@inproceedings{HofmannLimpertMatareetal.2019, author = {Hofmann, Till and Limpert, Nicolas and Matar{\´e}, Viktor and Ferrein, Alexander and Lakemeyer, Gerhard}, title = {Winning the RoboCup Logistics League with Fast Navigation, Precise Manipulation, and Robust Goal Reasoning}, series = {RoboCup 2019: Robot World Cup XXIII. RoboCup}, booktitle = {RoboCup 2019: Robot World Cup XXIII. RoboCup}, publisher = {Springer}, address = {Cham}, isbn = {978-3-030-35699-6}, doi = {10.1007/978-3-030-35699-6_41}, pages = {504 -- 516}, year = {2019}, language = {en} } @inproceedings{HoegenDonckerRuetters2020, author = {Hoegen, Anne von and Doncker, Rik W. De and R{\"u}tters, Ren{\´e}}, title = {Teaching Digital Control of Operational Amplifier Processes with a LabVIEW Interface and Embedded Hardware}, series = {The 23rd International Conference on Electrical Machines and Systems (ICEMS), Hamamatsu, Japan}, booktitle = {The 23rd International Conference on Electrical Machines and Systems (ICEMS), Hamamatsu, Japan}, doi = {10.23919/ICEMS50442.2020.9290928}, pages = {1117 -- 1122}, year = {2020}, language = {en} } @incollection{IbanezSanchezWolf2020, author = {Ibanez-Sanchez, Gema and Wolf, Martin}, title = {Interactive Process Mining-Induced Change Management Methodology for Healthcare}, series = {Interactive Process Mining in Healthcare}, booktitle = {Interactive Process Mining in Healthcare}, publisher = {Springer}, address = {Cham}, isbn = {978-3-030-53993-1 (Online)}, doi = {10.1007/978-3-030-53993-1_16}, pages = {267 -- 293}, year = {2020}, abstract = {The adoption of the Digital Health Transformation is a tremendous paradigm change in health organizations, which is not a trivial process in reality. For that reason, in this chapter, it is proposed a methodology with the objective to generate a changing culture in healthcare organisations. Such a change culture is essential for the successful implementation of any supporting methods like Interactive Process Mining. It needs to incorporate (mostly) new ways of team-based and evidence-based approaches for solving structural problems in a digital healthcare environment.}, language = {en} } @article{HueningBackes2020, author = {H{\"u}ning, Felix and Backes, Andreas}, title = {Direct observation of large Barkhausen jump in thin Vicalloy wires}, series = {IEEE Magnetics Letters}, volume = {11}, journal = {IEEE Magnetics Letters}, number = {Art. 2506504}, publisher = {IEEE}, address = {New York, NY}, isbn = {1949-307X}, doi = {10.1109/LMAG.2020.3046411}, pages = {1 -- 4}, year = {2020}, language = {en} } @inproceedings{HoegenDonckerBragardetal.2021, author = {Hoegen, Anne von and Doncker, Rik W. De and Bragard, Michael and Hoegen, Svenja von}, title = {Problem-Based Learning in Automation Engineering: Performing a Remote Laboratory Session Serving Various Educational Attainments}, series = {2021 IEEE Global Engineering Education Conference (EDUCON)}, booktitle = {2021 IEEE Global Engineering Education Conference (EDUCON)}, doi = {10.1109/EDUCON46332.2021.9453925}, pages = {1605 -- 1614}, year = {2021}, language = {en} } @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} } @article{AltherrEdererLorenzetal.2014, author = {Altherr, Lena and Ederer, Thorsten and Lorenz, Ulf and Pelz, Peter F. and P{\"o}ttgen, Philipp}, title = {Experimental validation of an enhanced system synthesis approach}, series = {Operations Research Proceedings 2014}, journal = {Operations Research Proceedings 2014}, editor = {L{\"u}bbecke, Marco and Koster, Arie and Letmathe, Peter and Madlener, Reihard and Peis, Britta and Walther, Grit}, publisher = {Springer}, address = {Basel}, isbn = {978-3-319-28695-2}, doi = {10.1007/978-3-319-28697-6_1}, pages = {6}, year = {2014}, abstract = {Planning the layout and operation of a technical system is a common task for an engineer. Typically, the workflow is divided into consecutive stages: First, the engineer designs the layout of the system, with the help of his experience or of heuristic methods. Secondly, he finds a control strategy which is often optimized by simulation. This usually results in a good operating of an unquestioned sys- tem topology. In contrast, we apply Operations Research (OR) methods to find a cost-optimal solution for both stages simultaneously via mixed integer program- ming (MILP). Technical Operations Research (TOR) allows one to find a provable global optimal solution within the model formulation. However, the modeling error due to the abstraction of physical reality remains unknown. We address this ubiq- uitous problem of OR methods by comparing our computational results with mea- surements in a test rig. For a practical test case we compute a topology and control strategy via MILP and verify that the objectives are met up to a deviation of 8.7\%.}, 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{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} }