@inproceedings{HueningStuettgen2021,
  author    = {H{\"u}ning, Felix and St{\"u}ttgen, Marcel},
  title     = {Work in Progress: Interdisciplinary projects in times of COVID-19 crisis - challenges, risks and chances},
  series = {2021 IEEE Global Engineering Education Conference (EDUCON)},
  booktitle = {2021 IEEE Global Engineering Education Conference (EDUCON)},
  doi       = {10.1109/EDUCON46332.2021.9454006},
  pages     = {1175 -- 1179},
  year      = {2021},
  language  = {en}
}
@inproceedings{AltherrEdererSchaenzleetal.2017,
  author    = {Altherr, Lena and Ederer, Thorsten and Sch{\"a}nzle, Christian and Lorenz, Ulf and Pelz, Peter F.},
  title     = {Algorithmic system design using scaling and affinity laws},
  series = {Operations Research Proceedings 2015},
  booktitle = {Operations Research Proceedings 2015},
  publisher = {Springer},
  address   = {Cham},
  isbn      = {978-3-319-42901-4},
  doi       = {10.1007/978-3-319-42902-1},
  pages     = {605 -- 611},
  year      = {2017},
  abstract  = {Energy-efficient components do not automatically lead to energy-efficient systems. Technical Operations Research (TOR) shifts the focus from the single component to the system as a whole and finds its optimal topology and operating strategy simultaneously. In previous works, we provided a preselected construction kit of suitable components for the algorithm. This approach may give rise to a combinatorial explosion if the preselection cannot be cut down to a reasonable number by human intuition. To reduce the number of discrete decisions, we integrate laws derived from similarity theory into the optimization model. Since the physical characteristics of a production series are similar, it can be described by affinity and scaling laws. Making use of these laws, our construction kit can be modeled more efficiently: Instead of a preselection of components, it now encompasses whole model ranges. This allows us to significantly increase the number of possible set-ups in our model. In this paper, we present how to embed this new formulation into a mixed-integer program and assess the run time via benchmarks. We present our approach on the example of a ventilation system design problem.},
  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{AltherrEdererVergeetal.2015,
  author    = {Altherr, Lena and Ederer, Thorsten and Verg{\´e}, Angela and Pelz, Peter F.},
  title     = {Algorithmische Struktursynthese eines hydrostatischen Getriebes},
  series = {Antriebssysteme 2015 : Elektrik, Mechanik, Fluidtechnik in der Anwendung},
  booktitle = {Antriebssysteme 2015 : Elektrik, Mechanik, Fluidtechnik in der Anwendung},
  publisher = {VDI-Verlag},
  address   = {D{\"u}sseldorf},
  isbn      = {978-3-18-092268-3},
  pages     = {145 -- 155},
  year      = {2015},
  language  = {de}
}
@inproceedings{SchaenzleAltherrEdereretal.2015,
  author    = {Sch{\"a}nzle, Christian and Altherr, Lena and Ederer, Thorsten and Lorenz, Ulf and Pelz, Peter F.},
  title     = {As good as it can be: Ventilation system design by a combined scaling and discrete optimization method},
  series = {Proceedings of FAN 2015},
  booktitle = {Proceedings of FAN 2015},
  pages     = {1 -- 11},
  year      = {2015},
  abstract  = {The understanding that optimized components do not automatically lead to energy-efficient systems sets the attention from the single component on the entire technical system. At TU Darmstadt, a new field of research named Technical Operations Research (TOR) has its origin. It combines mathematical and technical know-how for the optimal design of technical systems. We illustrate our optimization approach in a case study for the design of a ventilation system with the ambition to minimize the energy consumption for a temporal distribution of diverse load demands. By combining scaling laws with our optimization methods we find the optimal combination of fans and show the advantage of the use of multiple fans.},
  language  = {en}
}
@inproceedings{AltherrEdererFarnetaneetal.2017,
  author    = {Altherr, Lena and Ederer, Thorsten and Farnetane, Lucas S. and P{\"o}ttgen, Philipp and Verg{\´e}, Angela and Pelz, Peter F.},
  title     = {Multicriterial design of a hydrostatic transmission system via mixed-integer programming},
  series = {Operations Research Proceedings 2015},
  booktitle = {Operations Research Proceedings 2015},
  publisher = {Springer},
  address   = {Cham},
  isbn      = {978-3-319-42901-4},
  doi       = {10.1007/978-3-319-42902-1_41},
  pages     = {301 -- 307},
  year      = {2017},
  abstract  = {In times of planned obsolescence the demand for sustainability keeps growing. Ideally, a technical system is highly reliable, without failures and down times due to fast wear of single components. At the same time, maintenance should preferably be limited to pre-defined time intervals. Dispersion of load between multiple components can increase a system's reliability and thus its availability inbetween maintenance points. However, this also results in higher investment costs and additional efforts due to higher complexity. Given a specific load profile and resulting wear of components, it is often unclear which system structure is the optimal one. Technical Operations Research (TOR) finds an optimal structure balancing availability and effort. We present our approach by designing a hydrostatic transmission system.},
  language  = {en}
}
@inproceedings{LorenzAltherrPelz2020,
  author    = {Lorenz, Imke-Sophie and Altherr, Lena and Pelz, Peter F.},
  title     = {Resilience enhancement of critical infrastructure - graph-theoretical resilience analysis of the water distribution system in the German city of Darmstadt},
  series = {14th WCEAM Proceedings},
  booktitle = {14th WCEAM Proceedings},
  publisher = {Springer},
  address   = {Cham},
  isbn      = {978-3-030-64228-0},
  doi       = {10.1007/978-3-030-64228-0_13},
  pages     = {137 -- 149},
  year      = {2020},
  abstract  = {Water suppliers are faced with the great challenge of achieving high-quality and, at the same time, low-cost water supply. Since climatic and demographic influences will pose further challenges in the future, the resilience enhancement of water distribution systems (WDS), i.e. the enhancement of their capability to withstand and recover from disturbances, has been in particular focus recently. To assess the resilience of WDS, graph-theoretical metrics have been proposed. In this study, a promising approach is first physically derived analytically and then applied to assess the resilience of the WDS for a district in a major German City. The topology based resilience index computed for every consumer node takes into consideration the resistance of the best supply path as well as alternative supply paths. This resistance of a supply path is derived to be the dimensionless pressure loss in the pipes making up the path. The conducted analysis of a present WDS provides insight into the process of actively influencing the resilience of WDS locally and globally by adding pipes. The study shows that especially pipes added close to the reservoirs and main branching points in the WDS result in a high resilience enhancement of the overall WDS.},
  language  = {en}
}
@inproceedings{SchaenzleAltherrEdereretal.2015,
  author    = {Sch{\"a}nzle, Christian and Altherr, Lena and Ederer, Thorsten and Pelz, Peter},
  title     = {TOR - Towards the energetically optimal ventilation system},
  pages     = {1 Seite},
  year      = {2015},
  language  = {en}
}
@inproceedings{BensbergAuthCzarnecki2018,
  author    = {Bensberg, Frank and Auth, Gunnar and Czarnecki, Christian},
  title     = {Unterst{\"u}tzung von Wissenstransferprozessen durch Erschließung von Literaturdatenbanken - ein gestaltungsorientierter Ansatz auf Basis von Text Analytics},
  series = {Wissenstransfer in der Wirtschaftsinformatik Fachgespr{\"a}ch im Rahmen der MKWI 2018},
  booktitle = {Wissenstransfer in der Wirtschaftsinformatik Fachgespr{\"a}ch im Rahmen der MKWI 2018},
  editor    = {Hofmann, Georg Rainer and Alm, Wolfgang},
  publisher = {IMI-Verlag ; Hochschule Aschaffenburg, Information Management Institut},
  address   = {Aschaffenburg},
  isbn      = {9783981844207},
  pages     = {6 -- 15},
  year      = {2018},
  abstract  = {Angesichts des anhaltenden Wachstums wissenschaftlicher Ver{\"o}ffentlichungen werden Instrumente ben{\"o}tigt, um Literaturanalysen durch Digitalisierung produktiver zu gestalten. Dieser Beitrag stellt einen Ansatz vor, der bibliographische Daten aus der Literaturdatenbank EBSCO Discovery Service mithilfe von Text-Analytics-Methoden erschließt. Die L{\"o}sung basiert auf dem Textanalysesystem IBM Watson Explorer und eignet sich f{\"u}r explorative Literaturanalysen, um beispielsweise den Status quo emergierender Technologiefelder in der Literatur zu reflektieren. Die generierten Ergebnisse sind in den Kontext der zunehmenden Werkzeugunterst{\"u}tzung des Literaturrechercheprozesses einzuordnen und k{\"o}nnen f{\"u}r intra- sowie interinstitutionelle Wissenstransferprozesse in Forschungs- und Beratungskontexten genutzt werden.},
  language  = {de}
}
@inproceedings{SchnejiderWisselinkCzarnecki2017,
  author    = {Schnejider, Dominik and Wisselink, Frank and Czarnecki, Christian},
  title     = {Transformation von Wertsch{\"o}pfungsketten durch das Internet der Dinge - Bewertungsrahmen und Fallstudie},
  series = {GI Edition Proceedings Band 275 INFORMATIK 2017},
  booktitle = {GI Edition Proceedings Band 275 INFORMATIK 2017},
  editor    = {Eibl, Maximilian and Gaedke, Martin and Gesellschaft f{\"u}r Informatik e. V. (GI),},
  publisher = {K{\"o}llen},
  address   = {Bonn},
  isbn      = {978-3-88579-669-5},
  issn      = {1617-5468},
  doi       = {10.18420/in2017_208},
  pages     = {2081 -- 2094},
  year      = {2017},
  abstract  = {Momentan finden in vielen Branchen umfassende Ver{\"a}nderungen von M{\"a}rkten und Wertsch{\"o}pfungsketten statt, welche auch als Digitale Transformation bezeichnet werden. In diesem Zusammenhang wird das Internet der Dinge (Internet of Things, IoT) als ein wichtiger technischer Enabler der Ver{\"a}nderungen angesehen. Prim{\"a}re Ziele des IoT sind die Steuerung physischer Gegen-st{\"a}nde aus der Distanz und das Erfassen von Informationen aus dem Umfeld dieser Gegenst{\"a}nde. Welche neuen Gesch{\"a}fts-bzw. Partnermodelle entstehen durch die gemeinsame Nutzung von IoT-Daten und Big-Data-Technologien und welcher qualitative Mehrwert wird dadurch geschaffen? Als Antwort wird in diesem Beitrag ein Bewertungsrahmen zur qualitativen Wertsch{\"o}pfungsanalyse von IoT vorgeschlagen. Anhand dieses Bewertungsrahmens wird ein Anwendungsfall untersucht, der in anonymisierter Form an konkrete Praxisprojekte angelehnt ist. Konkret wird ein Anwendungsfall betrachtet, der eine Abfallwirtschaft 2.0 basierend auf dem Einsatz von IoT vorschl{\"a}gt. Aus den Untersuchungsergebnissen gehen beispielsweise Erkenntnisse hervor, wie Gesch{\"a}ftsmodelle auf Basis eines unentgeltlichen Informationsaustauschs durch IoT gestaltet werden k{\"o}nnen.},
  language  = {de}
}