@article{HueningLukenSchilderetal.2001,
  author    = {H{\"u}ning, Felix and Luken, Heiko and Schilder, Herbert and Eifert, Thomas},
  title     = {Magnetochemistry: Compounds and Concepts / Lueken, Heiko ; Schilder, Herbert ; Eifert, Thomas ; Handrick, Klaus ; H{\"u}ning, Felix},
  series = {Advances in Solid State Physics. 201 (2001)},
  journal   = {Advances in Solid State Physics. 201 (2001)},
  publisher = {-},
  pages     = {515 -- 532},
  year      = {2001},
  language  = {en}
}
@misc{Engels2008,
  author    = {Engels, Elmar},
  title     = {Magnetlesekopf, Magnetspeicherleseeinrichtung und Verfahren zum abgesicherten Auslesen und Verarbeiten von magnetisch gespeicherten Daten : Offenlegungsschrift : DE102006049162A1 ; Offenlegungstag: 24.04.2008},
  publisher = {Deutsches Patent- und Markenamt},
  address   = {M{\"u}nchen},
  pages     = {9 S.},
  year      = {2008},
  language  = {de}
}
@book{Huening2001,
  author    = {H{\"u}ning, Felix},
  title     = {Magnetische Eigenschaften niederdimensionaler Chrom-, Ruthenium- und Niobhalogenide},
  publisher = {Shaker},
  address   = {Aachen},
  isbn      = {3-8265-8551-8},
  pages     = {II, 122 S Ill., graph. Darst.},
  year      = {2001},
  language  = {en}
}
@article{SchmittSchollCaietal.2010,
  author    = {Schmitt, Robert and Scholl, Ingrid and Cai, Yu and Xia, Ji and Dziwoki, Paul and Harding, Martin and Pavim, Alberto},
  title     = {Machine Vision System for Inline Inspection in Carbide Insert Production},
  series = {Bildverarbeitung f{\"u}r die Medizin 2010 : Algorithmen, Systeme, Anwendungen ; Proceedings des Workshops vom 14. bis 16. M{\"a}rz in Aachen / Thomas M. Deserno ... (Hrsg.)},
  journal   = {Bildverarbeitung f{\"u}r die Medizin 2010 : Algorithmen, Systeme, Anwendungen ; Proceedings des Workshops vom 14. bis 16. M{\"a}rz in Aachen / Thomas M. Deserno ... (Hrsg.)},
  publisher = {Springer},
  address   = {Berlin},
  isbn      = {978-3-642-11967-5},
  pages     = {339 -- 342},
  year      = {2010},
  language  = {de}
}
@inproceedings{NikolovskiRekeElsenetal.2021,
  author    = {Nikolovski, Gjorgji and Reke, Michael and Elsen, Ingo and Schiffer, Stefan},
  title     = {Machine learning based 3D object detection for navigation in unstructured environments},
  series = {2021 IEEE Intelligent Vehicles Symposium Workshops (IV Workshops)},
  booktitle = {2021 IEEE Intelligent Vehicles Symposium Workshops (IV Workshops)},
  publisher = {IEEE},
  isbn      = {978-1-6654-7921-9},
  doi       = {10.1109/IVWorkshops54471.2021.9669218},
  pages     = {236 -- 242},
  year      = {2021},
  abstract  = {In this paper we investigate the use of deep neural networks for 3D object detection in uncommon, unstructured environments such as in an open-pit mine. While neural nets are frequently used for object detection in regular autonomous driving applications, more unusual driving scenarios aside street traffic pose additional challenges. For one, the collection of appropriate data sets to train the networks is an issue. For another, testing the performance of trained networks often requires tailored integration with the particular domain as well. While there exist different solutions for these problems in regular autonomous driving, there are only very few approaches that work for special domains just as well. We address both the challenges above in this work. First, we discuss two possible ways of acquiring data for training and evaluation. That is, we evaluate a semi-automated annotation of recorded LIDAR data and we examine synthetic data generation. Using these datasets we train and test different deep neural network for the task of object detection. Second, we propose a possible integration of a ROS2 detector module for an autonomous driving platform. Finally, we present the performance of three state-of-the-art deep neural networks in the domain of 3D object detection on a synthetic dataset and a smaller one containing a characteristic object from an open-pit mine.},
  language  = {en}
}
@incollection{StengerAltherrAbel2019,
  author    = {Stenger, David and Altherr, Lena and Abel, Dirk},
  title     = {Machine learning and metaheuristics for black-box optimization of product families: a case-study investigating solution quality vs. computational overhead},
  series = {Operations Research Proceedings 2018},
  booktitle = {Operations Research Proceedings 2018},
  publisher = {Springer},
  address   = {Cham},
  isbn      = {978-3-030-18499-5 (Print)},
  doi       = {10.1007/978-3-030-18500-8_47},
  pages     = {379 -- 385},
  year      = {2019},
  abstract  = {In product development, numerous design decisions have to be made. Multi-domain virtual prototyping provides a variety of tools to assess technical feasibility of design options, however often requires substantial computational effort for just a single evaluation. A special challenge is therefore the optimal design of product families, which consist of a group of products derived from a common platform. Finding an optimal platform configuration (stating what is shared and what is individually designed for each product) and an optimal design of all products simultaneously leads to a mixed-integer nonlinear black-box optimization model. We present an optimization approach based on metamodels and a metaheuristic. To increase computational efficiency and solution quality, we compare different types of Gaussian process regression metamodels adapted from the domain of machine learning, and combine them with a genetic algorithm. We illustrate our approach on the example of a product family of electrical drives, and investigate the trade-off between solution quality and computational overhead.},
  language  = {en}
}
@article{Heuermann1995,
  author    = {Heuermann, Holger},
  title     = {LZY: A Self-Calibration Approach in Competition to the LRM Method for On-Wafer Measurements},
  pages     = {129 -- 136},
  year      = {1995},
  language  = {en}
}
@article{SommerBitzStreckertetal.2007,
  author    = {Sommer, Angela M. and Bitz, Andreas and Streckert, Joachim and Hansen, Volkert W. and Lerchl, Alexander},
  title     = {Lymphoma development in mice chronically exposed to UMTS-modulated radiofrequency electromagnetic fields},
  series = {Radiation Research},
  volume    = {168},
  journal   = {Radiation Research},
  number    = {1},
  issn      = {1938-5404},
  doi       = {10.1667/RR0857.1},
  pages     = {72 -- 80},
  year      = {2007},
  language  = {en}
}
@article{Hagemann1978,
  author    = {Hagemann, Hans-J{\"u}rgen},
  title     = {Loss mechanisms and domain stabilization in doped BaTiO₃},
  series = {Journal of Physics C: Solid State Physics},
  volume    = {11},
  journal   = {Journal of Physics C: Solid State Physics},
  number    = {15},
  publisher = {n.a.},
  address   = {London},
  isbn      = {0022-3719},
  doi       = {10.1088/0022-3719/11/15/031},
  pages     = {3333 -- 3344},
  year      = {1978},
  language  = {en}
}
@article{FerreinSteinbauer2016,
  author    = {Ferrein, Alexander and Steinbauer, Gerald},
  title     = {Looking back on 20 Years of RoboCup},
  series = {KI - K{\"u}nstliche Intelligenz},
  volume    = {30},
  journal   = {KI - K{\"u}nstliche Intelligenz},
  number    = {3-4},
  publisher = {Springer},
  address   = {Berlin},
  issn      = {1610-1987},
  doi       = {10.1007/s13218-016-0443-y},
  pages     = {321 -- 323},
  year      = {2016},
  language  = {en}
}