@article{ThomaGardiFisheretal.2024, author = {Thoma, Andreas and Gardi, Alessandro and Fisher, Alex and Braun, Carsten}, title = {Improving local path planning for UAV flight in challenging environments by refining cost function weights}, series = {CEAS Aeronautical Journal}, journal = {CEAS Aeronautical Journal}, publisher = {Springer}, address = {Wien}, issn = {1869-5590 (eISSN)}, doi = {10.1007/s13272-024-00741-x}, pages = {12 Seiten}, year = {2024}, abstract = {Unmanned Aerial Vehicles (UAV) constantly gain in versatility. However, more reliable path planning algorithms are required until full autonomous UAV operation is possible. This work investigates the algorithm 3DVFH* and analyses its dependency on its cost function weights in 2400 environments. The analysis shows that the 3DVFH* can find a suitable path in every environment. However, a particular type of environment requires a specific choice of cost function weights. For minimal failure, probability interdependencies between the weights of the cost function have to be considered. This dependency reduces the number of control parameters and simplifies the usage of the 3DVFH*. Weights for costs associated with vertical evasion (pitch cost) and vicinity to obstacles (obstacle cost) have the highest influence on the failure probability of the local path planner. Environments with mainly very tall buildings (like large American city centres) require a preference for horizontal avoidance manoeuvres (achieved with high pitch cost weights). In contrast, environments with medium-to-low buildings (like European city centres) benefit from vertical avoidance manoeuvres (achieved with low pitch cost weights). The cost of the vicinity to obstacles also plays an essential role and must be chosen adequately for the environment. Choosing these two weights ideal is sufficient to reduce the failure probability below 10\%.}, language = {en} } @article{SaretzkiBergmannDahmannetal.2021, author = {Saretzki, Charlotte and Bergmann, Ole and Dahmann, Peter and Janser, Frank and Keimer, Jona and Machado, Patricia and Morrison, Audry and Page, Henry and Pluta, Emil and St{\"u}bing, Felix and K{\"u}pper, Thomas}, title = {Are small airplanes safe with regards to COVID-19 transmission?}, series = {Journal of Travel Medicine}, volume = {28}, journal = {Journal of Travel Medicine}, number = {7}, publisher = {Oxford University Press}, address = {Oxford}, issn = {1708-8305}, doi = {10.1093/jtm/taab105}, year = {2021}, language = {en} } @article{UlmerBraunChengetal.2023, author = {Ulmer, Jessica and Braun, Sebastian and Cheng, Chi-Tsun and Dowey, Steve and Wollert, J{\"o}rg}, title = {A human factors-aware assistance system in manufacturing based on gamification and hardware modularisation}, series = {International Journal of Production Research}, journal = {International Journal of Production Research}, publisher = {Taylor \& Francis}, issn = {0020-7543 (Print)}, doi = {10.1080/00207543.2023.2166140}, year = {2023}, abstract = {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.}, language = {en} } @article{SchopenNarayanBeckmannetal.2024, author = {Schopen, Oliver and Narayan, Sriram and Beckmann, Marvin and Najmi, Aezid-Ul-Hassan and Esch, Thomas and Shabani, Bahman}, title = {An EIS approach to quantify the effects of inlet air relative humidity on the performance of proton exchange membrane fuel cells: a pathway to developing a novel fault diagnostic method}, series = {International Journal of Hydrogen Energy}, volume = {58}, journal = {International Journal of Hydrogen Energy}, number = {8}, publisher = {Elsevier}, address = {Amsterdam}, isbn = {0360-3199 (print)}, issn = {1879-3487 (online)}, doi = {10.1016/j.ijhydene.2024.01.218}, pages = {1302 -- 1315}, year = {2024}, abstract = {In this work, the effect of low air relative humidity on the operation of a polymer electrolyte membrane fuel cell is investigated. An innovative method through performing in situ electrochemical impedance spectroscopy is utilised to quantify the effect of inlet air relative humidity at the cathode side on internal ionic resistances and output voltage of the fuel cell. In addition, algorithms are developed to analyse the electrochemical characteristics of the fuel cell. For the specific fuel cell stack used in this study, the membrane resistance drops by over 39 \% and the cathode side charge transfer resistance decreases by 23 \% after increasing the humidity from 30 \% to 85 \%, while the results of static operation also show an increase of ∼2.2 \% in the voltage output after increasing the relative humidity from 30 \% to 85 \%. In dynamic operation, visible drying effects occur at < 50 \% relative humidity, whereby the increase of the air side stoichiometry increases the drying effects. Furthermore, other parameters, such as hydrogen humidification, internal stack structure, and operating parameters like stoichiometry, pressure, and temperature affect the overall water balance. Therefore, the optimal humidification range must be determined by considering all these parameters to maximise the fuel cell performance and durability. The results of this study are used to develop a health management system to ensure sufficient humidification by continuously monitoring the fuel cell polarisation data and electrochemical impedance spectroscopy indicators.}, language = {en} } @article{FayyaziSardarThomasetal.2023, author = {Fayyazi, Mojgan and Sardar, Paramjotsingh and Thomas, Sumit Infent and Daghigh, Roonak and Jamali, Ali and Esch, Thomas and Kemper, Hans and Langari, Reza and Khayyam, Hamid}, title = {Artificial intelligence/machine learning in energy management systems, control, and optimization of hydrogen fuel cell vehicles}, volume = {15}, number = {6}, publisher = {MDPI}, address = {Basel}, doi = {10.3390/su15065249}, pages = {38}, year = {2023}, abstract = {Environmental emissions, global warming, and energy-related concerns have accelerated the advancements in conventional vehicles that primarily use internal combustion engines. Among the existing technologies, hydrogen fuel cell electric vehicles and fuel cell hybrid electric vehicles may have minimal contributions to greenhouse gas emissions and thus are the prime choices for environmental concerns. However, energy management in fuel cell electric vehicles and fuel cell hybrid electric vehicles is a major challenge. Appropriate control strategies should be used for effective energy management in these vehicles. On the other hand, there has been significant progress in artificial intelligence, machine learning, and designing data-driven intelligent controllers. These techniques have found much attention within the community, and state-of-the-art energy management technologies have been developed based on them. This manuscript reviews the application of machine learning and intelligent controllers for prediction, control, energy management, and vehicle to everything (V2X) in hydrogen fuel cell vehicles. The effectiveness of data-driven control and optimization systems are investigated to evolve, classify, and compare, and future trends and directions for sustainability are discussed.}, language = {en} }