TY - CHAP A1 - Thoma, Andreas A1 - Stiemer, Luc A1 - Braun, Carsten A1 - Fisher, Alex A1 - Gardi, Alessandro G. T1 - Potential of hybrid neural network local path planner for small UAV in urban environments T2 - AIAA SCITECH 2023 Forum N2 - This work proposes a hybrid algorithm combining an Artificial Neural Network (ANN) with a conventional local path planner to navigate UAVs efficiently in various unknown urban environments. The proposed method of a Hybrid Artificial Neural Network Avoidance System is called HANNAS. The ANN analyses a video stream and classifies the current environment. This information about the current Environment is used to set several control parameters of a conventional local path planner, the 3DVFH*. The local path planner then plans the path toward a specific goal point based on distance data from a depth camera. We trained and tested a state-of-the-art image segmentation algorithm, PP-LiteSeg. The proposed HANNAS method reaches a failure probability of 17%, which is less than half the failure probability of the baseline and around half the failure probability of an improved, bio-inspired version of the 3DVFH*. The proposed HANNAS method does not show any disadvantages regarding flight time or flight distance. Y1 - 2023 U6 - https://doi.org/10.2514/6.2023-2359 N1 - AIAA SCITECH 2023 Forum, 23-27 January 2023, National Harbor, Md & Online PB - AIAA CY - Reston, Va. 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 - TY - CHAP A1 - Christian, Esser A1 - Montag, Tim A1 - Schuba, Marko A1 - Allhof, Manuel T1 - Future critical infrastructure and security - cyberattacks on charging stations T2 - 31st International Electric Vehicle Symposium & Exhibition and International Electric Vehicle Technology Conference (EVS31 & EVTeC 2018) Y1 - 2018 SN - 978-1-5108-9157-9 SP - 665 EP - 671 PB - Society of Automotive Engineers of Japan (JSAE) CY - Tokyo ER - TY - CHAP A1 - Schuba, Marko A1 - Höfken, Hans-Wilhelm A1 - Linzbach, Sophie T1 - An ICS Honeynet for Detecting and Analyzing Cyberattacks in Industrial Plants T2 - 2021 International Conference on Electrical, Computer and Energy Technologies (ICECET) N2 - Cybersecurity of Industrial Control Systems (ICS) is an important issue, as ICS incidents may have a direct impact on safety of people or the environment. At the same time the awareness and knowledge about cybersecurity, particularly in the context of ICS, is alarmingly low. Industrial honeypots offer a cheap and easy to implement way to raise cybersecurity awareness and to educate ICS staff about typical attack patterns. When integrated in a productive network, industrial honeypots may not only reveal attackers early but may also distract them from the actual important systems of the network. Implementing multiple honeypots as a honeynet, the systems can be used to emulate or simulate a whole Industrial Control System. This paper describes a network of honeypots emulating HTTP, SNMP, S7communication and the Modbus protocol using Conpot, IMUNES and SNAP7. The nodes mimic SIMATIC S7 programmable logic controllers (PLCs) which are widely used across the globe. The deployed honeypots' features will be compared with the features of real SIMATIC S7 PLCs. Furthermore, the honeynet has been made publicly available for ten days and occurring cyberattacks have been analyzed KW - Conpot KW - honeypot KW - honeynet KW - ICS KW - cybersecurity Y1 - 2022 SN - 978-1-6654-4231-2 SN - 978-1-6654-4232-9 U6 - https://doi.org/10.1109/ICECET52533.2021.9698746 N1 - 2021 International Conference on Electrical, Computer and Energy Technologies (ICECET). 09-10 December 2021. Cape Town, South Africa. PB - IEEE 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 - Haugg, Albert Thomas A1 - Kreyer, Jörg A1 - Kemper, Hans A1 - Hatesuer, Katerina A1 - Esch, Thomas T1 - Heat exchanger for ORC. adaptability and optimisation potentials T2 - IIR International Rankine 2020 Conference N2 - The recovery of waste heat requires heat exchangers to extract it from a liquid or gaseous medium into another working medium, a refrigerant. In Organic Rankine Cycles (ORC) on Combustion Engines there are two major heat sources, the exhaust gas and the water/glycol fluid from the engine’s cooling circuit. A heat exchanger design must be adapted to the different requirements and conditions resulting from the heat sources, fluids, system configurations, geometric restrictions, and etcetera. The Stacked Shell Cooler (SSC) is a new and very specific design of a plate heat exchanger, created by AKG, which allows with a maximum degree of freedom the optimization of heat exchange rate and the reduction of the related pressure drop. This optimization in heat exchanger design for ORC systems is even more important, because it reduces the energy consumption of the system and therefore maximizes the increase in overall efficiency of the engine. Y1 - 2020 U6 - https://doi.org/10.18462/iir.rankine.2020.1224 N1 - IIR International Rankine 2020 Conference - Heating, Cooling, Power Generation. Glasgow, 2020. ER - TY - CHAP A1 - Merkens, Torsten A1 - Hebel, Christoph T1 - Sharing mobility concepts – flexible, sustainable, smart T2 - Proceedings of the 1st UNITED – Southeast Asia Automotive Interest Group (SAIG) International Conference KW - Sharing mobility KW - electro mobility KW - business models KW - mobility behaviour Y1 - 2021 SN - 978-3-902103-94-9 N1 - 1st UNITED-SAIG International Conference, 21-22 APR 2021, Chulalongkorn University, Thailand SP - 43 EP - 44 ER - TY - CHAP A1 - Thoma, Andreas A1 - Fisher, Alex A1 - Braun, Carsten T1 - Improving the px4 avoid algorithm by bio-inspired flight strategies T2 - DLRK2020 - „Luft- und Raumfahrt – Verantwortung in allen Dimensionen“ Y1 - 2020 U6 - https://doi.org/10.25967/530183 N1 - Deutscher Luft- und Raumfahrtkongress 2020, 1. bis 3. September 2020 – Online, „Luft- und Raumfahrt – Verantwortung in allen Dimensionen“ ER - TY - CHAP A1 - Thoma, Andreas A1 - Fisher, Alex A1 - Bertrand, Olivier A1 - Braun, Carsten ED - Vouloutsi, Vasiliki ED - Mura, Anna ED - Tauber, Falk ED - Speck, Thomas ED - Prescott, Tony J. ED - Verschure, Paul F. M. J. T1 - Evaluation of possible flight strategies for close object evasion from bumblebee experiments T2 - Living Machines 2020: Biomimetic and Biohybrid Systems KW - Obstacle avoidance KW - Bumblebees KW - Flight control KW - UAV KW - MAV Y1 - 2020 SN - 978-3-030-64312-6 U6 - https://doi.org/10.1007/978-3-030-64313-3_34 N1 - 9th International Conference, Living Machines 2020, Freiburg, Germany, July 28–30, 2020, Proceedings SP - 354 EP - 365 PB - Springer CY - Cham ER - TY - CHAP A1 - Götten, Falk A1 - Finger, Felix A1 - Braun, Carsten A1 - Havermann, Marc A1 - Bil, C. A1 - Gomez, F. T1 - Empirical Correlations for Geometry Build-Up of Fixed Wing Unmanned Air Vehicles T2 - APISAT 2018: The Proceedings of the 2018 Asia-Pacific International Symposium on Aerospace Technology (APISAT 2018) N2 - The results of a statistical investigation of 42 fixed-wing, small to medium sized (20 kg−1000 kg) reconnaissance unmanned air vehicles (UAVs) are presented. Regression analyses are used to identify correlations of the most relevant geometry dimensions with the UAV’s maximum take-off mass. The findings allow an empirical based geometry-build up for a complete unmanned aircraft by referring to its take-off mass only. This provides a bridge between very early design stages (initial sizing) and the later determination of shapes and dimensions. The correlations might be integrated into a UAV sizing environment and allow designers to implement more sophisticated drag and weight estimation methods in this process. Additional information on correlation factors for a rough drag estimation methodology indicate how this technique can significantly enhance the accuracy of early design iterations. KW - Unmanned Air Vehicle KW - Geometry KW - Correlations KW - Statistics KW - Drag Y1 - 2019 SN - 978-981-13-3305-7 U6 - https://doi.org/10.1007/978-981-13-3305-7_109 N1 - APISAT 2018 - Asia-Pacific International Symposium on Aerospace Technology. 16-18 October 2018. Chengdu, China. Lecture Notes in Electrical Engineering (LNEE, volume 459) SP - 1365 EP - 1381 PB - Springer CY - Singapore ER -