TY - CHAP A1 - Tamaldin, Noreffendy A1 - Esch, Thomas A1 - Tonoli, Andrea A1 - Reisinger, Karl Heinz A1 - Sprenger, Hanna A1 - Razuli, Hisham T1 - ERASMUS+ United CBHE Automotive International Collaboration from European to South East Asia T2 - Proceedings of the 2nd African International Conference on Industrial Engineering and Operations Management N2 - The industrial revolution especially in the IR4.0 era have driven many states of the art technologies to be introduced. The automotive industry as well as many other key industries have also been greatly influenced. The rapid development of automotive industries in Europe have created wide industry gap between European Union (EU) and developing countries such as in South East Asia (SEA). Indulging this situation, FH JOANNEUM, Austria together with European partners from FH Aachen, Germany and Politecnico di Torino, Italy are taking initiative to close down the gap utilizing the Erasmus+ United Capacity Building in Higher Education grant from EU. A consortium was founded to engage with automotive technology transfer using the European framework to Malaysian, Indonesian and Thailand Higher Education Institutions (HEI) as well as automotive industries in respective countries. This could be achieved by establishing Engineering Knowledge Transfer Unit (EKTU) in respective SEA institutions guided by the industry partners in their respective countries. This EKTU could offer updated, innovative and high-quality training courses to increase graduate’s employability in higher education institutions and strengthen relations between HEI and the wider economic and social environment by addressing University-industry cooperation which is the regional priority for Asia. It is expected that, the Capacity Building Initiative would improve the quality of higher education and enhancing its relevance for the labor market and society in the SEA partners. The outcome of this project would greatly benefit the partners in strong and complementary partnership targeting the automotive industry and enhanced larger scale international cooperation between the European and SEA partners. It would also prepare the SEA HEI in sustainable partnership with Automotive industry in the region as a mean of income generation in the future. KW - European Framework and South East Asia KW - Technology Transfer KW - Capacity Building Higher Education KW - Malaysian Automotive Industry Y1 - 2020 SN - 978-1-7923-6123-4 SN - 2169-8767 N1 - 2nd African International Conference on Industrial Engineering and Operations Management; Harare, Zimbabwe, December 7-10, 2020 SP - 2970 EP - 2972 PB - IEOM Society International CY - Southfield ER - TY - CHAP A1 - Tamaldin, Noreffendy A1 - Mansor, Muhd Rizuan A1 - Mat Yamin, Ahmad Kamal A1 - Bin Abdollah, Mohd Fazli A1 - Esch, Thomas A1 - Tonoli, Andrea A1 - Reisinger, Karl Heinz A1 - Sprenger, Hanna A1 - Razuli, Hisham ED - Bin Abdollah, Mohd Fadzli ED - Amiruddin, Hilmi ED - Singh, Amrik Singh Phuman ED - Munir, Fudhail Abdul ED - Ibrahim, Asriana T1 - Development of UTeM United Future Fuel Design Training Center Under Erasmus+ United Program T2 - Proceedings of the 7th International Conference and Exhibition on Sustainable Energy and Advanced Materials (ICE-SEAM 2021), Melaka, Malaysia N2 - The industrial revolution IR4.0 era have driven many states of the art technologies to be introduced especially in the automotive industry. The rapid development of automotive industries in Europe have created wide industry gap between European Union (EU) and developing countries such as in South-East Asia (SEA). Indulging this situation, FH Joanneum, Austria together with European partners from FH Aachen, Germany and Politecnico Di Torino, Italy is taking initiative to close the gap utilizing the Erasmus+ United grant from EU. A consortium was founded to engage with automotive technology transfer using the European ramework to Malaysian, Indonesian and Thailand Higher Education Institutions (HEI) as well as automotive industries. This could be achieved by establishing Engineering Knowledge Transfer Unit (EKTU) in respective SEA institutions guided by the industry partners in their respective countries. This EKTU could offer updated, innovative, and high-quality training courses to increase graduate’s employability in higher education institutions and strengthen relations between HEI and the wider economic and social environment by addressing Universityindustry cooperation which is the regional priority for Asia. It is expected that, the Capacity Building Initiative would improve the quality of higher education and enhancing its relevance for the labor market and society in the SEA partners. The outcome of this project would greatly benefit the partners in strong and complementary partnership targeting the automotive industry and enhanced larger scale international cooperation between the European and SEA partners. It would also prepare the SEA HEI in sustainable partnership with Automotive industry in the region as a mean of income generation in the future. KW - Erasmus+ United KW - technology transfer KW - UTeM Engineering Knowledge Transfer Unit KW - Malaysian automotive industry Y1 - 2022 SN - 978-981-19-3178-9 SN - 978-981-19-3179-6 (E-Book) U6 - http://dx.doi.org/10.1007/978-981-19-3179-6_50 SN - 2195-4356 N1 - The 7th International Conference and Exhibition on Sustainable Energy and Advanced Material (ICE-SEAM 2021) was organized by Universiti Teknikal Malaysia Melaka (UTeM), Malaysia, in association with the Universitas Sebelas Maret (UNS), Indonesia, on 23 November 2021. SP - 274 EP - 278 PB - Springer Nature CY - Singapore 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 - http://dx.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 - 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 N1 - Deutscher Luft- und Raumfahrtkongress 2020, 1. bis 3. September 2020 – Online, „Luft- und Raumfahrt – Verantwortung in allen Dimensionen“ ER - TY - RPRT A1 - Thoma, Andreas A1 - Laarmann, Lukas A1 - Merkens, Torsten A1 - Franzke, Till A1 - Möhren, Felix A1 - Buttermann, Lilly A1 - van der Weem, Dirk A1 - Fischer, Maximilian A1 - Misch, Philipp A1 - Böhme, Mirijam A1 - Röth, Thilo A1 - Hebel, Christoph A1 - Ritz, Thomas A1 - Franke, Marina A1 - Braun, Carsten T1 - Entwicklung eines intermodalen Mobilitätskonzeptes für die Pilotregion NRW/Rhein-Maas Euregio und Schaffung voller Kundenakzeptanz durch Transfer von Standards aus dem PKW-Bereich auf ein Flugtaxi : Schlussbericht : Projektakronym: SkyCab (Kategorie B) : Laufzeit in Monaten: 6 : Hauptthema: Kategorie B: Innovative Ideen mit Bezug zu UAS/Flugtaxis Y1 - 2020 N1 - Förderkennzeichen BMVI 45UAS1027A-F PB - FH Aachen CY - Aachen ER - 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 - http://dx.doi.org/10.2514/6.2023-2359 N1 - AIAA SCITECH 2023 Forum, 23-27 January 2023, National Harbor, MD & Online PB - AIAA ER - TY - JOUR A1 - Thoma, Andreas A1 - Thomessen, Karolin A1 - Gardi, Alessandro A1 - Fisher, A. A1 - Braun, Carsten T1 - Prioritising paths: An improved cost function for local path planning for UAV in medical applications JF - The Aeronautical Journal N2 - Even the shortest flight through unknown, cluttered environments requires reliable local path planning algorithms to avoid unforeseen obstacles. The algorithm must evaluate alternative flight paths and identify the best path if an obstacle blocks its way. Commonly, weighted sums are used here. This work shows that weighted Chebyshev distances and factorial achievement scalarising functions are suitable alternatives to weighted sums if combined with the 3DVFH* local path planning algorithm. Both methods considerably reduce the failure probability of simulated flights in various environments. The standard 3DVFH* uses a weighted sum and has a failure probability of 50% in the test environments. A factorial achievement scalarising function, which minimises the worst combination of two out of four objective functions, reaches a failure probability of 26%; A weighted Chebyshev distance, which optimises the worst objective, has a failure probability of 30%. These results show promise for further enhancements and to support broader applicability. KW - Path planning KW - Cost function KW - Multi-objective optimization Y1 - 2023 U6 - http://dx.doi.org/10.1017/aer.2023.68 SN - 0001-9240 (Print) SN - 2059-6464 (Online) IS - First View SP - 1 EP - 18 PB - Cambridge University Press CY - Cambridge ER - TY - JOUR A1 - Ulmer, Jessica A1 - Braun, Carsten A1 - Cheng, Chi-Tsun A1 - Dowey, Steve A1 - Wollert, Jörg T1 - A human factors-aware assistance system in manufacturing based on gamification and hardware modularisation JF - International Journal of Production Research N2 - Assistance systems have been widely adopted in the manufacturing sector to facilitate various processes and tasks in production environments. However, existing systems are mostly equipped with rigid functional logic and do not provide individual user experiences or adapt to their capabilities. This work integrates human factors in assistance systems by adjusting the hardware and instruction presented to the workers’ cognitive and physical demands. A modular system architecture is designed accordingly, which allows a flexible component exchange according to the user and the work task. Gamification, the use of game elements in non-gaming contexts, has been further adopted in this work to provide level-based instructions and personalised feedback. The developed framework is validated by applying it to a manual workstation for industrial assembly routines. KW - Human factors KW - assistance system KW - gamification KW - adaptive systems KW - manufacturing Y1 - 2023 U6 - http://dx.doi.org/10.1080/00207543.2023.2166140 SN - 0020-7543 (Print) SN - 1366-588X (Online) PB - Taylor & Francis ER - TY - CHAP A1 - Veettil, Yadu Krishna Morassery A1 - Rakshit, Shantam A1 - Schopen, Oliver A1 - Kemper, Hans A1 - Esch, Thomas A1 - Shabani, Bahman ED - Bin Abdollah, Mohd Fadzli ED - Amiruddin, Hilmi ED - Singh, Amrik Singh Phuman ED - Munir, Fudhail Abdul ED - Ibrahim, Asriana T1 - Automated Control System Strategies to Ensure Safety of PEM Fuel Cells Using Kalman Filters T2 - Proceedings of the 7th International Conference and Exhibition on Sustainable Energy and Advanced Materials (ICE-SEAM 2021), Melaka, Malaysia N2 - Having well-defined control strategies for fuel cells, that can efficiently detect errors and take corrective action is critically important for safety in all applications, and especially so in aviation. The algorithms not only ensure operator safety by monitoring the fuel cell and connected components, but also contribute to extending the health of the fuel cell, its durability and safe operation over its lifetime. While sensors are used to provide peripheral data surrounding the fuel cell, the internal states of the fuel cell cannot be directly measured. To overcome this restriction, Kalman Filter has been implemented as an internal state observer. Other safety conditions are evaluated using real-time data from every connected sensor and corrective actions automatically take place to ensure safety. The algorithms discussed in this paper have been validated thorough Model-in-the-Loop (MiL) tests as well as practical validation at a dedicated test bench. KW - control system KW - PEM fuel cells KW - Kalman filter Y1 - 2022 SN - 978-981-19-3178-9 SN - 978-981-19-3179-6 (E-Book) U6 - http://dx.doi.org/10.1007/978-981-19-3179-6_55 SN - 2195-4356 N1 - The 7th International Conference and Exhibition on Sustainable Energy and Advanced Material (ICE-SEAM 2021) was organized by Universiti Teknikal Malaysia Melaka (UTeM), Malaysia, in association with the Universitas Sebelas Maret (UNS), Indonesia, on 23 November 2021 SP - 296 EP - 299 PB - Springer Nature CY - Singapore ER - TY - CHAP A1 - Walter, Peter A1 - Elsen, Ingo A1 - Müller, Holger A1 - Kraiss, Karl-Friedrich T1 - 3D object recognition with a specialized mixtures of experts architecture T2 - IJCNN'99. International Joint Conference on Neural Networks. Proceedings N2 - Aim of the AXON2 project (Adaptive Expert System for Object Recogniton using Neuml Networks) is the development of an object recognition system (ORS) capable of recognizing isolated 3d objects from arbitrary views. Commonly, classification is based on a single feature extracted from the original image. Here we present an architecture adapted from the Mixtures of Eaqerts algorithm which uses multiple neuml networks to integmte different features. During tmining each neural network specializes in a subset of objects or object views appropriate to the properties of the corresponding feature space. In recognition mode the system dynamically chooses the most relevant features and combines them with maximum eficiency. The remaining less relevant features arz not computed and do therefore not decelerate the-recognition process. Thus, the algorithm is well suited for ml-time applications. Y1 - 1999 SN - 0-7803-5529-6 U6 - http://dx.doi.org/10.1109/IJCNN.1999.836243 SN - 1098-7576 N1 - Washington, DC 10-16.07.1999 SP - 3563 EP - 3568 PB - IEEE CY - New York ER -