2025
Zimmering, B.; Coelho, C.; Gupta, V.; Maleshkova, M.; Niggemann, O.: Breaking Free: Decoupling Forced Systems with Laplace Neural Networks, European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD), Porto, Portugal, 2025
Niggemann, O.; Beyerer, J.; Fey, G.; Kampker, A.; Diedrich, A.; Kühnert, C.; Kritl, A.: ML4CPS 2025 Workshop Proceedings, 2025, https://doi.org/10.24405/20018
Heesch, R.; Eilermann, S.; Windmann, A.; Diedrich, A.; Rosenthal, P.; Niggemann, O.: Evaluating Large Language Models for Real-World Engineering Tasks, 2025, https://doi.org/10.48550/arXiv.2505.13484
Diedrich, A.; Kühnert, C.; Maier, G.; Schraven, J.; Niggemann, O.: AITwin: A Uniform Digital Twin Interface for Artificial Intelligence Applications, In: Schulz, D., Bauckhage, C. (eds) Informed Machine Learning. Cognitive Technologies, Springer, Cham., 2025, https://doi.org/10.1007/978-3-031-83097-6_3
Hohmann, M.; Yiming, A.; Penter, L.; Ihlenfeldt, S.; Niggemann, O.: An AI approach for predicting the active surface of deep drawing tools in try-out, at – Automatisierungstechnik, 73(4): 251-260, 2025, https://doi.org/10.1515/auto-2024-0130
Rosenthal, P.; Liebert, A.; Niggemann, O.: Finding optimal solution principles in conceptual design, 25th International Conference on Engineering Design (ICED), Dallas, USA, 2025
Roche, J.P.; Niggemann, O.; Friebe, J.: Using Autoencoders and Automatic Differentiation to Reconstruct Missing Variables in a Set of Time Series. Springer Nature Computer Science. 2025
2024
Schulz, M.; Fay, A.; Niggemann, O.; Matiaske, W.; Schulz, D.: dtec.bw-Beiträge der Helmut-Schmidt-Universität / Universität der Bundeswehr Hamburg, Band 2, DTEC.bw, 2024, https://doi.org/10.24405/16768
Diedrich, A.; Heesch, R.; Bozzano, M.; Ludwig, B.; Cimatti, A.; Niggemann, O.: Inferring Sensor Placement Using Critical Pairs and Satisfiability Modulo Theory, The 35th International Conference on Principles of Diagnosis and Resilient Systems, Vienna, Austria, November 2024
Vranješ, D.; Ehrhardt, J.; Heesch, R.; Moddemann, L.; Steude, H.; Niggemann, O.: Design Principles for Falsifiable, Replicable and Reproducible Empirical Machine Learning Research, The 35th International Conference on Principles of Diagnosis and Resilient Systems, Vienna, Austria, November 2024
Heesch, R.; Cimatti, A.; Ehrhardt, J.; Diedrich, A.; Niggemann, O.: Summary of A Lazy Approach to Neural Numerical Planning with Control Parameters, The 35th International Conference on Principles of Diagnosis and Resilient Systems, Vienna, Austria, November 2024
Diedrich, A.; Windmann, S.; Niggemann, O.: Solving industrial fault diagnosis problems with quantum computers, Quantum Mach. Intell. 6, 66, 2024, https://doi.org/10.1007/s42484-024-00184-x
Eilermann, S.; Lüddecke, L.; Hohmann, M.; Zimmering, B.; Oertel, M.; Niggemann, O.: A Neural Ordinary Differential Equations Approach for 2D Flow Properties Analysis of Hydraulic Structures, 1st ECAI Workshop on Machine Learning Meets Differential Equations: From Theory to Applications, Santiago de Compostela, Spain, October 2024
Zimmering, B.; Coelho, C.; Niggemann, O.: Optimising Neural Fractional Differential Equations for Performance and Efficiency, 1st ECAI Workshop on Machine Learning Meets Differential Equations: From Theory to Applications, Santiago de Compostela, Spain, October 2024
Liebert, A.; Dethof, F.; Keßler, S; Niggemann, O.: Automated Impact Echo Spectrum Anomaly Detection using U-Net Autoencoder, PAIS24, Santiago de Compostela, Spain, October 2024
Heesch, R.; Cimatti, A; Ehrhardt, J.; Diedrich, A.; Niggemann, O.: A Lazy Approach to Neural Numerical Planning with Control Parameters, 27TH European Conference on Artificial Intelligence (ECAI), Santiago de Compostela, Spain, 2024
Boschmann, D.; Stieghorst, C; Knezevic, D; Kadri, L.; Niggemann, O.: Automation of PGAA Spectra Analysis with Deep Learning, 22nd IEEE International Conference on Industrial Informatics (INDIN), Beijing, China, 2024
Meyer, F.; Freitag, L.; Hinrichsen, S.; Niggemann, O.: Potentials of Large Language Models for Generating Assembly Instructions, 29th IEEE International Conference on Emerging Technologies and Factory Automation (ETFA), Padova, Italy, 2024
Widulle, N.; Meyer, F.; Niggemann, O.: Generating Assembly Instructions Using Reinforcement Learning in Combination with Large Language Models, 22nd IEEE International Conference on Industrial Informatics (INDIN), Beijing, China, 2024
Windmann, A.; Wittenberg, P.; Schieseck, M.; Niggemann, O.: Artificial Intelligence in Industry 4.0: A Review of Integration Challenges for Industrial Systems. 22nd IEEE International Conference on Industrial Informatics (INDIN), Beijing, China, 2024.
Moddemann, L.; Steude, H. S.; Diedrich, A.; Pill, I.; Niggemann, O.: Extracting Knowledge using Machine Learning for Anomaly Detection and Root-Cause Diagnosis, 29th IEEE International Conference on Emerging Technologies and Factory Automation (ETFA), Padova, Italy, 2024
Ehrhardt, J.; Overlöper, P; Vranjes, D; Steude, H; Diedrich, A.; Niggemann, O.: Using Modular Neural Networks for Anomaly Detection in Cyber-Physical Systems, 29th IEEE International Conference on Emerging Technologies and Factory Automation (ETFA), Padova, Italy, 2024
Ludwig, B.; Diedrich, A.; Niggemann, O.: Using Ontologies to Create Logical System Descriptions for Fault Diagnosis, 29th IEEE International Conference on Emerging Technologies and Factory Automation (ETFA), Padova, Italy, 2024
Overlöper, P.; Moddemann, L.; Hranisavljevic, N.; Windmann, A.; Niggemann, O.: Discretization of CPS Time Series with Neural Networks, 29th IEEE International Conference on Emerging Technologies and Factory Automation (ETFA), Padova, Italy, 2024
Zimmering, B.; Roche, J.P.; Niggemann, O.: Enhancing Nonlinear Electrical Circuit Modeling with Prior Knowledge-Infused Neural ODEs 29th IEEE International Conference on Emerging Technologies and Factory Automation (ETFA), Padova, Italy, 2024
Hohmann, M.; Eilermann, S.; Großmann, W.; Niggemann, O.: Design Automation: A Conditional VAE Approach to 3D Object Generation under Conditions, 29th IEEE International Conference on Emerging Technologies and Factory Automation (ETFA), Padova, Italy, 2024
Augustin, J.L.; Niggemann, O.: Self-Supervised Graph Structure Learning for Cyber-Physical Systems, ,12th IFAC Symposium on Fault Detection, Supervision and Safety for Technical Processes (SAFEPROCESS), Ferrara, Italy, 2024
Vranješ, D.; Niggemann, O.: Enhancing Cyber-Physical System Analysis with Structure-Aware Modular Neural Networks, 7th IEEE International Conference on Industrial Cyber-Physical Systems (ICPS), St. Louis, USA, 2024
Liebert, A.; Palani, A.; Rensmeyer, T.; Breuer, M.; Niggemann, O.: CNN-based Temperature Dynamics Approximation for Burning Rooms, SafeProcess24, Ferrara, Italy, June 2024
Steude, H. S.; Geier, C.; Moddemann, L.; Creutzenberg, M.; Pfeifer, J.; Turk, S.; Niggemann, O.: End-to-end MLOps Integration: A Case Study with ISS Telemetry Data, ML4CPS – Machine Learning for Cyber-Physical Systems, Berlin, Germany, 2024
Rosenthal, P.; Demke, N.; Mantwill, F.; Niggemann, O.: Plan-Based Derivation of General Functional Structures in Product Design, 7th IEEE International Conference on Industrial Cyber-Physical Systems (ICPS), St. Louis, USA, 2024
Zimmering, B.; Niggemann, O.: Integrating continuous-time neural networks in engineering: bridging machine learning and dynamical system modeling, Machine Learning for Cyber-Physical Systems (ML4CPS), Berlin, Germany, 2024, https://doi.org/10.24405/15313
Diedrich, A.; Moddemann, L.; Niggemann, O.: Learning System Descriptions for Cyber-Physical Systems, 12th IFAC Symposium on Fault Detection, Supervision and Safety for Technical Processes (SAFEPROCESS), Ferrara, Italy, 2024
Moddemann, L.; Steude, H.; Diedrich, A.; Niggemann, O.: Discret2Di – Deep Learning based Discretization for Model-based Diagnosis,12th IFAC Symposium on Fault Detection, Supervision and Safety for Technical Processes (SAFEPROCESS), Ferrara, Italy, 2024
Steude, H.; Moddemann, L.; Diedrich, A.; Ehrhardt, J.; Niggemann, O.: Diagnosis driven Anomaly Detection for Cyber-Physical Systems, 12th IFAC Symposium on Fault Detection, Supervision and Safety for Technical Processes (SAFEPROCESS), Ferrara, Italy, 2024
Rensmeyer, T.; Großmann, W.; Kramer, D.; Niggemann, O.: Bayesian Transfer Learning of Neural Network-Based Interatomic Force Models, The 38th Annual AAAI Conference on Artificial Intelligence | Workshop on AI to Accelerate Science and Engineering, Vancouver, Canada, 2024
Merkelbach, S.; Diedrich, A.; von Enzberg, Sebastian; Niggemann, O.; Dumitrescu, R.: Towards the Generation of Models for Fault Diagnosis of CPS using VQA Models, Machine Learning for Cyber-Physical Systems (ML4CPS), Berlin, Germany, 2024
Großmann, W.; Eilermann, S.; Rensmeyer, T.; Liebert, A.; Hohmann, M; Wittke, C.; Niggemann, O.: Position Paper on Materials Design — A Modern Approach, The 38th Annual AAAI Conference on Artificial Intelligence | Workshop on AI to Accelerate Science and Engineering, Vancouver, Canada, 2024, DOI: 10.48550/arXiv.2312.10996
2023
Steude, H. S.; Moddemann, L.; Diedrich, A.; Ehrhardt, J.; Niggemann, O.: Diagnosis Driven Anomaly Detection for CPS, Preprint, 2023
Petroll, C.; Eilermann, S.; Hofer, P.; Niggemann, O.: A Generative Neural Network Approach for 3D Multi-Criteria Design and Optimization of an Engine Mount for an Unmanned Air Vehicle, Preprint, 2023
Hinck, D; Schöttler, J.; Krantz, M.; Widulle, N.; Issleif, K.; Niggemann, O.: A next generation protective emblem: Cross-frequency protective options for non-combatants in the context of (fully) autonomous warfare, The International Review of the Red Cross, 2023
Eilermann, S.; Wehmeier, L.; Niggemann, O.; Deuter, A.: KIAAA: An AI Assistant for Teaching Programming in the Field of Automation, 2023 IEEE 21st International Conference on Industrial Informatics (INDIN), Lemgo, Germany, 2023, pp. 1-7, DOI: 10.1109/INDIN51400.2023.10218157
Moddemann, L.; Steude, H.; Niggemann, O.: Discret2Di – Deep Learning based Discretization for Model-based Diagnosis. 34th International Workshop on Principle of Diagnosis, Loma Mar, USA, 2023
Steude, H.; Moddemann, L.; Diedrich, A.; Ehrhardt, J.; Kalech, M.; Niggemann, O.: Diagnosis driven Anomaly Detection for CPS. 34th International Workshop on Principle of Diagnosis, Loma Mar, USA, 2023
Heesch, R.; Ehrhardt, J.; Niggemann, O.: Integrating Machine Learning into an SMT-based Planning Approach for Production Planning in Cyber-Physical Production Systems. International Workshop on Hybrid Models for Coupling Deductive and Inductive Reasoning (HYDRA), ECAI 2023 – European Conference on Artificial Intelligence, Krakau, Poland
Ehrhardt, J.; Heesch, R.; Niggemann, O.: Learning Process Steps as Dynamical Systems for a Sub-Symbolic Approach of Process Planning in Cyber-Physical Production Systems. International Workshop on Hybrid Models for Coupling Deductive and Inductive Reasoning (HYDRA), ECAI 2023 – European Conference on Artificial Intelligence, Krakau, Poland
Windmann, A.; Steude, H.; Niggemann, O.: Robustness and Generalization Performance of Deep Learning Models on Cyber-Physical Systems: A Comparative Study. Workshop of Artificial Intelligence for Time Series Analysis (AI4TS), IJCAI 2023 – International Joint Conference on Artificial Intelligence, Macao, China
Widulle, N.; Niggemann, O.: Using Reverse Reinforcement Learning for Assembly Tasks, PRL Workshop – Bridging the Gap Between AI Planning and Reinforcement Learning, IJCAI 2023 – International Joint Conference on Artificial Intelligence, Macao, China
Liebert, A.; Wittke, C.; Ehrhardt, J.; Jaufmann, R.; Widulle, N.; Eilermann, S.; Krantz, M.; Niggemann, O.: Using FliPSi to Generate Data for Machine Learning Algorithms, IEEE ETFA 2023 – IEEE International Conference on Emerging Technologies and Factory Automation, Siana, Romania
Krantz, M.; Widulle, N.; Niggemann, O.: Game Design Tools for ML Data Generation in CPS. In: 2023 9th International Conference on Automation, Robotics and Applications (ICARA), February 2023, to be published In: 2023 9th International Conference on Automation, Robotics and Applications (ICARA) Conference Proceedings, IEEE Xplore
Schöttler, J. J.; Bürklin, C.; Niggemann, O.: 54 Shapes of [naval] grey: AI based eo-sensor image classification of warships, NATO SET SET-311, NATO RESTRICTED, 2023
Schöttler, J. J.; Bürklin, C.; Niggemann, O.: Y inside: AI-based mapping of deinterleaved radar to a database, NATO SET SET-311, NATO RESTRICTED, 2023
Vieira da Silva, L.M.; Heesch, R.; Köcher, A.; Fay, A.: Transformation eines Fähigkeitsmodells in einen PDDL-Planungsansatz, at-Automatisierungstechnik 71.2, 2023, 105-115, DOI: 10.1515/auto-2022-0112
Niggemann, O.; Zimmering, B.; Steude, H.; Augustin, J.L.; Windmann, A.; Multaheb, S.: Machine Learning for Cyber-Physical Systems. In: Vogel-Heuser, B.; Wimmer, M. (eds) Digital Transformation. Springer Vieweg, Berlin, Heidelberg, 2023
https://doi.org/10.1007/978-3-662-65004-2_17
Kiefer, B.; Kristan, M.; Perš, J.; Žust, L.; Poiesi, F.; Andrade, F.; Bernardino, A.; Dawkins, M.; Raitoharju, J.; Quan, Y.; Atmaca, A.; Hofer, T.; Zhang, Q.; Xu, Y.; Zhang, J.; Tao, D.; Sommer, L.; Spraul, R.; Zhao, H.; Zhang, H.; Zhao, Y.; Augustin, J.; Jeon, E.i.; Lee, I.; Zedda, L.; Loddo, A.; Di Ruberto, C.; Verma, S.; Gupta, S.; Muralidhara, S.; Hegde, N.; Xing, D.; Evangeliou, N.; Tzes, A.; Bartl, V.; Špaňhel, J.; Herout, A.; Bhowmik, N.; Breckon, T.; Kundargi, S.; Anvekar, T.; Tabib, R.; Mudenagudi, U.; Vats, A.; Song, Y.; Liu, D.; Li, Y.; Li, S.; Tan, C.; Lan, L.; Somers, V.; De Vleeschouwer, C.; Alahi, A.; Huang, H.W.; Yang, C.Y.; Hwang, J.N.; Kim, P.K.; Kim, K.; Lee, K.; Jiang, S.; Li, H.; Ziqiang, Z.; Vu, T.A.; Nguyen-Truong, H.; Yeung, S.K.; Jia, Z.; Yang, S.; Hsu, C.C.; Hou, X.Y.; Jhang, Y.A.; Yang, S.; & Yang, M.T: 1st Workshop on Maritime Computer Vision (MaCVi) 2023: Challenge Results, Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV) Workshops (pp. 265-302), 2023
2022
Roche, J.P.; Friebe, J.; Niggemann, O.: Knowledge Based Grey Box Modeling of Inaccessible Circuits for System EMC-Simulation in Time Domain, 24th European Conference on Power Electronics and Applications (EPE’22 ECCE Europe), Hannover, Germany, September 2022.
Multaheb, S.; Bauer, F.; Bretschneider, P.; Niggemann, O.: Learning physically meaningful representations of energy systems with variational autoencoders, 27th IEEE International Conference on Emerging Technologies and Factory Automation, ETFA 2022, Stuttgart, Germany, September 6 ‐ 9, 2022
Rensmeyer, T.; Multaheb, S.; Putzke, J.; Zimmering, B.; Niggemann, O.: Using domain-knowledge to improve machine learning: A survey of recent advances, atp Magazin 8/2022, Vulkan Verlag
Nordhausen, A.; Ehrhardt, J; Möller, N: LaiLa Modellfabrik-Eine Validierungsplattform für Künstliche Intelligenz im Bereich Cyber-Physischer Produktionssysteme im Leichtbau, In: dtec.bw-Beiträge der Helmut-Schmidt-Universität / Universität der Bundeswehr Hamburg: Forschungsaktivitäten im Zentrum für Digitalisierungs- und Technologieforschung der Bundeswehr dtec.bw – Band 1 · 2022, Seiten 230-234, DOI: https://doi.org/10.24405/14522
Widulle, N.; Ehrhardt, J.; Krantz, M.; Liebert, A.; Nordhausen, A.; Niggemann, O.: Eine Simulationsumgebung für flexible Cyber-Physische Produktionssysteme zur Generierung realistischer Datensätze für maschinelle Lernverfahren, In: VDI-Berichte – Leitkongress Automation 2022 – Band 2399 · 2022, Seiten 533-545, ISSN: 0083-5560
Kelm, B.; Myschik, S.; Tappe, M.; Niggemann, O.: Anwendung eines modellbasierten Rekonfigurationsansatzes und Vorstellung eines Konzeptes zur qualitativen Systemüberwachung für das Lebenserhaltungssystems (ECLSS) des COLUMBUS-Moduls der ISS. In: dtec.bw-Beiträge der Helmut-Schmidt-Universität / Universität der Bundeswehr Hamburg: Forschungsaktivitäten im Zentrum für Digitalisierungs- und Technologieforschung der Bundeswehr dtec.bw – Band 1 · 2022, Seiten 117-122, DOI: https://doi.org/10.24405/14539
Ivanovic, Pavle; Schöttler, Jonas; Windmann, Alexander; Neumann, Philipp: SmartShip – AI-based Assistance Systems for the Maritime Sector. Forschungsaktivitäten im Zentrum für Digitalisierungs- und Technologieforschung der Bundeswehr 1 292-296, 2022
Widulle, N.; Vieira da Silva, L. M. ; Heesch, R. ; Putzke, J. ; Niggemann, O.: Investigating the Use of AI Planning Methods in Real-World CPS Use Cases. In: dtec.bw-Beiträge der Helmut-Schmidt-Universität / Universität der Bundeswehr Hamburg: Forschungsaktivitäten im Zentrum für Digitalisierungs- und Technologieforschung der Bundeswehr dtec.bw – Band 1 · 2022, Seiten 168-173, DOI: https://doi.org/10.24405/14547
Heesch, R.; Putzke, J.; Althoff, S.; Topalis, P.; Schieseck, M.; Fay, A.; Niggemann, O.: Anforderungen an eine Engineering-Plattform für die KI-basierte Automation. In: dtec.bw-Beiträge der Helmut-Schmidt-Universität / Universität der Bundeswehr Hamburg: Forschungsaktivitäten im Zentrum für Digitalisierungs- und Technologieforschung der Bundeswehr dtec.bw – Band 1 · 2022, Seiten 162-167, DOI: https://doi.org/10.24405/14546
Schieseck, S.; Topalis, P.; Heesch, R.; Putzke, J.; Fay, A.: Beschreibungsmittel für die modellbasierte KI-Entwicklung in Automatisierungssystemen. In: dtec.bw-Beiträge der Helmut-Schmidt-Universität / Universität der Bundeswehr Hamburg: Forschungsaktivitäten im Zentrum für Digitalisierungs- und Technologieforschung der Bundeswehr dtec.bw – Band 1 · 2022, Seiten 153-161, DOI: https://doi.org/10.24405/14545
Moddemann, L.; Steude, H.; Grashorn, P.; Niggemann, O.: Automated Anomaly Detection and Diagnosis of the Environmental Control System of the ISS. In: dtec.bw-Beiträge der Helmut-Schmidt-Universität / Universität der Bundeswehr Hamburg: Forschungsaktivitäten im Zentrum für Digitalisierungs- und Technologieforschung der Bundeswehr dtec.bw – Band 1 · 2022, Seiten 123-128, DOI: https://doi.org/10.24405/14540
Krantz, M.; Widulle, N.; Nordhausen, A.; Liebert, A.; Ehrhardt, J.; Eilermann, S.; Niggemann, O.: FliPSi: Generating Data for the Training of Machine Learning Algorithms for CPPS. In Annual Conference of the PHM Society (Vol. 14, No. 1), Oktober 2022
Großmann, W.; Horn, H.; Niggemann, O.: Improving remote material classification ability with thermal imagery. Sci Rep 12, 17288, 2022, https://doi.org/10.1038/s41598-022-21588-4
Krantz, M.; Windmann, A.; Heesch, R.; Moddemann, L.; Niggemann, O.: “On a Uniform Causality Model for Industrial Automation”, arXiv:2209.09618, 2022
Kelm, Benjamin; Balzereit, Kaja; Moddemann, Lukas; Myschik, Stephan; Niggemann, Oliver: Application of a Model-based Reconfiguration Approach for the ISS Columbus Environmental Control and Life Support System (ECLSS), 33rd International Workshop on Principle of Diagnosis, Toulouse, France, 2022
Heesch, René; Widulle, Niklas; Köcher, Aljosha; Nordhausen, Anna; Vieira da Silva, Luis Miguel; Putzke, Julian; Niggemann, Oliver: Methoden der künstlichen Intelligenz für die automatisierte Planung von modularen Produktionsprozessen, Automation 2022, 2022
Vranješ, Daniel; Topalis, Philip; Niggemann, Oliver: Chancen und Herausforderungen für Künstliche Intelligenz in kleinen und mittelständischen Unternehmen, Automation 2022, 2022
Ehrhardt, Jonas; Ramonat, Malte; Heesch, René; Balzereit, Kaja; Diedrich, Alexander; Niggemann, Oliver: An AI benchmark for Diagnosis, Reconfiguration & Planning, 27th IEEE International Conference on Emerging Technologies and Factory Automation (ETFA2022), 2022
Schöttler, Jonas; Otto, Claus; Großmann, Willi; Niggemann, Oliver: Opportunities of AI for maritime forces in an in- and outward-looking view, ICMCIS2022, Elsevier Procedia, May 2022
Diedrich, Alexander; Niggemann, Oliver: On Residual-based Diagnosis of Physical Systems. Elsevier Engineering Applications of Artificial Intelligence, Volume 109, March 2022, 104636.
Vranješ, Daniel; Niggemann, Oliver: Anomaly detection based on time series data from industrial automatic sewing machines, The 5th IEEE International Conference on Industrial Cyber-Physical Systems (ICPS), Coventry, England, 2022.
Liebert, Artur; Weber, Wolfgang; Reif, Sebastian; Niggemann, Oliver: Anomaly Detection with Autoencoders as a Tool for Detecting Sensor Malfunctions, The 5th IEEE International Conference on Industrial Cyber-Physical Systems (ICPS), Coventry, England, 2022.
Balzereit, Kaja; Niggemann, Oliver: AutoConf: A New Algorithm for Reconfiguration of Cyber-Physical Production Systems, IEEE Transactions on Industrial Informatics, January 2022
Köcher, Aljosha; Heesch, René; Widulle, Niklas; Nordhausen, Anna; Putzke, Julian; Windmann, Alexander; Niggemann, Oliver: A Research Agenda for AI Planning in the Field of Flexible Production Systems, The 5th IEEE International Conference on Industrial Cyber-Physical Systems (ICPS), Coventry, England, 2022.
Steude, Henrik; Windmann, Alexander; Niggemann, Oliver: Learning Physical Concepts in Cyber-Physical Systems: A Case Study, The 11th IFAC Symposium on Fault Detection, Supervision, and Safety of Technical Processes (SAFEPROCESS 2022), Pafos, Cyprus, June 2022
2021
Dietrich, Alexander; Niggemann, Oliver: Diagnosing Systems through Approximated Information, Proceedings of the Annual Conference of the PHM Society, 2021.
Balzereit, Kaja; Niggemann, Oliver: Sound and Complete Reconfiguration for a Class of Hybrid Systems, 32nd International Workshop on Principle of Diagnosis, 2021, Hamburg, Germany
Roche, Jan-Philipp; Friebe, Jens; Niggemann, Oliver: Neural Network Modeling of Nonlinear Filters for EMC Simulation in Discrete Time Domain, 47th Annual Conference of the IEEE Industrial Electronics Society (IECON 2021), Toronto, Canada, 2021.
Rosenthal, Philipp; Niggemann, Oliver: Problem examination for AI methods in conceptual product design, IJCAI 2021 Workshop – AI and Product Design, Montreal, Canada, 2021.
Balzereit, Kaja; Diedrich, Alexander; Ginster, Jonas; Niggemann, Oliver: An Ensemble of Benchmarks for the Evaluation of AI Methods for Fault Handling in CPPS, IEEE International Conference on Industrial Informatics (INDIN 2021), Palma de Mallorca, Spain, 2021
Balzereit, Kaja; Niggemann, Oliver: Gradient-based Reconfiguration of Cyber-Physical Production Systems, IEEE International Conference on Industrial Cyber-Physical Systems (ICPS 2021), Victoria, BC, Canada, 2021.
Zimmering, Bernd; Niggemann, Oliver; Hasterok, Constanze; Pfannstiel, Erik; Ramming, Dario; Pfrommer, Julius: Generating Artificial Sensor Data for the Comparison of Unsupervised Machine Learning Methods, Journal Sensors 2021, 21(7), 2397, https://doi.org/10.3390/s21072397
Niggemann, Oliver; Diedrich, Alexander, Pfannstiel, Erik; Schraven, Joshua; Kühnert, Christian: A Generic DigitalTwin Model for Artificial Intelligence Applications, IEEE International Conference on Industrial Cyber-Physical Systems (ICPS), Victoria, BC, Canada, May 2021
Multaheb, Samim; Zimmering, Bernd; Niggemann, Oliver: Expressing uncertainty in neural networks for production systems, at – Automatisierungstechnik 69(3):221-230, DOI: 10.1515/auto-2020-0122, March 2021
Book: Beyerer, Jürgen; Maier, Alexander; Niggemann, Oliver: Machine Learning for Cyber Physical Systems Selected papers from the International Conference ML4CPS 2020: Selected papers from the International Conference ML4CPS 2020, January 2021
2020
Roche, Jan-Philipp; Friebe, Jens; Niggemann, Oliver: Machine Learning for Grey Box Modeling of Electric Components for Circuit- and EMC-Simulation, PCIM Europe Conference, 2020.
Niggemann, O., Biswas, G., Kinnebrew, J., Bunte, A., Hranisavljevic, N.: Handbuch Industrie 4.0 – Konzeptualisierung als Kernfrage des Maschinellen Lernens in der Produktion, Springer Verlag, 2020
Bunte, Andreas; Li, Peng Li; Niggemann: Learned Abstraction: Knowledge Based Concept Learning for Cyber Physical Systems, Machine Learning for Cyber Physical Systems – Selected papers from the International Conference ML4CPS, DOI: 10.1007/978-3-662-59084-3_6, Springer Vieweg, January 2020
Balzereit, Kaja; Niggemann, Oliver: Modeling Quantitative Effects for the Reconfiguration of Hybrid Systems, 31st International Workshop on Principles of Diagnosis DX, 2020
Diedrich, Alexander; Balzereit, Kaja; Niggemann, Oliver: First Approaches to Automatically Diagnose and Reconfigure Hybrid Cyber-Physical Systems, Machine Learning for Cyber Physical Systems – Selected papers from the International Conference ML4CPS, Springer Vieweg, January 2020
Balzereit, Kaja; Fullen, Marta; Niggemann, Oliver: A Concept for the Automated Reconfiguration of Quadcopters, Conference LWDA 2020, September 2020
Voß, Carlo; Eiteneuer, Benedikt; Niggemann, Oliver: Incorporating Uncertainty into Unsupervised Machine Learning for Cyber-Physical Systems, 3rd IEEE International Conference on Industrial Cyber-Physical Systems (ICPS), Tampere, Finland, June 2020
Balzereit, Kaja; Niggemann, Oliver: Automated Reconfiguration of Cyber-Physical Production Systems using Satisfiability Modulo Theories, 3rd IEEE International Conference on Industrial Cyber-Physical Systems (ICPS), Tampere, Finland, June 2020
Li, Peng; Niggemann, Oliver: A Non-Convex Archetypal Analysis for One-class Classification based Anomaly Detection in Cyber-Physical Systems, IEEE Transactions on Industrial Informatics PP(99):1-1, DOI: 10.1109/TII.2020.3009106, July 2020
Hranisavljevic, Nemanja; Maier, Alexander; Niggemann, Oliver: Discretization of hybrid CPPS data into timed automaton using restricted Boltzmann machines, Engineering Applications of Artificial Intelligence 95:103826, DOI: 10.1016/j.engappai.2020.103826, August 2020
Giese, Katharina; Eickmeyer, Jens; Niggemann, Oliver: Differential Evolution in Production Process Optimization of Cyber Physical Systems, In book: Machine Learning for Cyber Physical Systems, Springer Vieweg, January 2020, DOI:10.1007/978-3-662-59084-3_3
Fullen, Marta; Schüller, Peter; Niggemann, Oliver: Semi-supervised Case-based Reasoning Approach to Alarm Flood Analysis, In book: Machine Learning for Cyber Physical Systems, Springer Vieweg, January 2020, DOI:10.1007/978-3-662-59084-3_7
Bunte, Andreas; Li, Peng; Niggemann, Oliver: Learned Abstraction: Knowledge Based Concept Learning for Cyber Physical Systems. In book: Machine Learning for Cyber Physical Systems – Selected papers from the International Conference ML4CPS, Springer Vieweg, January 2020, DOI:10.1007/978-3-662-59084-3_6
2019
Li, Peng; Niggemann, Oliver: Non-convex hull based anomaly detection in CPPS, Elsevier Engineering Applications of Artificial Intelligence 87, October 2019
Balzereit, Kaja; Niggemann, Oliver Niggemann: Automated Reconfiguration of Cyber-Physical Production Systems using Satisfiability Modulo Theories, 30th International Workshop on Principles of Diagnosis (DX-19) November 2019
Bunte, Andreas; Wunderlich, Paul; Moriz, Natalia; Li, Peng; Mankowski, André, Rogalla, Antje; Niggemann, Oliver: Why Symbolic AI is a Key Technology for Self-Adaption in the Context of CPPS, IEEE Emerging Technologies and Factory Automation (ETFA), September 2019.
Bunte, Andreas; Fischbach, Andreas; Strohschein, Jan; Bartz-Beielstein, Thomas; Faeskorn-Woyke, Heide; Niggemann Oliver: Evaluation of Cognitive Architectures for Cyber-Physical Production Systems, 24nd IEEE International Conference on Emerging Technologies and Factory Automation (ETFA), Sept. 2019.
Zhang, Fan; Pinkal, Kevin; Wefing, Patrick; Conradi, Florian; Schneider, Jan; Niggemann, Oliver: Quality Control of Continuous Wort Production through Production Data Analysis in Latent Space. In: 20th IEEE International Conference on Industrial Technology (ICIT), Melbourne, Australia, Feb 2019.
Eiteneuer, Benedikt; Hranisavljevic, Nemanja; Niggemann, Oliver: Dimensionality Reduction and Anomaly Detection for CPPS Data using Autoencoder. In: 20th IEEE International Conference on Industrial Technology (ICIT), Melbourne, Australien, Feb 2019.
Li, Peng; Niggemann, Oliver; Hammer, Barbara: On the Identification of Decision Boundaries for Anomaly Detection in CPPS. In: 20th IEEE International Conference on Industrial Technology (ICIT 2019) Melbourne, Australia, Feb 2019.
Diedrich, Alexander; Niggemann, Oliver: Model-based Diagnosis of Hybrid Systems using Satisfiability Modulo Theory. Thirty-Third AAAI Conference on Artificial Intelligence (AAAI-19), Hawaii, USA, Jan 2019.
Bunte, Andreas; Stein, Benno; Niggemann, Oliver: Model-Based Diagnosis for Cyber-Physical Production Systems Based on Machine Learning and Residual-Based Diagnosis Models. Thirty-Third AAAI Conference on Artificial Intelligence (AAAI-19), Hawaii, USA, Jan 2019.
Windmann, S., Balzereit, K. & Niggemann, O: Model-based routing in flexible manufacturing systems. at – Automatisierungstechnik, 67(2), 2019
Balzereit, Kaja; Maier, Alexander; Björn, Barig, Niggemann, Oliver: Data-driven Identification of Causal Dependencies in Cyber-Physical Production Systems, 11th International Conference on Agents and Artificial Intelligence, Prague, Czech Republic, February 2019
2018
Li, Peng; Niggemann, Oliver; Hammer, Barbara: A Geometric Approach to Clustering Based Anomaly Detection for Industrial Applications. In: 44th Annual Conference of the IEEE Industrial Electronics Society (IECON) Oct 2018.
Li, Peng; Niggemann, Oliver: A Data Provenance based Architecture to Enhance the Reliability of Data Analysis for Industry 4.0. In: 23th IEEE International Conference on Emerging Technologies and Factory Automation (ETFA) Sep 2018.
Rogalla, Antje; Fay, Alexander; Niggemann, Oliver: Improved Domain Modeling for Realistic Automated Planning and Scheduling in Discrete Manufacturing. In: 23th IEEE International Conference on Emerging Technologies and Factory Automation (ETFA) Sep 2018.
Bunte, Andreas; Niggemann, Oliver; Stein, Benno: Integrating OWL Ontologies for Smart Services into AutomationML and OPC UA. In: 23th IEEE International Conference on Emerging Technologies and Factory Automation (ETFA) Sep 2018.
Wunderlich, Paul; Niggemann, Oliver: Inference Methods for Detecting the Root Cause of Alarm Floods in Causal Models. In: 23rd International Conference on Methods and Models in Automation and Robotics (MMAR) Międzyzdroje, Poland, Aug 2018.
Eiteneuer, Benedikt; Niggemann, Oliver: LSTM for model-based Anomaly Detection in Cyber-Physical Systems. In: Proceedings of the 29th International Workshop on Principles of Diagnosis Warsaw, Poland, Aug 2018.
von Birgelen, Alexander; Niggemann, Oliver: Enable learning of Hybrid Timed Automata in Absence of Discrete Events through Self-Organizing Maps. S.: 37-54, Springer Vieweg, Aug 2018.
von Birgelen, Alexander; Niggemann, Oliver: Anomaly Detection and Localization for Cyber-Physical Production Systems with Self-Organizing Maps. S.: 55-71, Springer Vieweg, Aug 2018.
Wunderlich, Paul; Niggemann, Oliver: Concept for Alarm Flood Reduction with Bayesian Networks by Identifying the Root Cause. In: IMPROVE – Innovative Modelling Approaches for Production Systems to Raise Validatable Efficiency: Intelligent Methods for the Factory of the Future S.: 111-129, Springer Vieweg, Aug 2018.
Windmann, Stefan; Niggemann, Oliver: A Sampling-Based Method for Robust and Efficient Fault Detection in Industrial Automation Processes. Springer Vieweg, Aug 2018.
Fullen, Marta; Schüller, Peter; Niggemann, Oliver: Validation of similarity measures for industrial alarm flood analysis. Springer Vieweg, Aug 2018.
Niggemann, Oliver; Schüller, Peter: IMPROVE – Innovative Modelling Approaches for Production Systems to Raise Validatable Efficiency. Springer Vieweg, Aug 2018.
Wunderlich, Paul; Niggemann, Oliver: Challenges in Learning Causal Models of Alarms in Industrial Plants. In: 16th IEEE International Conference on Industrial Informatics (INDIN) Porto, Portugal, Jul 2018.
Specht, Felix; Otto, Jens; Niggemann, Oliver; Hammer, Barbara: Generation of Adversarial Examples to Prevent Misclassification of Deep Neural Network based Condition Monitoring Systems for Cyber-Physical Production Systems. In: IEEE 16th International Conference on Industrial Informatics (INDIN) Jul 2018.
Lang, Dorota; Wunderlich, Paul; Heinz, Mario; Wisniewski, Lukasz; Jasperneite, Jürgen; Niggemann, Oliver; Röcker, Carsten: Assistance System to Support Troubleshooting of Complex Industrial Systems. In: 14th IEEE International Workshop on Factory Communication Systems (WFCS) Imperia (Italy), Jun 2018.
von Birgelen, Alexander; Buratti, Davide; Mager, Jens; Niggemann, Oliver: Self-Organizing Maps for Anomaly Localization and Predictive Maintenance in Cyber-Physical Production Systems. In: 51st CIRP Conference on Manufacturing Systems (CIRP CMS 2018) CIRP-CMS, May 2018.
Conradi, Florian; Wefing, Patrick; Pinkal, Kevin; Zhang, Fan; Niggemann, Oliver; Schneider, Jan: Inline progress measurement of the ß-amylase rest in the mashing process employing a near infrared transflectance probe. In: Trends in Brewing, Gent Apr 2018.
Wefing, Patrick; Conradi, Florian; Fuchs, Lukas; Schoppmeier, Jan; Pinkal, Kevin; Niggemann, Oliver; Schneider, Jan: Laboratory Plant for a closed loop-controlled continuous (CLCC) Mashing. In: Trends in Brewing, Gent Apr 2018.
Windmann, Stefan; Niggemann, Oliver; Stichweh, Heiko: Computation of energy efficient driving speeds in conveying systems. In: at – Automatisierungstechnik Mar 2018.
Nisic, Tatjana; Conradi, Florian; Niggemann, Oliver; Pinkal, Kevin; Schneider, Jan; Wefing, Patrick; Zhang, Fan: Food meets IT: der Digitale Zwilling erobert die Lebensmittelindustrie. In: IM+io Mar 2018.
Otto, Jens; Vogel-Heuser, Birgit; Niggemann, Oliver: Automatic Parameter Estimation for Reusable Software Components of Modular and Reconfigurable Cyber Physical Production Systems in the Domain of Discrete Manufacturing. In: IEEE Transactions on Industrial Informatics IEEE, Jan 2018.
Bunte, Andreas; Li, Peng; Niggemann, Oliver: Mapping Data Sets to Concepts Using Machine Learning and a Knowledge Based Approach. In: International Conference on Agents and Artificial Intelligence (ICAART) SCITEPRESS, Madeira, Portugal, Jan 2018.
2017
von Birgelen, Alexander; Niggemann, Oliver: Using Self-Organizing Maps to Learn Hybrid Timed Automata in Absence of Discrete Events. In: 22nd IEEE International Conference on Emerging Technologies and Factory Automation (ETFA 2017), Sep 2017.
Wunderlich, Paul; Niggemann, Oliver: Structure Learning Methods for Bayesian Networks to Reduce Alarm Floods by Identifying the Root Cause. In: 22nd IEEE International Conference on Emerging Technologies and Factory Automation (ETFA 2017), Sep 2017.
Rogalla, Antje; Niggemann, Oliver: Automated Process Planning for Cyber-Physical Production Systems. In: 22nd IEEE International Conference on Emerging Technologies and Factory Automation (ETFA), Sep 2017.
Pethig, Florian; Niggemann, Oliver; Walter, Armin: Towards Industrie 4.0 Compliant Configuration of Condition Monitoring Services. In: 15th IEEE International Conference on Industrial Informatics (INDIN 2017), Jul 2017.
Pinkal, Kevin; Niggemann, Oliver: A New Approach to Model-Based Test Case Generation for Industrial Automation Systems. In: 15th IEEE International Conference on Industrial Informatics (INDIN 2017), Jul 2017.
Fullen, Marta; Schüller, Peter; Niggemann, Oliver: Defining and validating similarity measures for industrial alarm flood analysis. In: 2017 IEEE 15th International Conference on Industrial Informatics (INDIN), Jul 2017.
Bunte, Andreas; Li, Peng; Niggemann, Oliver: Learned Abstraction: Knowledge Based Concept Learning for Cyber Physical Systems. In: 3rd Conference on Machine Learning for Cyber Physical Systems and Industry 4.0 (ML4CPS), October 2017.
Windmann, Stefan; Lang, Dorota; Niggemann, Oliver: Learning Parallel Automata of PLCs. In: 22nd IEEE International Conference on Emerging Technologies And Factory Automation, September 2017.
Windmann, Stefan; Niggemann, Oliver: A Self-Configurable Fault Detection System for Industrial Ethernet Networks. In: at – Automatisierungstechnik at – Automatisierungstechnik, May 2017.
Diedrich, Christian; Niggemann, Oliver; Pethig, Florian; Kraft, Andreas; Bock, Jürgen; Gössling, Andreas; Hänisch, Rolf; Reich, Johannes; Vollmar, Friedrich; Wende, Jörg: Interaktionsmodell für Industrie 4.0 Komponenten. In: At Automatisierungstechnik Jan 2017.
2016
Bunte, Andreas; Diedrich, Alexander; Niggemann, Oliver: Semantics Enable Standardized User Interfaces for Diagnosis in Modular Production Systems. In: International Workshop on the Principles of Diagnosis (DX) Denver, CO, USA, Oct 2016
Hranisavljevic, Nemanja; Niggemann, Oliver; Maier, Alexander: A Novel Anomaly Detection Algorithm for Hybrid Production Systems based on Deep Learning and Timed Automata. In: International Workshop on the Principles of Diagnosis (DX) Denver, Oct 2016
Diedrich, Alexander; Feldman, Alexander ; Perdomo-Ortiz, Alejandro ; Abreu, Rui ; Niggemann, Oliver; de Kleer, Johan: Applying Simulated Annealing to Problems in Model-based Diagnosis. In: International Workshop on the Principles of Diagnosis (DX) Denver, CO, USA, Oct 2016.
Bunte, Andreas; Diedrich, Alexander; Niggemann, Oliver: Integrating Semantics for Diagnosis of Manufacturing Systems. In: 21th IEEE International Conference on Emerging Technologies and Factory Automation (ETFA) Berlin, Sep 2016.
Henning, Steffen; Niggemann, Oliver; Otto, Jens: Pattern-Based Control-Code Synthesis. In: 14th International IEEE Conference on Industrial Informatics (INDIN), Politiers (France) Jul 2016.
Bunte, Andreas; Diedrich, Alexander; Niggemann, Oliver: Integrating Semantics for Diagnosis of Manufacturing Systems. In: 21th IEEE International Conference on Emerging Technologies and Factory Automation (ETFA) Berlin, Sep 2016.
Windmann, Stefan; Niggemann, Oliver: A GPU-Based Method for Robust and Efficient Fault Detection in Industrial Automation Processes. In: 14th International IEEE Conference on Industrial Informatics (INDIN), Poltiers (France), Jul 2016.
Li, Peng; Niggemann, Oliver: Improving Clustering Based Anomaly Detection with Concave Hull: An Application in Condition Monitoring of Wind Turbines. In: 14th IEEE International Conference on Industrial Informatics (INDIN 2016), Poltiers (France) Jul 2016.
Niggemann, Oliver; Biswas, Gautam; Khorasgani, Hamed; Volgmann, Sören; Bunte, Andreas: Datenanalyse in der intelligenten Fabrik. Springer Berlin Heidelberg, Berlin, Heidelberg, Jun 2016.
Maier, Alexander; Schriegel, Sebastian; Niggemann, Oliver: Big Data and Machine Learning for the Smart Factory – Solutions for Condition Monitoring, Diagnosis and Optimization. In: Industrial Internet of Things: Cybermanufacturing Systems, Springer Verlag, Jun 2016.
2015
Niggemann, Oliver; Frey, Christian: Data-Driven Anomaly Detection in Cyber-Physical Production Systems,, at – Automatisierungstechnik(63) S.: 821–832, Oct 2015
Vogel-Heuser, Birgit; Schütz, Daniel ; Schöler, Thorsten ; Pröll , Sebastian ; Jeschke, Sabina ; Ewert , Daniel ; Niggemann, Oliver; Windmann, Stefan; Berger, Ulrich ; Lehmann, Christian : Agentenbasierte cyber-physische Produktionssysteme – Anwendungen für Industrie 4.0. In: atp edition, DIV Vulkan-Verlag, München, Sep 2015.
Shrestha, Ganesh Man; Niggemann, Oliver: Hybrid Approach Combining Bayesian Network and Rule-based Systems for Resource Optimization in Industrial Cleaning Processes. In: 20th IEEE International Conference on Emerging Technologies and Factory Automation (ETFA 2015) Luxembourg, Sep 2015.
Windmann, Stefan; Jungbluth, Florian ; Niggemann, Oliver: A HMM-Based Fault Detection Method for Piecewise Stationary Industrial Processes. In: IEEE International Conference on Emerging Technologies and Factory Automation (ETFA 2015) Luxembourg, Sep 2015.
Windmann, Stefan; Niggemann, Oliver: MapReduce Algorithms for Efficient Generation of CPS Models from Large Historical Data Sets. In: IEEE International Conference on Emerging Technologies and Factory Automation (ETFA 2015) Luxembourg, Sep 2015.
Specht, Felix; Flatt, Holger; Eickmeyer, Jens; Niggemann, Oliver: Exploiting Multicore Processors in PLCs using Libraries for IEC 61131-3. In: IEEE International Conference on Emerging Technologies and Factory Automation (ETFA 2015) Luxembourg , Sep 2015.
Eickmeyer, Jens; Li, Peng; Pethig, Florian; Niggemann, Oliver: Data Driven Modeling for System-Level Condition Monitoring on Wind Power Plants. In: International Workshop on the Principles of Diagnosis (DX) Paris, France, Aug 2015.
Maier, Alexander; Niggemann, Oliver: On the Learning of Timing Behavior for Anomaly Detection in Cyber-Physical Production Systems. In: International Workshop on the Principles of Diagnosis (DX) Paris, France, Aug 2015.
Niggemann, Oliver; Biswas, Gautam; Kinnebrew, John S.; Khorasgani, Hamed; Volgmann, Sören; Bunte, Andreas: Data-Driven Monitoring of Cyber-Physical Systems Leveraging on Big Data and the Internet-of-Things for Diagnosis and Control. In: International Workshop on the Principles of Diagnosis (DX); Paris, France In: International Workshop on the Principles of Diagnosis (DX) Paris, France, Aug 2015.
Windmann, Stefan; Niggemann, Oliver: Efficient Fault Detection for Industrial Automation Processes with Observable Process Variables. In: IEEE International Conference on Industrial Informatics (INDIN 2015) Cambridge, UK, Jul 2015.
Windmann, Stefan; Niggemann, Oliver; Stichweh, Heiko: Energy efficiency optimization by automatic coordination of motor speeds in conveying systems. In: IEEE International Conference on Industrial Technology (ICIT 2015) Mar 2015.
Windmann, Stefan; Niggemann, Oliver: Automatic model separation and application to diagnosis in industrial automation systems. In: IEEE International Conference on Industrial Technology (ICIT 2015) Mar 2015.
Lohweg, Volker; Niggemann, Oliver: On the Diagnosis of Cyber-Physical Production Systems – State-of-the-Art and Research Agenda. In: Twenty-Ninth Conference on Artificial Intelligence (AAAI-15) Austin, Texas, USA, Jan 2015.
2014
Henning, Steffen; Otto, Jens; Niggemann, Oliver; Schriegel, Sebastian: A Descriptive Engineering Approach for Cyber-Physical Systems. In: 19th IEEE International Conference on Emerging Technologies and Factory Automation (ETFA) Barcelona, Spain, Sep 2014.
Niggemann, Oliver; Kroll, Bjoern: On the applicability of model based software development to cyber physical production systems. In: CyPhERS 2nd Experts Workshop CPSWeek 2014 CPSWeek 2014, Berlin, Germany, April 14 2014 , Apr 2014.
Kroll, Bjoern; Schaffranek, David; Schriegel, Sebastian; Niggemann, Oliver: System modeling based on machine learning for anomaly detection and predictive maintenance in industrial plants. In: 19th IEEE International Conference on Emerging Technologies and Factory Automation (ETFA) Sep 2014.
Niggemann, Oliver; Windmann, Stefan; Volgmann, Sören; Bunte, Andreas; Stein, Benno: Using Learned Models for the Root Cause Analysis of Cyber-Physical Production Systems. In: International Workshop on the Principles of Diagnosis (DX) Graz, Austria, Sep 2014.
Shrestha, Ganesh Man; Niggemann, Oliver: A Bayesian Predictive Assistance System for Resource Optimization – A Case Study in Industrial Cleaning Process. In: 19th IEEE International Conference on Emerging Technologies and Factory Automation (ETFA) Barcelona, Sep 2014.
Moriz, Natalia; Böttcher, Björn; Niggemann, Oliver; Lackhove, Josef: Assisted Design for Automation Systems – from Formal Requirements to Final Designs. In: 19th IEEE International Conference on Emerging Technologies and Factory Automation (ETFA) Barcelona, Spain, Sep 2014.
Frey, Christian; Heinzmann, Michael; Jasperneite, Jürgen; Niggemann, Oliver; Sauer, Olaf; Schleipen, Miriam; Usländer, Thomas: IKT in der Fabrik der Zukunft – Ein Diskussionsbeitrag zu Industrie 4.0. In: atp edition DIV Vulkan Verlag, München, Aug 2014.
Volgmann, Sören; Rangel, Francisco; Niggemann, Oliver; Rosso, Paolo: Emotional Trends in Social Media – A State Space Approach. In: 21st European Conference on Artificial Intelligence Frontiers in Artificial Intelligence and Applications, 2014 S.: Vol. 263, pp. 1123-1124, IOS Press, Aug 2014.
Böttcher, Björn; Moriz, Natalia; Niggemann, Oliver: From Formal Requirements on Technical Systems to Complete Designs – A Holistic Approach. In: 21st European Conference on Artificial Intelligence (ECAI 2014) In: 21st European Conference on Artificial Intelligence Frontiers in Artificial Intelligence and Applications, 2014 S.: pp. 977-978, Vol. 263, Prague, Czech Republic, Aug 2014.
Niggemann, Oliver; Jasperneite, Jürgen: Konzepte und Anwendungsfälle für die intelligente Fabrik. In: Bauernhansl, Thomas; ten Hompel, Michael; Vogel-Heuser, Birgit (Hrsg.): Industrie 4.0 in Produktion, Automatisierung und Logistik Springer-Verlag, Jun 2014.
Anis, Anas; Schäfer, Wilhelm; Niggemann, Oliver: A Comparison of Modeling Approaches for Planning in Cyber Physical Production Systems. In: 19th IEEE International Conference on Emerging Technologies and Factory Automation (ETFA) Barcelona, Jun 2014.
Niggemann, Oliver: Industrie 4.0 ohne modellbasierte Softwareentwicklung – Und warum es ohne Modelle nicht gehen wird. In: atp edition(Ausgabe 05 ) May 2014.
Windmann, Stefan; Niggemann, Oliver: Intelligente Assistenzsysteme für die Automation – Menschen bei der Prozessführung besser unterstützen. In: atp edition – Ausgabe 04 2014 Apr 2014.
Frey, Christian; Heinzmann, Michael; Jasperneite, Jürgen; Niggemann, Oliver; Sauer, Olaf; Schleipen, Miriam; Usländer, Thomas; Voit, Michael: IKT in der Fabrik der Zukunft – Ein Diskussionsbeitrag zu Industrie 4.0. In: atp edition Mar 2014.
2013
Niggemann, Oliver; Vodenčarević, Asmir; Maier, Alexander; Windmann, Stefan; Kleine Büning, Hans: A Learning Anomaly Detection Algorithm for Hybrid Manufacturing Systems. In: The 24th International Workshop on Principles of Diagnosis (DX-2013) Jerusalem, Israel, Oct 2013.
Kroll, Bjoern; Schriegel, Sebastian; Niggemann, Oliver: A Software Architecture for the Analysis of Energy- and Process-Data. In: 18th IEEE International Conference on Emerging Technologies and Factory Automation (ETFA) Sep 2013.
Böttcher, Björn; Badinger, Johann; Moriz, Natalia; Niggemann, Oliver: Design of Industrial Automation Systems – Formal Requirements in the Engineering Process. In: 18thIEEE International Conference on Emerging Technologies and Factory Automation (ETFA) Cagliari, Italy, Sep 2013.
Gilani, Syed Sheraz; Windmann, Stefan; Pethig, Florian; Kroll, Bjoern; Niggemann, Oliver: The Importance of Model-Learning for the Analysis of the Energy Consumption of Production Plant. In: 18th IEEE International Conference on Emerging Technologies & Factory Automation (ETFA), Cagliari, Italy Sep 2013.
Windmann, Stefan; Jiao, Shuo; Niggemann, Oliver; Borcherding, Holger: A Stochastic Method for the Detection of Anomalous Energy Consumption in Hybrid Industrial Systems. In: 11th International IEEE Conference on Industrial Informatics 2013 Bochum, Germany, May 2013.
Faltinski, Sebastian ; Jäger, Michael; Niggemann, Oliver; Marek, Frank: Auf dem Weg vom Spielzeug zum Werkzeug. In: atp edition May 2013.
Schetinin, Nikolai; Moriz, Natalia; Kumar, Barath; Faltinski, Sebastian ; Niggemann, Oliver; Maier, Alexander: Why do verification approaches in automation rarely use HIL-test? In: IEEE International Conference on Industrial Technology (ICIT) 25.-27. February 2013, Cape Town, South Africa, Feb 2013.
Maier, Alexander; Köster, Markus; Paiz Gatica, Carlos; Niggemann, Oliver: Automated Generation of Timing Models in Distributed Production Plants. In: IEEE International Conference on Industrial Technology (ICIT 2013), Cape Town, South Africa, Feb 2013 Feb 2013.
2012
Pethig, Florian; Kroll, Bjoern; Niggemann, Oliver; Maier, Alexander; Tack, Tim: A Generic Synchronized Data Acquisition Solution for Distributed Automation Systems. In: 18th IEEE International Conference on Emerging Technologies and Factory Automation Krakow, Poland, Sep 2012.
Jasperneite, Jürgen; Niggemann, Oliver: Intelligente Assistenzsysteme zur Beherrschung der Systemkomplexität in der Automation. In: ATP edition – Automatisierungstechnische Praxis 9/2012 Oldenbourg Verlag, München, Sep 2012.
Niggemann, Oliver; Stein, Benno; Vodenčarević, Asmir; Maier, Alexander; Kleine Büning, Hans: Learning Behavior Models for Hybrid Timed Systems. In: Twenty-Sixth Conference on Artificial Intelligence (AAAI-12) Jul 2012.
Faltinski, Sebastian ; Flatt, Holger; Pethig, Florian; Kroll, Bjoern; Vodenčarević, Asmir; Maier, Alexander; Niggemann, Oliver: Detecting Anomalous Energy Consumptions in Distributed Manufacturing Systems. In: IEEE 10th International Conference on Industrial Informatics (INDIN), 2012 Beijing, China, Jul 2012.
Faltinski, Sebastian ; Niggemann, Oliver; Moriz, Natalia; Mankowski, Andre: AutomationML: From Data Exchange to System Planning and Simulation.. In: 2012 IEEE International Conference on Industrial Technology (ICIT) Athen, Griechenland, Mar 2012.
2011
Vodenčarević, Asmir; Kleine Büning, Hans; Niggemann, Oliver; Maier, Alexander: Identifying Behavior Models for Process Plants. In: 16th IEEE International Conference on Emerging Technologies and Factory Automation ETFA’2011, Toulouse, France, 2011 In: 16th IEEE International Conference on Emerging Technologies & Factory Automation (ETFA) Sep 2011.
Jäger, Michael; Just, Roman; Niggemann, Oliver: Using automatic Topology Discovery to diagnose PROFINET networks. In: 16th IEEE International Conference on Emerging Technologies and Factory Automation (ETFA 2011) Toulouse, France, Sep 2011.
Wienke, Michael; Faltinski, Sebastian Niggemann, Oliver; Jasperneite, Jürgen: mINA-DL: A Novel Description Language Enabling Dynamic Reconfiguration in Industrial Automation. In: 16th IEEE International Conference on Emerging Technologies and Factory Automation (ETFA 2011) Toulouse, France, Sep 2011.
2010
Niggemann, Oliver; Lohweg, Volker; Tack, Tim: A Probabilistic MajorClust Variant for the Clustering of Near-Homogeneous Graphs. In: 33rd Annual German Conference on Artificial Intelligence (KI 2010) Sep 2010.
Niggemann, Oliver: System-Level Design and Simulation of Automation Systems. In: 8th IEEE International Workshop on Factory Communication Systems – COMMUNICATION in AUTOMATION (WFCS 2010) May 2010.
2009
Kumar, Barath; Niggemann, Oliver; Jasperneite, Jürgen: Timed Automata for Modeling Network Traffic. In: Machine Learning in Real-Time Applications (MLRTA 09) (in conjunction with 32nd Annual Conference on Artificial Intelligence (KI 2009)) Paderborn, Germany, Sep 2009.
Niggemann, Oliver et al.: Using Simulation to Verify Diagnosis Algorithms of Electronic Systems. To be published in SAE World Congress, 2009. , Mar 2009.
2008
Niggemann, Oliver et al.: Durchgängige Systemtests von der virtuellen Integration bis zum Verbundtest.. In: ATZ elektronik Nov 2008.
Stichling, Dirk; Niggemann, Oliver; Fleischer, Dirk: Combining Automotive System and Function Models to support Code Generation and Early System Verification. Convergence 2008, October 20-22, 2008, Detroit, Michigan, USA , Oct 2008.
Otterbach, Rainer; Niggemann, Oliver; Fleischer, Dirk; Jogi, Santhosh: Using Software Architecture Models in Automotive Development Processes. SAE 2008 Commercial Vehicle Engineering Congress, October 7-9, 2008, Rosemont, Illinois, USA, Oct 2008.
Niggemann, Oliver; Stroop, Joachim: Models for Model’s Sake. Proceedings of the 30th International Conference on Software Engineering (ICSE) – Experience Track on Automotive Systems, Leipzig, Germany, 10 – 18 May 2008 , May 2008.
2007
Niggemann, Oliver; Eisemann, Ulrich; Beine, Michael; Kiffmeier, Ulrich: Behavior Modeling Tools in an Architecture-Driven Development Process – From Function Models to AUTOSAR. SAE World Congress & Exhibition, April 2007, Detroit, USA , Apr 2007.
2006
Stein, Benno; Niggemann, Oliver; Balzer, Heinrich: Diagnosis in Automotive Applications. 3rd Monet Workshop on Model-Based Systems (MBS-06) at the ECAI 06 at Riva de Gard, Italy, 2006 , Aug 2006.
Stein, Benno; Niggemann, Oliver; Lettmann, Theodor: Speeding up Model-based Diagnosis by a Heuristic Approach to Solving SAT. In Proceedings of the IASTED International Conference on Artificial Intelligence and Applications (AIA 06), Innsbruck, Austria, Anaheim, Calgary, Zurich, February 2006. , Feb 2006.
2002
Bach, Roland; Zeller, Vitus; Cheleg, Alexei; Niggemann, Oliver: Assessing the Influence of Linear/Nonlinear Effects on Different Q-Factor Measurement Methods. 18th National Fiber Optic Engineer Conference (NFOEC), Dallas, USA, 2002 , Jul 2002.
2001
Niggemann, Oliver; Stein, Benno: Generation of Similarity Measures from Different Sources. The Fourteenth International Conference on Industrial and Engineering Applications of Artificial Intelligence and Expert Systems (IEA/AIE-2001). Springer-Verlag (Lecture Notes in the Computer Science/Lecture Notes in Artificial Intelligence), 2001 , Sep 2001.
Niggemann, Oliver; Stein, Benno; Tölle, Jens: Visualization of Traffic Structures. IEEE International Conference on Communications (ICC) 2001, Helsinki, Finland , Aug 2001.
Niggemann, Oliver: Visual Data Mining of Graph-Based Data. University of Paderborn, 2001. An electronic copy is available from the library of the University of Paderborn (http://ubdata.uni-paderborn.de/ediss/17/2001/niggeman/). , Jun 2001.
Lappe, Michael; Parl, Jong; Niggemann, Oliver; Holm, Liisa: Generating Protein Interaction Maps from Incomplete Data: Application to Fold Assignment. „9th International Conference on Intelligent Systems for Molecular Biology“. „9th International Conference on Intelligent Systems for Molecular Biology“, Copenhagen, Denmark, 2001 , Mar 2001.
1997-2000
Niggemann, Oliver; Stein, Benno: Learning the optimal Graph-Drawing Method for clustered Graphs. In Advanced Visual Interfaces 2000, ACM , Jul 2000.
B. Stein and O. Niggemann. On the nature of structure and its identification. In 25th International Workshop on Graph-Theoretic Concepts in Computer Science, Lecture Notes In Computer Science, Springer Verlag, 1999
Oliver Niggemann, Benno Stein and Michael Suermann. Network Configuration: Approaches for Solving the Cable Management Problem. International Symposium on Mathematical Programming (ISMP 97). Swiss Federal Institute of Technology, Lausanne (EPFL), August 1997.
Letzte Änderung: 27. Mai 2025