TY - CHAP A1 - El Moussaoui, Noureddine A1 - Kassmi, Khalil A1 - Alexopoulos, Spiros A1 - Schwarzer, Klemens A1 - Chayeb, Hamid A1 - Bachiri, Najib T1 - Simulation studies on a new innovative design of a hybrid solar distiller MSDH alimented with a thermal and photovoltaic energy T2 - Materialstoday: Proceedings N2 - In this paper, we present the structure, the simulation the operation of a multi-stage, hybrid solar desalination system (MSDH), powered by thermal and photovoltaic (PV) (MSDH) energy. The MSDH system consists of a lower basin, eight horizontal stages, a field of four flat thermal collectors with a total area of 8.4 m2, 3 Kw PV panels and solar batteries. During the day the system is heated by thermal energy, and at night by heating resistors, powered by solar batteries. These batteries are charged by the photovoltaic panels during the day. More specifically, during the day and at night, we analyse the temperature of the stages and the production of distilled water according to the solar irradiation intensity and the electric heating power, supplied by the solar batteries. The simulations were carried out in the meteorological conditions of the winter month (February 2020), presenting intensities of irradiance and ambient temperature reaching 824 W/m2 and 23 °C respectively. The results obtained show that during the day the system is heated by the thermal collectors, the temperature of the stages and the quantity of water produced reach 80 °C and 30 Kg respectively. At night, from 6p.m. the system is heated by the electric energy stored in the batteries, the temperature of the stages and the quantity of water produced reach respectively 90 °C and 104 Kg for an electric heating power of 2 Kw. Moreover, when the electric power varies from 1 Kw to 3 Kw the quantity of water produced varies from 92 Kg to 134 Kg. The analysis of these results and their comparison with conventional solar thermal desalination systems shows a clear improvement both in the heating of the stages, by 10%, and in the quantity of water produced by a factor of 3. Y1 - 2021 U6 - https://doi.org/10.1016/j.matpr.2021.03.115 SN - 2214-7853 N1 - The Fourth edition of the International Conference on Materials & Environmental Science (ICMES 2020), virtual conference, November 18-28, 2020, Morocco VL - 45 IS - 8 SP - 7653 EP - 7660 PB - Elsevier CY - Amsterdam ER - TY - CHAP A1 - Butenweg, Christoph ED - Kuzmanović, Vladan ED - Ignjatović, Ivan T1 - Integrated approach for monitoring and management of buildings with digital building models and modern sensor technologies T2 - Proceedings of the International Conference Civil Engineering 2021 - Achievements and Visions N2 - 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). Y1 - 2021 N1 - Civil Engineering 2021 – Achievements and Visions, University of Belgrade, October 25 – 26, 2021 Belgrade, Serbia SP - 67 EP - 75 PB - University of Belgrade CY - Belgrade ER - TY - CHAP A1 - Dey, Thomas A1 - Elsen, Ingo A1 - Ferrein, Alexander A1 - Frauenrath, Tobias A1 - Reke, Michael A1 - Schiffer, Stefan ED - Makedon, Fillia T1 - CO2 Meter: a do-it-yourself carbon dioxide measuring device for the classroom T2 - PETRA '21: Proceedings of the 14th Pervasive Technologies Related to Assistive Environments Conference N2 - 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. KW - embedded hardware KW - sensor networks KW - information systems KW - education KW - do-it-yourself Y1 - 2021 SN - 9781450387927 U6 - https://doi.org/10.1145/3453892.3462697 N1 - PETRA '21: The 14th PErvasive Technologies Related to Assistive Environments Conference Corfu Greece 29 June 2021- 2 July 2021 SP - 292 EP - 299 PB - Association for Computing Machinery CY - New York ER - TY - CHAP A1 - Chajan, Eduard A1 - Schulte-Tigges, Joschua A1 - Reke, Michael A1 - Ferrein, Alexander A1 - Matheis, Dominik A1 - Walter, Thomas T1 - GPU based model-predictive path control for self-driving vehicles T2 - IEEE Intelligent Vehicles Symposium (IV) N2 - 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. KW - Heuristic algorithms KW - Computational modeling KW - model-predictive control KW - GPU KW - autonomous driving Y1 - 2021 SN - 978-1-7281-5394-0 U6 - https://doi.org/10.1109/IV48863.2021.9575619 N1 - 2021 IEEE Intelligent Vehicles Symposium (IV), July 11-17, 2021. Nagoya, Japan SP - 1243 EP - 1248 PB - IEEE CY - New York, NY ER - TY - CHAP A1 - Ferrein, Alexander A1 - Meeßen, Marcus A1 - Limpert, Nicolas A1 - Schiffer, Stefan ED - Lepuschitz, Wilfried T1 - Compiling ROS schooling curricula via contentual taxonomies T2 - Robotics in Education N2 - 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. Y1 - 2021 SN - 978-3-030-67411-3 U6 - https://doi.org/10.1007/978-3-030-67411-3_5 N1 - RiE: International Conference on Robotics in Education (RiE); Advances in Intelligent Systems and Computing book series (AISC, volume 1316) SP - 49 EP - 60 PB - Springer CY - Cham ER - TY - CHAP A1 - Schulte, Maximilian A1 - Eggert, Mathias T1 - Predicting hourly bitcoin prices based on long short-term memory neural networks T2 - Proceedings of the International Conference on Wirtschaftsinformatik (WI) 2021 N2 - 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 %. Y1 - 2021 N1 - 16th International Conference on Wirtschaftsinformatik, March 2021, Essen, Germany ER - TY - CHAP A1 - Heuermann, Holger A1 - Harzheim, Thomas A1 - Mühmel, Marc T1 - A maritime harmonic radar search and rescue system using passive and active tags T2 - 2020 17th European Radar Conference (EuRAD) N2 - 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. KW - Harmonic Radar KW - Rescue System KW - Frequency Doubler KW - Transponder KW - Tag Y1 - 2021 SN - 978-2-87487-061-3 U6 - https://doi.org/10.1109/EuRAD48048.2021.00030 N1 - 17th European Radar Conference, 13th - 15th January 2021, Utrecht, Netherlands SP - 73 EP - 76 PB - IEEE CY - New York, NY ER - TY - CHAP A1 - Bornheim, Tobias A1 - Grieger, Niklas A1 - Bialonski, Stephan T1 - FHAC at GermEval 2021: Identifying German toxic, engaging, and fact-claiming comments with ensemble learning T2 - Proceedings of the GermEval 2021 Workshop on the Identification of Toxic, Engaging, and Fact-Claiming Comments : 17th Conference on Natural Language Processing KONVENS 2021 Y1 - 2021 U6 - https://doi.org/10.48415/2021/fhw5-x128 N1 - KONVENS (Konferenz zur Verarbeitung natürlicher Sprache/Conference on Natural Language Processing) 2021, 6. - 9. September 2021, Düsseldorf SP - 105 EP - 111 PB - Heinrich Heine University CY - Düsseldorf ER -