@inproceedings{SchwagerAngeleSchwarzboezletal.2023, author = {Schwager, Christian and Angele, Florian and Schwarzb{\"o}zl, Peter and Teixeira Boura, Cristiano Jos{\´e} and Herrmann, Ulf}, title = {Model predictive assistance for operational decision making in molten salt receiver systems}, series = {SolarPACES: Solar Power \& Chemical Energy Systems}, booktitle = {SolarPACES: Solar Power \& Chemical Energy Systems}, number = {2815 / 1}, publisher = {AIP conference proceedings / American Institute of Physics}, address = {Melville, NY}, isbn = {978-0-7354-4623-6}, issn = {1551-7616 (online)}, doi = {10.1063/5.0151514}, pages = {8 Seiten}, year = {2023}, abstract = {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.}, language = {en} } @inproceedings{SteuerDankert2023, author = {Steuer-Dankert, Linda}, title = {Training future skills - sustainability, interculturality \& innovation in a digital design thinking format}, series = {Proceedings of the 19th International CDIO Conference}, booktitle = {Proceedings of the 19th International CDIO Conference}, pages = {12 Seiten}, year = {2023}, abstract = {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.}, language = {en} } @inproceedings{LahrsKrisamHerrmann2023, author = {Lahrs, Lennart and Krisam, Pierre and Herrmann, Ulf}, title = {Envisioning a collaborative energy system planning platform for the energy transition at the district level}, series = {ECOS 2023. The 36th International Conference on Efficiency, Cost, Optimization, Simulation and Environmental Impact of Energy Systems}, booktitle = {ECOS 2023. The 36th International Conference on Efficiency, Cost, Optimization, Simulation and Environmental Impact of Energy Systems}, publisher = {Procedings of ECOS 2023}, doi = {10.52202/069564-0284}, pages = {3163 -- 3170}, year = {2023}, abstract = {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.}, language = {en} } @inproceedings{NierlePieper2023, author = {Nierle, Elisabeth and Pieper, Martin}, title = {Measuring social impacts in engineering education to improve sustainability skills}, series = {European Society for Engineering Education (SEFI)}, booktitle = {European Society for Engineering Education (SEFI)}, doi = {10.21427/QPR4-0T22}, pages = {9 Seiten}, year = {2023}, abstract = {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.}, language = {en} } @inproceedings{AlhaskirTschescheLinkeetal.2023, author = {Alhaskir, Mohamed and Tschesche, Matteo and Linke, Florian and Schriewer, Elisabeth and Weber, Yvonne and Wolking, Stefan and R{\"o}hrig, Rainer and Koch, Henner and Kutafina, Ekaterina}, title = {ECG matching: an approach to synchronize ECG datasets for data quality comparisons}, series = {Proceedings of the 68th Annual Meeting of the German Association of Medical Informatics, Biometry, and Epidemiology e.V. (gmds) 2023}, volume = {307}, booktitle = {Proceedings of the 68th Annual Meeting of the German Association of Medical Informatics, Biometry, and Epidemiology e.V. (gmds) 2023}, editor = {R{\"o}hrig, Rainer and Grabe, Niels and Haag, Martin and H{\"u}bner, Ursula and Sax, Ulrich and Schmidt, Carsten Oliver and Sedlmayr, Martin and Zapf, Antonia}, publisher = {IOS Press}, isbn = {978-1-64368-428-4 (Print)}, doi = {10.3233/SHTI230718}, pages = {225 -- 232}, year = {2023}, abstract = {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.}, language = {en} } @inproceedings{TischbeinKeanVertgewalletal.2023, author = {Tischbein, Franziska and Kean, Kilian and Vertgewall, Chris Martin and Ulbig, Andreas and Altherr, Lena}, title = {Determination of the topology of low-voltage distribution grids using cluster methods}, series = {27th International Conference on Electricity Distribution (CIRED 2023)}, booktitle = {27th International Conference on Electricity Distribution (CIRED 2023)}, publisher = {IEEE}, isbn = {978-1-83953-855-1}, doi = {10.1049/icp.2023.0478}, pages = {1 -- 5}, year = {2023}, abstract = {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.}, language = {en} } @inproceedings{SchulteTiggesMatheisRekeetal.2023, author = {Schulte-Tigges, Joschua and Matheis, Dominik and Reke, Michael and Walter, Thomas and Kaszner, Daniel}, title = {Demonstrating a V2X enabled system for transition of control and minimum risk manoeuvre when leaving the operational design domain}, series = {HCII 2023: HCI in Mobility, Transport, and Automotive Systems}, booktitle = {HCII 2023: HCI in Mobility, Transport, and Automotive Systems}, editor = {Kr{\"o}mker, Heidi}, publisher = {Springer}, address = {Cham}, isbn = {978-3-031-35677-3 (Print)}, doi = {10.1007/978-3-031-35678-0_12}, pages = {200 -- 210}, year = {2023}, abstract = {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.}, language = {en} } @inproceedings{BuesgenKloeserKohletal.2023, author = {B{\"u}sgen, Andr{\´e} and Kl{\"o}ser, Lars and Kohl, Philipp and Schmidts, Oliver and Kraft, Bodo and Z{\"u}ndorf, Albert}, title = {From cracked accounts to fake IDs: user profiling on German telegram black market channels}, series = {Data Management Technologies and Applications}, booktitle = {Data Management Technologies and Applications}, editor = {Cuzzocrea, Alfredo and Gusikhin, Oleg and Hammoudi, Slimane and Quix, Christoph}, publisher = {Springer}, address = {Cham}, isbn = {978-3-031-37889-8 (Print)}, doi = {10.1007/978-3-031-37890-4_9}, pages = {176 -- 202}, year = {2023}, abstract = {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.}, language = {en} } @inproceedings{KohlFreyerKraemeretal.2023, author = {Kohl, Philipp and Freyer, Nils and Kr{\"a}mer, Yoka and Werth, Henri and Wolf, Steffen and Kraft, Bodo and Meinecke, Matthias and Z{\"u}ndorf, Albert}, title = {ALE: a simulation-based active learning evaluation framework for the parameter-driven comparison of query strategies for NLP}, series = {Deep Learning Theory and Applications}, booktitle = {Deep Learning Theory and Applications}, editor = {Conte, Donatello and Fred, Ana and Gusikhin, Oleg and Sansone, Carlo}, publisher = {Springer}, address = {Cham}, isbn = {978-3-031-39058-6 (Print)}, doi = {10.1007/978-3-031-39059-3_16}, pages = {235 -- 253}, year = {2023}, abstract = {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.}, language = {en} } @inproceedings{FreyerThewesMeinecke2023, author = {Freyer, Nils and Thewes, Dustin and Meinecke, Matthias}, title = {GUIDO: a hybrid approach to guideline discovery \& ordering from natural language texts}, series = {Proceedings of the 12th International Conference on Data Science, Technology and Applications DATA - Volume 1}, booktitle = {Proceedings of the 12th International Conference on Data Science, Technology and Applications DATA - Volume 1}, editor = {Gusikhin, Oleg and Hammoudi, Slimane and Cuzzocrea, Alfredo}, isbn = {978-989-758-664-4}, issn = {2184-285X}, doi = {10.5220/0012084400003541}, pages = {335 -- 342}, year = {2023}, abstract = {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.}, language = {en} }