TY - CHAP A1 - Hoegen, Anne von A1 - Doncker, Rik W. De A1 - Rütters, René T1 - Teaching Digital Control of Operational Amplifier Processes with a LabVIEW Interface and Embedded Hardware T2 - The 23rd International Conference on Electrical Machines and Systems (ICEMS), Hamamatsu, Japan Y1 - 2020 U6 - http://dx.doi.org/10.23919/ICEMS50442.2020.9290928 SP - 1117 EP - 1122 ER - TY - CHAP A1 - Ibanez-Sanchez, Gema A1 - Wolf, Martin T1 - Interactive Process Mining-Induced Change Management Methodology for Healthcare T2 - Interactive Process Mining in Healthcare N2 - 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. KW - Methodology KW - Change culture KW - Lean thinking KW - Interactive process mining KW - Objective data Y1 - 2020 SN - 978-3-030-53993-1 (Online) SN - 978-3-030-53992-4 (Print) U6 - http://dx.doi.org/10.1007/978-3-030-53993-1_16 SP - 267 EP - 293 PB - Springer CY - Cham ER - TY - JOUR A1 - Hüning, Felix A1 - Backes, Andreas T1 - Direct observation of large Barkhausen jump in thin Vicalloy wires JF - IEEE Magnetics Letters Y1 - 2020 SN - 1949-307X U6 - http://dx.doi.org/10.1109/LMAG.2020.3046411 VL - 11 IS - Art. 2506504 SP - 1 EP - 4 PB - IEEE CY - New York, NY ER - TY - CHAP A1 - Hoegen, Anne von A1 - Doncker, Rik W. De A1 - Bragard, Michael A1 - Hoegen, Svenja von T1 - Problem-Based Learning in Automation Engineering: Performing a Remote Laboratory Session Serving Various Educational Attainments T2 - 2021 IEEE Global Engineering Education Conference (EDUCON) Y1 - 2021 U6 - http://dx.doi.org/10.1109/EDUCON46332.2021.9453925 SP - 1605 EP - 1614 ER - TY - CHAP A1 - Hüning, Felix A1 - Stüttgen, Marcel T1 - Work in Progress: Interdisciplinary projects in times of COVID-19 crisis – challenges, risks and chances T2 - 2021 IEEE Global Engineering Education Conference (EDUCON) Y1 - 2021 U6 - http://dx.doi.org/10.1109/EDUCON46332.2021.9454006 SP - 1175 EP - 1179 ER - TY - JOUR A1 - Altherr, Lena A1 - Ederer, Thorsten A1 - Lorenz, Ulf A1 - Pelz, Peter F. A1 - Pöttgen, Philipp ED - Lübbecke, Marco ED - Koster, Arie ED - Letmathe, Peter ED - Madlener, Reihard ED - Peis, Britta ED - Walther, Grit T1 - Experimental validation of an enhanced system synthesis approach JF - Operations Research Proceedings 2014 N2 - 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%. Y1 - 2014 SN - 978-3-319-28695-2 U6 - http://dx.doi.org/10.1007/978-3-319-28697-6_1 PB - Springer CY - Basel ER - TY - JOUR A1 - Vergé, Angela A1 - Pöttgen, Philipp A1 - Altherr, Lena A1 - Ederer, Thorsten A1 - Pelz, Peter F. ED - Greuloch, Ivo ED - Weber, Manfred ED - Meier, Miles T1 - Lebensdauer als Optimierungsziel: Algorithmische Struktursynthese am Beispiel eines hydrostatischen Getriebes JF - O+P – Ölhydraulik und Pneumatik N2 - Verfügbarkeit und Nachhaltigkeit sind wichtige Anforderungen bei der Planung langlebiger technischer Systeme. Meist werden bei Lebensdaueroptimierungen lediglich einzelne Komponenten vordefinierter Systeme untersucht. Ob eine optimale Lebensdauer eine gänzlich andere Systemvariante bedingt, wird nur selten hinterfragt. Technical Operations Research (TOR) erlaubt es, aus Obermengen technischer Systeme automatisiert die lebensdaueroptimale Systemstruktur auszuwählen. Der Artikel zeigt dies am Beispiel eines hydrostatischen Getriebes. Y1 - 2016 SN - 1614-9602 VL - 60 IS - 1-2 SP - 114 EP - 121 PB - Vereinigte Fachverl. CY - Mainz ER - TY - CHAP A1 - Altherr, Lena A1 - Ederer, Thorsten A1 - Schänzle, Christian A1 - Lorenz, Ulf A1 - Pelz, Peter F. T1 - Algorithmic system design using scaling and affinity laws T2 - Operations Research Proceedings 2015 N2 - 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. KW - Optimal Topology KW - Piecewise Linearization KW - Ventilation System KW - Similarity Theory Y1 - 2017 SN - 978-3-319-42901-4 SN - 978-3-319-42902-1 U6 - http://dx.doi.org/10.1007/978-3-319-42902-1 N1 - International Conference of the German, Austrian and Swiss Operations Research Societies (GOR, ÖGOR, SVOR/ASRO), University of Vienna, Austria, September 1-4, 2015 SP - 605 EP - 611 PB - Springer CY - Cham ER - TY - CHAP A1 - Nikolovski, Gjorgji A1 - Reke, Michael A1 - Elsen, Ingo A1 - Schiffer, Stefan T1 - Machine learning based 3D object detection for navigation in unstructured environments T2 - 2021 IEEE Intelligent Vehicles Symposium Workshops (IV Workshops) N2 - 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. KW - 3D object detection KW - LiDAR KW - autonomous driving KW - Deep learning KW - Three-dimensional displays Y1 - 2021 SN - 978-1-6654-7921-9 U6 - http://dx.doi.org/10.1109/IVWorkshops54471.2021.9669218 N1 - 2021 IEEE Intelligent Vehicles Symposium Workshops (IV Workshops), 11-17 July 2021, Nagoya, Japan. SP - 236 EP - 242 PB - IEEE ER - TY - CHAP A1 - Altherr, Lena A1 - Ederer, Thorsten A1 - Vergé, Angela A1 - Pelz, Peter F. T1 - Algorithmische Struktursynthese eines hydrostatischen Getriebes T2 - Antriebssysteme 2015 : Elektrik, Mechanik, Fluidtechnik in der Anwendung Y1 - 2015 SN - 978-3-18-092268-3 N1 - Antriebssysteme 2015 - Elektrik, Mechanik, Fluidtechnik in der Anwendung. VDI/VDE-Fachtagung. 11.11.15-12.11.15, Aachen. Veröffentlicht in der Reihe VDI-Berichte, Bandnummer 2268. SP - 145 EP - 155 PB - VDI-Verlag CY - Düsseldorf ER -