@inproceedings{ThomaFisherBraun2020,
  author    = {Thoma, Andreas and Fisher, Alex and Braun, Carsten},
  title     = {Improving the px4 avoid algorithm by bio-inspired flight strategies},
  series = {DLRK2020 - „Luft- und Raumfahrt - Verantwortung in allen Dimensionen"},
  booktitle = {DLRK2020 - „Luft- und Raumfahrt - Verantwortung in allen Dimensionen"},
  pages     = {10 Seiten},
  year      = {2020},
  language  = {en}
}
@inproceedings{HauggKreyerKemperetal.2020,
  author    = {Haugg, Albert Thomas and Kreyer, J{\"o}rg and Kemper, Hans and Hatesuer, Katerina and Esch, Thomas},
  title     = {Heat exchanger for ORC. adaptability and optimisation potentials},
  series = {IIR International Rankine 2020 Conference},
  booktitle = {IIR International Rankine 2020 Conference},
  doi       = {10.18462/iir.rankine.2020.1224},
  pages     = {10 Seiten},
  year      = {2020},
  abstract  = {The recovery of waste heat requires heat exchangers to extract it from a liquid or gaseous medium into another working medium, a refrigerant. In Organic Rankine Cycles (ORC) on Combustion Engines there are two major heat sources, the exhaust gas and the water/glycol fluid from the engine's cooling circuit. A heat exchanger design must be adapted to the different requirements and conditions resulting from the heat sources, fluids, system configurations, geometric restrictions, and etcetera. The Stacked Shell Cooler (SSC) is a new and very specific design of a plate heat exchanger, created by AKG, which allows with a maximum degree of freedom the optimization of heat exchange rate and the reduction of the related pressure drop. This optimization in heat exchanger design for ORC systems is even more important, because it reduces the energy consumption of the system and therefore maximizes the increase in overall efficiency of the engine.},
  language  = {en}
}
@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}
}
@inproceedings{FiedlerGottschlichMuellerMelcher2021,
  author    = {Fiedler, Gerda and Gottschlich-M{\"u}ller, Birgit and Melcher, Karin},
  title     = {Online-Pr{\"u}fungen mit STACK Aufgaben},
  series = {Tagungsband ASIM Workshop STS/GMMS/EDU 2021},
  booktitle = {Tagungsband ASIM Workshop STS/GMMS/EDU 2021},
  editor    = {Liu-Henke, Xiaobo and Durak, Umut},
  publisher = {ARGESIM Verlag},
  address   = {Wien},
  isbn      = {978-3-901608-69-8},
  doi       = {10.11128/arep.45},
  pages     = {6 Seiten},
  year      = {2021},
  abstract  = {Wir stellen hier exemplarisch STACK Aufgaben vor, die frei von der Problematik sind, welche sich durch diverse Kommunikationswege und (webbasierte) Computer Algebra Systeme (CAS) ergibt. Daher sind sie insbesondere f{\"u}r eine Open-Book Online Pr{\"u}fung geeignet, da eine faire Pr{\"u}fungssituation gew{\"a}hrleistet werden kann.},
  language  = {de}
}
@article{HahnHebelManz2022,
  author    = {Hahn, Geogr W. and Hebel, Christoph and Manz, W.},
  title     = {Die neuen Empfehlungen f{\"u}r Verkehrsnachfragemodellierung im Personenverkehr},
  series = {Straßenverkehrstechnik},
  volume    = {66},
  journal   = {Straßenverkehrstechnik},
  number    = {10},
  publisher = {Kirschbaum Verlag GmbH},
  address   = {Bonn},
  issn      = {0039-2219},
  doi       = {10.53184/SVT10-2022-1},
  pages     = {721 -- 726},
  year      = {2022},
  abstract  = {Die neu erschienenen „Empfehlungen zum Einsatz von Verkehrsnachfragemodellen f{\"u}r den Personenverkehr" liefern erstmals als Empfehlungspapier der Forschungsgesellschaft f{\"u}r Straßen- und Verkehrswesen einen umfassenden {\"U}berblick zu den verschiedenen Aspekten der Modellierung und geben dem Fachplaner konkrete Hilfestellung f{\"u}r die Konzeption von Nachfragemodellen. Das Empfehlungspapier zielt unter anderem darauf ab, die Erwartungen und das Anspruchsniveau in Hinblick auf Sachgerechtigkeit der Modelle, die erzielbare Modellqualit{\"a}t und den Detaillierungsgrad der Modellaussagen zu harmonisieren.},
  language  = {de}
}
@article{EmigHebelSchwark2022,
  author    = {Emig, J. and Hebel, Christoph and Schwark, A.},
  title     = {Einsatzbereiche f{\"u}r Verkehrsnachfragemodelle},
  series = {Straßenverkehrstechnik},
  volume    = {66},
  journal   = {Straßenverkehrstechnik},
  number    = {10},
  publisher = {Kirschbaum Verlag GmbH},
  address   = {Bonn},
  issn      = {0039-2219},
  doi       = {10.53184/SVT10-2022-2},
  pages     = {727 -- 736},
  year      = {2022},
  abstract  = {In der Praxis bestehen vielf{\"a}ltige Einsatzbereiche f{\"u}r Verkehrsnachfragemodelle. Mit ihnen k{\"o}nnen Kenngr{\"o}ßen des Verkehrsangebots und der Verkehrsnachfrage f{\"u}r den heutigen Zustand wie auch f{\"u}r zuk{\"u}nftige Zust{\"a}nde bereitgestellt werden, um so die Grundlagen f{\"u}r verkehrsplanerische Entscheidungen zu liefern. Die neuen „Empfehlungen zum Einsatz von Verkehrsnachfragemodellen f{\"u}r den Personenverkehr" (EVNM-PV) (FGSV 2022) veranschaulichen anhand von typischen Planungsaufgaben, welche differenzierten Anforderungen daraus f{\"u}r die Modellkonzeption und -erstellung resultieren. Vor dem Hintergrund der konkreten Aufgabenstellung sowie deren spezifischer planerischer Anforderungen bildet die abzuleitende Modellspezifikation die verabredete Grundlage zwischen Auftraggeber und Modellersteller f{\"u}r die konkrete inhaltliche, fachliche Ausgestaltung des Verkehrsmodells.},
  language  = {de}
}
@inproceedings{FingerdeVriesVosetal.2020,
  author    = {Finger, Felix and de Vries, Reynard and Vos, Roelof and Braun, Carsten and Bil, Cees},
  title     = {A comparison of hybrid-electric aircraft sizing methods},
  series = {AIAA Scitech 2020 Forum},
  booktitle = {AIAA Scitech 2020 Forum},
  doi       = {10.2514/6.2020-1006},
  pages     = {31 Seiten},
  year      = {2020},
  language  = {en}
}
@inproceedings{KreyerMuellerEsch2020,
  author    = {Kreyer, J{\"o}rg and M{\"u}ller, Marvin and Esch, Thomas},
  title     = {A Map-Based Model for the Determination of Fuel Consumption for Internal Combustion Engines as a Function of Flight Altitude},
  series = {Deutscher Luft- und Raumfahrtkongress 2019, „Luft- und Raumfahrt - technologische Br{\"u}cke in die Zukunft", Darmstadt, 30. September bis 2. Oktober 2019},
  booktitle = {Deutscher Luft- und Raumfahrtkongress 2019, „Luft- und Raumfahrt - technologische Br{\"u}cke in die Zukunft", Darmstadt, 30. September bis 2. Oktober 2019},
  publisher = {Deutsche Gesellschaft f{\"u}r Luft- und Raumfahrt - Lilienthal-Oberth e.V},
  address   = {Bonn},
  doi       = {10.25967/490162},
  pages     = {13 Seiten},
  year      = {2020},
  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{DinghoferHartung2020,
  author    = {Dinghofer, Kai and Hartung, Frank},
  title     = {Analysis of Criteria for the Selection of Machine Learning Frameworks},
  series = {2020 International Conference on Computing, Networking and Communications (ICNC)},
  booktitle = {2020 International Conference on Computing, Networking and Communications (ICNC)},
  doi       = {10.1109/ICNC47757.2020.9049650},
  pages     = {373 -- 377},
  year      = {2020},
  language  = {en}
}