzen wird durch den Klimawandel in den nächsten Jahren immer größere Bedeutung gewinnen. Eine Möglichkeit, das für Pflanzen zur Verfügung stehende Wasserpotential beurteilen zu können, ist die Nutzung von klimatischen Faktoren und Computermodellen. In diesem Beitrag wird die Entwicklung eines auf dem Forschungsprojekt agriOpenLink basierten Softwaresystems zur Abschätzung der Evapotranspiration auf einer landwirtschaftlichen Nutzfläche beschrieben. Der Fokus dieses Berichts liegt auf dem Softwareframework, welches auf semantischer Technologie basiert und formalisiertes landwirtschaftliches Wissen sowie Computermodelle zur Abschätzung der Evapotranspiration beinhaltet. Es werden erste experimentelle Ergebnisse diskutiert und die semantische Technologie bezüglich praktischer Nutzung evaluiert. KW - Semantic Modelling 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 - 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 - 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 KW - Digital Manufacturing KW - Robotics KW - Industrial Engineering Y1 - ER - 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 - 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 - 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 - TY - JOUR A1 - Wöber, Wilfried A1 - Novotny, Georg A1 - Mehnen, Lars A1 - Olaverri-Monreal, Cristina T1 - Autonomous Vehicles: Vehicle Parameter Estimation Using Variational Bayes and Kinematics JF - Applied Sciences KW - Variational bayes KW - Vehicle parameter estimation KW - Probabilistic robotics Y1 - VL - 10 IS - 18 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 -