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Comparison of Material Flow Models and Acceleration of the Macroscopic Flow Model for Virtual Commissioning

A. Kienzlen, and A. Verl. 34th Annual European Simulation Simulation and Modelling Conference: Modelling and Simulation 2020 (ESM), page 168 - 175. (2020)

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Annika Kienzlen University of Stuttgart

Annika Liebgott

Automated reference-free assessment of MR image quality using an active learning approach: Comparison of Support Vector Machine versus Deep Neural Network classificationS. Gatidis, A. Liebgott, M. Schwartz, P. Martirosian, F. Schick, K. Nikolaou, B. Yang, and T. Küstner. Proceedings of the International Society for Magnetic Resonance in Medicine (ISMRM), Honolulu, Hawaii, USA, (April 2017)

Automated reference-free assessment of MR image quality using an active learning approach: Comparison of Support Vector Machine versus Deep Neural Network classificationS. Gatidis, A. Liebgott, M. Schwartz, P. Martirosian, F. Schick, K. Nikolaou, B. Yang, and T. Küstner. Proceedings of the International Society for Magnetic Resonance in Medicine (ISMRM), Honolulu, Hawaii, USA, (April 2017)An easy-to-use image labeling platform for automatic Magnetic Resonance Image Quality AssessmentT. Küstner, P. Wolf, M. Schwartz, A. Liebgott, F. Schick, S. Gatidis, and B. Yang. Proceedings of the IEEE International Symposium on Biomedical Imaging (ISBI), Melbourne, Australia, (April 2017)Automated image quality assessment in whole-body MRIS. Gatidis, A. Liebgott, H. Schmidt, N. Schwenzer, P. Martirosian, K. Nikolaou, F. Schick, B. Yang, and T. Küstner. Proceedings of the Radiological Society of North America (RSNA), Chicago, USA, (November 2016)

Annika Ackermann University of Stuttgart

Annika Wagner University of Stuttgart

Annika Neubauer University of Stuttgart

Other publications of authors with the same name

Der Materialfluss des digitalen ZwillingsA. Verl, and A. Kienzlen. SPS Magazin, (October 2019)A Signal-Based Approach to Switching Between Micro and Macro Material Flow Models for Production SystemsA. Kienzlen, and A. Verl. Production at the Leading Edge of Technology, page 491--501. Cham, Springer Nature Switzerland, (2024)Simulative Optimierung der Steuerungsparameter eines Materialflusslayouts mit BandförderernA. Kienzlen, J. Weißen, A. Verl, and S. Göttlich. Forschung im Ingenieurwesen, (Oct 27, 2020)Recurrent Neural Network for Modelling a Contractive Soft ActuatorA. Kienzlen, M. Zuern, S. Kazemi, W. Xu, L. Cheng, M. Strommel, and A. Verl. ISR Europe 2023; 56th International Symposium on Robotics, page 419-425. (2023)Complex Physics with Graph Networks for Industrial Material Flow SimulationF. Jaensch, K. Herburger, E. Bobe, A. Csiszar, A. Kienzlen, and A. Verl. Procedia CIRP, (2023)KI ebnet den Weg für autonome KatheternavigationA. Kienzlen, and A. Verl. mt | medizintechnik, 143 (6): 10-16 (2023)Materialflusssimulation für die Virtuelle Inbetriebnahme in SteuerungsechtzeitS. Göttlich, S. Hoher, A. Verl, J. Weißen, and A. Kienzlen. page 121--140. Springer Berlin Heidelberg, Berlin, Heidelberg, (2024)Konzept zur Integration eines kontinuierlichen Materialflussmodells in die Virtuelle Inbetriebnahme durch SignalgenerierungA. Kienzlen, and A. Verl. Simulation in Produktion und Logistik 2021, page 585--594. Göttingen, Cuvillier Verlag, (September 2021)Predicting coupling signals in a material flow real-time co-simulation with a Kalman filterA. Kienzlen, C. Scheifele, and A. Verl. 88, page 9--14. Elsevier BV, (2020)Comparative Analysis of Factory Simulation Description Models for Comprehensive Description of Model DesignD. Brovkina, A. Kienzlen, and O. Riedel. 2020 IEEE Eurasia Conference on IOT, Communication and Engineering (ECICE), page 318-321. (2020)