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Jan Niclas Reimann, M.Sc.

FB Elektrische Energietechnik

Soest FB EET Gebäude 4

Publikationen

2023

Reimann, J.N., Shukla, B.B., Schwung, A., Ding, S.X. (2023). Similarity Constrained Conditional Generative Auto-encoder with Generalized Dilated Networks. In: De Marsico, M., Sanniti di Baja, G., Fred, A. (eds) Pattern Recognition Applications and Methods. ICPRAM ICPRAM 2021 2022. Lecture Notes in Computer Science, vol 13822. Springer, Cham.

2022

Jan Niclas Reimann, Andreas Schwung, and Steven X. Ding, “Adopting attention-mechanisms for Neural Logic Rule Layers,” at- Automatisierungstechnik, vol. 70, no. 3, pp. 257–266, 2022

Jan Niclas Reimann, Andreas Schwung, and Steven X. Ding, “Neural Logic Rule Layers,” Information Sciences, vol. 596, pp. 185–201, 2022

2021
G.S. Chadha, J.N. Reimann, A. Schwung: „Generalized Dilation Structures in Convolutional Neural Networks“; Proc. of the International Conference on Pattern Recognition Applications and Methods, accepted for publication, 2021

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

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

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 International Conference on Intelligent Systems, Funchal, Portugal, 2018