@misc{11330,
  abstract     = {{With the increasing complexity in manual assembly and a demographic decline in skilled workforce, the importance of well-documented processes through assembly instructions has grown. Creating these instructions is a time-consuming and knowledge-intensive task that typically relies on experienced employees. Although various automation solutions have been proposed to assist in generating assembly instructions, they often fall short in providing detailed textual guidance. With the rise of generative artificial intelligence (AI), new potentials arise in this domain. Therefore, this paper explores these potentials by employing various large language models (LLMs), prompting techniques and input data in an experimental setup for generating detailed assembly instructions, including the planning of assembly sequences as well as textual guidance on tools, assembly activities, and quality assurance measures. The findings reveal promising opportunities in leveraging LLMs but also substantial challenges, particularly in assembly sequence planning. To improve the reliability of generating assembly instructions, we propose a multi-agent concept that decomposes the complex task into simpler subtasks, each managed by specialized agents.}},
  author       = {{Meyer, Frederic and Freitag, Lennart and Hinrichsen, Sven and Niggemann, Oliver}},
  booktitle    = {{2024 IEEE 29th International Conference on Emerging Technologies and Factory Automation (ETFA)}},
  isbn         = {{979-8-3503-6123-0}},
  keywords     = {{assembly instruction, GPT, large language model, LLM, prompt}},
  location     = {{Padova, Italy}},
  publisher    = {{IEEE}},
  title        = {{{Potentials of Large Language Models for Generating Assembly Instructions}}},
  doi          = {{https://doi.org/10.1109/ETFA61755.2024.10710806}},
  volume       = {{78}},
  year         = {{2024}},
}

@misc{12993,
  abstract     = {{In computer science and related technical fields, researchers, educators, and practitioners are continuously automating recurring tasks for high efficiency in a wide variety of fields. In higher education, such tasks that educators face are the recurring review and assessment process of students' programming coursework. Thus, various attempts exist to automate the assessment and feedback generation for course homework and practicals in higher education. Those approaches for automated programming task assessment often comprise running automated tests to check for limited functional correctness and potentially style checking for various violations (LINTing). Educators familiar with large-scale automated task assessment are likely used to seeing hard-coded solutions specifically or accidentally designed to just pass the required tests, ignoring or misinterpreting the actual task requirements. Detecting such issues in arbitrary code is non-trivial and an ongoing research topic in software engineering. Software engineering research has yielded various semantic analysis frameworks, such as GitHub's CodeQL, which can be adapted for programming task assessment. We present a work-in-progress programming task analysis framework which employs CodeQL's analysis technology to identify the actual use of task-description-mandated syntactic and semantic elements such as loop structures or the use of mandated data blocks in branching conditions. This allows extending existing course work analysis frameworks to include a semantic check of an uploaded program which exceeds the relatively simple set of input-output test cases provided by unit tests. We use a running example of entry level programming tasks and several solution attempts to introduce and explain our proposed control flow and data flow -based analysis method. We discuss the benefits of including semantic analysis as an additional method in the automated programming task assessment toolbox. Our main contribution is the adaptation of an semantic analysis code framework to analyse syntactic and semantic components in students' programming coursework.}},
  author       = {{Wehmeier, Leon and Eilermann, Sebastian and Niggemann, Oliver and Deuter, Andreas}},
  booktitle    = {{FIE 2023 : College Station, TX, USA, October 18-21, 2023 : conference proceedings  / 2023 IEEE Frontiers in Education Conference (FIE)}},
  isbn         = {{979-8-3503-3643-6}},
  keywords     = {{Codes, Electronic learning, Soft sensors, Semantics, Education, Syntactics, Task analysis}},
  location     = {{Texas}},
  publisher    = {{IEEE}},
  title        = {{{Task-fidelity Assessment for Programming Tasks Using Semantic Code Analysis}}},
  doi          = {{10.1109/fie58773.2023.10342916}},
  year         = {{2024}},
}

@misc{11333,
  author       = {{Stuke, Tobias and Rauschenbach, Thomas and Bartsch, Thomas}},
  booktitle    = {{Machine Learning for Cyber-Physical Systems: Selected papers from the International Conference ML4CPS 2023 }},
  editor       = {{Niggemann, Oliver and Beyerer ,  Jürgen  and Krantz,  Maria and Kühnert, Christian }},
  isbn         = {{978-3-031-47061-5}},
  location     = {{Hamburg}},
  pages        = {{8}},
  publisher    = {{Springer Verlag}},
  title        = {{{Development of a Reinforcement Learning Approach for Industrial Bin Picking}}},
  doi          = {{10.1007/978-3-031-47062-2_5}},
  volume       = {{18}},
  year         = {{2023}},
}

@misc{10782,
  abstract     = {{With the trend towards shorter product lifecycles, smaller batch sizes, and more product variants, the complexity of manual assembly activities is increasing. To support employees in carrying out complex assembly tasks, the use of assembly instructions is indispensable to ensure high process capability and work productivity. However, the creation of assembly instructions is often time-consuming. Thus, the use of automation approaches can be a way to simplify the creation of assembly instructions. Therefore, this paper introduces a promising automation concept for applying robotic process automation (RPA) to generate assembly instructions automatically. Finally, the automation concept is demonstrated in a practical use case that illustrates the associated automation potential of RPA.}},
  author       = {{Meyer, Frederic and Hinrichsen, Sven and Niggemann, Oliver}},
  booktitle    = {{Human Interaction & Emerging Technologies (IHIET 2023): Artificial Intelligence & Future Applications}},
  issn         = {{2771-0718}},
  keywords     = {{Digital Assembly Instruction, Industrial Engineering, Manual Assembly, Robotic Process Automation, RPA, Work Instruction}},
  location     = {{NIzza}},
  pages        = {{629--638}},
  publisher    = {{AHFE International}},
  title        = {{{How to Generate Assembly Instructions with Robotic Process Automation}}},
  doi          = {{10.54941/ahfe1004070}},
  volume       = {{111}},
  year         = {{2023}},
}

@inbook{4786,
  author       = {{Niggemann, Oliver and Biswas, Gautam and Kinnebrew, John S. and Khorasgani, Hamed and Hranisavljevic, Nemanja and Bunte, Andreas}},
  booktitle    = {{Handbuch Industrie 4.0: Produktion, Automatisierung und Logistik}},
  editor       = {{ten Hompel, Michael and Vogel-Heuser, Birgit and Bauernhansl, Thomas}},
  isbn         = {{978-3-662-45537-1}},
  pages        = {{800}},
  publisher    = {{Springer}},
  title        = {{{Konzeptualisierung als Kernfrage des Maschinellen Lernens in der Produktion}}},
  year         = {{2020}},
}

@inproceedings{5443,
  author       = {{Zhang, Fan and Pinkal, K. and Conradi, Florian and Wefing, Patrick and Schneider, Jan and Niggemann, Oliver}},
  location     = {{Melbourne, Australia}},
  title        = {{{Quality Control of Continuous Wort Production through Production Data Analysis in Latent Space}}},
  year         = {{2019}},
}

@inproceedings{4780,
  author       = {{Bunte, Andreas and Wunderlich, Paul and Moriz, Natalia and Li, Peng and Mankowski, Andre and Rogalla, Antje and Niggemann, Oliver}},
  booktitle    = {{24nd IEEE International Conference on Emerging Technologies and Factory Automation (ETFA)}},
  title        = {{{Why Symbolic AI is a Key Technology for Self-Adaption in the Context of CPPS}}},
  year         = {{2019}},
}

@inproceedings{4781,
  abstract     = {{Cyber-physical production systems (CPPS) integrate physical and computational resources due to increasingly available sensors and processing power. This enables the usage of data, to create additional benefit, such as condition monitoring or optimization. These capabilities can lead to cognition, such that the system is able to adapt independently to changing circumstances by learning from additional sensors information. Developing a reference architecture for the design of CPPS and standardization of machines and software interfaces is crucial to enable compatibility of data usage between different machine models and vendors. This paper analysis existing reference architecture regarding their cognitive abilities, based on requirements that are derived from three different use cases. The results from the evaluation of the reference architectures, which include two instances that stem from the field of cognitive science, reveal a gap in the applicability of the architectures regarding the generalizability and the level of abstraction. While reference architectures from the field of automation are suitable to address use case specific requirements, and do not address the general requirements, especially w.r.t. adaptability, the examples from the field of cognitive science are well usable to reach a high level of adaption and cognition. It is desirable to merge advantages of both classes of architectures to address challenges in the field of CPPS in Industrie 4.0.}},
  author       = {{Bunte, Andreas and Fischbach, Andreas and Strohschein, Jan and Bartz-Beielstein, Thomas and Faeskorn-Woyke, Heide and Niggemann, Oliver}},
  booktitle    = {{24nd IEEE International Conference on Emerging Technologies and Factory Automation (ETFA)}},
  isbn         = {{978-1-7281-0303-7}},
  issn         = {{1946-0759}},
  keywords     = {{Reference Architecture, Cognition, Industrie 4.0}},
  location     = {{Zaragoza, SPAIN}},
  pages        = {{729--736}},
  publisher    = {{IEEE}},
  title        = {{{Evaluation of Cognitive Architectures for Cyber-Physical Production Systems}}},
  doi          = {{10.1109/etfa.2019.8869038}},
  year         = {{2019}},
}

@article{4784,
  author       = {{Bunte, Andreas and Fischbach, Andreas and Strohschein, Jan and Bartz-Beielstein, Thomas and Faeskorn-Woyke, Heide and Niggemann, Oliver}},
  journal      = {{arXiv e-prints}},
  title        = {{{Evaluation of Cognitive Architectures for Cyber-Physical ProductionSystems}}},
  year         = {{2019}},
}

@inproceedings{4785,
  author       = {{Bunte, Andreas and Stein, Benno and Niggemann, Oliver}},
  publisher    = {{Thirty-Third AAAI Conference on Artificial Intelligence (AAAI-19)}},
  title        = {{{Model-Based Diagnosis for Cyber-Physical Production Systems Based on Machine Learning and Residual-Based Diagnosis Models}}},
  year         = {{2019}},
}

@misc{12808,
  abstract     = {{Along with the constantly increasing complexity of industrial automation systems, machine learning methods have been widely applied to detecting abnormal states in such systems. Anomaly detection tasks can be treated as one-class classification problems in machine learning. Geometric methods can give an intuitive solution to such problems. In this paper, we propose a new geometric structure, oriented non-convex hulls, to represent decision boundaries used for one-class classification. Based on this geometric structure, a novel boundary based one-class classification algorithm is developed to solve the anomaly detection problem. Compared with traditional boundary-based approaches such as convex hulls based methods and one-class support vector machines, the proposed approach can better reflect the true geometry of target data and needs little effort for parameter tuning. The effectiveness of this approach is evaluated with artificial and real world data sets to solve the anomaly detection problem in Cyber-Physical-Production-Systems (CPPS). The evaluation results also show that the proposed approach has higher generality than the used baseline algorithms.}},
  author       = {{Li, Peng and Niggemann, Oliver}},
  booktitle    = {{Engineering Applications of Artificial Intelligence}},
  issn         = {{1873-6769}},
  keywords     = {{One-class classification, n-dimensional oriented non-convex hull, Anomaly detection, CPPS}},
  publisher    = {{Elsevier BV}},
  title        = {{{Non-convex hull based anomaly detection in CPPS}}},
  doi          = {{10.1016/j.engappai.2019.103301}},
  volume       = {{87}},
  year         = {{2019}},
}

@inproceedings{4327,
  abstract     = {{In ever changing world, the industrial systems become more and more complex. Machine feedback in the form of alarms and notifications, due to its growing volume, becomes overwhelming for the operator. In addition, expectations in relation to system availability are growing as well. Therefore, there exists strong need for new solutions guaranteeing fast troubleshooting of problems that arise during system operation. The approach proposed in this study uses advantages of the Asset Administration Shell, machine learning, and human-machine interaction in order to create the assistance system which holistically addresses the issue of troubleshooting complex industrial systems.}},
  author       = {{Lang, Dorota and Wunderlich, Paul and Heinz, Mario and Wisniewski, Lukasz and Jasperneite, Jürgen and Niggemann, Oliver and Röcker, Carsten}},
  booktitle    = {{14th IEEE International Workshop on Factory Communication Systems (WFCS)}},
  keywords     = {{Maintenance engineering, Adaptation models, Machine learning, Data models, Standards, Software, Bayes methods}},
  location     = {{Imperia, Italy }},
  publisher    = {{IEEE}},
  title        = {{{Assistance System to Support Troubleshooting of Complex Industrial Systems}}},
  doi          = {{10.1109/WFCS.2018.8402380}},
  year         = {{2018}},
}

@article{727,
  abstract     = {{Produkte bestehen aus einer zunehmenden Anzahl unterschiedlicher Teile. Dieser Trend hängt mit einer steigenden Variantenanzahl und der Integration zusätzlicher Produktfunktionen zusammen. Dadurch steigen die Anforderungen an Kommissionierprozesse in der Montage. Es stellt sich daher die Frage der optimalen Anordnung der Teile, um unnötige Gehwege beim Kommissionieren zu vermeiden. Im Rahmen des Beitrages wird ein Optimierungsalgorithmus vorgestellt, mit dem Bauteile so klassifiziert werden, dass Gehwege reduziert werden können.  }},
  author       = {{Sehr, Philip and Bendzioch, Sven and Niggemann, Oliver}},
  issn         = {{1437-7624}},
  journal      = {{Wissenschaftliche Berichte : wissenschaftliche Zeitschrift der Hochschule Mittweida (FH)}},
  location     = {{Mittweida}},
  number       = {{n.a.}},
  pages        = {{18--21}},
  publisher    = {{Hochschule Mittweida}},
  title        = {{{Mathematische Optimierung der Teileanordnung in der Kommissionierung}}},
  year         = {{2018}},
}

@inproceedings{5630,
  author       = {{Conradi, F. P.  and Wefing, Patrick and Pinkal, K. and Zhang, Fan and Niggemann, Oliver and Schneider, Jan}},
  location     = {{Gent}},
  title        = {{{Inline progress measurement of the ß-amylase rest in the mashing process employing a near infrared transflectance probe}}},
  year         = {{2018}},
}

@inproceedings{5631,
  author       = {{Wefing, Patrick and Conradi, F. P.  and Fuchs, Lara and Schoppmeier, J. W.  and Pinkal, K. and Niggemann, Oliver and Schneider, Jan}},
  location     = {{Gent}},
  title        = {{{Laboratory plant for a closed loop-controlled continuous (CLCC) mashing}}},
  year         = {{2018}},
}

@inproceedings{5633,
  author       = {{Conradi, F. P.  and Wefing, Patrick and Pinkal, K. and Zhang, Fan and Niggemann, Oliver and Schneider, Jan}},
  location     = {{Berlin}},
  title        = {{{Inline progress measurement of the ß-amylase rest in the mashing process employing a near infrared transflectance probe}}},
  year         = {{2018}},
}

@inproceedings{4787,
  author       = {{Bunte, Andreas and Niggemann, Oliver and Stein, Benno}},
  booktitle    = {{23th IEEE International Conference on Emerging Technologies and Factory Automation (ETFA)}},
  title        = {{{Integrating OWL Ontologies for Smart Services into AutomationML and OPC UA}}},
  year         = {{2018}},
}

@inproceedings{4788,
  author       = {{Bunte, Andreas and Li, Peng and Niggemann, Oliver}},
  booktitle    = {{International Conference on Agents and Artificial Intelligence (ICAART)}},
  publisher    = {{SCITEPRESS}},
  title        = {{{Mapping Data Sets to Concepts Using Machine Learning and a Knowledge Based Approach}}},
  year         = {{2018}},
}

@inproceedings{4254,
  abstract     = {{The current trend of integrating machines and factories into cyber-physical systems (CPS) creates an enormous complexity for operators of such systems. Especially the search for the root cause of cascading failures becomes highly time-consuming. Within this paper, we address the question on how to help human users to better and faster understand root causes of such situations. We propose a concept of interactive alarm flood reduction and present the implementation of a first vertical prototype for such a system. We consider this prototype as a first artifact to be discussed by the research community and aim towards an incremental further development of the system in order to support humans in complex error situations.}},
  author       = {{Büttner, Sebastian and Wunderlich, Paul and Heinz, Mario and Niggemann, Oliver and Röcker, Carsten}},
  booktitle    = {{ Machine Learning and Knowledge Extraction : First IFIP TC 5, WG 8.4, 8.9, 12.9 International Cross-Domain Conference, CD-MAKE 2017, Reggio, Italy, August 29 – September 1, 2017, Proceedings}},
  editor       = {{Holzinger, Andreas}},
  isbn         = {{978-3-319-66807-9}},
  keywords     = {{Alarm flood reduction, Machine learning, Assistive system}},
  location     = {{Reggio, Italy}},
  pages        = {{69--82}},
  publisher    = {{Springer}},
  title        = {{{Managing Complexity: Towards Intelligent Error-Handling Assistance Trough Interactive Alarm Flood Reduction}}},
  volume       = {{10410}},
  year         = {{2017}},
}

@book{4595,
  author       = {{Pethig, Florian and Schriegel, Sebastian and Maier, Alexander and Otto, Jens and Windmann, Stefan and Böttcher, Björn and Niggemann, Oliver and Jasperneite, Jürgen}},
  isbn         = {{ 978-3-8163-0709-9}},
  publisher    = {{VDMA Guideline, Publisher: VDMA Verlag GmbH, Editor: Verband Deutscher Maschinen- und Anlagenbau e.V.}},
  title        = {{{Industrie 4.0 Communication Guideline Based on OPC UA}}},
  year         = {{2017}},
}

@inproceedings{267,
  abstract     = {{Sich verkürzende Innovations- und Produktlebenszyklen sowie eine zunehmende Variantenvielfalt verbunden mit kleineren Losgrößen bis hin zur kundenindividuellen Produktkonfiguration führen zu veränderten Anforderungen an die Gestaltung von Montagesystemen. Gleichzeitig bieten technologische Entwicklungen auf dem Gebiet der Assistenzsysteme neue Gestaltungsmöglichkeiten, Beschäftigte bei der Ausführung ihrer Arbeitsprozesse zu unterstützten. Der vorliegende Beitrag zeigt anhand eines Fallbeispiels, eines Montagearbeitsplatzes aus der SmartFactoryOWL in Lemgo, wie ein Montagearbeitsplatz über Assistenzfunktionen weiter entwickelt wurde und welche Auswirkungen diese Entwicklungen auf die Arbeit haben. Der Arbeitsplatz wurde im Rahmen eines Kooperationsprojektes des Labors für Industrial Engineering der Hochschule Ostwestfalen-Lippe und der Unternehmen Turck und Brandt Kantentechnik in der SmartFactory-OWL realisiert. Er verfügt über ein Assistenzsystem, welches dem Monteur auftrags- und arbeitsschrittbezogene Informationen bereitstellt. Das System besteht maßgeblich aus einem Pick-to-light System verbunden mit einem Touchscreen zur Visualisierung der Informationen. Der Montageauftrag wird über eine RFID-Applikation angemeldet. Damit lassen sich neue Produktvarianten flexibel hinzufügen und bestehende ändern. Zudem können die über Sensoren erfassten Daten zu Kennzahlen aggregiert und entsprechend der Anforderungen der Nutzer visualisiert werden. Im Rahmen des Beitrages werden die Auswirkungen dieser technologischen Weiterentwicklungen auf die Arbeit erörtert.}},
  author       = {{Kleineberg, Tim and Hinrichsen, Sven and Niggemann, Oliver}},
  booktitle    = {{Bericht zum 63. Frühjahrskongress der Gesellschaft für Arbeitswissenschaft e.V.}},
  isbn         = {{978-3-936804-22-5}},
  location     = {{Brugg-Windisch}},
  number       = {{30}},
  publisher    = {{GfA-Press}},
  title        = {{{Auswirkungen von Assistenzsystemen auf die Arbeit in der manuellen Montage}}},
  year         = {{2017}},
}

@inproceedings{4789,
  author       = {{Bunte, Andreas and Li, Peng and Niggemann, Oliver}},
  booktitle    = {{3rd Conference on Machine Learning for Cyber Physical Systems and Industry 4.0 (ML4CPS)}},
  title        = {{{Learned Abstraction: Knowledge Based Concept Learning for Cyber Physical Systems}}},
  year         = {{2017}},
}

@inproceedings{4782,
  abstract     = {{Die zunehmende Individualisierung von Produkten stellt neue Anforderun-
gen an Produktionsanlagen, wodurch deren Komplexit¨at steigt. Damit komplexe An-
lagen effizient bedient werden k¨onnen, vor allem im Fall eines Fehlers, sind neue Inter-
aktionskonzepte erforderlich. In dieser Arbeit wurde ein Konzept f¨ur die Sprachverar-
beitung entworfen und prototypisch implementiert. Zus¨atzlich zur Sprachverarbeitung
ist eine semantische Schicht notwendig um eine Steuerung ¨uber Sprache zu realisieren}},
  author       = {{Bunte, Andreas and Diedrich, Alexander and Niggemann, Oliver}},
  booktitle    = {{Tagungsband des Entwicklerforums "HMI – Komponenten Lösungen"}},
  publisher    = {{HMI}},
  title        = {{{Natürlichsprachliche Schnittstelle für Produktionssysteme}}},
  year         = {{2016}},
}

@inproceedings{4790,
  author       = {{Bunte, Andreas and Diedrich, Alexander and Niggemann, Oliver}},
  booktitle    = {{International Workshop on the Principles of Diagnosis (DX)}},
  title        = {{{Semantics Enable Standardized User Interfaces for Diagnosis in Modular Production Systems}}},
  year         = {{2016}},
}

@inproceedings{4791,
  author       = {{Bunte, Andreas and Diedrich, Alexander and Niggemann, Oliver}},
  booktitle    = {{21th IEEE International Conference on Emerging Technologies and Factory Automation (ETFA)}},
  title        = {{{Integrating Semantics for Diagnosis of Manufacturing Systems}}},
  year         = {{2016}},
}

@inbook{4792,
  author       = {{Niggemann, Oliver and Biswas, Gautam and Khorasgani, Hamed and Volgmann, Sören and Bunte, Andreas}},
  booktitle    = {{Handbuch Industrie 4.0 Bd. 2}},
  editor       = {{Vogel-Hauser, Birgit and Bauernhansl, Thomas and ten Hompel, Michael}},
  publisher    = {{Springer Berlin Heidelberg}},
  title        = {{{Datenanalyse in der intelligenten Fabrik}}},
  year         = {{2016}},
}

@inproceedings{4863,
  author       = {{Jasperneite, Jürgen and Niggemann, Oliver}},
  booktitle    = {{Vortrag anlässlich des 50 jährigen Bestehens der Lemgoer Elektrotechnik}},
  title        = {{{Wie intelligent werden Maschinen?}}},
  year         = {{2016}},
}

@article{428,
  abstract     = {{Der Trend einer zunehmenden Produkt- und Variantenvielfalt macht es notwendig, Betriebsmittel so zu planen, dass diese möglichst für ein breites Produktspektrum einsetzbar oder einfach auf neue Produkte oder Produktvarianten umzurüsten sind. Hierdurch entsteht in vielen Branchen der Bedarf nach wandlungsfähigen Fertigungssystemen. Wandlungsfähigkeit oder Rekonfiguration bedeutet, ein Fertigungssystem mit einfachen Mitteln umbauen zu können, um die Systemfunktionen an veränderte oder neue Anforderungen strukturell anzupassen. Diese Anpassungen sollten möglichst ohne Experten erfolgen können. Dazu kann das an die Computertechnik angelehnte Prinzip des „Plug and Produce“ vorteilhaft genutzt werden. In diesem Beitrag wird dieses Prinzip an zwei Fertigungssystemen der Smart Factory OWL, einer gemeinsamen Initiative der Fraunhofer-Gesellschaft und der Hochschule Ostwestfalen-Lippe in Lemgo, erläutert.}},
  author       = {{Jasperneite, Jürgen and Hinrichsen, Sven and Niggemann, Oliver}},
  issn         = {{0170-6012}},
  journal      = {{Informatik-Spektrum}},
  number       = {{3}},
  pages        = {{183--190}},
  publisher    = {{Springer-Verlag GmbH}},
  title        = {{{,,Plug-and-Produce“ für Fertigungssysteme - Anwendungsfälle und Lösungsansätze}}},
  volume       = {{38}},
  year         = {{2015}},
}

@inproceedings{2167,
  abstract     = {{Cyber-Physical Production Systems (CPPSs) are in the focus of research, industry and politics: By applying new IT and new computer science solutions, production systems will become more adaptable, more resource ef- ficient and more user friendly. The analysis and diagnosis of such systems is a major part of this trend: Plants should detect automatically wear, faults and suboptimal configurations. This paper reflects the current state-of- the-art in diagnosis against the requirements of CPPSs, identifies three main gaps and gives application scenarios to outline first ideas for potential solutions to close these gaps.
}},
  author       = {{Niggemann, Oliver and Lohweg, Volker}},
  booktitle    = {{Twenty-Ninth Conference on Artificial Intelligence (AAAI-15)}},
  keywords     = {{Cyber-Physical Systems, Machine Learning, Diagnosis, Anomaly Detection}},
  title        = {{{On the Diagnosis of Cyber-Physical Production Systems - State-of-the-Art and Research Agenda}}},
  year         = {{2015}},
}

@inproceedings{4793,
  author       = {{Diedrich, Alexander and Bunte, Andreas and Maier, Alexander and Niggemann, Oliver}},
  booktitle    = {{Machine Learning for Cyber Physical Systems and Industry 4.0 (ML4CPS)}},
  title        = {{{Kognitive Architektur zum Konzeptlernen in technischen Systemen}}},
  year         = {{2015}},
}

@inproceedings{4794,
  author       = {{Niggemann, Oliver and Biswas, Gautam and Kinnebrew, John S. and Khorasgani, Hamed and Volgmann, Sören and Bunte, Andreas}},
  booktitle    = {{International Workshop on the Principles of Diagnosis (DX)}},
  title        = {{{Data-Driven Monitoring of Cyber-Physical Systems Leveraging on Big Data and the Internet-of-Things for Diagnosis and Control}}},
  year         = {{2015}},
}

@inproceedings{4802,
  author       = {{Böttcher, Björn and Moriz, Natalia and Niggemann, Oliver}},
  booktitle    = {{Automation 2015}},
  publisher    = {{VDI-Verlag}},
  title        = {{{Intelligente Entwurfsassistenz für Automatisierungssysteme - Vorteile deklarativer Paradigmen im Systementwurf}}},
  year         = {{2015}},
}

@article{4552,
  author       = {{Frey, Christian and Heinzmann, Michael and Jasperneite, Jürgen and Niggemann, Oliver and Sauer, Olaf and Schleipen, Miriam and Usländer, Thomas and Voit, Michael}},
  journal      = {{ATP edition - Automatisierungstechnische Praxis}},
  title        = {{{IKT in der Fabrik der Zukunft - Ein Diskussionsbeitrag zu Industrie 4.0}}},
  year         = {{2014}},
}

@inbook{4556,
  author       = {{Niggemann, Oliver and Jasperneite, Jürgen}},
  booktitle    = {{Industrie 4.0 in Produktion, Automatisierung und Logistik }},
  editor       = {{Bauernhansl, Thomas and ten Hompel, Michael and Vogel-Heuser, Birgit}},
  publisher    = {{Springer-Verlag}},
  title        = {{{Konzepte und Anwendungsfälle für die intelligente Fabrik}}},
  year         = {{2014}},
}

@article{4561,
  author       = {{Frey, Christian and Heinzmann, Michael and Jasperneite, Jürgen and Niggemann, Oliver and Sauer, Olaf and Schleipen, Miriam and Usländer, Thomas}},
  journal      = {{ATP edition - Automatisierungstechnische Praxis}},
  publisher    = {{DIV Vulkan Verlag}},
  title        = {{{IKT in der Fabrik der Zukunft - Ein Diskussionsbeitrag zu Industrie 4.0}}},
  year         = {{2014}},
}

@inproceedings{4573,
  author       = {{Schriegel, Sebastian and Niggemann, Oliver and Jasperneite, Jürgen}},
  location     = {{Boppard}},
  title        = {{{Plug-and-Work für verteilte Echtzeitsysteme mit Zeitsynchronisation}}},
  year         = {{2014}},
}

@inproceedings{4795,
  author       = {{Niggemann, Oliver and Windmann, Stefan and Volgmann, Sören and Bunte, Andreas and Stein, Benno}},
  booktitle    = {{International Workshop on the Principles of Diagnosis (DX)}},
  publisher    = {{Graz, Austria}},
  title        = {{{Using Learned Models for the Root Cause Analysis of Cyber-Physical Production Systems}}},
  year         = {{2014}},
}

@inproceedings{4803,
  author       = {{Moriz, Natalia and Böttcher, Björn and Niggemann, Oliver and Lackhove, Josef}},
  booktitle    = {{19th IEEE International Conference on Emerging Technologies and Factory Automation (ETFA)}},
  title        = {{{Assisted Design for Automation Systems - from Formal Requirements to Final Designs}}},
  year         = {{2014}},
}

@inproceedings{4804,
  author       = {{Böttcher, Björn and Moriz, Natalia and Niggemann, Oliver}},
  booktitle    = {{21st European Conference on Artificial Intelligence Frontiers in Artificial Intelligence and Applications, 2014}},
  editor       = {{Schaub, T., Friedrich, G. O'Sullivan, B.}},
  pages        = {{pp. 977--978}},
  publisher    = {{Vol. 263}},
  title        = {{{From Formal Requirements on Technical Systems to Complete Designs - A Holistic Approach}}},
  year         = {{2014}},
}

@article{4199,
  author       = {{Niggemann, Oliver and Borcherding, Holger and Köster, Markus and Windmann, Stefan and Ehlich, Martin}},
  issn         = {{1436-4980}},
  journal      = {{Werkstattstechnik : wt}},
  number       = {{5}},
  pages        = {{416 -- 422}},
  publisher    = {{Springer}},
  title        = {{{Energieeffizienz in der Intralogistik : Elektrische Antriebstechnik - intelligent und nachhaltig}}},
  year         = {{2013}},
}

@inproceedings{4276,
  abstract     = {{In the presented work, the detection of anomalous energy consumption in hybrid industrial production systems is investigated. A model-based approach with a timed hybrid automaton as overall system model is employed for anomaly detection. The approach is based on the assumption of several system modes, i.e. phases with continuous system behavior. Transitions between the modes are attributed to discrete control events such as on/off signals. The underlying discrete event system which comprises both system modes and transitions is modeled as finite state machine. The focus of this paper is set on the modeling of the energy consumption in the particular system modes. Sequences of stochastic state space models are employed for this purpose. Model learning and anomaly detection for this approach are considered. The proposed approach is further evaluated in a small model factory. The experimental results show significant improvements compared to existing approaches to anomaly detection in hybrid industrial systems.}},
  author       = {{Windmann, Stefan and Jiao, Shuo and Niggemann, Oliver and Borcherding, Holger}},
  booktitle    = {{11th International IEEE Conference on Industrial Informatics}},
  location     = {{Bochum}},
  pages        = {{194 -- 199}},
  publisher    = {{IEEE}},
  title        = {{{A Stochastic Method for the Detection of Anomalous Energy Consumption in Hybrid Industrial Systems}}},
  year         = {{2013}},
}

@inproceedings{4805,
  author       = {{Böttcher, Björn and Badinger, Johann and Moriz, Natalia and Niggemann, Oliver}},
  booktitle    = {{18thIEEE International Conference on Emerging Technologies and Factory Automation (ETFA)}},
  title        = {{{Design of Industrial Automation Systems - Formal Requirements in the Engineering Process}}},
  year         = {{2013}},
}

@inproceedings{4806,
  author       = {{Schetinin, Nikolai and Moriz, Natalia and Kumar, Barath and Faltinski, Sebastian and Niggemann, Oliver and Maier, Alexander}},
  booktitle    = {{International Conference on Industrial Technology (ICIT) 25.-27. February 2013, Cape Town, South Africa, }},
  title        = {{{Why do verification approaches in automation rarely use HIL-test?}}},
  year         = {{2013}},
}

@article{4527,
  author       = {{Kumar, Barath and Niggemann, Oliver and Schäfer, Wilhelm and Jasperneite, Jürgen}},
  journal      = {{Advances in Intelligent and Soft Computing}},
  pages        = {{1027--1034}},
  title        = {{{Modeling and Testing of Automation Systems}}},
  volume       = {{133}},
  year         = {{2012}},
}

@article{4535,
  author       = {{Jasperneite, Jürgen and Niggemann, Oliver}},
  journal      = {{ATP edition - Automatisierungstechnische Praxis }},
  number       = {{9}},
  publisher    = {{Oldenbourg Verlag}},
  title        = {{{Intelligente Assistenzsysteme zur Beherrschung der Systemkomplexität in der Automation}}},
  year         = {{2012}},
}

@inproceedings{4540,
  author       = {{Kumar, Barath and Toensfeuerborn, Andreas and Niggemann, Oliver and Schäfer, Wilhelm and Jasperneite, Jürgen}},
  location     = {{Tallinn, Estonia}},
  title        = {{{Experience in deploying MBT for industrial automation}}},
  year         = {{2012}},
}

@inproceedings{2105,
  author       = {{Dicks, Alexander and Bator, Martyna and Lohweg, Volker and Faltinski, Sebastian and Niggemann, Oliver}},
  booktitle    = {{Cyber-Physical Systems – Enabling Multi-Nature Systems (CPMNS), Dresden, April 18, }},
  isbn         = {{978-3-8396-0398-7 }},
  pages        = {{51--56}},
  publisher    = {{Fraunhofer-Verlag}},
  title        = {{{Cyber-Physical Systems im Maschinen- und Anlagenbau – ein Konzept für die Zukunft?}}},
  year         = {{2012}},
}

@inproceedings{4807,
  author       = {{Faltinski, Sebastian and Moriz, Natalia and Schetinin, Nikolai and Niggemann, Oliver}},
  booktitle    = {{AUTOMATION 2012}},
  title        = {{{AutomationML als Grundlage für einen durchgängigen Modellierung-, Simulations- und Integrationsprozess in der Anlageplanung. }}},
  year         = {{2012}},
}

@inproceedings{4808,
  author       = {{Faltinski, Sebastian and Niggemann, Oliver and Moriz, Natalia and Mankowski, Andre}},
  booktitle    = {{2012 IEEE International Conference on Industrial Technology (ICIT)}},
  title        = {{{AutomationML: From Data Exchange to System Planning and Simulation}}},
  year         = {{2012}},
}

@inproceedings{4683,
  author       = {{Faltinski, Sebastian and Wienke, Michael and Niggemann, Oliver and Jasperneite, Jürgen}},
  booktitle    = {{2. Jahreskolloquium Kommunikation in der Automation (KommA 2011)}},
  title        = {{{mINA - Eine echtzeitfähige Middleware für die Industrieautomation zur Realisierung semantikbasierter Wandelbarkeit }}},
  year         = {{2011}},
}

@inproceedings{4688,
  author       = {{Wienke, Michael and Faltinski, Sebastian and Niggemann, Oliver and Jasperneite, Jürgen}},
  booktitle    = {{16th IEEE International Conference on Emerging Technologies and Factory Automation (ETFA 2011)}},
  title        = {{{mINA-DL: A Novel Description Language Enabling Dynamic Reconfiguration in Industrial Automation}}},
  year         = {{2011}},
}

@inproceedings{4703,
  author       = {{Niggemann, Oliver and Jasperneite, Jürgen}},
  booktitle    = {{8. Symposium Informationstechnologien für Entwicklung und Produktion in der Verfahrenstechnik Dechema}},
  publisher    = {{Frankfurt}},
  title        = {{{Systemmodelle für wandelbare Automatisierungssysteme}}},
  year         = {{2011}},
}

@inproceedings{4809,
  author       = {{Gräser, Olaf and Kumar, Barath and Moriz, Natalia and Maier, Alexander and Niggemann, Oliver}},
  title        = {{{AutomationML as a Basis for Offline- and Realtime-Simulation}}},
  year         = {{2011}},
}

@inproceedings{4810,
  author       = {{Moriz, Natalia and Faltinski, Sebastian and Gräser, Olaf and Niggemann, Oliver and Barth, Mike and Fay, Alexander}},
  booktitle    = {{VDI Kongress AUTOMATION 2011 }},
  publisher    = {{Baden Baden}},
  title        = {{{Integration und Anwendung von objektorientierten Simulationsmodellen in AutomationML}}},
  year         = {{2011}},
}

@inproceedings{4454,
  author       = {{Kumar, Barath and Niggemann, Oliver and Jasperneite, Jürgen}},
  location     = {{Cape Town, South Africa}},
  title        = {{{Statistical Models of Network Traffic}}},
  year         = {{2010}},
}

@inproceedings{4455,
  author       = {{Niggemann, Oliver and Maier, Alexander and Jasperneite, Jürgen}},
  title        = {{{Model-based Development of Automation Systems}}},
  year         = {{2010}},
}

@inproceedings{4464,
  author       = {{Kumar, Barath and Niggemann, Oliver and Jasperneite, Jürgen}},
  location     = {{Magdeburg, Germany}},
  title        = {{{Test Generation for Hybrid, Probabilistic Control Models}}},
  year         = {{2010}},
}

@inproceedings{2088,
  abstract     = {{Clustering remains a major topic in machine learning; it is used e.g. for document categorization, for data mining, and for image analysis. In all these application areas, clustering algorithms try to identify groups of related data in large data sets.

In this paper, the established clustering algorithm MajorClust ([12]) is improved; making it applicable to data sets with few structure on the local scale—so called near-homogeneous graphs. This new algorithm MCProb is verified empirically using the problem of image clustering. Furthermore, MCProb is analyzed theoretically. For the applications examined so-far, MCProb outperforms other established clustering techniques.}},
  author       = {{Niggemann, Oliver and Lohweg, Volker and Tack, Tim}},
  booktitle    = {{33rd Annual German Conference on Artificial Intelligence (KI 2010)}},
  isbn         = {{978-3-642-16110-0}},
  keywords     = {{Markov Chain, Cluster Algorithm, Edge Weight, Spectral Cluster, Stable Distribution}},
  pages        = {{184--194}},
  publisher    = {{Springer}},
  title        = {{{A Probabilistic MajorClust Variant for the Clustering of Near-Homogeneous Graphs}}},
  doi          = {{https://doi.org/10.1007/978-3-642-16111-7_21}},
  volume       = {{6359}},
  year         = {{2010}},
}

@inproceedings{2083,
  author       = {{Lohweg, Volker and Niggemann, Oliver}},
  booktitle    = {{Lemgoer Schriften zur industriellen Informationstechnik (Lemgo Series on Industrial Information Technology), Vol. 3, ISSN 1869-2087, Lemgo 2009}},
  title        = {{{Machine Learning in Real-time Applications (MLRTA09 - KI 2009 Workshop)}}},
  year         = {{2009}},
}

@inproceedings{4715,
  author       = {{Kumar, Barath and Niggemann, Oliver and Jasperneite, Jürgen}},
  booktitle    = {{Machine Learning in Real-Time Applications (MLRTA 09) (in conjunction with 32nd Annual Conference on Artificial Intelligence (KI 2009))}},
  title        = {{{Timed Automata for Modeling Network Traffic}}},
  year         = {{2009}},
}