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 - TY - CHAP A1 - Müller, Tim M. A1 - Schmitt, Andreas A1 - Leise, Philipp A1 - Meck, Tobias A1 - Altherr, Lena A1 - Pelz, Peter F. A1 - Pfetsch, Marc E. T1 - Validation of an optimized resilient water supply system T2 - Uncertainty in Mechanical Engineering N2 - Component failures within water supply systems can lead to significant performance losses. One way to address these losses is the explicit anticipation of failures within the design process. We consider a water supply system for high-rise buildings, where pump failures are the most likely failure scenarios. We explicitly consider these failures within an early design stage which leads to a more resilient system, i.e., a system which is able to operate under a predefined number of arbitrary pump failures. We use a mathematical optimization approach to compute such a resilient design. This is based on a multi-stage model for topology optimization, which can be described by a system of nonlinear inequalities and integrality constraints. Such a model has to be both computationally tractable and to represent the real-world system accurately. We therefore validate the algorithmic solutions using experiments on a scaled test rig for high-rise buildings. The test rig allows for an arbitrary connection of pumps to reproduce scaled versions of booster station designs for high-rise buildings. We experimentally verify the applicability of the presented optimization model and that the proposed resilience properties are also fulfilled in real systems. KW - Optimization KW - Mixed-integer nonlinear programming KW - Water distribution system KW - Resilience KW - Validation Y1 - 2021 SN - 978-3-030-77255-0 SN - 978-3-030-77256-7 U6 - https://doi.org/10.1007/978-3-030-77256-7_7 N1 - Proceedings of the 4th International Conference on Uncertainty in Mechanical Engineering (ICUME 2021), June 7–8, 2021 SP - 70 EP - 80 PB - Springer CY - Cham ER - TY - CHAP A1 - Mandekar, Swati A1 - Jentsch, Lina A1 - Lutz, Kai A1 - Behbahani, Mehdi A1 - Melnykowycz, Mark T1 - Earable design analysis for sleep EEG measurements T2 - UbiComp '21 N2 - Conventional EEG devices cannot be used in everyday life and hence, past decade research has been focused on Ear-EEG for mobile, at-home monitoring for various applications ranging from emotion detection to sleep monitoring. As the area available for electrode contact in the ear is limited, the electrode size and location play a vital role for an Ear-EEG system. In this investigation, we present a quantitative study of ear-electrodes with two electrode sizes at different locations in a wet and dry configuration. Electrode impedance scales inversely with size and ranges from 450 kΩ to 1.29 MΩ for dry and from 22 kΩ to 42 kΩ for wet contact at 10 Hz. For any size, the location in the ear canal with the lowest impedance is ELE (Left Ear Superior), presumably due to increased contact pressure caused by the outer-ear anatomy. The results can be used to optimize signal pickup and SNR for specific applications. We demonstrate this by recording sleep spindles during sleep onset with high quality (5.27 μVrms). KW - EEG KW - sensors KW - Impedance Spectroscopy KW - Sleep EEG KW - biopotential electrodes Y1 - 2021 U6 - https://doi.org/10.1145/3460418.3479328 N1 - UbiComp '21: Adjunct Proceedings of the 2021 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2021 ACM International Symposium on Wearable Computers, September 21–26, 2021, Virtual, USA SP - 171 EP - 175 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 - Hüning, Felix A1 - Wache, Franz-Josef A1 - Magiera, David T1 - Redundant bus systems using dual-mode radio T2 - Proceedings of Sixth International Congress on Information and Communication Technology N2 - Communication via serial bus systems, like CAN, plays an important role for all kinds of embedded electronic and mechatronic systems. To cope up with the requirements for functional safety of safety-critical applications, there is a need to enhance the safety features of the communication systems. One measure to achieve a more robust communication is to add redundant data transmission path to the applications. In general, the communication of real-time embedded systems like automotive applications is tethered, and the redundant data transmission lines are also tethered, increasing the size of the wiring harness and the weight of the system. A radio link is preferred as a redundant transmission line as it uses a complementary transmission medium compared to the wired solution and in addition reduces wiring harness size and weight. Standard wireless links like Wi-Fi or Bluetooth cannot meet the requirements for real-time capability with regard to bus communication. Using the new dual-mode radio enables a redundant transmission line meeting all requirements with regard to real-time capability, robustness and transparency for the data bus. In addition, it provides a complementary transmission medium with regard to commonly used tethered links. A CAN bus system is used to demonstrate the redundant data transfer via tethered and wireless CAN. Y1 - 2021 SN - 978-981-16-2379-0 SN - 978-981-16-2380-6 U6 - https://doi.org/10.1007/978-981-16-2380-6_73 N1 - Sixth International Congress on Information and Communication Technology, ICICT 2021, Brunel University, London, February 25–26, 2021 SP - 835 EP - 842 PB - Springer CY - Singapore ER - TY - CHAP A1 - Klöser, Lars A1 - Kohl, Philipp A1 - Kraft, Bodo A1 - Zündorf, Albert T1 - Multi-attribute relation extraction (MARE): simplifying the application of relation extraction T2 - Proceedings of the 2nd International Conference on Deep Learning Theory and Applications DeLTA - Volume 1 N2 - Natural language understanding’s relation extraction makes innovative and encouraging novel business concepts possible and facilitates new digitilized decision-making processes. Current approaches allow the extraction of relations with a fixed number of entities as attributes. Extracting relations with an arbitrary amount of attributes requires complex systems and costly relation-trigger annotations to assist these systems. We introduce multi-attribute relation extraction (MARE) as an assumption-less problem formulation with two approaches, facilitating an explicit mapping from business use cases to the data annotations. Avoiding elaborated annotation constraints simplifies the application of relation extraction approaches. The evaluation compares our models to current state-of-the-art event extraction and binary relation extraction methods. Our approaches show improvement compared to these on the extraction of general multi-attribute relations. Y1 - 2021 SN - 978-989-758-526-5 U6 - https://doi.org/10.5220/0010559201480156 N1 - 2nd International Conference on Deep Learning Theory and Applications, DeLTA2021, July 7-9, 2021 SP - 148 EP - 156 PB - SciTePress CY - Setúbal ER - TY - CHAP A1 - Adenacker, J. A1 - Gerhards, Benjamin A1 - Otten, Christian A1 - Schleser, Markus T1 - Laserstrahlschweißen von Aluminium-Kupfer-Werkstoffkombinationen für die Elektromobilität T2 - DVS CONGRESS 2021 Y1 - 2021 SN - 978-3-96144-146-4 N1 - DVS CONGRESS 2021, 14. – 17. September 2021, Essen. Große Schweißtechnische Tagung 2021, DVS CAMPUS 2021. DVS Berichte, Band: 371 SP - 31 EP - 38 PB - DVS Media GmbH CY - Düsseldorf ER - TY - CHAP A1 - Kohl, Philipp A1 - Schmidts, Oliver A1 - Klöser, Lars A1 - Werth, Henri A1 - Kraft, Bodo A1 - Zündorf, Albert T1 - STAMP 4 NLP – an agile framework for rapid quality-driven NLP applications development T2 - Quality of Information and Communications Technology. QUATIC 2021 N2 - The progress in natural language processing (NLP) research over the last years, offers novel business opportunities for companies, as automated user interaction or improved data analysis. Building sophisticated NLP applications requires dealing with modern machine learning (ML) technologies, which impedes enterprises from establishing successful NLP projects. Our experience in applied NLP research projects shows that the continuous integration of research prototypes in production-like environments with quality assurance builds trust in the software and shows convenience and usefulness regarding the business goal. We introduce STAMP 4 NLP as an iterative and incremental process model for developing NLP applications. With STAMP 4 NLP, we merge software engineering principles with best practices from data science. Instantiating our process model allows efficiently creating prototypes by utilizing templates, conventions, and implementations, enabling developers and data scientists to focus on the business goals. Due to our iterative-incremental approach, businesses can deploy an enhanced version of the prototype to their software environment after every iteration, maximizing potential business value and trust early and avoiding the cost of successful yet never deployed experiments. KW - Machine learning KW - Process model KW - Natural language processing Y1 - 2021 SN - 978-3-030-85346-4 SN - 978-3-030-85347-1 U6 - https://doi.org/10.1007/978-3-030-85347-1_12 N1 - International Conference on the Quality of Information and Communications Technology, QUATIC 2021, 8-11 September, Algarve, Portugal SP - 156 EP - 166 PB - Springer CY - Cham ER - TY - CHAP A1 - Schmidts, Oliver A1 - Kraft, Bodo A1 - Winkens, Marvin A1 - Zündorf, Albert T1 - Catalog integration of heterogeneous and volatile product data T2 - DATA 2020: Data Management Technologies and Applications N2 - The integration of frequently changing, volatile product data from different manufacturers into a single catalog is a significant challenge for small and medium-sized e-commerce companies. They rely on timely integrating product data to present them aggregated in an online shop without knowing format specifications, concept understanding of manufacturers, and data quality. Furthermore, format, concepts, and data quality may change at any time. Consequently, integrating product catalogs into a single standardized catalog is often a laborious manual task. Current strategies to streamline or automate catalog integration use techniques based on machine learning, word vectorization, or semantic similarity. However, most approaches struggle with low-quality or real-world data. We propose Attribute Label Ranking (ALR) as a recommendation engine to simplify the integration process of previously unknown, proprietary tabular format into a standardized catalog for practitioners. We evaluate ALR by focusing on the impact of different neural network architectures, language features, and semantic similarity. Additionally, we consider metrics for industrial application and present the impact of ALR in production and its limitations. Y1 - 2021 SN - 978-3-030-83013-7 U6 - https://doi.org/10.1007/978-3-030-83014-4_7 N1 - International Conference on Data Management Technologies and Applications, DATA 2020, 7-9 July SP - 134 EP - 153 PB - Springer CY - Cham ER - 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 -