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Chair for Technologies and Management of Digital Transformation


Univ. Prof. Dr. Ing. Tobias Meisen

Industrial Deep Learning

In our research, we address the development and verification of deep learning methods for the use in industrial processes and services. On the one hand, our research aims at establishing deep learning as an enabler of new services such as predictive quality and predictive maintenance. On the other hand, we investigate reinforcement learning methods for training intelligent, self-learning agents to solve planning tasks. Central reserach topics are the formalization of industrial use cases as learning problems and their solution under consideration of criteria such as reliability, robustness and accuracy. Furthermore, we address the issue of developing data-efficient and sustainable deep learning models for the manufacturing industry. Our main research areas here cover bridging the reality gap of simulations by means of transfer learning methods and the continual training of deep learning models across process and system changes.

Main Topics

  • Deep Reinforcement Learning
  • Supervised Learning and Anomaly Detection
  • Sim2Real Transfer Learning
  • Continual Learning

Application Areas

  • Predictive Quality and Predictive Maintenance
  • Planning and control of industrial robots
  • Job shop scheduling and intelligent production planning
  • Usage anlalytics of digital products and services

Contact

Hasan Tercan, M.Sc.

Selected relevant publications

References
Hasan Tercan; Philipp Deibert; Tobias Meisen
Continual learning of neural networks for quality prediction in production using memory aware synapses and weight transfer
Journal of Intelligent Manufacturing,
2021
ISSN: 0956-5515

Keywords: research-industrial-deep-learning

Yannik Steiniger; Jannis Stoppe; Dieter Kraus; Tobias Meisen
Erzeugung von synthetischen Seitensichtsonar-Bildern mittels Generative Adversarial Networks
Hydrographische Nachrichten, (119):30--34
2021
ISSN: 1866-9204

Keywords: research-industrial-deep-learning

Christian Scheiderer; Nik Dorndorf; Tobias Meisen
Effects of Domain Randomization on Simulation-to-Reality Transfer of Reinforcement Learning Policies for Industrial Robots
Arabnia, Hamid R. and Ferens, Ken and de La Fuente, David and Kozerenko, Elena B. and Olivas Varela, José Angel and Tinetti, Fernando G., editor, Advances in Artificial Intelligence and Applied Cognitive Computing of Springer eBook Collection
page 157--169.
Publisher: Springer International Publishing and Imprint Springer, Cham
2021
ISBN: 978-3-030-70295-3

Keywords: research-industrial-deep-learning

Hasan Tercan; Christian Bitter; Todd Bodnar; Philipp Meisen; Tobias Meisen
Evaluating a Session-based Recommender System using Prod2vec in a Commercial Application
Proceedings of the 23rd International Conference on Enterprise Information Systems , page 610--617.
Publisher: SCITEPRESS - Science and Technology Publications,
2021
ISBN: 978-989-758-509-8

Keywords: research-industrial-deep-learning

Robert F. Maack; Hasan Tercan; Alexia F. Solvay; Maximilian Mieth; Tobias Meisen
Fault Detection in Railway Switches using Deformable Convolutional Neural Networks
2021 IEEE 19th International Conference on Industrial Informatics (INDIN) , page 1--6.
Publisher: IEEE,
2021
ISBN: 978-1-7281-4395-8

Keywords: research-industrial-deep-learning

Christian Bitter; Hasan Tercan; Tobias Meisen; Todd Bodnar; Philipp Meisen
When to Message: Investigating User Response Prediction with Machine Learning for Advertisement Emails
2021 4th International Conference on Artificial Intelligence for Industries (AI4I) , page 25--29.
Publisher: IEEE,
2021
ISBN: 978-1-6654-3410-2

Keywords: research-industrial-deep-learning

Christian Scheiderer; Timo Thun; Christian Idzik; Andrés Felipe Posada-Moreno; Alexander Krämer; Johannes Lohmar; Gerhard Hirt; Tobias Meisen
Simulation-as-a-Service for Reinforcement Learning Applications by Example of Heavy Plate Rolling Processes
Procedia Manufacturing, 51:897--903
2020
ISSN: 2351-9789

Keywords: research-industrial-deep-learning

Fabian Scheidt; Jifei Ou; Hiroshi Ishii; Tobias Meisen
deepKnit: Learning-based Generation of Machine Knitting Code
Procedia Manufacturing, 51:485--492
2020
ISSN: 2351-9789

Keywords: research-industrial-deep-learning

Schirin Baer; Punit Kumar Mohanty; Danielle Chelsea Turner; Tobias Meisen
Multi Agent Deep Q-Network Approach for Online Job Shop Scheduling in Flexible Manufacturing
International Conference on Manufacturing Systems and Multiple Machies (ICMSMM 2020)
2020

Keywords: research-industrial-deep-learning

Schirin Baer; Danielle Chelsea Turner; Punit Kumar Mohanty; Vladimir Samsonov; Jupiter Romuald Bakekeu; Tobias Meisen
Multi Agent Deep Q-Network Approach for Online Job Shop Scheduling in Flexible Manufacturing
Proceedings of the 7th International Conference on Industrial Engineering and Applications (ICIEA)
2020

Keywords: research-industrial-deep-learning

Christian Scheiderer; Malte Mosbach; Andres Felipe Posada-Moreno; Tobias Meisen
Transfer of Hierarchical Reinforcement Learning Structures for Robotic Manipulation Tasks
2020 International Conference on Computational Science and Computational Intelligence (CSCI) , page 504--509.
Publisher: IEEE,
2020
ISBN: 978-1-7281-7624-6

Keywords: research-industrial-deep-learning

Christian Scheiderer; Timo Thun; Tobias Meisen
Bézier Curve Based Continuous and Smooth Motion Planning for Self-Learning Industrial Robots
Procedia Manufacturing, 38:423--430
2019
ISSN: 2351-9789

Keywords: research-industrial-deep-learning

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