TY - CHAP A1 - Schwager, Christian A1 - Angele, Florian A1 - Schwarzbözl, Peter A1 - Teixeira Boura, Cristiano José A1 - Herrmann, Ulf T1 - Model predictive assistance for operational decision making in molten salt receiver systems T2 - SolarPACES: Solar Power & Chemical Energy Systems N2 - Despite the challenges of pioneering molten salt towers (MST), it remains the leading technology in central receiver power plants today, thanks to cost effective storage integration and high cost reduction potential. The limited controllability in volatile solar conditions can cause significant losses, which are difficult to estimate without comprehensive modeling [1]. This paper presents a Methodology to generate predictions of the dynamic behavior of the receiver system as part of an operating assistance system (OAS). Based on this, it delivers proposals if and when to drain and refill the receiver during a cloudy period in order maximize the net yield and quantifies the amount of net electricity gained by this. After prior analysis with a detailed dynamic two-phase model of the entire receiver system, two different reduced modeling approaches where developed and implemented in the OAS. A tailored decision algorithm utilizes both models to deliver the desired predictions efficiently and with appropriate accuracy. KW - Power plants KW - Associated liquids KW - Decision theory KW - Electrochemistry Y1 - 2023 SN - 978-0-7354-4623-6 U6 - https://doi.org/10.1063/5.0151514 SN - 1551-7616 (online) SN - 0094-243X (print) N1 - SolarPACES: SOLAR POWER & CHEMICAL ENERGY SYSTEMS: 27th International Conference on Concentrating Solar Power and Chemical Energy Systems, 27 September–1 October 2021, Online IS - 2815 / 1 PB - AIP conference proceedings / American Institute of Physics CY - Melville, NY ER - TY - CHAP A1 - Steuer-Dankert, Linda T1 - Training future skills - sustainability, interculturality & innovation in a digital design thinking format T2 - Proceedings of the 19th International CDIO Conference N2 - The complex questions of today for a world of tomorrow are characterized by their global impact. Solutions must therefore not only be sustainable in the sense of the three pillars of sustainability (economic, environmental, and social) but must also function globally. This goes hand in hand with the need for intercultural acceptance of developed services and products. To achieve this, engineers, as the problem solvers of the future, must be able to work in intercultural teams on appropriate solutions, and be sensitive to intercultural perspectives. To equip the engineers of the future with the so-called future skills, teaching concepts are needed in which students can acquire these methods and competencies in application-oriented formats. The presented course "Applying Design Thinking - Sustainability, Innovation and Interculturality" was developed to teach future skills from the competency areas Digital Key Competencies, Classical Competencies and Transformative Competencies. The CDIO Standard 3.0, in particular the standards 5, 6, 7 and 8, was used as a guideline. The course aims to prepare engineering students from different disciplines and cultures for their future work in an international environment by combining a digital teaching format with an interdisciplinary, transdisciplinary and intercultural setting for solving sustainability challenges. The innovative moment lies in the digital application of design thinking and the inclusion of intercultural as well as trans- and interdisciplinary perspectives in innovation development processes. In this paper, the concept of the course will be presented in detail and the particularities of a digital implementation of design thinking will be addressed. Subsequently, the potentials and challenges will be reflected and practical advice for integrating design thinking in engineering education will be given. KW - Design Thinking KW - Sustainability KW - Future Skills KW - Interculturality KW - Interdisciplinarity Y1 - 2023 N1 - 19th International CDIO Conference, hosted by NTNU, Trondheim, Norway, June 26-29, 2023 ER - TY - CHAP A1 - Lahrs, Lennart A1 - Krisam, Pierre A1 - Herrmann, Ulf T1 - Envisioning a collaborative energy system planning platform for the energy transition at the district level T2 - ECOS 2023. The 36th International Conference on Efficiency, Cost, Optimization, Simulation and Environmental Impact of Energy Systems N2 - Residential and commercial buildings account for more than one-third of global energy-related greenhouse gas emissions. Integrated multi-energy systems at the district level are a promising way to reduce greenhouse gas emissions by exploiting economies of scale and synergies between energy sources. Planning district energy systems comes with many challenges in an ever-changing environment. Computational modelling established itself as the state-of-the-art method for district energy system planning. Unfortunately, it is still cumbersome to combine standalone models to generate insights that surpass their original purpose. Ideally, planning processes could be solved by using modular tools that easily incorporate the variety of competing and complementing computational models. Our contribution is a vision for a collaborative development and application platform for multi-energy system planning tools at the district level. We present challenges of district energy system planning identified in the literature and evaluate whether this platform can help to overcome these challenges. Further, we propose a toolkit that represents the core technical elements of the platform. Lastly, we discuss community management and its relevance for the success of projects with collaboration and knowledge sharing at their core. KW - Energy system planning KW - District energy planning platform KW - District data model KW - Renewable energy integration Y1 - 2023 U6 - https://doi.org/10.52202/069564-0284 N1 - ECOS 2023. The 36th International Conference on Efficiency, Cost, Optimization, Simulation and Environmental Impact of Energy Systems, 25-30 JUNE, 2023, Las Palmas de Gran Canaria, Spain SP - 3163 EP - 3170 PB - Procedings of ECOS 2023 ER - TY - CHAP A1 - Nierle, Elisabeth A1 - Pieper, Martin T1 - Measuring social impacts in engineering education to improve sustainability skills T2 - European Society for Engineering Education (SEFI) N2 - In times of social climate protection movements, such as Fridays for Future, the priorities of society, industry and higher education are currently changing. The consideration of sustainability challenges is increasing. In the context of sustainable development, social skills are crucial to achieving the United Nations Sustainable Development Goals (SDGs). In particular, the impact that educational activities have on people, communities and society is therefore coming to the fore. Research has shown that people with high levels of social competence are better able to manage stressful situations, maintain positive relationships and communicate effectively. They are also associated with better academic performance and career success. However, especially in engineering programs, the social pillar is underrepresented compared to the environmental and economic pillars. In response to these changes, higher education institutions should be more aware of their social impact - from individual forms of teaching to entire modules and degree programs. To specifically determine the potential for improvement and derive resulting change for further development, we present an initial framework for social impact measurement by transferring already established approaches from the business sector to the education sector. To demonstrate the applicability, we measure the key competencies taught in undergraduate engineering programs in Germany. The aim is to prepare the students for success in the modern world of work and their future contribution to sustainable development. Additionally, the university can include the results in its sustainability report. Our method can be applied to different teaching methods and enables their comparison. KW - Social impact measurement KW - Key competences KW - Sustainable engineering education KW - Future skills Y1 - 2023 U6 - https://doi.org/10.21427/QPR4-0T22 N1 - 51st Annual Conference of the European Society for Engineering Education, Technological University Dublin, 10th-14th September, 2023 N1 - Corresponding Author: Elisabeth Nierle ER - TY - CHAP A1 - Alhaskir, Mohamed A1 - Tschesche, Matteo A1 - Linke, Florian A1 - Schriewer, Elisabeth A1 - Weber, Yvonne A1 - Wolking, Stefan A1 - Röhrig, Rainer A1 - Koch, Henner A1 - Kutafina, Ekaterina ED - Röhrig, Rainer ED - Grabe, Niels ED - Haag, Martin ED - Hübner, Ursula ED - Sax, Ulrich ED - Schmidt, Carsten Oliver ED - Sedlmayr, Martin ED - Zapf, Antonia T1 - ECG matching: an approach to synchronize ECG datasets for data quality comparisons T2 - Proceedings of the 68th Annual Meeting of the German Association of Medical Informatics, Biometry, and Epidemiology e.V. (gmds) 2023 N2 - Clinical assessment of newly developed sensors is important for ensuring their validity. Comparing recordings of emerging electrocardiography (ECG) systems to a reference ECG system requires accurate synchronization of data from both devices. Current methods can be inefficient and prone to errors. To address this issue, three algorithms are presented to synchronize two ECG time series from different recording systems: Binned R-peak Correlation, R-R Interval Correlation, and Average R-peak Distance. These algorithms reduce ECG data to their cyclic features, mitigating inefficiencies and minimizing discrepancies between different recording systems. We evaluate the performance of these algorithms using high-quality data and then assess their robustness after manipulating the R-peaks. Our results show that R-R Interval Correlation was the most efficient, whereas the Average R-peak Distance and Binned R-peak Correlation were more robust against noisy data. KW - Electrocardiography KW - Wearable electronic device KW - Sensors comparison KW - Time-series synchronization Y1 - 2023 SN - 978-1-64368-428-4 (Print) SN - 978-1-64368-429-1 (Online) U6 - https://doi.org/10.3233/SHTI230718 N1 - Proceedings of the 68th Annual Meeting of the German Association of Medical Informatics, Biometry, and Epidemiology e.V. (gmds) 2023, 17. - 21.9.23, Heilbronn, Germany Part of the series: Studies in Health Technology and Informatics VL - 307 SP - 225 EP - 232 PB - IOS Press ER - TY - CHAP A1 - Tischbein, Franziska A1 - Kean, Kilian A1 - Vertgewall, Chris Martin A1 - Ulbig, Andreas A1 - Altherr, Lena T1 - Determination of the topology of low-voltage distribution grids using cluster methods T2 - 27th International Conference on Electricity Distribution (CIRED 2023) N2 - Due to the decarbonization of the energy sector, the electric distribution grids are undergoing a major transformation, which is expected to increase the load on the operating resources due to new electrical loads and distributed energy resources. Therefore, grid operators need to gradually move to active grid management in order to ensure safe and reliable grid operation. However, this requires knowledge of key grid variables, such as node voltages, which is why the mass integration of measurement technology (smart meters) is necessary. Another problem is the fact that a large part of the topology of the distribution grids is not sufficiently digitized and models are partly faulty, which means that active grid operation management today has to be carried out largely blindly. It is therefore part of current research to develop methods for determining unknown grid topologies based on measurement data. In this paper, different clustering algorithms are presented and their performance of topology detection of low voltage grids is compared. Furthermore, the influence of measurement uncertainties is investigated in the form of a sensitivity analysis. Y1 - 2023 SN - 978-1-83953-855-1 U6 - https://doi.org/10.1049/icp.2023.0478 N1 - 27th International Conference on Electricity Distribution (CIRED 2023), 12-15 June 2023, Rome, Italy. SP - 1 EP - 5 PB - IEEE ER - TY - CHAP A1 - Schulte-Tigges, Joschua A1 - Matheis, Dominik A1 - Reke, Michael A1 - Walter, Thomas A1 - Kaszner, Daniel ED - Krömker, Heidi T1 - Demonstrating a V2X enabled system for transition of control and minimum risk manoeuvre when leaving the operational design domain T2 - HCII 2023: HCI in Mobility, Transport, and Automotive Systems N2 - Modern implementations of driver assistance systems are evolving from a pure driver assistance to a independently acting automation system. Still these systems are not covering the full vehicle usage range, also called operational design domain, which require the human driver as fall-back mechanism. Transition of control and potential minimum risk manoeuvres are currently research topics and will bridge the gap until full autonomous vehicles are available. The authors showed in a demonstration that the transition of control mechanisms can be further improved by usage of communication technology. Receiving the incident type and position information by usage of standardised vehicle to everything (V2X) messages can improve the driver safety and comfort level. The connected and automated vehicle’s software framework can take this information to plan areas where the driver should take back control by initiating a transition of control which can be followed by a minimum risk manoeuvre in case of an unresponsive driver. This transition of control has been implemented in a test vehicle and was presented to the public during the IEEE IV2022 (IEEE Intelligent Vehicle Symposium) in Aachen, Germany. KW - V2X KW - Transiton of Control KW - Minimum Risk Manoeuvre KW - Operational Design Domain KW - Connected Automated Vehicle Y1 - 2023 SN - 978-3-031-35677-3 (Print) SN - 978-3-031-35678-0 (Online) U6 - https://doi.org/10.1007/978-3-031-35678-0_12 N1 - 5th International Conference, MobiTAS 2023, Held as Part of the 25th HCI International Conference, HCII 2023, Copenhagen, Denmark, July 23–28, 2023. SP - 200 EP - 210 PB - Springer CY - Cham ER - TY - CHAP A1 - Büsgen, André A1 - Klöser, Lars A1 - Kohl, Philipp A1 - Schmidts, Oliver A1 - Kraft, Bodo A1 - Zündorf, Albert ED - Cuzzocrea, Alfredo ED - Gusikhin, Oleg ED - Hammoudi, Slimane ED - Quix, Christoph T1 - From cracked accounts to fake IDs: user profiling on German telegram black market channels T2 - Data Management Technologies and Applications N2 - Messenger apps like WhatsApp and Telegram are frequently used for everyday communication, but they can also be utilized as a platform for illegal activity. Telegram allows public groups with up to 200.000 participants. Criminals use these public groups for trading illegal commodities and services, which becomes a concern for law enforcement agencies, who manually monitor suspicious activity in these chat rooms. This research demonstrates how natural language processing (NLP) can assist in analyzing these chat rooms, providing an explorative overview of the domain and facilitating purposeful analyses of user behavior. We provide a publicly available corpus of annotated text messages with entities and relations from four self-proclaimed black market chat rooms. Our pipeline approach aggregates the extracted product attributes from user messages to profiles and uses these with their sold products as features for clustering. The extracted structured information is the foundation for further data exploration, such as identifying the top vendors or fine-granular price analyses. Our evaluation shows that pretrained word vectors perform better for unsupervised clustering than state-of-the-art transformer models, while the latter is still superior for sequence labeling. KW - Clustering KW - Natural language processing KW - Information extraction KW - Profile extraction KW - Text mining Y1 - 2023 SN - 978-3-031-37889-8 (Print) SN - 978-3-031-37890-4 (Online) U6 - https://doi.org/10.1007/978-3-031-37890-4_9 N1 - 10th International Conference, DATA 2021, Virtual Event, July 6–8, 2021, and 11th International Conference, DATA 2022, Lisbon, Portugal, July 11-13, 2022 SP - 176 EP - 202 PB - Springer CY - Cham ER - TY - CHAP A1 - Kohl, Philipp A1 - Freyer, Nils A1 - Krämer, Yoka A1 - Werth, Henri A1 - Wolf, Steffen A1 - Kraft, Bodo A1 - Meinecke, Matthias A1 - Zündorf, Albert ED - Conte, Donatello ED - Fred, Ana ED - Gusikhin, Oleg ED - Sansone, Carlo T1 - ALE: a simulation-based active learning evaluation framework for the parameter-driven comparison of query strategies for NLP T2 - Deep Learning Theory and Applications N2 - Supervised machine learning and deep learning require a large amount of labeled data, which data scientists obtain in a manual, and time-consuming annotation process. To mitigate this challenge, Active Learning (AL) proposes promising data points to annotators they annotate next instead of a subsequent or random sample. This method is supposed to save annotation effort while maintaining model performance. However, practitioners face many AL strategies for different tasks and need an empirical basis to choose between them. Surveys categorize AL strategies into taxonomies without performance indications. Presentations of novel AL strategies compare the performance to a small subset of strategies. Our contribution addresses the empirical basis by introducing a reproducible active learning evaluation (ALE) framework for the comparative evaluation of AL strategies in NLP. The framework allows the implementation of AL strategies with low effort and a fair data-driven comparison through defining and tracking experiment parameters (e.g., initial dataset size, number of data points per query step, and the budget). ALE helps practitioners to make more informed decisions, and researchers can focus on developing new, effective AL strategies and deriving best practices for specific use cases. With best practices, practitioners can lower their annotation costs. We present a case study to illustrate how to use the framework. KW - Active learning KW - Query learning KW - Natural language processing KW - Deep learning KW - Reproducible research Y1 - 2023 SN - 978-3-031-39058-6 (Print) SN - 978-3-031-39059-3 (Online) U6 - https://doi.org/10.1007/978-3-031-39059-3_16 N1 - 4th International Conference, DeLTA 2023, Rome, Italy, July 13–14, 2023. SP - 235 EP - 253 PB - Springer CY - Cham ER - TY - CHAP A1 - Freyer, Nils A1 - Thewes, Dustin A1 - Meinecke, Matthias ED - Gusikhin, Oleg ED - Hammoudi, Slimane ED - Cuzzocrea, Alfredo T1 - GUIDO: a hybrid approach to guideline discovery & ordering from natural language texts T2 - Proceedings of the 12th International Conference on Data Science, Technology and Applications DATA - Volume 1 N2 - Extracting workflow nets from textual descriptions can be used to simplify guidelines or formalize textual descriptions of formal processes like business processes and algorithms. The task of manually extracting processes, however, requires domain expertise and effort. While automatic process model extraction is desirable, annotating texts with formalized process models is expensive. Therefore, there are only a few machine-learning-based extraction approaches. Rule-based approaches, in turn, require domain specificity to work well and can rarely distinguish relevant and irrelevant information in textual descriptions. In this paper, we present GUIDO, a hybrid approach to the process model extraction task that first, classifies sentences regarding their relevance to the process model, using a BERT-based sentence classifier, and second, extracts a process model from the sentences classified as relevant, using dependency parsing. The presented approach achieves significantly better resul ts than a pure rule-based approach. GUIDO achieves an average behavioral similarity score of 0.93. Still, in comparison to purely machine-learning-based approaches, the annotation costs stay low. KW - Natural Language Processing KW - Text Mining KW - Process Model Extraction KW - Business Process Intelligence Y1 - 2023 SN - 978-989-758-664-4 U6 - https://doi.org/10.5220/0012084400003541 SN - 2184-285X N1 - 12th International Conference on Data Science, Technology and Applications, July 11-13, 2023, in Rome, Italy. SP - 335 EP - 342 ER -