@inproceedings{NierlePieper2023,
  author    = {Nierle, Elisabeth and Pieper, Martin},
  title     = {Measuring social impacts in engineering education to improve sustainability skills},
  series = {European Society for Engineering Education (SEFI)},
  booktitle = {European Society for Engineering Education (SEFI)},
  doi       = {10.21427/QPR4-0T22},
  pages     = {9 Seiten},
  year      = {2023},
  abstract  = {In times of social climate protection movements, such as Fridays for Future, the priorities of society, industry and higher education are currently changing. The consideration of sustainability challenges is increasing. In the context of sustainable development, social skills are crucial to achieving the United Nations Sustainable Development Goals (SDGs). In particular, the impact that educational activities have on people, communities and society is therefore coming to the fore. Research has shown that people with high levels of social competence are better able to manage stressful situations, maintain positive relationships and communicate effectively. They are also associated with better academic performance and career success. However, especially in engineering programs, the social pillar is underrepresented compared to the environmental and economic pillars. In response to these changes, higher education institutions should be more aware of their social impact - from individual forms of teaching to entire modules and degree programs. To specifically determine the potential for improvement and derive resulting change for further development, we present an initial framework for social impact measurement by transferring already established approaches from the business sector to the education sector. To demonstrate the applicability, we measure the key competencies taught in undergraduate engineering programs in Germany. The aim is to prepare the students for success in the modern world of work and their future contribution to sustainable development. Additionally, the university can include the results in its sustainability report. Our method can be applied to different teaching methods and enables their comparison.},
  language  = {en}
}
@inproceedings{AltherrConzenElsenetal.2023,
  author    = {Altherr, Lena and Conzen, Max and Elsen, Ingo and Frauenrath, Tobias and Lyrmann, Andreas},
  title     = {Sensor retrofitting of existing buildings in an interdisciplinary teaching project at university level},
  series = {Tagungsband AALE 2023 : mit Automatisierung gegen den Klimawandel},
  booktitle = {Tagungsband AALE 2023 : mit Automatisierung gegen den Klimawandel},
  editor    = {Reiff-Stephan, J{\"o}rg and J{\"a}kel, Jens and Schwarz, Andr{\´e}},
  publisher = {le-tex publishing services GmbH},
  address   = {Leipzig},
  isbn      = {978-3-910103-01-6},
  doi       = {10.33968/2023.04},
  pages     = {31 -- 40},
  year      = {2023},
  abstract  = {Existing residential buildings have an average lifetime of 100 years. Many of these buildings will exist for at least another 50 years. To increase the efficiency of these buildings while keeping costs at reasonable rates, they can be retrofitted with sensors that deliver information to central control units for heating, ventilation and electricity. This retrofitting process should happen with minimal intervention into existing infrastructure and requires new approaches for sensor design and data transmission. At FH Aachen University of Applied Sciences, students of different disciplines work together to learn how to design, build, deploy and operate such sensors. The presented teaching project already created a low power design for a combined CO2, temperature and humidity measurement device that can be easily integrated into most home automation systems},
  language  = {en}
}
@inproceedings{ArndtConzenElsenetal.2023,
  author    = {Arndt, Tobias and Conzen, Max and Elsen, Ingo and Ferrein, Alexander and Galla, Oskar and K{\"o}se, Hakan and Schiffer, Stefan and Tschesche, Matteo},
  title     = {Anomaly detection in the metal-textile industry for the reduction of the cognitive load of quality control workers},
  series = {PETRA '23: Proceedings of the 16th International Conference on PErvasive Technologies Related to Assistive Environments},
  booktitle = {PETRA '23: Proceedings of the 16th International Conference on PErvasive Technologies Related to Assistive Environments},
  publisher = {ACM},
  isbn      = {9798400700699},
  doi       = {10.1145/3594806.3596558},
  pages     = {535 -- 542},
  year      = {2023},
  abstract  = {This paper presents an approach for reducing the cognitive load for humans working in quality control (QC) for production processes that adhere to the 6σ -methodology. While 100\% QC requires every part to be inspected, this task can be reduced when a human-in-the-loop QC process gets supported by an anomaly detection system that only presents those parts for manual inspection that have a significant likelihood of being defective. This approach shows good results when applied to image-based QC for metal textile products.},
  language  = {en}
}
@inproceedings{ThomaStiemerBraunetal.2023,
  author    = {Thoma, Andreas and Stiemer, Luc and Braun, Carsten and Fisher, Alex and Gardi, Alessandro G.},
  title     = {Potential of hybrid neural network local path planner for small UAV in urban environments},
  series = {AIAA SCITECH 2023 Forum},
  booktitle = {AIAA SCITECH 2023 Forum},
  publisher = {AIAA},
  doi       = {10.2514/6.2023-2359},
  pages     = {13 Seiten},
  year      = {2023},
  abstract  = {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.},
  language  = {en}
}