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2020 – today
- 2024
[c9]M. Mierzwa, K. Mäde, R. Sharma
, Uwe Reisgen, J. Wiartalla, Markus Schmitz, Mathias Hüsing, Burkhard Corves:
Monitoring and control of Additive Manufacturing with the Robot Operating System. ICCAD 2024: 1-6- [i2]Tobias Fischer, Isabel Paredes, Michael Batchelor, Thorsten Beier, Jesse Haviland, Silvio Traversaro, Wolf Vollprecht, Markus Schmitz, Michael Milford:
ROS2WASM: Bringing the Robot Operating System to the Web. CoRR abs/2409.09941 (2024) - 2023
- [j3]Dan Bürger, Stefan Pastel, Chien-Hsi Chen, Katharina Petri, Markus Schmitz, Leon Wischerath, Kerstin Witte:
Suitability test of virtual reality applications for older people considering the spatial orientation ability. Virtual Real. 27(3): 1751-1764 (2023) - 2022
- [b1]Markus Schmitz:
Machine Learning in Industrial Applications: Insights Gained from Selected Studies. University of Erlangen-Nuremberg, Germany, 2022 - [j2]Lars Leyendecker, Markus Schmitz, Hans Aoyang Zhou, Vladimir Samsonov, Marius Rittstieg, Daniel Lütticke:
Deep Reinforcement Learning for Robotic Control in High-Dexterity Assembly Tasks - A Reward Curriculum Approach. Int. J. Semantic Comput. 16(3): 381-402 (2022) - [c8]Markus Schmitz, Dilara Yesilbas:
An Application-centric Perspective on Industrial Artificial Intelligence. AMCIS 2022 - [c7]Markus Schmitz, Florian Menz, Mathias Hüsing, Burkhard Corves:
Robotic Analysation Methodology for Multidirectional Additive Manufacturing Process Configuration. ARK 2022: 462-469 - 2021
- [c6]Marius Hebecker, Jens Lambrecht, Markus Schmitz:
Towards Real-World Force-Sensitive Robotic Assembly through Deep Reinforcement Learning in Simulations. AIM 2021: 1045-1051 - [c5]Majid Shirazi, Markus Schmitz, Simon Janssen, Anabelle Thies, Georgij Safronov, Amr Rizk, Peter Mayr, Philipp Engelhardt:
Verifying the Applicability of Synthetic Image Generation for Object Detection in Industrial Quality Inspection. ICMLA 2021: 1365-1372 - [c4]Lars Leyendecker, Markus Schmitz, Hans Aoyang Zhou, Vladimir Samsonov, Marius Rittstieg, Daniel Lütticke:
Deep Reinforcement Learning for Robotic Control in High-Dexterity Assembly Tasks - A Reward Curriculum Approach. IRC 2021: 35-42 - [i1]Jibinraj Antony, Florian Schlather, Georgij Safronov, Markus Schmitz, Kristof Van Laerhoven:
Toward Fault Detection in Industrial Welding Processes with Deep Learning and Data Augmentation. CoRR abs/2106.10160 (2021) - 2020
- [j1]Markus Schmitz:
Auf dem Weg zu einer ethisch verantwortungsvollen Datennutzung. Inform. Spektrum 43(6): 366-373 (2020) - [c3]Markus Schmitz, Florian Pinsker, Alexander Ruhri, Beibei Jiang, Georgij Safronov:
Enabling Rewards for Reinforcement Learning in Laser Beam Welding processes through Deep Learning. ICMLA 2020: 1424-1431
2010 – 2019
- 2018
- [c2]Patrick Straus, Markus Schmitz, René Wöstmann, Jochen Deuse:
Enabling of Predictive Maintenance in the Brownfield through Low-Cost Sensors, an IIoT-Architecture and Machine Learning. IEEE BigData 2018: 1474-1483 - [p1]Markus Schmitz:
Die Digitale Agenda der BA - Wie sich ein öffentlicher Dienstleister der Digitalisierung stellt. Digitalisierung im Spannungsfeld von Politik, Wirtschaft, Wissenschaft und Recht (1) 2018: 409-418 - 2012
- [c1]Tjorben Bogon, Ingo J. Timm, Ulrich Jessen, Markus Schmitz, Sigrid Wenzel, Andreas D. Lattner, Dimitris C. Paraskevopoulos, Sven Spieckermann:
Towards assisted input and output data analysis in manufacturing simulation: the EDASim approach. WSC 2012: 257:1-257:13
Coauthor Index
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