Hochschule Düsseldorf
University of Applied Sciences
Fachbereich Elektro- & Informationstechnik
Faculty of Electrical Engineering & Information Technology

​Forschung

  • Entwicklung von selbstoptimierenden, selbstlernenden und autonomen Systemen
  • Intelligente Datenanalyse für industrielle Anwendungen
  • Augmented & Virtual Reality sowie Ubiquitous Computing für bedienerfreundliche Mensch-Maschine Schnittstellen
  • Industrie 4.0 & Smart Factory
  • Maschinelles Lernen & Spieltheorie

Publikationen

2021

D. Schwung, S. Yuwono, A. Schwung, S. X. Ding: „Decentralized learning of energy optimal production policies using PLC-informed reinforcement learning“; in Computers & Chemical Engineering, Volume 152, 107382, 2021

D. Schwung: „Maschinelle Lernalgorithmen zur Selbstoptimierung in verteilten Produktionssystemen basierend auf spieltheoretischen Konzepten", Shaker Verlag, ISBN: 978-3-8440-7840-4, 2021


2020

D. Schwung, V. Patel, A. Schwung, S. X. Ding: „Optimierung in verteilten Produktionssystemen - Maschinelles Lernen mittels Spieltheorie auf der SPS“; in atp magazin, 62 (11-12), S. 66-77, 2020


D. Schwung, A. Schwung, S. X. Ding: „Self Optimization of Modular Production Units: A State-Based Potential Game Approach“; in IEEE Transactions on Cybernetics, accepted for publication, 2020


D. Schwung, J. N. Reimann, A. Schwung, S. X. Ding: „Smart Manufacturing Systems: A game theory-based approach“; In book: Intelligent Systems: Theory, Research and Innovation in Applications, Chapter: 3, Springer, 2020


2019

D. Schwung, A. Schwung, S. X. Ding: „Actor-Critic Reinforcement Learning for Energy Optimization in hybrid Production Environments“; in International Journal of Computing, 18(4), S. 360-371, 2019


D. Schwung, M. Modali, A. Schwung: „Self-Optimization in Smart Production Systems using Distributed Reinforcement Learning“; Proc. of the IEEE International Conference on Systems, Man and Cybernatics (SMC 2019), Bari, Italy, 2019


D. Schwung, J. N. Reimann, A. Schwung, S. X. Ding: „Potential Game based Distributed Optimization of Modular Production Units“; Proc. of the IEEE International Conference on Industrial Informatics (INDIN 2019), Helsinki, Finland, 2019


A. Schwung, D. Schwung, M.S. Abdul Hameed: „ Cooperative Robot Control in Flexible Manufacturing Cells: Centralized vs. Distributed Approaches“; Proc. of the IEEE International Conference on Industrial Informatics (INDIN 2019), Helsinki, Finland, 2019


2018

D. Schwung, A. Schwung, S. X. Ding: „On-line Energy Optimization of Hybrid Production Systems using Actor-Critic Reinforcement Learning“; Proc. of the 9th IEEE International Conference on Intelligent Systems, Funchal, Portugal, 2018


D. Schwung, J. N. Reimann, A. Schwung, S. X. Ding: „Self Learning in Flexible Manufacturing Units: A Reinforcement Learning Approach“; Proc. of the 9th IEEE International Conference on Intelligent Systems, Funchal, Portugal, 2018


2017

D. Schwung, F. Csaplar, A. Schwung, S. X. Ding: „An Application of Reinforcement Learning Algorithms to Industrial Multi-Robot Stations for cooperative handling operation“ Proc. of the 15th IEEE International Conference on Industrial Informatics (INDIN 2017), Emden, Germany, 2017


D. Schwung, T. Kempe, A. Schwung, S. X. Ding: „Self-optimization of energy consumption in complex bulk good processes using reinforcement learning“, Proc. of the 15th IEEE International Conference on Industrial Informatics (INDIN 2017), Emden, Germany, 2017


A. Schwung, A. Elbel, D. Schwung: „System reconfiguration of modular production units using a SOA-based control structure“, Proc. of the 15th IEEE International Conference on Industrial Informatics (INDIN 2017), Emden, Germany, 2017