@inproceedings{RitschelStenzelCzarneckietal.2021, author = {Ritschel, Konstantin and Stenzel, Adina and Czarnecki, Christian and Hong, Chin-Gi}, title = {Realizing robotic process automation potentials: an architectural perspective on a real-life implementation case}, series = {GI Edition Proceedings Band 314 "INFORMATIK 2021" Computer Science \& Sustainability}, booktitle = {GI Edition Proceedings Band 314 "INFORMATIK 2021" Computer Science \& Sustainability}, editor = {Gesellschaft f{\"u}r Informatik e.V. (GI),}, publisher = {K{\"o}llen}, address = {Bonn}, isbn = {9783885797081}, issn = {1617-5468}, doi = {10.18420/informatik2021-108}, pages = {1303 -- 1311}, year = {2021}, abstract = {The initial idea of Robotic Process Automation (RPA) is the automation of business processes through a simple emulation of user input and output by software robots. Hence, it can be assumed that no changes of the used software systems and existing Enterprise Architecture (EA) is required. In this short, practical paper we discuss this assumption based on a real-life implementation project. We show that a successful RPA implementation might require architectural work during analysis, implementation, and migration. As practical paper we focus on exemplary lessons-learned and new questions related to RPA and EA.}, 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} } @incollection{LeiseAltherr2021, author = {Leise, Philipp and Altherr, Lena}, title = {Experimental evaluation of resilience metrics in a fluid system}, series = {Mastering Uncertainty in Mechanical Engineering}, booktitle = {Mastering Uncertainty in Mechanical Engineering}, publisher = {Springer}, address = {Cham}, isbn = {978-3-030-78356-3}, pages = {442 -- 447}, year = {2021}, language = {en} } @incollection{AltherrLeisePfetschetal.2021, author = {Altherr, Lena and Leise, Philipp and Pfetsch, Marc E. and Schmitt, Andreas}, title = {Optimal design of resilient technical systems on the example of water supply systems}, series = {Mastering Uncertainty in Mechanical Engineering}, booktitle = {Mastering Uncertainty in Mechanical Engineering}, publisher = {Springer}, address = {Cham}, isbn = {978-3-030-78356-3}, pages = {429 -- 433}, year = {2021}, language = {en} } @incollection{AltherrLeise2021, author = {Altherr, Lena and Leise, Philipp}, title = {Resilience as a concept for mastering uncertainty}, series = {Mastering Uncertainty in Mechanical Engineering}, booktitle = {Mastering Uncertainty in Mechanical Engineering}, publisher = {Springer}, address = {Cham}, isbn = {978-3-030-78353-2}, doi = {10.1007/978-3-030-78354-9}, pages = {412 -- 417}, year = {2021}, language = {en} } @article{LeiseEsserEichenlaubetal.2021, author = {Leise, Philipp and Eßer, Arved and Eichenlaub, Tobias and Schleiffer, Jean-Eric and Altherr, Lena and Rinderknecht, Stephan and Pelz, Peter F.}, title = {Sustainable system design of electric powertrains - comparison of optimization methods}, series = {Engineering Optimization}, journal = {Engineering Optimization}, publisher = {Taylor \& Francis}, address = {London}, issn = {0305-215X}, doi = {10.1080/0305215X.2021.1928660}, year = {2021}, abstract = {The transition within transportation towards battery electric vehicles can lead to a more sustainable future. To account for the development goal 'climate action' stated by the United Nations, it is mandatory, within the conceptual design phase, to derive energy-efficient system designs. One barrier is the uncertainty of the driving behaviour within the usage phase. This uncertainty is often addressed by using a stochastic synthesis process to derive representative driving cycles and by using cycle-based optimization. To deal with this uncertainty, a new approach based on a stochastic optimization program is presented. This leads to an optimization model that is solved with an exact solver. It is compared to a system design approach based on driving cycles and a genetic algorithm solver. Both approaches are applied to find efficient electric powertrains with fixed-speed and multi-speed transmissions. Hence, the similarities, differences and respective advantages of each optimization procedure are discussed.}, language = {en} } @inproceedings{Huening2021, author = {H{\"u}ning, Felix}, title = {Sustainable changes beyond covid-19 for a second semester physics course for electrical engineering students}, series = {Blended Learning in Engineering Education: challenging, enlightening - and lasting?}, booktitle = {Blended Learning in Engineering Education: challenging, enlightening - and lasting?}, isbn = {978-2-87352-023-6}, pages = {1424 -- 1428}, year = {2021}, abstract = {The course Physics for Electrical Engineering is part of the curriculum of the bachelor program Electrical Engineering at University of Applied Science Aachen. Before covid-19 the course was conducted in a rather traditional way with all parts (lecture, exercise and lab) face-to-face. This teaching approach changed fundamentally within a week when the covid-19 limitations forced all courses to distance learning. All parts of the course were transformed to pure distance learning including synchronous and asynchronous parts for the lecture, live online-sessions for the exercises and self-paced labs at home. Using these methods, the course was able to impart the required knowledge and competencies. Taking the teacher's observations of the student's learning behaviour and engagement, the formal and informal feedback of the students and the results of the exams into account, the new methods are evaluated with respect to effectiveness, sustainability and suitability for competence transfer. Based on this analysis strong and weak points of the concept and countermeasures to solve the weak points were identified. The analysis further leads to a sustainable teaching approach combining synchronous and asynchronous parts with self-paced learning times that can be used in a very flexible manner for different learning scenarios, pure online, hybrid (mixture of online and presence times) and pure presence teaching.}, language = {en} } @inproceedings{MuellerSchmittLeiseetal.2021, author = {M{\"u}ller, Tim M. and Schmitt, Andreas and Leise, Philipp and Meck, Tobias and Altherr, Lena and Pelz, Peter F. and Pfetsch, Marc E.}, title = {Validation of an optimized resilient water supply system}, series = {Uncertainty in Mechanical Engineering}, booktitle = {Uncertainty in Mechanical Engineering}, publisher = {Springer}, address = {Cham}, isbn = {978-3-030-77255-0}, doi = {10.1007/978-3-030-77256-7_7}, pages = {70 -- 80}, year = {2021}, abstract = {Component failures within water supply systems can lead to significant performance losses. One way to address these losses is the explicit anticipation of failures within the design process. We consider a water supply system for high-rise buildings, where pump failures are the most likely failure scenarios. We explicitly consider these failures within an early design stage which leads to a more resilient system, i.e., a system which is able to operate under a predefined number of arbitrary pump failures. We use a mathematical optimization approach to compute such a resilient design. This is based on a multi-stage model for topology optimization, which can be described by a system of nonlinear inequalities and integrality constraints. Such a model has to be both computationally tractable and to represent the real-world system accurately. We therefore validate the algorithmic solutions using experiments on a scaled test rig for high-rise buildings. The test rig allows for an arbitrary connection of pumps to reproduce scaled versions of booster station designs for high-rise buildings. We experimentally verify the applicability of the presented optimization model and that the proposed resilience properties are also fulfilled in real systems.}, language = {en} } @incollection{PfetschAbeleAltherretal.2021, author = {Pfetsch, Marc E. and Abele, Eberhard and Altherr, Lena and B{\"o}lling, Christian and Br{\"o}tz, Nicolas and Dietrich, Ingo and Gally, Tristan and Geßner, Felix and Groche, Peter and Hoppe, Florian and Kirchner, Eckhard and Kloberdanz, Hermann and Knoll, Maximilian and Kolvenbach, Philip and Kuttich-Meinlschmidt, Anja and Leise, Philipp and Lorenz, Ulf and Matei, Alexander and Molitor, Dirk A. and Niessen, Pia and Pelz, Peter F. and Rexer, Manuel and Schmitt, Andreas and Schmitt, Johann M. and Schulte, Fiona and Ulbrich, Stefan and Weigold, Matthias}, title = {Strategies for mastering uncertainty}, series = {Mastering uncertainty in mechanical engineering}, booktitle = {Mastering uncertainty in mechanical engineering}, publisher = {Springer}, address = {Cham}, isbn = {978-3-030-78353-2}, doi = {10.1007/978-3-030-78354-9_6}, pages = {365 -- 456}, year = {2021}, abstract = {This chapter describes three general strategies to master uncertainty in technical systems: robustness, flexibility and resilience. It builds on the previous chapters about methods to analyse and identify uncertainty and may rely on the availability of technologies for particular systems, such as active components. Robustness aims for the design of technical systems that are insensitive to anticipated uncertainties. Flexibility increases the ability of a system to work under different situations. Resilience extends this characteristic by requiring a given minimal functional performance, even after disturbances or failure of system components, and it may incorporate recovery. The three strategies are described and discussed in turn. Moreover, they are demonstrated on specific technical systems.}, language = {en} } @inproceedings{ChajanSchulteTiggesRekeetal.2021, author = {Chajan, Eduard and Schulte-Tigges, Joschua and Reke, Michael and Ferrein, Alexander and Matheis, Dominik and Walter, Thomas}, title = {GPU based model-predictive path control for self-driving vehicles}, series = {IEEE Intelligent Vehicles Symposium (IV)}, booktitle = {IEEE Intelligent Vehicles Symposium (IV)}, publisher = {IEEE}, address = {New York, NY}, isbn = {978-1-7281-5394-0}, doi = {10.1109/IV48863.2021.9575619}, pages = {1243 -- 1248}, year = {2021}, abstract = {One central challenge for self-driving cars is a proper path-planning. Once a trajectory has been found, the next challenge is to accurately and safely follow the precalculated path. The model-predictive controller (MPC) is a common approach for the lateral control of autonomous vehicles. The MPC uses a vehicle dynamics model to predict the future states of the vehicle for a given prediction horizon. However, in order to achieve real-time path control, the computational load is usually large, which leads to short prediction horizons. To deal with the computational load, the control algorithm can be parallelized on the graphics processing unit (GPU). In contrast to the widely used stochastic methods, in this paper we propose a deterministic approach based on grid search. Our approach focuses on systematically discovering the search area with different levels of granularity. To achieve this, we split the optimization algorithm into multiple iterations. The best sequence of each iteration is then used as an initial solution to the next iteration. The granularity increases, resulting in smooth and predictable steering angle sequences. We present a novel GPU-based algorithm and show its accuracy and realtime abilities with a number of real-world experiments.}, language = {en} }