TY  - CHAP
A1  - Altherr, Lena
A1  - Conzen, Max
A1  - Elsen, Ingo
A1  - Frauenrath, Tobias
A1  - Lyrmann, Andreas
ED  - Reiff-Stephan, Jörg
ED  - Jäkel, Jens
ED  - Schwarz, André
T1  - Sensor retrofitting of existing buildings in an interdisciplinary teaching project at university level
T2  - Tagungsband AALE 2023 : mit Automatisierung gegen den Klimawandel
N2  - 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
KW  - Building Automation
KW  - Smart Building
KW  - CO2
KW  - Carbon Dioxide
KW  - Education
Y1  - 2023
SN  - 978-3-910103-01-6
U6  - http://dx.doi.org/10.33968/2023.04
N1  - 19. AALE-Konferenz. Luxemburg, 08.03.-10.03.2023.
BTS Connected Buildings & Cities Luxemburg
(Tagungsband unter https://doi.org/10.33968/2023.01)
SP  - 31
EP  - 40
PB  - le-tex publishing services GmbH
CY  - Leipzig
ER  - 
TY  - CHAP
A1  - Arndt, Tobias
A1  - Conzen, Max
A1  - Elsen, Ingo
A1  - Ferrein, Alexander
A1  - Galla, Oskar
A1  - Köse, Hakan
A1  - Schiffer, Stefan
A1  - Tschesche, Matteo
T1  - Anomaly detection in the metal-textile industry for the reduction of the cognitive load of quality control workers
T2  - PETRA '23: Proceedings of the 16th International Conference on PErvasive Technologies Related to Assistive Environments
N2  - 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.
KW  - Datasets
KW  - Neural networks
KW  - Anomaly detection
KW  - Quality control
KW  - Process optimization
Y1  - 2023
SN  - 9798400700699
U6  - http://dx.doi.org/10.1145/3594806.3596558
N1  - PETRA '23: Proceedings of the 16th International Conference on Pervasive Technologies Related to Assistive Environments, Corfu Greece, July 5 - 7, 2023.
SP  - 535
EP  - 542
PB  - ACM
ER  -