@inproceedings{WoeberNovotnyAburaiaetal., author = {W{\"o}ber, Wilfried and Novotny, Georg and Aburaia, Mohamed and Otrebski, Richard and Kubinger, Wilfried}, title = {Estimating a Sparse Representation of Gaussian Processes Using Global Optimization and the Bayesian Information Criterion}, series = {Austrian Robotics Workshop 2018}, booktitle = {Austrian Robotics Workshop 2018}, subject = {Mobile Robotics}, language = {en} } @inproceedings{DannerederEngelhardtNowitzkiMarkletal., author = {Dannereder, Florian and Engelhardt-Nowitzki, Corinna and Markl, Erich and Lackner, Maximilian and Kreith, Josef and Pachschw{\"o}ll, Paul Herwig and Aburaia, Mohamed and Shooman, Diane}, title = {Development of a 3D-Printed Bionic Hand with Muscle- and Force Control}, series = {Proceedings of the Austrian Robotics Workshop 2018}, booktitle = {Proceedings of the Austrian Robotics Workshop 2018}, subject = {Robotics}, language = {en} } @inproceedings{WoeberSchulmeisterAschaueretal., author = {W{\"o}ber, Wilfried and Schulmeister, Klemens and Aschauer, Christian and Gronauer, Andreas and Tomic, Dana Kathrin and Fensel, Anna and Riegler, Thomas and Handler, Franz and H{\"o}rmann, Sandra and Otte, Marcel and Auer, Wolfgang}, title = {Adaptive Agricultural Processes via Open Interfaces and Linked Services}, series = {Referate der 34. GIL-Jahrestagung - IT-Standards in der Agrar- und Ern{\"a}hrungswissenschaft}, booktitle = {Referate der 34. GIL-Jahrestagung - IT-Standards in der Agrar- und Ern{\"a}hrungswissenschaft}, pages = {157 -- 160}, subject = {Adaption}, language = {en} } @book{OrsolitsLackner, author = {Orsolits, Horst and Lackner, Maximilian}, title = {Virtual Reality und Augmented Reality in der Digitalen Produktion}, editor = {Lackner, Maximilian and Orsolits, Horst}, publisher = {Springer Gabler}, isbn = {978-3-658-29009-2}, publisher = {Fachhochschule Technikum Wien}, subject = {Virtual Reality}, language = {de} } @inproceedings{GonzalezGutierrezTreitlerSpoerketal., author = {Gonzalez-Gutierrez, Joamin and Treitler, Manuel and Spoerk, Martin and Arbeiter, Florian and Schuschnigg, Stephan and Lammer, Herfried and Lackner, Maximilian and Aburaia, Mohamed and Poszvek, G{\"u}nther and Zhang, Haiguang and Sapkota, Janak and Holzer, Clemes}, title = {Carbon fiber reinforced thermoplastics for material extrusion additive manufacturing}, series = {Conference proceedings of 35th International Conference of the Polymer Processing Society}, booktitle = {Conference proceedings of 35th International Conference of the Polymer Processing Society}, pages = {5}, abstract = {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.}, subject = {Additive Manufacturing}, language = {en} } @inproceedings{RauerWoeberAburaia, author = {Rauer, Johannes and W{\"o}ber, Wilfried and Aburaia, Mohamed}, title = {An Autonomous Mobile Handling Robot Using Object Recognition}, series = {Proceedings of ARW \& OAGM Workshop 2019}, booktitle = {Proceedings of ARW \& OAGM Workshop 2019}, subject = {Mobile Robotics}, language = {en} } @inproceedings{WoeberAburaiaAburaiaetal., author = {W{\"o}ber, Wilfried and Aburaia, Ali and Aburaia, Mohamed and Kubinger, Wilfried and Otrebski, Richard and Engelhardt-Nowitzki, Corinna and Markl, Erich}, title = {Digital Manufacturing \& Robotics im Department Industrial Engineering}, series = {Konferenz der Mechatronik Plattform Autonome mechatronische Systeme}, booktitle = {Konferenz der Mechatronik Plattform Autonome mechatronische Systeme}, pages = {20 -- 21}, abstract = {Digitalisierung und Automatisierung in produzierender Industrie sind Schlagw{\"o}rter heutiger Forschungsbarbeiten am Department Industrial Engineering der Fachhochschule Technikum Wien. K{\"u}nstliche Intelligenz, vernetzte Sensorsysteme oder {\"u}bergreifende Prozessplanung erm{\"o}glichen Optimierung hinsichtlich Produktionszeit oder Fertigung von individualisierten Produkten, was zur Losgr{\"o}ße 1 f{\"u}hrt. In diesem Dokument werden Forschungst{\"a}tigkeiten des Departments Industrial Engineering im Kontext Vernetzung, Servicerobotik sowie Industrierobotik zusammengefasst.}, subject = {Industrial Engineering}, language = {de} } @misc{WoeberAburaiaKubingeretal., author = {W{\"o}ber, Wilfried and Aburaia, Ali and Kubinger, Wilfried and Otrebski, Richard and Engelhardt-Nowitzki, Corinna and Markl, Erich and Aburaia, Mohamed}, title = {Digital Manufacturing \& Robotics im Department Industrial Engineering}, subject = {Industrial Engineering}, language = {de} } @inproceedings{AburaiaLacknerGruenbichleretal., author = {Aburaia, Mohamed and Lackner, Maximilian and Gr{\"u}nbichler, Hannes and Engelhardt-Nowitzki, Corinna and Markl, Erich and Lammer, H. and Zhang, Haiguang and Wang, J. and Sapotka, J. and Janics, T. and Hailberger, M.}, title = {Freeform-FDM process development using natural fibre reinforced biopolymers}, series = {2nd International Conference on 3D Prinitng Technology and Innovation March 19-20, 2018 London, UK}, booktitle = {2nd International Conference on 3D Prinitng Technology and Innovation March 19-20, 2018 London, UK}, subject = {Freeform Printing}, language = {en} } @inproceedings{DannerederPachschwoellAburaiaetal., author = {Dannereder, Florian and Pachschw{\"o}ll, Paul and Aburaia, Mohamed and Markl, Erich and Lackner, Maximilian and Engelhardt-Nowitzki, Corinna and Shooman, Diane}, title = {Development of a 3D-printed Bionic Hand with Muscle- and Force Control}, series = {Austrian Robotics Workshop 2018}, booktitle = {Austrian Robotics Workshop 2018}, subject = {Robotics}, language = {en} } @inproceedings{TomicDrenjanacLazendicetal., author = {Tomic, Dana Kathrin and Drenjanac, Domagoj and Lazendic, Goran and H{\"o}rmann, Sandra and Handler, Franz and W{\"o}ber, Wilfried and Schulmeister, Klemens and Otte, Marcel and Auer, Wolfgang}, title = {Semantic Services for Adaptive Processes in Livestock Farming}, series = {International Conference of Agricultural Engineering (AgEng 2014)}, booktitle = {International Conference of Agricultural Engineering (AgEng 2014)}, pages = {8}, subject = {Agriculture}, language = {en} } @article{TomicDrenjanacLazendicetal., author = {Tomic, Dana Kathrin and Drenjanac, Domagoj and Lazendic, Goran and H{\"o}rmann, Sandra and Handler, Franz and W{\"o}ber, Wilfried and Aschauer, Christian and Auer, Wolfgang}, title = {Ontologies and semantic services for process optimization in agricultural production}, series = {e\&i Elektrotechnik und Informationstechnik}, journal = {e\&i Elektrotechnik und Informationstechnik}, subject = {Semantics}, language = {en} } @article{KamravamaneshPflueglNischkaueretal., author = {Kamravamanesh, Donya and Pfl{\"u}gl, Stefan and Nischkauer, Winfried and Limbeck, Andreas and Lackner, Maximilian and Herwig, Christoph}, title = {Photosynthetic poly-β-hydroxybutyrate accumulation in unicellular cyanobacterium Synechocystis sp. PCC 6714}, series = {AMB Express}, volume = {143}, journal = {AMB Express}, number = {7}, subject = {Bacteria}, language = {en} } @misc{KamravamaneshPflueglLackneretal., author = {Kamravamanesh, Donya and Pfl{\"u}gl, Stefan and Lackner, Maximilian and Herwig, Christoph}, title = {Photosynthetic poly-β-hydroxybutyrate accumulation in unicellular cyanobacterium Synechocystis sp. PCC 6714}, subject = {Bacteria}, language = {en} } @book{LacknerGrabowStadler, author = {Lackner, Maximilian and Grabow, Wilhelm and Stadler, Philipp}, title = {Handbook of Online and Near-real-time Methods in Microbiology}, publisher = {Fachhochschule Technikum Wien}, subject = {Microbiology}, language = {en} } @misc{Lackner, author = {Lackner, Maximilian}, title = {PBAT - A versatile bioplastics}, subject = {Bioplastics}, language = {en} } @book{OrsolitsLackner, author = {Orsolits, Horst and Lackner, Maximilian}, title = {Virtual Reality und Augmented Reality in der Digitalen Produktion}, publisher = {Gabler}, isbn = {978-3-658-29008-5}, publisher = {Fachhochschule Technikum Wien}, subject = {Virtual Reality}, language = {de} } @book{Lackner, author = {Lackner, Maximilian}, title = {Chemical Engineering Vocabulary: Bilingual}, publisher = {Fachhochschule Technikum Wien}, subject = {Chemical Engineering}, language = {en} } @article{SpitzerSonnleitnerKempeLackner, author = {Spitzer-Sonnleitner, Birgit and Kempe, Andre and Lackner, Maximilian}, title = {Influence of halide solutions on collagen networks - measurements of physical properties by atomic force microscopy (AFM)}, series = {Influence of halide solutions on collagen networks}, journal = {Influence of halide solutions on collagen networks}, subject = {Collagen Networks}, language = {en} } @article{WoeberMehnenCurtoetal., author = {W{\"o}ber, Wilfried and Mehnen, Lars and Curto, Manuel and Dias Tibihika, Papius and Tesfaye, Genanaw and Meimberg, Harald}, title = {Investigating Shape Variation Using Generalized Procrustes Analysis and Machine Learning}, series = {Applied Sciences}, volume = {2022}, journal = {Applied Sciences}, number = {12(6), 3158}, pages = {26}, abstract = {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}, subject = {generalized procrustes analysis}, language = {en} } @article{WoeberCurtoTibihikaetal., author = {W{\"o}ber, Wilfried and Curto, Manuel and Tibihika, Papius D. and Meulenboek, Paul and Alemayehu, Esayas and Mehnen, Lars and Meimberg, Harald and Sykacek, Peter}, title = {Identifying geographically differentiated features of Ethopian Nile tilapia (Oreochromis niloticus) morphology with machine learning}, series = {PlosONE}, volume = {16}, journal = {PlosONE}, number = {4}, subject = {Machine Learning}, language = {en} } @inproceedings{WoeberTibihikaOlaverriMonrealetal., author = {W{\"o}ber, Wilfried and Tibihika, Papius D and Olaverri-Monreal, Cristina and Mehnen, Lars and Sykacek, Peter and Meimberg, Harald}, title = {Comparison of Unsupervised Learning Methods for Natural Image Processing}, series = {Biodiversity Information Science and Standards}, booktitle = {Biodiversity Information Science and Standards}, number = {3}, subject = {Machine Learning}, language = {en} } @inproceedings{WoeberSzuegyiKubingeretal., author = {W{\"o}ber, Wilfried and Szuegyi, Daniel and Kubinger, Wilfried and Mehnen, Lars}, title = {A principal component analysis based object detection for thermal infra-red images}, series = {Proceedings of the 55th International Symposium ELMAR}, booktitle = {Proceedings of the 55th International Symposium ELMAR}, subject = {Principal Component Analysis}, language = {en} } @misc{SpulakOtrebskiKubinger, author = {Spulak, David and Otrebski, Richard and Kubinger, Wilfried}, title = {Object Tracking by Combining Supervised and Adaptive Online Learning through Sensor Fusion of Multiple Stereo Camera Systems}, subject = {Object Tracking}, language = {en} } @inproceedings{WoeberAburaiaOlaverriMonreal, author = {W{\"o}ber, Wilfried and Aburaia, Mohamed and Olaverri-Monreal, Cristina}, title = {Classification of Streetsigns Using Gaussian Process Latent Variable Models}, series = {2019 IEEE International Conference on Connected Vehicles and Expo (ICCVE), Graz, Austria}, booktitle = {2019 IEEE International Conference on Connected Vehicles and Expo (ICCVE), Graz, Austria}, subject = {Streetsigns}, language = {en} } @article{SattingerPapaStujaetal., author = {Sattinger, Vinzenz and Papa, Maximilian and Stuja, Kemajl and Kubinger, Wilfried}, title = {Methodik zur Entwicklung sicherer kollaborativer Produktionssysteme im Rahmen von Industrie 4.0}, series = {e \& i Elektrotechnik und Informationstechnik}, journal = {e \& i Elektrotechnik und Informationstechnik}, subject = {Robotics}, language = {de} } @inproceedings{PapaKaselautzkeRadingeretal., author = {Papa, Maximilian and Kaselautzke, David and Radinger, Thomas and Stuja, Kemajl}, title = {Development of a safety industry 4.0 production environment}, series = {28th DAAAM International Symposium on Intelligent Manufacturing and Automation}, booktitle = {28th DAAAM International Symposium on Intelligent Manufacturing and Automation}, subject = {Saftey}, language = {en} } @book{LacknerChenSuzuki, author = {Lackner, Maximilian and Chen, Wei-Ying, and Suzuki, Toshio}, title = {Handbook of Climate Change Mitigation and Adaptation}, publisher = {Fachhochschule Technikum Wien}, subject = {Climate Change}, language = {en} } @article{WoeberMehnenSykaceketal., author = {W{\"o}ber, Wilfried and Mehnen, Lars and Sykacek, Peter and Meimberg, Harald}, title = {Investigating Explanatory Factors of Machine Learning Models for Plant Classification}, series = {Plants}, volume = {2021}, journal = {Plants}, number = {10(12):2674}, pages = {20}, abstract = {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}, subject = {deep learning}, language = {en} }