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Veröffentlichungen

Brand, F., Lott, K., Malburg, L., Hoffmann, M., & Bergmann, R. (2023). Using Deep Reinforcement Learning for the Adaptation of Semantic Workflows. In: Lukas Malburg und Deepika Verma (Hg.): Proceedings of the Workshops at the 31st International Conference on Case-Based Reasoning (ICCBR-WS 2023) co-located with the 31st International Conference on Case-Based Reasoning (ICCBR 2023), Aberdeen, Scotland, UK, July 17, 2023, Bd. 3438: CEUR-WS.org (CEUR Workshop Proceedings), S. 55–70.

Creutz, L., Schneider, J., & Dartmann, G. (2022). Distributed Hash Table with Extensible Remote Procedure Calls. In: 2022 5th International Conference on Computational Intelligence and Networks (CINE) (pp. 1-6). IEEE.

Creutz, L., Wagner, K., & Dartmann, G. (2022). Cyber-Physical Contracts in Offline Regions. In: 2022 IEEE International Conferences on Internet of Things (iThings) and IEEE Green Computing & Communications (GreenCom) and IEEE Cyber, Physical & Social Computing (CPSCom) and IEEE Smart Data (SmartData) and IEEE Congress on Cybermatics (Cybermatics) (pp. 461-469). IEEE.

Fazlic, L. B., Halawa, A., Schmeink, A., Lipp, R., Martin, L., Peine, A., ... & Dartmann, G. (2022). A Novel Hybrid Methodology for Anomaly Detection in Time Series. In: International Journal of Computational Intelligence Systems 15.1 p. 50, 2022.

Grumbach, L., & Bergmann, R. (2021). SEMAFLEX: A novel approach for implementing workflow flexibility by deviation based on constraint satisfaction problem solving. In: Expert Systems, 38(7), e12385.

Grumbach, L., & Bergmann, R. (2023, July). A Case-Based Approach for Workflow Flexibility by Deviation. In: International Conference on Case-Based Reasoning (pp. 294-308). Cham: Springer Nature Switzerland.

Guldner, A., & Murach, J. (2022). Measuring and assessing the resource and energy efficiency of artificial intelligence of things devices and algorithms. In Environmental Informatics (pp. 185-199). Cham: Springer International Publishing. In: Wohlgemuth, V. et al. (eds) Advances and New Trends in Environmental Informatics. EnviroInfo 2022. Progress in IS. Springer, Cham.

Guldner, A., Hoffmann, M., Lohr, C., Machhamer, R., Malburg, L., Morgen, M., ... & Weyers, B. (2023). A Framework for AI-Based Self-Adaptive CyberPhysical Process Systems. In: it - Information Technology 65.3, pp. 113–128.  

Guldner, A., Kreten, S., & Naumann, S. (2021). Exploration and systematic assessment of the resource efficiency of Machine Learning. In: Gesellschaft für Informatik e.V. (GI) (Hrsg.) INFORMATIK 2021 - Computer Science & Sustainability, pp. 287–299, Lecture Notes in Informatics (LNI), Bonn.

Hoffmann, M., & Bergmann, R. (2022). Using graph embedding techniques in process-oriented case-based reasoning. In: Algorithms, 15(2), 27.

Hoffmann, M., & Bergmann, R. (2023). Ranking-Based Case Retrieval with Graph Neural Networks in Process-Oriented Case-Based Reasoning. In: The International FLAIRS Conference Proceedings (Vol. 36).

Hoffmann, M., Malburg, L., & Bergmann, R. (2021). ProGAN: toward a framework for process monitoring and flexibility by change via generative adversarial networks. In: International Conference on Business Process Management (pp. 43-55). Cham: Springer International Publishing.

Hoffmann, M., Malburg, L., & Bergmann, R. (2021). ProGAN: toward a framework for process monitoring and flexibility by change via generative adversarial networks. In: International Conference on Business Process Management (pp. 43-55). Cham: Springer International Publishing.

Hoffmann, M., Malburg, L., Bach, N., & Bergmann, R. (2022). GPU-based graph matching for accelerating similarity assessment in process-oriented case-based reasoning. In: International Conference on Case-Based Reasoning (pp. 240-255). Cham: Springer International Publishing.

Junger, D., Westing, M., Freitag, C., Guldner, A., Mittelbach, K., Weber, S., ... & Wohlgemuth, V. (2023). Potentials of Green Coding-Findings and Recommendations for Industry, Education and Science. In: INFORMATIK 2023 – Designing Futures: Zukünfte gestalten. Gesellschaft für Informatik e.V. (GI). Lecture Notes in Informatics (LNI), pp. 1289–1299.

Knebel, P., Appold, C., Guldner, A., Horbach, M., Juncker, Y., Müller, S., & Matheis, A. (2022). An Artificial Intelligence of Things based Method for Early Detection of Bark Beetle Infested Trees. In: Wohlgemuth, V. et al. (Hrsg.), EnviroInfo 2022. Lecture Notes in Informatics (LNI). Bonn: Gesellschaft für Informatik e.V. p. 111.

Kumar, R., Schultheis, A., Malburg, L., Hoffmann, M., & Bergmann, R. (2022). Considering inter-case dependencies during similarity-based retrieval In: Proceedings of the 35th International Florida Artificial Intelligence Research Society Conference, FLAIRS 2022, Hutchinson Island, Jensen Beach, Florida, USA, 2022.

Lisa Grumbach (2023): Flexible Workflows - A Constraint- and Case-Based Approach. Diss. Artif. Intell. 354, IOS Press 2023, ISBN 978-1-64368-396-6, pp. 1-318

Machhamer, R., Fazlic, L. B., Guven, E., Junk, D., Kurt, G. K., Naumann, S., ... & Dartmann, G. (2023). Likelihood-based Sensor Calibration for Expert-Supported Distributed Learning Algorithms in IoT Systems. arXiv preprint arXiv:2309.11526.

Malburg, L., Hoffmann, M., & Bergmann, R. (2023). Applying MAPE-K control loops for adaptive workflow management in smart factories. In: Journal of Intelligent Information Systems, 1-29.

Malburg, L., Hoffmann, M., Trumm, S., & Bergmann, R. (2021). Improving similarity-based retrieval efficiency by using graphic processing units in case-based reasoning. In: Proceedings of the 34th International Florida Artificial Intelligence Research Society Conference, FLAIRS 2021, North Miami Beach, Florida, USA.

Malburg, L., Rieder, M. P., Seiger, R., Klein, P., & Bergmann, R. (2021). Object detection for smart factory processes by machine learning. In: Procedia Computer Science, 184, 581-588.

Maletzki, C., Grumbach, L., Rietzke, E., & Bergmann, R. (2023). Towards Hybrid Intelligent Support Systems for Emergency Call Handling. In: Proceedings of the AAAI 2023 Spring Symposium on Challenges Requiring the Combination of Machine Learning and Knowledge Engineering, Hyatt Regency, San Francisco Airport, California, USA, March 27-29.

Morgen, M., Fazlic, L. B., Peine, A., Martin, L., Schmeink, A., Hallawa, A., & Dartmann, G. (2022, November). A Visualization and Benchmarking Simulator for Clinical Data. In: PHealth 2022: Proceedings of the 19th International Conference on Wearable Micro and Nano Technologies for Personalized Health (Vol. 299, p. 223). IOS Press.

Pauli, J., Hoffmann, M., & Bergmann, R. (2023). Similarity-Based Retrieval in Process-Oriented Case-Based Reasoning Using Graph Neural Networks and Transfer Learning. In: The International FLAIRS Conference Proceedings (Vol. 36).

Rietzke, E., Maletzki, C., Bergmann, R., & Kuhn, N. (2021). Execution of knowledge-intensive processes by utilizing ontology-based reasoning: ODD-BP: an ontology-and data-driven business process model. In: Journal on Data Semantics, 10(1-2), 3-18.

Rodermund, S. C., Buettner, R., & Timm, I. J. (2020). Towards simulation-based preplanning for experimental analysis of nudging. 15th International Conference on Wirtschaftsinformatik (WI 2020) (pp. 1219–1233), Potsdam, Germany, 2020.

Rodermund, S. C., Lorig, F., & Timm, I. J. (2019). Ethical Challenges in Modeling and Simulation of Nudging in Care. In: EMoWI@ Wirtschaftsinformatik (pp. 35-41).

Rodermund, S. C., Neuerburg, B., Lorig, F., & Timm, I. J. (2019). Simulating Strain and Motivation in Human Work Performance: An Agent-Based Modeling Approach Using the Job Demands-Resources Model. In: Proceedings of the Eleventh Conference on Advances in System Simulation (SIMUL 2019), Valencia, Spain (pp. 8-13).

Rodermund, S. C., Neuerburg, B., Lorig, F., & Timm, I. J. Agent-Based Simulation of Strain and Motivation in Human Work Performance in Human Work Performance, International Journal on Advances in Systems and Measurements, 13 (3&4), 2020.

Schäfer, F., Schaupeter, L., & Vette-Steinkamp, M. (2023). Konzept eines wandlungsfähigen Demontagesystems für Hochvoltbatterien in Tagungsband AALE 2023: Mit Automatisierung gegen den Klimawandel, J. Reiff-Stephan, J. Jäkel, and A. Schwarz, Eds., 2023, pp. 21–29.

Julien Murach et al. “Development of an Autonomous Edge-AI Board Prototype for Local, Automated Training and Inference of TinyML Algorithms”. In: IEEE Systems Journal (2024). issn: 1937-9234

Schaupeter, L., & Vette-Steinkamp, M. (2022). Geschlossener Informationsfluss über den gesamten Produktlebenszyklus als Wegbereiter zur effizienten Refabrikation. In: Wissenstransfer im Spannungsfeld von Autonomisierung und Fachkräftemangel, Hochschule für Technik, Wirtschaft und Kultur Leipzig.

Schuler, N., Hoffmann, M., Beise, H. P., & Bergmann, R. (2023). Semi-supervised Similarity Learning in Process-Oriented Case-Based Reasoning. In: International Conference on Innovative Techniques and Applications of Artificial Intelligence (pp. 159-173). Cham: Springer Nature Switzerland.

Schultheis, A., Hoffmann, M., Malburg, L., & Bergmann, R. (2023). Explanation of similarities in process-oriented case-based reasoning by visualization. In: International Conference on Case-Based Reasoning (pp. 53-68). Cham: Springer Nature Switzerland.

Theusch, F., Seemann, L., Guldner, A., Naumann, S., & Bergmann, R. (2023). Towards Machine Learning-based Digital Twins in Cyber-Physical Systems. In: First Workshop on AI for Digital Twins and Cyber-Physical Applications in conjunction with 32nd International Joint Conference on Artificial Intelligence (AI4DT&CP @IJCAI 2023). Macao S.A.R, pp. 1–16.

Theusch, F.; Klein, P.; Bergmann, R.; Wilke, W.; Bock, W.; Weber, A.: Fault Detection and Condition Monitoring in District Heating Using Smart Meter Data. In: Proceedings of the European Conference of the PHM Society 2021, pp. 407 – 417.

Urschel, B., Fazlic, L. B., Morgen, M., Machhamer, R., Dartmann, G., & Gollmer, K. U. (2022). A Machine Learning Approach for Optimal Ventilation based on Data from CO 2 Sensors. In: 2022 Sensor Data Fusion: Trends, Solutions, Applications (SDF) (pp. 1-6). IEEE.

Weber, S., Guldner, A., Begic Fazlic, L., Dartmann, G., & Naumann, S. (2023). Sustainability in Artificial Intelligence-Towards a Green AI Reference Model. In: INFORMATIK 2023 – Designing Futures: Zukünfte gestalten. Gesellschaft für Informatik e.V. (GI). Lecture Notes in Informatics (LNI), 2023, pp. 1503–1514.