Publikationen
2025
P. Wohlgenannt, L. Moosbrugger, S. Hegenbart, M. Kolhe, P. Kepplinger, „Optimal Sizing and Operation of a Battery Energy Storage System for Demand Response in a Food Processing Plant using Deep Learning for Load Forecasting”, 6th Interdisciplinary Data Science Conference (iDSC'25), Salzburg 26-28 May 2025
P. Steurer, A. Skuta, S. Hegenbart, R. Hoch, T. Lorünser, „Data-Driven Modeling for Privacy-Preserving Energy Prediction of Industrial Robots”, 5th IFSA Winter Conference on Automation, Robotics & Communications for Industry 4.0/5.0 (ARCI' 2025), Granada, 19-21 Februar 2025, https://www.researchgate.net/publication/389901305
2024
P. Wohlgenannt, S. Hegenbart, E. Eder, M. Kolhe, P. Kepplinger, "Energy Demand Response in a Food-Processing Plant: A Deep Reinforcement Learning Approach", Energies 2024, 17(24), 6430; https://doi.org/10.3390/en17246430
A. Vogell, U. Schilcher, J.F. Schmidt, C. Bettstetter, “Chimera states in pulse-coupled oscillator systems”, Physical Review E., accepted 10.9.2024
Schilcher, U., Toumpis, S., Borkotoky, S., Schmidt, J., Bettstetter, C., “Calculating the traffic density in LPWANs with finite retransmissions”, in Proc. IEEE Vehicular Technology Conf. (VTC), Singapore, June 2024
Rathi, S., Schmidt, J., Schilcher, U., Borkotoky, S., “Energy-aware relaying in dense LoRa networks with heterogeneous batteries”, IEEE Networking Letters, 6(1): 41–45, 2024.
2023
Sonnberger M., Merz R., Schmidt J., “Testing the limits of 5G communication with articulated robots in various traffic conditions," in Proc. IEEE Conf. on Standards for Commun. and Networking, Munich, Germany, November 2023.
Luckeneder, C., Hoch, R., Kaindl, H., " Towards Using Structural Abstraction for Model Checking", Proceedings of the 10th International Conference on Dependable Systems and Their Applications (DSA 2023), August 2023
Stippel, C., Schwendinger, B., Kammerhofer, M., Hoch, R. Kaindl, H., Sauter, T., "Towards Optimized Schedules for Charging Electric Vehicles on Austrian Highways using Genetic Algorithms", GECCO '23 Companion: Proceedings of the Companion Conference on Genetic and Evolutionary Computation, Juli 2023
Schmidt, J., Schilcher, U., Vogell, A., Bettstetter, C., "Using Randomization in Self-Organized Synchronization for Wireless Networks", ACM Transactions on Autonomous and Adaptive Systems, Juni 2023
Sparr, K., Steurer, P., Drexel, D., Hoch, R., "Using Digital Twins in Learning Factories for Simulation and Optimization", Proceedings of the 13th Conference on Learning Factories (CLF 2023), Juni 2023
2022
Drexel, D., Hoch, R., "Three-faceted manufacturing knowledge representation in cloud environments", 10th IFAC Conference on Manufacturing Modelling, Management and Control MIM 2022, Nantes, 22. - 24. Juni 2022
Merz, R., Hoch, R., Drexel, D., "Flexibilisierung von Lieferketten", nachhaltige technologien, AEE intec, Ausgabe 01, 2022, S. 15-17
2021
Drexel, D., "Automatically linking concepts in distributed, cloud-based manufacturing environments", Proceedings of the 15th International Rule Challenge, 7th Industry Track, and 5th Doctoral Consortium @RuleML+RR 2021. Leuven, Belgium (virtual due to Covid-19 pandemic), 8. - 15. September 2021
Sparr, K., Drexel, D., Hoch, R., "Using an Auction-Based System in Cloud Manufacturing for Selecting Manufacturing-as-a-Service Providers", in: Dolgui, A., Bernard, A., Lemoine, D., von Cieminski, G., Romero, D. (eds) Advances in Production Management Systems. Artificial Intelligence for Sustainable and Resilient Production Systems. APMS 2021. IFIP Advances in Information and Communication Technology, vol 634. Springer, August 2021
2020
Merz, R., Hoch, R., Drexel, D., "A Cloud-Based Research and Learning Factory for Industrial Productions", 10th Conference on Learning Factories, CLF2020, Procedia Manufacturing Vol. 45 (2020) 215–221
2019
Plankensteiner, K., "Zuverlässigkeitsanalyse von Schweißprozessen mit Supervised Learning Methoden", in: Werth, W., "Machine Learning und Data Science in der Mechatronik", Tagungsband, FH Kärnten, Campus Villach, 21. November 2019
2018
Poulain, B., Plankensteiner, K., Rigger, E., Schöch, R., Merz, R., "Concept of probabilistic modeling for real-time prediction of product quality and design automation", Efficiency, Flexibility, Integration, Wiener Produktionstechnik Kongress 2018, WPK 2018, 26. - 27. September 2018
Malin, S., Plankensteiner, K., Merz, R., Mayr, R., Schöndorfer, S., Thomas, M., "Smart recommendation system to simplify projecting for an HMI/SCADA platform", Data Science – Analytics and Applications, Proceedings of the 2nd International Data Science Conference – iDSC2019, Springer, 2019
Zumtobel, M., Plankensteiner, K., "Using supervised learning to predict the reliability of a welding process", Data Science – Analytics and Applications, Proceedings of the 2nd International Data Science Conference – iDSC2019, Springer, 2019