TY - CHAP A1 - Peterson, Leif Arne A1 - Röth, Thilo A1 - Uibel, Thomas ED - Uibel, Thormas ED - Peterson, Leif Arne ED - Baumann, Marcus T1 - Holzwerkstoffe in Karosseriestrukturen T2 - Tagungsband Aachener Holzbautagung 2017 Y1 - 2017 SN - 2197-4489 SP - 34 EP - 45 ER - TY - CHAP A1 - Röth, Thilo A1 - Deutskens, Christoph A1 - Kreisköther, Kai A1 - Heimes, Heiner Hans A1 - Schittny, Bastian A1 - Ivanescu, Sebastian A1 - Kleine Büning, Max A1 - Reinders, Christian A1 - Wessels, Saskia A1 - Haunreiter, Andreas A1 - Reisgen, Uwe A1 - Thiele, Regina A1 - Hameyer, Kay A1 - Doncker, Rik W. de A1 - Sauer, Uwe A1 - Hoek, Hauke van A1 - Hübner, Mareike A1 - Hennen, Martin A1 - Stolze, Thilo A1 - Vetter, Andreas A1 - Hagedorn, Jürgen A1 - Müller, Dirk A1 - Rewitz, Kai A1 - Wesseling, Mark A1 - Flieger, Björn T1 - Entwicklung von elektrofahrzeugspezifischen Systemen T2 - Elektromobilität N2 - Die Batterie ist eine der absolut zentralen Komponenten des Elektrofahrzeugs. Die serielle Entwicklung und Produktion dieser Batterien und die Verbesserung der Leistungen wird entscheidend für den Erfolg der Elektromobilität sein. Die Batterie ist jedoch nicht das einzige elektrofahrzeugspezifische System, das neu entwickelt, umkonzipiert oder verbessert werden muss. So sind ebenso die Entwicklung der neuen Fahrzeugstruktur sowie des elektrifizierten Antriebsstranges Teil dieses Kapitels. Weiterhin wird ein Blick auf das bedeutende Thema des Thermomanagements geworfen. Y1 - 2018 SN - 978-3-662-53137-2 U6 - https://doi.org/10.1007/978-3-662-53137-2_6 SP - 279 EP - 386 PB - Springer Vieweg CY - Berlin, Heidelberg ER - TY - JOUR A1 - Wilbring, Daniela A1 - Enning, Manfred A1 - Pfaff, Raphael A1 - Schmidt, Bernd T1 - Neue Perspektiven für die Bahn in der Produktions- und Distributionslogistik durch Prozessautomation JF - ETR - Eisenbahntechnische Rundschau Y1 - 2020 SN - 0013-2845 VL - 69 IS - 3 SP - 15 EP - 19 ER - TY - CHAP A1 - Dinghofer, Kai A1 - Hartung, Frank T1 - Analysis of Criteria for the Selection of Machine Learning Frameworks T2 - 2020 International Conference on Computing, Networking and Communications (ICNC) N2 - With the many achievements of Machine Learning in the past years, it is likely that the sub-area of Deep Learning will continue to deliver major technological breakthroughs [1]. In order to achieve best results, it is important to know the various different Deep Learning frameworks and their respective properties. This paper provides a comparative overview of some of the most popular frameworks. First, the comparison methods and criteria are introduced and described with a focus on computer vision applications: Features and Uses are examined by evaluating papers and articles, Adoption and Popularity is determined by analyzing a data science study. Then, the frameworks TensorFlow, Keras, PyTorch and Caffe are compared based on the previously described criteria to highlight properties and differences. Advantages and disadvantages are compared, enabling researchers and developers to choose a framework according to their specific needs. Y1 - 2020 U6 - https://doi.org/10.1109/ICNC47757.2020.9049650 N1 - 2020 International Conference on Computing, Networking and Communications (ICNC), 17-20 February 2020, Big Island, HI, USA SP - 373 EP - 377 PB - IEEE CY - New York, NY ER - TY - CHAP A1 - Kreyer, Jörg A1 - Müller, Marvin A1 - Esch, Thomas T1 - A Map-Based Model for the Determination of Fuel Consumption for Internal Combustion Engines as a Function of Flight Altitude N2 - In addition to very high safety and reliability requirements, the design of internal combustion engines (ICE) in aviation focuses on economic efficiency. The objective must be to design the aircraft powertrain optimized for a specific flight mission with respect to fuel consumption and specific engine power. Against this background, expert tools provide valuable decision-making assistance for the customer. In this paper, a mathematical calculation model for the fuel consumption of aircraft ICE is presented. This model enables the derivation of fuel consumption maps for different engine configurations. Depending on the flight conditions and based on these maps, the current and the integrated fuel consumption for freely definable flight emissions is calculated. For that purpose, an interpolation method is used, that has been optimized for accuracy and calculation time. The mission boundary conditions flight altitude and power requirement of the ICE form the basis for this calculation. The mathematical fuel consumption model is embedded in a parent program. This parent program presents the simulated fuel consumption by means of an example flight mission for a representative airplane. The focus of the work is therefore on reproducing exact consumption data for flight operations. By use of the empirical approaches according to Gagg-Farrar [1] the power and fuel consumption as a function of the flight altitude are determined. To substantiate this approaches, a 1-D ICE model based on the multi-physical simulation tool GT-Suite® has been created. This 1-D engine model offers the possibility to analyze the filling and gas change processes, the internal combustion as well as heat and friction losses for an ICE under altitude environmental conditions. Performance measurements on a dynamometer at sea level for a naturally aspirated ICE with a displacement of 1211 ccm used in an aviation aircraft has been done to validate the 1-D ICE model. To check the plausibility of the empirical approaches with respect to the fuel consumption and performance adjustment for the flight altitude an analysis of the ICE efficiency chain of the 1-D engine model is done. In addition, a comparison of literature and manufacturer data with the simulation results is presented. Y1 - 2020 U6 - https://doi.org/10.25967/490162 N1 - 68. Deutscher Luft- und Raumfahrtkongress 30.09.-02.10.2019, Darmstadt PB - DGLR CY - Bonn ER - TY - CHAP A1 - Finger, Felix A1 - de Vries, Reynard A1 - Vos, Roelof A1 - Braun, Carsten A1 - Bil, Cees T1 - A comparison of hybrid-electric aircraft sizing methods T2 - AIAA Scitech 2020 Forum N2 - The number of case studies focusing on hybrid-electric aircraft is steadily increasing, since these configurations are thought to lead to lower operating costs and environmental impact than traditional aircraft. However, due to the lack of reference data of actual hybrid-electric aircraft, in most cases, the design tools and results are difficult to validate. In this paper, two independently developed approaches for hybrid-electric conceptual aircraft design are compared. An existing 19-seat commuter aircraft is selected as the conventional baseline, and both design tools are used to size that aircraft. The aircraft is then re-sized under consideration of hybrid-electric propulsion technology. This is performed for parallel, serial, and fully-electric powertrain architectures. Finally, sensitivity studies are conducted to assess the validity of the basic assumptions and approaches regarding the design of hybrid-electric aircraft. Both methods are found to predict the maximum take-off mass (MTOM) of the reference aircraft with less than 4% error. The MTOM and payload-range energy efficiency of various (hybrid-) electric configurations are predicted with a maximum difference of approximately 2% and 5%, respectively. The results of this study confirm a correct formulation and implementation of the two design methods, and the data obtained can be used by researchers to benchmark and validate their design tools. Y1 - 2020 U6 - https://doi.org/10.2514/6.2020-1006 N1 - AIAA Scitech 2020 Forum, Driving aerospace solutions for global challenges, Orlando, 06. - 10. January 2020 ER - TY - JOUR A1 - Khayyam, Hamid A1 - Jamali, Ali A1 - Bab-Hadiashar, Alireza A1 - Esch, Thomas A1 - Ramakrishna, Seeram A1 - Jalil, Mahdi A1 - Naebe, Minoo T1 - A Novel Hybrid Machine Learning Algorithm for Limited and Big Data Modelling with Application in Industry 4.0 JF - IEEE Access N2 - To meet the challenges of manufacturing smart products, the manufacturing plants have been radically changed to become smart factories underpinned by industry 4.0 technologies. The transformation is assisted by employment of machine learning techniques that can deal with modeling both big or limited data. This manuscript reviews these concepts and present a case study that demonstrates the use of a novel intelligent hybrid algorithms for Industry 4.0 applications with limited data. In particular, an intelligent algorithm is proposed for robust data modeling of nonlinear systems based on input-output data. In our approach, a novel hybrid data-driven combining the Group-Method of Data-Handling and Singular-Value Decomposition is adapted to find an offline deterministic model combined with Pareto multi-objective optimization to overcome the overfitting issue. An Unscented-Kalman-Filter is also incorporated to update the coefficient of the deterministic model and increase its robustness against data uncertainties. The effectiveness of the proposed method is examined on a set of real industrial measurements. Y1 - 2020 SN - 2169-3536 U6 - https://doi.org/10.1109/ACCESS.2020.2999898 SP - 1 EP - 12 PB - IEEE CY - New York, NY 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 - TY - CHAP A1 - Hebel, Christoph A1 - Herrmann, Ulf A1 - Ritz, Thomas A1 - Röth, Thilo A1 - Anthrakidis, Anette A1 - Böker, Jörg A1 - Franzke, Till A1 - Grodzki, Thomas A1 - Merkens, Torsten A1 - Schöttler, Mirjam T1 - FlexSHARE – Methodisches Framework zur innovativen Gestaltung der urbanen Mobilität durch Sharing- Angebote T2 - Transforming Mobility – What Next? N2 - Das Ziel des INTERREG-Projektes „SHAREuregio“ (FKZ: 34.EFRE-0300134) ist es, grenzüberschreitende Mobilität in der Euregio Rhein-Maas-Nord zu ermöglichen und zu fördern. Dazu soll ein elektromobiles Car- und Bikesharing- System entwickelt und in der Stadt Mönchengladbach, im Kreis Viersen sowie in den Gemeinden Roermond und Venlo (beide NL) zusammen mit den Partnern Wirtschaftsförderung Mönchengladbach, Wirtschaftsförderung für den Kreis Viersen, NEW AG, Goodmoovs (NL), Greenflux (NL) und der FH Aachen implementiert werden. Zunächst richtet sich das Angebot, bestehend aus 40 Elektroautos und 40 Elektrofahrrädern, an Unternehmen und wird nach einer Erprobungsphase, mit einer größeren Anzahl an Fahrzeugen, auch für Privatpersonen verfügbar gemacht werden. Die Fahrzeuge stehen bei den jeweiligen Anwendungspartnern in Deutschland und den Niederlanden. Im Rahmen dieses Projektes hat die FH Aachen „FlexSHARE“ entwickelt – ein methodisches Framework zur innovativen Gestaltung urbaner Sharing- Angebote. Das Framework ermöglicht es, anhand von messbaren Kenngrößen, bedarfsgerechte und auf die Region abgestimmte Sharing-Systeme zu entwickeln. Y1 - 2022 SN - 978-3-658-36429-8 U6 - https://doi.org/10.1007/978-3-658-36430-4_10 N1 - Tagungsband zum 13. Wissenschaftsforum Mobilität, Beiträge des Wissenschaftsforums SP - 153 EP - 169 PB - Springer Gabler CY - Wiesbaden ER - TY - CHAP A1 - Fiedler, Gerda A1 - Gottschlich-Müller, Birgit A1 - Melcher, Karin ED - Liu-Henke, Xiaobo ED - Durak, Umut T1 - Online-Prüfungen mit STACK Aufgaben T2 - Tagungsband ASIM Workshop STS/GMMS/EDU 2021 N2 - Wir stellen hier exemplarisch STACK Aufgaben vor, die frei von der Problematik sind, welche sich durch diverse Kommunikationswege und (webbasierte) Computer Algebra Systeme (CAS) ergibt. Daher sind sie insbesondere für eine Open-Book Online Prüfung geeignet, da eine faire Prüfungssituation gewährleistet werden kann. Y1 - 2021 SN - 978-3-901608-69-8 U6 - https://doi.org/10.11128/arep.45 N1 - Virtueller Workshop, ASIM STS/GMMS & EDU 2021, 11.-12. März 2021 SP - 173 EP - 178 PB - ARGESIM Verlag CY - Wien ER -