TY  - CHAP
A1  - Hofmann, Till
A1  - Limpert, Nicolas
A1  - Mataré, Viktor
A1  - Ferrein, Alexander
A1  - Lakemeyer, Gerhard
T1  - Winning the RoboCup Logistics League with Fast Navigation, Precise Manipulation, and Robust Goal Reasoning
T2  - RoboCup 2019: Robot World Cup XXIII. RoboCup
Y1  - 2019
SN  - 978-3-030-35699-6
U6  - http://dx.doi.org/10.1007/978-3-030-35699-6_41
N1  - Lecture Notes in Computer Science, vol 11531
SP  - 504
EP  - 516
PB  - Springer
CY  - Cham
ER  - 
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  - 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  - Schänzle, Christian
A1  - Altherr, Lena
A1  - Ederer, Thorsten
A1  - Lorenz, Ulf
A1  - Pelz, Peter F.
T1  - As good as it can be: Ventilation system design by a combined scaling and discrete optimization method
T2  - Proceedings of FAN 2015
N2  - 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.
Y1  - 2015
N1  - Proceedings of FAN 2015, Lyon (France), 15 – 17 April 2015
SP  - 1
EP  - 11
ER  -