@inproceedings{UlmerBraunWollert2018,
  author    = {Ulmer, Jessica and Braun, Sebastian and Wollert, J{\"o}rg},
  title     = {Generische IoT Adapter f{\"u}r semantische Maschinenschnittstellen},
  series = {Internet of Things - vom Sensor bis zur Cloud},
  booktitle = {Internet of Things - vom Sensor bis zur Cloud},
  pages     = {1 -- 5},
  year      = {2018},
  language  = {de}
}
@inproceedings{UlmerLaiChengetal.2019,
  author    = {Ulmer, Jessica and Lai, Chow Yin and Cheng, Chi-Tsun and Wollert, J{\"o}rg},
  title     = {Integration von VR und AR in Produktlebenszyklen - Eine {\"U}bersicht {\"u}ber die Nutzung virtueller Technologien im industriellen Umfeld},
  series = {Automation 2019},
  booktitle = {Automation 2019},
  pages     = {1 -- 12},
  year      = {2019},
  language  = {de}
}
@inproceedings{UlmerBraunLaietal.2019,
  author    = {Ulmer, Jessica and Braun, Sebastian and Lai, Chow Yin and Cheng, Chi-Tsun and Wollert, J{\"o}rg},
  title     = {Generic integration of VR and AR in product lifecycles based on CAD models},
  series = {Proceedings of The 23rd World Multi-Conference on Systemics, Cybernetics and Informatics: WMSCI 2019},
  booktitle = {Proceedings of The 23rd World Multi-Conference on Systemics, Cybernetics and Informatics: WMSCI 2019},
  year      = {2019},
  language  = {en}
}
@article{WilbringEnningPfaffetal.2020,
  author    = {Wilbring, Daniela and Enning, Manfred and Pfaff, Raphael and Schmidt, Bernd},
  title     = {Neue Perspektiven f{\"u}r die Bahn in der Produktions- und Distributionslogistik durch Prozessautomation},
  series = {ETR - Eisenbahntechnische Rundschau},
  volume    = {69},
  journal   = {ETR - Eisenbahntechnische Rundschau},
  number    = {3},
  issn      = {0013-2845},
  pages     = {15 -- 19},
  year      = {2020},
  language  = {de}
}
@inproceedings{GerhardsSchleserOttenetal.2019,
  author    = {Gerhards, Benjamin and Schleser, Markus and Otten,, Christian and Schwarz, Alexander and Gebhardt, Andreas},
  title     = {Innovative Laser Beam Joining Technology for Additive Manufactured Parts},
  series = {Conference Proceedings 72nd IIW Annual Assembly and International Conference, 7-12 July 2019, Bratislava},
  booktitle = {Conference Proceedings 72nd IIW Annual Assembly and International Conference, 7-12 July 2019, Bratislava},
  pages     = {1 -- 8},
  year      = {2019},
  language  = {en}
}
@inproceedings{GerhardsSchleserOtten2019,
  author    = {Gerhards, Benjamin and Schleser, Markus and Otten, Christian},
  title     = {Advancements of mobile vacuum laser welding for industrial thick sheet applications},
  series = {Conference Proceedings 72nd IIW Annual Assembly and International Conference, 7-12 July 2019, Bratislava},
  booktitle = {Conference Proceedings 72nd IIW Annual Assembly and International Conference, 7-12 July 2019, Bratislava},
  pages     = {1 -- 8},
  year      = {2019},
  language  = {en}
}
@inproceedings{BraunChengLaietal.2019,
  author    = {Braun, Sebastian and Cheng, Chi-Tsun and Lai, Chow Yin and Wollert, J{\"o}rg},
  title     = {Microservice Architecture for Automation - Realization by the example of a model-factory's manufacturing execution system},
  series = {Proceedings of the 23rd World Multi-Conference on Systemics, Cybernetics and Informatics: WMSCI 2019},
  booktitle = {Proceedings of the 23rd World Multi-Conference on Systemics, Cybernetics and Informatics: WMSCI 2019},
  pages     = {33 -- 37},
  year      = {2019},
  language  = {en}
}
@article{Thomas2019,
  author    = {Thomas, Axel},
  title     = {Die Wiederbelebung der Mitarbeiterwohnungsidee bei kommunalen Unternehmen},
  series = {VM Verwaltung und Management},
  volume    = {25},
  journal   = {VM Verwaltung und Management},
  number    = {6},
  publisher = {Nomos-Verl.-Ges.},
  address   = {Baden-Baden},
  issn      = {0947-9856},
  doi       = {10.5771/0947-9856-2019-6-286},
  pages     = {286 -- 291},
  year      = {2019},
  language  = {de}
}
@article{EngelnMuellges1982,
  author    = {Engeln-M{\"u}llges, Gisela},
  title     = {Entartungsbedingungen f{\"u}r Gleitfl{\"a}chennomogramme und Nomographierbarkeitsbedingungen f{\"u}r Fluchtebenennomogramme},
  series = {ZAMM - Journal of Applied Mathematics and Mechanics / Zeitschrift f{\"u}r Angewandte Mathematik und Mechanik},
  volume    = {62},
  journal   = {ZAMM - Journal of Applied Mathematics and Mechanics / Zeitschrift f{\"u}r Angewandte Mathematik und Mechanik},
  number    = {2},
  issn      = {1521-4001},
  doi       = {10.1002/zamm.19820620203},
  pages     = {77 -- 93},
  year      = {1982},
  language  = {de}
}
@incollection{EngemannDuKallweitetal.2020,
  author    = {Engemann, Heiko and Du, Shengzhi and Kallweit, Stephan and Ning, Chuanfang and Anwar, Saqib},
  title     = {AutoSynPose: Automatic Generation of Synthetic Datasets for 6D Object Pose Estimation},
  series = {Machine Learning and Artificial Intelligence. Proceedings of MLIS 2020},
  booktitle = {Machine Learning and Artificial Intelligence. Proceedings of MLIS 2020},
  publisher = {IOS Press},
  address   = {Amsterdam},
  isbn      = {978-1-64368-137-5},
  doi       = {10.3233/FAIA200770},
  pages     = {89 -- 97},
  year      = {2020},
  abstract  = {We present an automated pipeline for the generation of synthetic datasets for six-dimension (6D) object pose estimation. Therefore, a completely automated generation process based on predefined settings is developed, which enables the user to create large datasets with a minimum of interaction and which is feasible for applications with a high object variance. The pipeline is based on the Unreal 4 (UE4) game engine and provides a high variation for domain randomization, such as object appearance, ambient lighting, camera-object transformation and distractor density. In addition to the object pose and bounding box, the metadata includes all randomization parameters, which enables further studies on randomization parameter tuning. The developed workflow is adaptable to other 3D objects and UE4 environments. An exemplary dataset is provided including five objects of the Yale-CMU-Berkeley (YCB) object set. The datasets consist of 6 million subsegments using 97 rendering locations in 12 different UE4 environments. Each dataset subsegment includes one RGB image, one depth image and one class segmentation image at pixel-level.},
  language  = {en}
}