TY - JOUR A1 - Fagan, Andrew J. A1 - Bitz, Andreas A1 - Björkman-Burtscher, Isabella M. A1 - Collins, Christopher M. A1 - Kimbrell, Vera A1 - Raaijmakers, Alexander J. E. T1 - 7T MR Safety JF - Journal of Magnetic Resonance Imaging (JMRI) Y1 - 2021 U6 - https://doi.org/10.1002/jmri.27319 SN - 1522-2586 VL - 53 IS - 2 SP - 333 EP - 346 PB - Wiley CY - Weinheim ER - TY - JOUR A1 - Serror, Martin A1 - Hack, Sacha A1 - Henze, Martin A1 - Schuba, Marko A1 - Wehrle, Klaus T1 - Challenges and Opportunities in Securing the Industrial Internet of Things JF - IEEE Transactions on Industrial Informatics Y1 - 2021 U6 - https://doi.org/10.1109/TII.2020.3023507 SN - 1941-0050 VL - 17 IS - 5 SP - 2985 EP - 2996 PB - IEEE CY - New York 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 aession aerving various educational attainments T2 - 2021 IEEE Global Engineering Education Conference (EDUCON) N2 - During the Covid-19 pandemic, vocational colleges, universities of applied science and technical universities often had to cancel laboratory sessions requiring students’ attendance. These above of all are of decisive importance in order to give learners an understanding of theory through practical work.This paper is a contribution to the implementation of distance learning for laboratory work applicable for several upper secondary educational facilities. Its aim is to provide a paradigm for hybrid teaching to analyze and control a non-linear system depicted by a tank model. For this reason, we redesign a full series of laboratory sessions on the basis of various challenges. Thus, it is suitable to serve different reference levels of the European Qualifications Framework (EQF).We present problem-based learning through online platforms to compensate the lack of a laboratory learning environment. With a task deduced from their future profession, we give students the opportunity to develop own solutions in self-defined time intervals. A requirements specification provides the framework conditions in terms of time and content for students having to deal with the challenges of the project in a self-organized manner with regard to inhomogeneous previous knowledge. If the concept of Complete Action is introduced in classes before, they will automatically apply it while executing the project.The goal is to combine students’ scientific understanding with a procedural knowledge. We suggest a series of remote laboratory sessions that combine a problem formulation from the subject area of Measurement, Control and Automation Technology with a project assignment that is common in industry by providing extracts from a requirements specification. Y1 - 2021 U6 - https://doi.org/10.1109/EDUCON46332.2021.9453925 N1 - 2021 IEEE Global Engineering Education Conference (EDUCON), 21-23 April 2021, Vienna, Austria SP - 1605 EP - 1614 PB - IEEE CY - New York, NY 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) N2 - Project work and inter disciplinarity are integral parts of today's engineering work. It is therefore important to incorporate these aspects into the curriculum of academic studies of engineering. At the faculty of Electrical Engineering and Information Technology an interdisciplinary project is part of the bachelor program to address these topics. Since the summer term 2020 most courses changed to online mode during the Covid-19 crisis including the interdisciplinary projects. This online mode introduces additional challenges to the execution of the projects, both for the students as well as for the lecture. The challenges, but also the risks and chances of this kind of project courses are subject of this paper, based on five different interdisciplinary projects Y1 - 2021 U6 - https://doi.org/10.1109/EDUCON46332.2021.9454006 N1 - 2021 IEEE Global Engineering Education Conference (EDUCON), 21-23 April 2021, Vienna, Austria SP - 1175 EP - 1179 PB - IEEE CY - New York, NY ER - TY - CHAP A1 - Hüning, Felix T1 - Sustainable changes beyond covid-19 for a second semester physics course for electrical engineering students T2 - Blended Learning in Engineering Education: challenging, enlightening – and lasting? N2 - 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. Y1 - 2021 SN - 978-2-87352-023-6 N1 - SEFI 49th Annual Conference, Technische Universität Berlin (online), 13 – 16 September 2021 SP - 1424 EP - 1428 ER - TY - JOUR A1 - Leise, Philipp A1 - Eßer, Arved A1 - Eichenlaub, Tobias A1 - Schleiffer, Jean-Eric A1 - Altherr, Lena A1 - Rinderknecht, Stephan A1 - Pelz, Peter F. T1 - Sustainable system design of electric powertrains - comparison of optimization methods JF - Engineering Optimization N2 - 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. KW - Powertrain KW - stochastic optimization KW - global optimization KW - genetic algorithm Y1 - 2021 U6 - https://doi.org/10.1080/0305215X.2021.1928660 SN - 0305-215X PB - Taylor & Francis CY - London ER - TY - CHAP A1 - Altherr, Lena A1 - Leise, Philipp T1 - Resilience as a concept for mastering uncertainty T2 - Mastering Uncertainty in Mechanical Engineering Y1 - 2021 SN - 978-3-030-78353-2 U6 - https://doi.org/10.1007/978-3-030-78354-9 N1 - Unterkapitel 6.3.1 des Kapitels "Strategies for Mastering Uncertainty" SP - 412 EP - 417 PB - Springer CY - Cham ER - TY - CHAP A1 - Altherr, Lena A1 - Leise, Philipp A1 - Pfetsch, Marc E. A1 - Schmitt, Andreas T1 - Optimal design of resilient technical systems on the example of water supply systems T2 - Mastering Uncertainty in Mechanical Engineering Y1 - 2021 SN - 978-3-030-78356-3 N1 - Unterkapitel des Kapitels "Strategies for Mastering Uncertainty" SP - 429 EP - 433 PB - Springer CY - Cham ER - TY - CHAP A1 - Leise, Philipp A1 - Altherr, Lena T1 - Experimental evaluation of resilience metrics in a fluid system T2 - Mastering Uncertainty in Mechanical Engineering Y1 - 2021 SN - 978-3-030-78356-3 N1 - Unterkapitel des Kapitels "Strategies for Mastering Uncertainty" SP - 442 EP - 447 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 - https://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 -