@inproceedings{DeyElsenFerreinetal.2021,
  author    = {Dey, Thomas and Elsen, Ingo and Ferrein, Alexander and Frauenrath, Tobias and Reke, Michael and Schiffer, Stefan},
  title     = {CO2 Meter: a do-it-yourself carbon dioxide measuring device for the classroom},
  series = {PETRA 2021: The 14th PErvasive Technologies Related to Assistive Environments Conference},
  booktitle = {PETRA 2021: The 14th PErvasive Technologies Related to Assistive Environments Conference},
  editor    = {Makedon, Fillia},
  publisher = {Association for Computing Machinery},
  address   = {New York},
  isbn      = {9781450387927},
  doi       = {10.1145/3453892.3462697},
  pages     = {292 -- 299},
  year      = {2021},
  abstract  = {In this paper we report on CO2 Meter, a do-it-yourself carbon dioxide measuring device for the classroom. Part of the current measures for dealing with the SARS-CoV-2 pandemic is proper ventilation in indoor settings. This is especially important in schools with students coming back to the classroom even with high incidents rates. Static ventilation patterns do not consider the individual situation for a particular class. Influencing factors like the type of activity, the physical structure or the room occupancy are not incorporated. Also, existing devices are rather expensive and often provide only limited information and only locally without any networking. This leaves the potential of analysing the situation across different settings untapped. Carbon dioxide level can be used as an indicator of air quality, in general, and of aerosol load in particular. Since, according to the latest findings, SARS-CoV-2 can be transmitted primarily in the form of aerosols, carbon dioxide may be used as a proxy for the risk of a virus infection. Hence, schools could improve the indoor air quality and potentially reduce the infection risk if they actually had measuring devices available in the classroom. Our device supports schools in ventilation and it allows for collecting data over the Internet to enable a detailed data analysis and model generation. First deployments in schools at different levels were received very positively. A pilot installation with a larger data collection and analysis is underway.},
  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{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}
}
@inproceedings{ChristianMontagSchubaetal.2018,
  author    = {Christian, Esser and Montag, Tim and Schuba, Marko and Allhof, Manuel},
  title     = {Future critical infrastructure and security - cyberattacks on charging stations},
  series = {31st International Electric Vehicle Symposium \& Exhibition and International Electric Vehicle Technology Conference (EVS31 \& EVTeC 2018)},
  booktitle = {31st International Electric Vehicle Symposium \& Exhibition and International Electric Vehicle Technology Conference (EVS31 \& EVTeC 2018)},
  publisher = {Society of Automotive Engineers of Japan (JSAE)},
  address   = {Tokyo},
  isbn      = {978-1-5108-9157-9},
  pages     = {665 -- 671},
  year      = {2018},
  language  = {en}
}
@inproceedings{SchubaHoefkenLinzbach2022,
  author    = {Schuba, Marko and H{\"o}fken, Hans-Wilhelm and Linzbach, Sophie},
  title     = {An ICS Honeynet for Detecting and Analyzing Cyberattacks in Industrial Plants},
  series = {2021 International Conference on Electrical, Computer and Energy Technologies (ICECET)},
  booktitle = {2021 International Conference on Electrical, Computer and Energy Technologies (ICECET)},
  publisher = {IEEE},
  isbn      = {978-1-6654-4231-2},
  doi       = {10.1109/ICECET52533.2021.9698746},
  pages     = {6 Seiten},
  year      = {2022},
  abstract  = {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},
  language  = {en}
}
@inproceedings{NikolovskiRekeElsenetal.2021,
  author    = {Nikolovski, Gjorgji and Reke, Michael and Elsen, Ingo and Schiffer, Stefan},
  title     = {Machine learning based 3D object detection for navigation in unstructured environments},
  series = {2021 IEEE Intelligent Vehicles Symposium Workshops (IV Workshops)},
  booktitle = {2021 IEEE Intelligent Vehicles Symposium Workshops (IV Workshops)},
  publisher = {IEEE},
  isbn      = {978-1-6654-7921-9},
  doi       = {10.1109/IVWorkshops54471.2021.9669218},
  pages     = {236 -- 242},
  year      = {2021},
  abstract  = {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.},
  language  = {en}
}
@inproceedings{HauggKreyerKemperetal.2020,
  author    = {Haugg, Albert Thomas and Kreyer, J{\"o}rg and Kemper, Hans and Hatesuer, Katerina and Esch, Thomas},
  title     = {Heat exchanger for ORC. adaptability and optimisation potentials},
  series = {IIR International Rankine 2020 Conference},
  booktitle = {IIR International Rankine 2020 Conference},
  doi       = {10.18462/iir.rankine.2020.1224},
  pages     = {10 Seiten},
  year      = {2020},
  abstract  = {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.},
  language  = {en}
}
@inproceedings{MerkensHebel2021,
  author    = {Merkens, Torsten and Hebel, Christoph},
  title     = {Sharing mobility concepts - flexible, sustainable, smart},
  series = {Proceedings of the 1st UNITED - Southeast Asia Automotive Interest Group (SAIG)},
  booktitle = {Proceedings of the 1st UNITED - Southeast Asia Automotive Interest Group (SAIG)},
  isbn      = {978-3-902103-94-9},
  pages     = {43 -- 44},
  year      = {2021},
  language  = {en}
}
@inproceedings{ThomaFisherBraun2020,
  author    = {Thoma, Andreas and Fisher, Alex and Braun, Carsten},
  title     = {Improving the px4 avoid algorithm by bio-inspired flight strategies},
  series = {DLRK2020 - „Luft- und Raumfahrt - Verantwortung in allen Dimensionen"},
  booktitle = {DLRK2020 - „Luft- und Raumfahrt - Verantwortung in allen Dimensionen"},
  pages     = {10 Seiten},
  year      = {2020},
  language  = {en}
}
@inproceedings{ThomaFisherBertrandetal.2020,
  author    = {Thoma, Andreas and Fisher, Alex and Bertrand, Olivier and Braun, Carsten},
  title     = {Evaluation of possible flight strategies for close object evasion from bumblebee experiments},
  series = {Living Machines 2020: Biomimetic and Biohybrid Systems},
  booktitle = {Living Machines 2020: Biomimetic and Biohybrid Systems},
  editor    = {Vouloutsi, Vasiliki and Mura, Anna and Tauber, Falk and Speck, Thomas and Prescott, Tony J. and Verschure, Paul F. M. J.},
  publisher = {Springer},
  address   = {Cham},
  isbn      = {978-3-030-64312-6},
  doi       = {10.1007/978-3-030-64313-3_34},
  pages     = {354 -- 365},
  year      = {2020},
  language  = {en}
}