@inproceedings{Butenweg2021, author = {Butenweg, Christoph}, title = {Integrated approach for monitoring and management of buildings with digital building models and modern sensor technologies}, series = {Proceedings of the International Conference Civil Engineering 2021 - Achievements and Visions}, booktitle = {Proceedings of the International Conference Civil Engineering 2021 - Achievements and Visions}, editor = {Kuzmanović, Vladan and Ignjatović, Ivan}, publisher = {University of Belgrade}, address = {Belgrade}, pages = {67 -- 75}, year = {2021}, abstract = {Nowadays modern high-performance buildings and facilities are equipped with monitoring systems and sensors to control building characteristics like energy consumption, temperature pattern and structural safety. The visualization and interpretation of sensor data is typically based on simple spreadsheets and non-standardized user-oriented solutions, which makes it difficult for building owners, facility managers and decision-makers to evaluate and understand the data. The solution of this problem in the future are integrated BIM-Sensor approaches which allow the generation of BIM models incorporating all relevant information of monitoring systems. These approaches support both the dynamic visualization of key structural performance parameters, the effective long-term management of sensor data based on BIM and provide a user-friendly interface to communicate with various stakeholders. A major benefit for the end user is the use of the BIM software architecture, which is the future standard anyway. In the following, the application of the integrated BIM-Sensor approach is illustrated for a typical industrial facility as a part of an early warning and rapid response system for earthquake events currently developed in the research project "ROBUST" with financial support by the German Federal Ministry for Economic Affairs and Energy (BMWI).}, language = {en} } @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 '21: Proceedings of the 14th Pervasive Technologies Related to Assistive Environments Conference}, booktitle = {PETRA '21: Proceedings of 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{ChajanSchulteTiggesRekeetal.2021, author = {Chajan, Eduard and Schulte-Tigges, Joschua and Reke, Michael and Ferrein, Alexander and Matheis, Dominik and Walter, Thomas}, title = {GPU based model-predictive path control for self-driving vehicles}, series = {IEEE Intelligent Vehicles Symposium (IV)}, booktitle = {IEEE Intelligent Vehicles Symposium (IV)}, publisher = {IEEE}, address = {New York, NY}, isbn = {978-1-7281-5394-0}, doi = {10.1109/IV48863.2021.9575619}, pages = {1243 -- 1248}, year = {2021}, abstract = {One central challenge for self-driving cars is a proper path-planning. Once a trajectory has been found, the next challenge is to accurately and safely follow the precalculated path. The model-predictive controller (MPC) is a common approach for the lateral control of autonomous vehicles. The MPC uses a vehicle dynamics model to predict the future states of the vehicle for a given prediction horizon. However, in order to achieve real-time path control, the computational load is usually large, which leads to short prediction horizons. To deal with the computational load, the control algorithm can be parallelized on the graphics processing unit (GPU). In contrast to the widely used stochastic methods, in this paper we propose a deterministic approach based on grid search. Our approach focuses on systematically discovering the search area with different levels of granularity. To achieve this, we split the optimization algorithm into multiple iterations. The best sequence of each iteration is then used as an initial solution to the next iteration. The granularity increases, resulting in smooth and predictable steering angle sequences. We present a novel GPU-based algorithm and show its accuracy and realtime abilities with a number of real-world experiments.}, language = {en} } @inproceedings{FerreinMeessenLimpertetal.2021, author = {Ferrein, Alexander and Meeßen, Marcus and Limpert, Nicolas and Schiffer, Stefan}, title = {Compiling ROS schooling curricula via contentual taxonomies}, series = {Robotics in Education}, booktitle = {Robotics in Education}, editor = {Lepuschitz, Wilfried}, publisher = {Springer}, address = {Cham}, isbn = {978-3-030-67411-3}, doi = {10.1007/978-3-030-67411-3_5}, pages = {49 -- 60}, year = {2021}, abstract = {The Robot Operating System (ROS) is the current de-facto standard in robot middlewares. The steadily increasing size of the user base results in a greater demand for training as well. User groups range from students in academia to industry professionals with a broad spectrum of developers in between. To deliver high quality training and education to any of these audiences, educators need to tailor individual curricula for any such training. In this paper, we present an approach to ease compiling curricula for ROS trainings based on a taxonomy of the teaching contents. The instructor can select a set of dedicated learning units and the system will automatically compile the teaching material based on the dependencies of the units selected and a set of parameters for a particular training. We walk through an example training to illustrate our work.}, language = {en} } @inproceedings{SchulteEggert2021, author = {Schulte, Maximilian and Eggert, Mathias}, title = {Predicting hourly bitcoin prices based on long short-term memory neural networks}, series = {Proceedings of the International Conference on Wirtschaftsinformatik (WI) 2021}, booktitle = {Proceedings of the International Conference on Wirtschaftsinformatik (WI) 2021}, pages = {16 Seiten}, year = {2021}, abstract = {Bitcoin is a cryptocurrency and is considered a high-risk asset class whose price changes are difficult to predict. Current research focusses on daily price movements with a limited number of predictors. The paper at hand aims at identifying measurable indicators for Bitcoin price movements and the development of a suitable forecasting model for hourly changes. The paper provides three research contributions. First, a set of significant indicators for predicting the Bitcoin price is identified. Second, the results of a trained Long Short-term Memory (LSTM) neural network that predicts price changes on an hourly basis is presented and compared with other algorithms. Third, the results foster discussions of the applicability of neural nets for stock price predictions. In total, 47 input features for a period of over 10 months could be retrieved to train a neural net that predicts the Bitcoin price movements with an error rate of 3.52 \%.}, language = {en} } @inproceedings{HeuermannHarzheimMuehmel2021, author = {Heuermann, Holger and Harzheim, Thomas and M{\"u}hmel, Marc}, title = {A maritime harmonic radar search and rescue system using passive and active tags}, series = {2020 17th European Radar Conference (EuRAD)}, booktitle = {2020 17th European Radar Conference (EuRAD)}, publisher = {IEEE}, address = {New York, NY}, isbn = {978-2-87487-061-3}, doi = {10.1109/EuRAD48048.2021.00030}, pages = {73 -- 76}, year = {2021}, abstract = {This article introduces a new maritime search and rescue system based on S-band illumination harmonic radar (HR). Passive and active tags have been developed and tested attached to life jackets and a rescue boat. This system was able to detect and range the active tags up to a range of 5800 m in tests on the Baltic Sea with an antenna input power of only 100 W. All electronic GHz components of the system, excluding the S-band power amplifier, were custom developed for this purpose. Special attention is given to the performance and conceptual differences between passive and active tags used in the system and integration with a maritime X-band navigation radar is demonstrated.}, language = {en} } @inproceedings{BornheimGriegerBialonski2021, author = {Bornheim, Tobias and Grieger, Niklas and Bialonski, Stephan}, title = {FHAC at GermEval 2021: Identifying German toxic, engaging, and fact-claiming comments with ensemble learning}, series = {Proceedings of the GermEval 2021 Workshop on the Identification of Toxic, Engaging, and Fact-Claiming Comments : 17th Conference on Natural Language Processing KONVENS 2021}, booktitle = {Proceedings of the GermEval 2021 Workshop on the Identification of Toxic, Engaging, and Fact-Claiming Comments : 17th Conference on Natural Language Processing KONVENS 2021}, publisher = {Heinrich Heine University}, address = {D{\"u}sseldorf}, doi = {10.48415/2021/fhw5-x128}, pages = {105 -- 111}, year = {2021}, language = {en} } @inproceedings{MohanGrossMenzeletal.2021, author = {Mohan, Nijanthan and Groß, Rolf Fritz and Menzel, Karsten and Theis, Fabian}, title = {Opportunities and Challenges in the Implementation of Building Information Modeling for Prefabrication of Heating, Ventilation and Air Conditioning Systems in Small and Medium-Sized Contracting Companies in Germany - A Case Study}, series = {WIT Transactions on The Built Environment, Vol. 205}, booktitle = {WIT Transactions on The Built Environment, Vol. 205}, publisher = {WIT Press}, address = {Southampton}, issn = {1743-3509}, doi = {10.2495/BIM210101}, pages = {117 -- 126}, year = {2021}, abstract = {Even though BIM (Building Information Modelling) is successfully implemented in most of the world, it is still in the early stages in Germany, since the stakeholders are sceptical of its reliability and efficiency. The purpose of this paper is to analyse the opportunities and obstacles to implementing BIM for prefabrication. Among all other advantages of BIM, prefabrication is chosen for this paper because it plays a vital role in creating an impact on the time and cost factors of a construction project. The project stakeholders and participants can explicitly observe the positive impact of prefabrication, which enables the breakthrough of the scepticism factor among the small-scale construction companies. The analysis consists of the development of a process workflow for implementing prefabrication in building construction followed by a practical approach, which was executed with two case studies. It was planned in such a way that, the first case study gives a first-hand experience for the workers at the site on the BIM model so that they can make much use of the created BIM model, which is a better representation compared to the traditional 2D plan. The main aim of the first case study is to create a belief in the implementation of BIM Models, which was succeeded by the execution of offshore prefabrication in the second case study. Based on the case studies, the time analysis was made and it is inferred that the implementation of BIM for prefabrication can reduce construction time, ensures minimal wastes, better accuracy, less problem-solving at the construction site. It was observed that this process requires more planning time, better communication between different disciplines, which was the major obstacle for successful implementation. This paper was carried out from the perspective of small and medium-sized mechanical contracting companies for the private building sector in Germany.}, language = {en} }