@misc{BeimlerLeisslEbneretal., author = {Beimler, Josef and Leißl, Caroline and Ebner, Lena and Elsner, Michael and M{\"u}hlbauer, Gerhard and Kohlert, Dieter and Schubert, Martin J. W. and Weiß, Andreas P. and Sterner, Michael and Raith, Thomas and Afranseder, Martin and Krapf, Tobias and Mottok, J{\"u}rgen and Siemers, Christian and Großmann, Benjamin and H{\"o}cherl, Johannes and Schlegl, Thomas and Schneider, Ralph and Milaev, Johannes and Rampelt, Christina and Roduner, Christian and Glowa, Christoph and Bachl, Christoph and Schliekmann, Claus and Gnan, Alfons and Grill, Martin and Ruhland, Karl and Piehler, Thomas and Friers, Daniel and Wels, Harald and Pflug, Kenny and Kucera, Markus and Waas, Thomas and Schlachetzki, Felix and Boy, Sandra and Pemmerl, Josef and Leis, Alexander and Welsch, Andreas F.X. and Graf, Franz and Zenger, Gerhard and Volbert, Klaus and Waas, Thomas and Scherzinger, Stefanie and Klettke, Meike and St{\"o}rl, Uta and Heyl, C. and Boldenko, A. and Monkman, Gareth J. and Kujat, Richard and Briem, Ulrich and Hierl, Stefan and Talbot, Sebastian and Schmailzl, Anton and Ławrowski, Robert Damian and Prommesberger, Christian and Langer, Christoph and Dams, Florian and Schreiner, Rupert and Valentino, Piergiorgio and Romano, Marco and Ehrlich, Ingo and Furgiuele, Franco and Gebbeken, Norbert and Eisenried, Michael and Jungbauer, Bastian and Hutterer, Albert and Bauhuber, Michael and Mikrievskij, Andreas and Argauer, Monika and Hummel, Helmut and Lechner, Alfred and Liebetruth, Thomas and Schumm, Michael and Joseph, Saskia and Reschke, Michael and Soska, Alexander and Schroll-Decker, Irmgard and Putzer, Michael and Rasmussen, John and Dendorfer, Sebastian and Weber, Tim and Al-Munajjed, Amir Andreas and Verkerke, Gijsbertus Jacob and Renkawitz, Tobias and Haug, Sonja and Rudolph, Clarissa and Zeitler, Annika and Schaubeck, Simon and Steffens, Oliver and Rechenauer, Christian and Schulz-Brize, Thekla and Fleischmann, Florian and Kusterle, Wolfgang and Beer, Anne and Wagner, Bernd and Neidhart, Thomas}, title = {Forschungsbericht 2013}, editor = {Baier, Wolfgang}, address = {Regensburg}, organization = {Ostbayerische Technische Hochschule Regensburg}, doi = {10.35096/othr/pub-799}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:898-opus4-7990}, pages = {80}, language = {de} } @misc{AppelhansKampmannMottoketal., author = {Appelhans, Marie-Luise and Kampmann, Matthias and Mottok, J{\"u}rgen and Riederer, Michael and Nagl, Klaus and Steffens, Oliver and D{\"u}nnweber, Jan and Wildgruber, Markus and Roth, Julius and Stadler, Timo and Palm, Christoph and Weiß, Martin Georg and Rochholz, Sandra and Bierl, Rudolf and Gschossmann, Andreas and Haug, Sonja and Schmidbauer, Simon and Koch, Anna and Westner, Markus and Bary, Benedikt von and Ellermeier, Andreas and V{\"o}gele, Daniel and Maiwald, Frederik and Hierl, Stefan and Schlamp, Matthias and Ehrlich, Ingo and Siegl, Marco and H{\"u}ntelmann, Sven and Wildfeuer, Matthias and Br{\"u}ckl, Oliver and Sterner, Michael and Hofrichter, Andreas and Eckert, Fabian and Bauer, Franz and Dawoud, Belal and Rabl, Hans-Peter and Gamisch, Bernd and Schmidt, Ottfried and Heberl, Michael and Thema, Martin and Mayer, Ulrike and Eller, Johannes and Sippenauer, Thomas and Adelt, Christian and Haslbeck, Matthias and Vogl, Bettina and Mauerer, Wolfgang and Ramsauer, Ralf and Lohmann, Daniel and Sax, Irmengard and Gabor, Thomas and Feld, Sebastian and Linnhoff-Popien, Claudia and Ławrowski, Robert Damian and Langer, Christoph and Schreiner, Rupert and Sellmair, Josef}, title = {Forschung 2019}, editor = {Baier, Wolfgang}, address = {Regensburg}, organization = {Ostbayerische Technische Hochschule Regensburg}, isbn = {978-3-9818209-7-3}, doi = {10.35096/othr/pub-789}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:898-opus4-7890}, pages = {72}, abstract = {Bericht mit Forschungsprojekten aus verschiedenen Bereichen der OTH Regensburg mit dem Schwerpunktthema "K{\"u}nstliche Intelligenz" und einem Gespr{\"a}ch zur "Medizin der Zukunft"}, subject = {Forschung}, language = {de} } @unpublished{BockWeiss, author = {Bock, Sebastian and Weiß, Martin Georg}, title = {Local Convergence of Adaptive Gradient Descent Optimizers}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:898-opus4-7546}, abstract = {Adaptive Moment Estimation (ADAM) is a very popular training algorithm for deep neural networks and belongs to the family of adaptive gradient descent optimizers. However to the best of the authors knowledge no complete convergence analysis exists for ADAM. The contribution of this paper is a method for the local convergence analysis in batch mode for a deterministic fixed training set, which gives necessary conditions for the hyperparameters of the ADAM algorithm. Due to the local nature of the arguments the objective function can be non-convex but must be at least twice continuously differentiable. Then we apply this procedure to other adaptive gradient descent algorithms and show for most of them local convergence with hyperparameter bounds.}, language = {en} } @unpublished{BockWeiss, author = {Bock, Sebastian and Weiß, Martin Georg}, title = {Rotation Detection of Components with Convolutional Neural Networks}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:898-opus4-4120}, abstract = {The main issues in many image processing applications are object recognition and detection of objects, which answers the questions whether an object is present and if it is present, where it is located. Popular object detection algorithms like YOLO use a regression formulation for the whole problem, especially for the bounding box parameters. In production industry the setting usually is different: One usually knows the object type and rather wants to know with high precision where the object is. We study a prototype application in this area where we identify the rotation of an object in a plane. To solve this problem use a regression approach with a CNN architecture as a function approximator. We compare our results to standard image processing algorithms, which do not use neural networks, and present quantitative results on the accuracy. CNNs seem at least competitive to classical image processing.}, language = {en} } @inproceedings{BockWeiss, author = {Bock, Sebastian and Weiß, Martin Georg}, title = {Non-Convergence and Limit Cycles in the Adam Optimizer}, series = {Proceedings of the 28th International Conference on Artificial Neural Networks, 2019, Munich, Germany, September 17-19}, booktitle = {Proceedings of the 28th International Conference on Artificial Neural Networks, 2019, Munich, Germany, September 17-19}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:898-opus4-490}, pages = {232 -- 243}, abstract = {One of the most popular training algorithms for deep neural networks is the Adaptive Moment Estimation (Adam) introduced by Kingma and Ba. Despite its success in many applications there is no satisfactory convergence analysis: only local convergence can be shown for batch mode under some restrictions on the hyperparameters, counterexamples exist for incremental mode. Recent results show that for simple quadratic objective functions limit cycles of period 2 exist in batch mode, but only for atypical hyperparameters, and only for the algorithm without bias correction. We extend the convergence analysis to all choices of the hyperparameters for quadratic functions. This finally answers the question of convergence for Adam in batch mode to the negative. We analyze the stability of these limit cycles and relate our analysis to other results where approximate convergence was shown, but under the additional assumption of bounded gradients which does not apply to quadratic functions. The investigation heavily relies on the use of computer algebra due to the complexity of the equations.}, subject = {Neuronales Netz}, language = {en} } @inproceedings{BockWeiss, author = {Bock, Sebastian and Weiß, Martin Georg}, title = {A Proof of Local Convergence for the Adam Optimizer}, series = {Proceedings of the 2019 International Joint Conference on Neural Networks (IJCNN), 2019, Budapest, Hungary, July 14-19}, volume = {2019}, booktitle = {Proceedings of the 2019 International Joint Conference on Neural Networks (IJCNN), 2019, Budapest, Hungary, July 14-19}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:898-opus4-501}, pages = {1 -- 8}, abstract = {Adaptive Moment Estimation (Adam) is a very popular training algorithm for deep neural networks, implemented in many machine learning frameworks. To the best of the authors knowledge no complete convergence analysis exists for Adam. The contribution of this paper is a method for the local convergence analysis in batch mode for a deterministic fixed training set, which gives necessary conditions for the hyperparameters of the Adam algorithm. Due to the local nature of the arguments the objective function can be non-convex but must be at least twice continuously differentiable.}, subject = {Neuronales Netz}, language = {en} } @unpublished{Weiss, author = {Weiß, Martin Georg}, title = {Optimization of Cartesian Tasks with Configuration Selection}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:898-opus4-20176}, abstract = {A basic task in the design of an industrial robot application is the relative placement of robot and workpiece. Process points are defined in Cartesian coordinates relative to the workpiece coordinate system, and the workpiece has to be located such that the robot can reach all points. Finding such a location is still an iterative procedure based on the developers' intuition. One difficulty is the choice of one of the several solutions of the backward transform of a typical 6R robot. We present a novel algorithm that simultaneously optimizes the workpiece location and the robot configuration at all process points using higher order optimization algorithms. A key ingredient is the extension of the robot with a virtual prismatic axis. The practical feasibility of the approach is shown with an example using a commercial industrial robot.}, subject = {Intralogistik}, language = {en} } @inproceedings{SchubertSeignDaietal., author = {Schubert, Martin J. W. and Seign, Stefan and Dai, Q. and Hinterseer, Sebastian and Pielmeier, F. and Pietsch, Alexander and Seebauer, C. and Weiß, Josef and Yu, C. and Zenger, Stefan}, title = {Capacitive Sensor Technology for Soil Moisture Monitoring Networks}, series = {24th IEEE International Conference on Electronics, Circuits and Systems (ICECS), 5-8 Dec. 2017, Batumi, Georgia}, booktitle = {24th IEEE International Conference on Electronics, Circuits and Systems (ICECS), 5-8 Dec. 2017, Batumi, Georgia}, doi = {10.1109/ICECS.2017.8292018}, pages = {190 -- 193}, abstract = {A sensor technology for tracking soil-moisture in agricultural fields is presented. A linear measurement method was developed to satisfy the need for cheap sensors with wide range of capacitance change such as factor 30. A combined wired and wireless network transmits data to a server. Various battery aspects are compared with respect to use in agriculture.}, language = {en} } @inproceedings{Weiss, author = {Weiß, Martin Georg}, title = {Optimization of Cartesian Tasks with Configuration Selection}, series = {2nd IMA Conference on Mathematics of Robotics: online September 8-10, 2021}, booktitle = {2nd IMA Conference on Mathematics of Robotics: online September 8-10, 2021}, editor = {Holderbaum, William and Selig, J. M.}, publisher = {Springer International Publishing}, address = {Cham}, isbn = {978-3-030-91351-9}, doi = {10.1007/978-3-030-91352-6_16}, pages = {153 -- 160}, abstract = {A basic task in the design of an industrial robot application is the relative placement of robot and workpiece. Process points are defined in Cartesian coordinates relative to the workpiece coordinate system, and the workpiece has to be located such that the robot can reach all points. Finding such a location is still an iterative procedure based on the developers' intuition. One difficulty is the choice of one of the several solutions of the backward transform of a typical 6R robot. We present a novel algorithm that simultaneously optimizes the workpiece location and the robot configuration at all process points using higher order optimization algorithms. A key ingredient is the extension of the robot with a virtual prismatic axis. The practical feasibility of the approach is shown with an example using a commercial industrial robot.}, language = {en} } @inproceedings{WeissVolbert, author = {Weiß, Martin Georg and Volbert, Klaus}, title = {Intelligente Steuerung von Industrierobotern}, series = {Zweite OTH-Clusterkonferenz 18. Januar 2017 Techbase, Regensburg}, booktitle = {Zweite OTH-Clusterkonferenz 18. Januar 2017 Techbase, Regensburg}, editor = {Falter, Thomas}, address = {Regensburg}, organization = {OTH Regensburg und OTH Amberg-Weiden}, language = {de} } @article{BayerstadlerBecquinBinderetal., author = {Bayerstadler, Andreas and Becquin, Guillaume and Binder, Julia and Botter, Thierry and Ehm, Hans and Ehmer, Thomas and Erdmann, Marvin and Gaus, Norbert and Harbach, Philipp and Hess, Maximilian and Klepsch, Johannes and Leib, Martin and Luber, Sebastian and Luckow, Andre and Mansky, Maximilian and Mauerer, Wolfgang and Neukart, Florian and Niedermeier, Christoph and Palackal, Lilly and Pfeiffer, Ruben and Polenz, Carsten and Sepulveda, Johanna and Sievers, Tammo and Standen, Brian and Streif, Michael and Strohm, Thomas and Utschig-Utschig, Clemens and Volz, Daniel and Weiss, Horst and Winter, Fabian}, title = {Industry quantum computing applications}, series = {EPJ Quantum Technology}, volume = {8}, journal = {EPJ Quantum Technology}, publisher = {Springer}, organization = {Springer}, doi = {10.1140/epjqt/s40507-021-00114-x}, pages = {1 -- 17}, abstract = {Quantum computing promises to overcome computational limitations with better and faster solutions for optimization, simulation, and machine learning problems. Europe and Germany are in the process of successfully establishing research and funding programs with the objective to dvance the technology's ecosystem and industrialization, thereby ensuring digital sovereignty, security, and competitiveness. Such an ecosystem comprises hardware/software solution providers, system integrators, and users from research institutions, start-ups, and industry. The vision of the Quantum Technology and Application Consortium (QUTAC) is to establish and advance the quantum computing ecosystem, supporting the ambitious goals of the German government and various research programs. QUTAC is comprised of ten members representing different industries, in particular automotive manufacturing, chemical and pharmaceutical production, insurance, and technology. In this paper, we survey the current state of quantum computing in these sectors as well as the aerospace industry and identify the contributions of QUTAC to the ecosystem. We propose an application-centric approach for the industrialization of the technology based on proven business impact. This paper identifies 24 different use cases. By formalizing high-value use cases into well-described reference problems and benchmarks, we will guide technological progress and eventually commercialization. Our results will be beneficial to all ecosystem participants, including suppliers, system integrators, software developers, users, policymakers, funding program managers, and investors.}, language = {en} } @inproceedings{Weiss, author = {Weiß, Martin Georg}, title = {Optimal Object Placement Using a Virtual Axis}, series = {Advances in Robot Kinematics, ARK 2018}, volume = {8}, booktitle = {Advances in Robot Kinematics, ARK 2018}, editor = {Lenarcic, Jadran and Parenti-Castelli, Vincenzo}, publisher = {Springer International Publishing}, address = {Cham}, isbn = {978-3-319-93187-6}, doi = {10.1007/978-3-319-93188-3_14}, pages = {116 -- 123}, abstract = {A basic task in the design of a robotic production cell is the relative placement of robot and workpiece. The fundamental requirement is that the robot can reach all process positions; only then one can think further optimization. Therefore an algorithm that automatically places an object into the workspace is very desirable. However many iterative optimization algorithms cannot guarantee that all intermediate steps are reachable, resulting in complicated procedures. We present a novel approach which extends a robot by a virtual prismatic joint - which measures the distance to the workspace - such that any TCP frames are reachable. This allows higher order nonlinear programming algorithms to be used for placement of an object alone as well as the optimal placement under some differentiable criterion.}, language = {en} } @misc{MauererRexhepajMonkmanetal., author = {Mauerer, Wolfgang and Rexhepaj, Tanja and Monkman, Gareth J. and Sindersberger, Dirk and Diermeier, Andreas and Neidhart, Thomas and Wolfrum, Dominik and Sterner, Michael and Heberl, Michael and Nusko, Robert and Maier, Georg and Nagl, Klaus and Reuter, Monika and Hofrichter, Andreas and Lex, Thomas and Lesch, Florian and Kieninger, B{\"a}rbel and Szalo, Alexander Eduard and Zehner, Alexander and Palm, Christoph and Joblin, Mitchell and Apel, Sven and Ramsauer, Ralf and Lohmann, Daniel and Westner, Markus and Strasser, Artur and Munndi, Maximilian and Ebner, Lena and Elsner, Michael and Weiß, Nils and Segerer, Matthias and Hackenberg, Rudolf and Steger, Sebastian and Schmailzl, Anton and Dostalek, Michael and Armbruster, Dominik and Koch, Fabian and Hierl, Stefan and Thumann, Philipp and Swidergal, Krzysztof and Wagner, Marcus and Briem, Ulrich and Diermeier, Andreas and Spreitzer, Stefan and Beiderbeck, Sabrina and Hook, Christian and Zobel, Martin and Weber, Tim and Groß, Simon and Penzkofer, Rainer and Dendorfer, Sebastian and Schillitz, Ingo and Bauer, Thomas and Rudolph, Clarissa and Schmidt, Katja and Liebetruth, Thomas and Hamer, Markus and Haug, Sonja and Vernim, Matthias and Weber, Karsten and Saßmannshausen, Sean Patrick and Books, Sebastian and Neuleitner, Nikolaus and Rechenauer, Christian and Steffens, Oliver and Kusterle, Wolfgang and G{\"o}mmel, Roland and Wellnitz, Felix and Stierstorfer, Johannes and Stadler, Dominik and Hofmann, Matthias J. and Motschmann, Hubert and Shamonin (Chamonine), Mikhail and Bleicher, Veronika and Fischer, Sebastian and Hackenberg, Rudolf and Horn, Anton and Kawasch, Raphael and Petzenhauser, Michael and Probst, Tobias and Udalzow, Anton and Dams, Florian and Schreiner, Rupert and Langer, Christoph and Prommesberger, Christian and Ławrowski, Robert Damian}, title = {Forschungsbericht 2016}, editor = {Baier, Wolfgang}, address = {Regensburg}, organization = {Ostbayerische Technische Hochschule Regensburg}, doi = {10.35096/othr/pub-1384}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:898-opus4-13840}, language = {de} } @misc{WeberDendorferSuessetal., author = {Weber, Karsten and Dendorfer, Sebastian and S{\"u}ß, Franz and Kubowitsch, Simone and Schratzenstaller, Thomas and Haug, Sonja and Mohr, Christa and Kiesl, Hans and Drechsler, J{\"o}rg and Westner, Markus and Kobus, J{\"o}rn and Schubert, Martin J. W. and Zenger, Stefan and Pietsch, Alexander and Weiß, Josef and Hinterseer, Sebastian and Schieck, Roland and Scherzinger, Stefanie and Klettke, Meike and Ringlstetter, Andreas and St{\"o}rl, Uta and Bissyand{\´e}, Tegawend{\´e} F. and Seeburger, Achim and Schindler, Timo and Ramsauer, Ralf and Kiszka, Jan and K{\"o}lbl, Andreas and Lohmann, Daniel and Mauerer, Wolfgang and Maier, Johannes and Scorna, Ulrike and Palm, Christoph and Soska, Alexander and Mottok, J{\"u}rgen and Ellermeier, Andreas and V{\"o}gele, Daniel and Hierl, Stefan and Briem, Ulrich and Buschmann, Knut and Ehrlich, Ingo and Pongratz, Christian and Pielmeier, Benjamin and Tyroller, Quirin and Monkman, Gareth J. and Gut, Franz and Roth, Carina and Hausler, Peter and Bierl, Rudolf and Prommesberger, Christian and Ławrowski, Robert Damian and Langer, Christoph and Schreiner, Rupert and Huang, Yifeng and She, Juncong and Ottl, Andreas and Rieger, Walter and Kraml, Agnes and Poxleitner, Thomas and Hofer, Simon and Heisterkamp, Benjamin and Lerch, Maximilian and Sammer, Nike and Golde, Olivia and Wellnitz, Felix and Schmid, Sandra and Muntschick, Claudia and Kusterle, Wolfgang and Paric, Ivan and Br{\"u}ckl, Oliver and Haslbeck, Matthias and Schmidt, Ottfried and Schwanzer, Peter and Rabl, Hans-Peter and Sterner, Michael and Bauer, Franz and Steinmann, Sven and Eckert, Fabian and Hofrichter, Andreas}, title = {Forschungsbericht 2017}, editor = {Baier, Wolfgang}, address = {Regensburg}, organization = {Ostbayerische Technische Hochschule Regensburg}, isbn = {978-3-9818209-3-5}, doi = {10.35096/othr/pub-1383}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:898-opus4-13835}, subject = {Forschung}, language = {de} } @techreport{SchwarzBockWeiss, author = {Schwarz, Philipp and Bock, Sebastian and Weiß, Martin}, title = {Abschlussbericht Projekt LeaP - Learning Poses : Posenerkennung mit Neuronalen Netzen}, address = {Regensburg}, doi = {10.35096/othr/pub-8851}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:898-opus4-88514}, pages = {45}, abstract = {Die Sch{\"a}tzung der 6D-Pose bekannter Objekte findet Anwendungen in der Robotik, der Luft- und Raumfahrtsteuerung und automatisierten Produktionsumgebungen. Es ist nach wie vor g{\"a}ngige Praxis, klassische Bildverarbeitungsmethoden zu verwenden, um eine hohe Pr{\"a}zision zu erreichen. Diese Methoden erfordern jedoch eine manuelle Parametrisierung der verwendeten Erkennungswerkzeuge f{\"u}r jedes einzelne Objekt. Dies wirft die Frage auf, ob maschinelle Lerntechniken, insb. convolutional neural networks, Netzwerke, so trainiert werden k{\"o}nnen, dass sie die in der Industrie geforderte Pr{\"a}zision ohne individuelle Programmierung erreichen k{\"o}nnen. Dar{\"u}ber sollen in Produktionsumgebungen Umwelteinfl{\"u}sse wie Lichtverh{\"a}ltnisse minimiert werden. Es werden verschiedene Netzstrukturen entwickelt und auf ihre Leistung bei diesem Szenario untersucht, mit 1 bis 6 Freiheitsgraden in der Aufgabenstellung und einer oder mehreren Kameras. Zus{\"a}tzlich wird ein Ansatz mit reinforcement learning entwickelt und untersucht.}, language = {de} } @unpublished{FeessWeiss, author = {Feeß, Alexander and Weiß, Martin}, title = {Uniqueness Domains of Offset Wrist Robots via a 4D Workspace Representation}, address = {Regensburg}, doi = {10.35096/othr/pub-8863}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:898-opus4-88635}, pages = {9}, abstract = {We present a new method for studying the workspace of 6R robots using a four-dimensional representation. This is applied to numerically determine connected workspace components with a constant number of solutions of the inverse kinematics, and to describe critical values that form the boundaries these regions. To define and maximize uniqueness domains, nonsingular transition of these boundaries are investigated. This provides a method to determine workspace regions in which the robot can move avoiding configuration changes and singularities.}, language = {en} } @unpublished{FeessWeiss, author = {Feeß, Alexander and Weiß, Martin}, title = {Analysis of a Cuspidal 6R Robot}, address = {Regensburg}, doi = {10.35096/othr/pub-8862}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:898-opus4-88626}, pages = {8}, abstract = {We present a theoretical and numerical analysis of the kinematics for the Transpressor, a cuspidal 6R robot. It admits up to 16 inverse kinematics solutions which are described geometrically. For special target poses, we provide the solutions analytically and present a simple numerical solver for the general case. Moreover, an analytical estimate of the Jacobian determinant on a path between two solutions proves cuspidality for a class of robots similar to the transpressor.}, subject = {Kinematik}, language = {en} } @inproceedings{BockSchwarzWeiss, author = {Bock, Sebastian and Schwarz, Philipp and Weiß, Martin G.}, title = {U-Shape Phenomenon with Gaussian Noise and Clipped Inputs}, series = {Proceedings of Eighth International Congress on Information and Communication Technology. - (ICICT 2023, London, Volume 3)}, booktitle = {Proceedings of Eighth International Congress on Information and Communication Technology. - (ICICT 2023, London, Volume 3)}, editor = {Yang, Xin-She}, publisher = {Springer}, address = {Singapore}, isbn = {978-981-99-3042-5}, doi = {10.1007/978-981-99-3043-2_45}, pages = {569 -- 579}, abstract = {Object detection and recognition is a core task in machine vision. Both the position and the rotation of the objects are of interest. Common algorithms for object recognition based on neural networks use a regression formulation and are aimed at changing environments. In industrial applications, the setting is different: One would like to determine with high accuracy, the pose of known objects. We are investigating a prototype application in this field, where we identify the rotation of an object in a plane. We use common network architectures and provide an overview of how well they are suited for this purpose. In addition, we compare our results with standard image processing algorithms, paying particular attention to the accuracy with metrics relevant in the industry. Moreover, an U-shape phenomenon with disturbed data occurred during the experiments, which is partially explained analytically.}, language = {en} }