@unpublished{DeckertSchomakerGrasmik2026, author = {Deckert, Carsten and Schomaker, Rahel M. and Grasmik, Jeanna}, title = {Universalism and Innovation: The Influence of Impartiality and Inclusiveness on National Innovativeness}, series = {Queios}, journal = {Queios}, publisher = {Qeios Ltd}, doi = {10.32388/KNI4FN}, year = {2026}, abstract = {The innovative capacity of a country is a key factor in its economic development. While classical approaches primarily emphasize technological and economic aspects, cultural and moral influences are increasingly coming into focus in research. In particular, the treatment of out-groups and the intensity of in-group preferences play a crucial role, as expressed in the societal contrast between social cohesion and individual freedom. A central aspect of this debate is the moral dimension, particularly the tension between universalism, relativism, and particularism. This study examines the impact of universalism on a country's innovativeness by situating the concept within cultural theory and analyzing it empirically. The findings provide new insights into the relationship between cultural values and innovation potential with regard to moral capital in a society.}, language = {en} } @unpublished{SulaimanSaberHasanetal.2025, author = {Sulaiman, Mohammed Abdulqader and Saber, Hindren Ali and Hasan, Hasan Fahmi and Benim, Ali Cemal}, title = {Latent Heat Cold Storage Integrated Direct Evaporative Cooler: A Neoteric Practical Design for Energy and Water Saving Potential with Thermal Comfort Improvement}, series = {Social Science Research Network (SSRN)}, journal = {Social Science Research Network (SSRN)}, publisher = {Elsevier}, doi = {10.2139/ssrn.5352056}, pages = {90}, year = {2025}, subject = {Klimaanlage}, language = {en} } @unpublished{BushuievBushuievdeJongeetal.2025, author = {Bushuiev, Roman and Bushuiev, Anton and de Jonge, Niek F. and Young, Adamo and Kretschmer, Fleming and Samusevich, Raman and Heirman, Janne and Wang, Fei and Zhang, Luke and D{\"u}hrkop, Kai and Ludwig, Marcus and Haupt, Nils A. and Kalia, Apurva and Brungs, Corinna and Schmid, Robin and Greiner, Russell and Wang, Bo and Wishart, David S. and Liu, Li-Ping and Rousu, Juho and Bittremieux, Wout and R{\"o}st, Hannes and Mak, Tytus D. and Hassoun, Soha and Huber, Florian and van der Hooft, Justin J.J. and Stravs, Michael A. and B{\"o}cker, Sebastian and Sivic, Josef and Pluskal, Tom{\´a}š}, title = {MassSpecGym: A benchmark for the discovery and identification of molecules}, series = {arXiv}, journal = {arXiv}, edition = {v3}, publisher = {arXiv}, doi = {10.48550/arXiv.2410.23326}, pages = {49}, year = {2025}, abstract = {The discovery and identification of molecules in biological and environmental samples is crucial for advancing biomedical and chemical sciences. Tandem mass spectrometry (MS/MS) is the leading technique for high-throughput elucidation of molecular structures. However, decoding a molecular structure from its mass spectrum is exceptionally challenging, even when performed by human experts. As a result, the vast majority of acquired MS/MS spectra remain uninterpreted, thereby limiting our understanding of the underlying (bio)chemical processes. Despite decades of progress in machine learning applications for predicting molecular structures from MS/MS spectra, the development of new methods is severely hindered by the lack of standard datasets and evaluation protocols. To address this problem, we propose MassSpecGym -- the first comprehensive benchmark for the discovery and identification of molecules from MS/MS data. Our benchmark comprises the largest publicly available collection of high-quality labeled MS/MS spectra and defines three MS/MS annotation challenges: de novo molecular structure generation, molecule retrieval, and spectrum simulation. It includes new evaluation metrics and a generalization-demanding data split, therefore standardizing the MS/MS annotation tasks and rendering the problem accessible to the broad machine learning community. MassSpecGym is publicly available at this https URL [https://github.com/pluskal-lab/MassSpecGym].}, subject = {Maschinelles Lernen}, language = {en} } @unpublished{YuwonoRanaSchwungetal.2025, author = {Yuwono, Steve and Rana, Muhammad Uzair and Schwung, Dorothea and Schwung, Andreas}, title = {Real Time Self-Tuning Adaptive Controllers on Temperature Control Loops using Event-based Game Theory}, series = {arXiv}, journal = {arXiv}, edition = {v1}, address = {arXiv}, doi = {10.48550/arXiv.2506.13164}, url = {http://nbn-resolving.de/urn:nbn:de:hbz:due62-opus-55462}, pages = {11}, year = {2025}, abstract = {This paper presents a novel method for enhancing the adaptability of Proportional-Integral-Derivative (PID) controllers in industrial systems using event-based dynamic game theory, which enables the PID controllers to self-learn, optimize, and fine-tune themselves. In contrast to conventional self-learning approaches, our proposed framework offers an event-driven control strategy and game-theoretic learning algorithms. The players collaborate with the PID controllers to dynamically adjust their gains in response to set point changes and disturbances. We provide a theoretical analysis showing sound convergence guarantees for the game given suitable stability ranges of the PID controlled loop. We further introduce an automatic boundary detection mechanism, which helps the players to find an optimal initialization of action spaces and significantly reduces the exploration time. The efficacy of this novel methodology is validated through its implementation in the temperature control loop of a printing press machine. Eventually, the outcomes of the proposed intelligent self-tuning PID controllers are highly promising, particularly in terms of reducing overshoot and settling time.}, subject = {Intelligente Fertigung}, language = {en} } @unpublished{MuellerBraunKeuper2025, author = {M{\"u}ller, Patrick and Braun, Alexander and Keuper, Margret}, title = {Examining the Impact of Optical Aberrations to Image Classification and Object Detection Models}, edition = {v1}, publisher = {arXiv}, doi = {10.48550/arXiv.2504.18510}, url = {http://nbn-resolving.de/urn:nbn:de:hbz:due62-opus-55457}, pages = {32}, year = {2025}, abstract = {Deep neural networks (DNNs) have proven to be successful in various computer vision applications such that models even infer in safety-critical situations. Therefore, vision models have to behave in a robust way to disturbances such as noise or blur. While seminal benchmarks exist to evaluate model robustness to diverse corruptions, blur is often approximated in an overly simplistic way to model defocus, while ignoring the different blur kernel shapes that result from optical systems. To study model robustness against realistic optical blur effects, this paper proposes two datasets of blur corruptions, which we denote OpticsBench and LensCorruptions. OpticsBench examines primary aberrations such as coma, defocus, and astigmatism, i.e. aberrations that can be represented by varying a single parameter of Zernike polynomials. To go beyond the principled but synthetic setting of primary aberrations, LensCorruptions samples linear combinations in the vector space spanned by Zernike polynomials, corresponding to 100 real lenses. Evaluations for image classification and object detection on ImageNet and MSCOCO show that for a variety of different pre-trained models, the performance on OpticsBench and LensCorruptions varies significantly, indicating the need to consider realistic image corruptions to evaluate a model's robustness against blur.}, subject = {Maschinelles Lernen}, language = {de} } @unpublished{HuberPollmann2025, author = {Huber, Florian and Pollmann, Julian}, title = {Count your bits: more subtle similarity measures using larger radius count vectors}, publisher = {bioRXiv}, doi = {10.1101/2025.06.16.659994}, url = {http://nbn-resolving.de/urn:nbn:de:hbz:due62-opus-54632}, pages = {29}, year = {2025}, abstract = {Quantifying molecular similarity is a cornerstone of cheminformatics, underpinning applications from virtual screening to chemical space visualization. A wide range of molecular fingerprints and similarity metrics, most notably Tanimoto scores, are employed, but their effectiveness is highly context-dependent. In this study, we systematically evaluate several 2D fingerprint types, including circular, path-based, and distance-encoded variants, using both binary and count representations. We highlight the consequences of fingerprint choice, vector folding, and similarity metric selection, revealing critical issues such as fingerprint duplication, mass dependent score biases, and high bit collision rates. Sparse and count-based fingerprints consistently outperform fixed-size binary vectors in preserving structural distinctions. Furthermore, we introduce percentile-based normalization, propose inverse-document-frequency (IDF) weighting, and benchmark all methods against graph-based MCES similarities. Our results offer practical guidance for selecting molecular similarity measures, emphasizing the need for conscious, task-aware fingerprinting choices in large-scale chemical analyses.}, subject = {Computational chemistry}, language = {en} } @unpublished{BeckmannDenfeldKoehnetal.2024, author = {Beckmann, Felix and Denfeld, Alexander and Koehn, Konrad and Medebach, Ina and Steffens, Jochen and Weinzierl, Stefan}, title = {Untersuchung der akustischen Qualit{\"a}t von Restaurants in Berlin und D{\"u}sseldorf}, isbn = {978-3-939296-22-5}, pages = {271 -- 274}, year = {2024}, abstract = {Widrige akustische Bedingungen stellen einen der Hauptgr{\"u}nde f{\"u}r Beschwerden in Restaurants dar; insbesondere bei h{\"o}rbeeintr{\"a}chtigten G{\"a}sten kann eine schlechte Akustik den Besuch im Restaurant negativ beeinflussen oder gar fr{\"u}hzeitig enden lassen. Um Empfehlungen hinsichtlich optimaler Nachhallzeiten sowie beobachtete Zusammenh{\"a}nge zwischen akustischen und nichtakustischen Faktoren bei Restaurantbesuchen zu {\"u}berpr{\"u}fen und im Hinblick auf verschiedene Restaurantkategorien zu erweitern, wurden in der vorliegenden Studie 20 Restaurants in Berlin und NRW mit insgesamt mehr als 600 G{\"a}sten untersucht. Hierbei wurden sowohl die Nachhallzeit als auch der Lautst{\"a}rkepegel sowie die Anzahl der G{\"a}ste im Raum {\"u}ber die Zeit erfasst. Mittels elektronischem Fragebogen wurden Soundscape-Parameter, Besuchsmotive sowie die empfundene Sprachverst{\"a}ndlichkeit und Restaurantqualit{\"a}t erhoben. Erste Ergebnisse zeigen einen großen Zusammenhang zwischen objektiven akustischen Gr{\"o}ßen und wahrgenommener akustischer Angenehmheit und Sprachverst{\"a}ndlichkeit, welcher je nach Restaurantkategorie variiert. Die Daten zeigen weiterhin, dass sich die akustische Angenehmheit nicht vollst{\"a}ndig durch die Sprachverst{\"a}ndlichkeit im Restaurant aufkl{\"a}ren l{\"a}sst. Implikationen f{\"u}r das akustische Design von Restaurants im Spannungsfeld zwischen Ruhe und Lebendigkeit werden diskutiert.}, language = {de} } @unpublished{WolfBraunUlrich2024, author = {Wolf, Dominik Werner and Braun, Alexander and Ulrich, Markus}, title = {Optical aberrations in autonomous driving: Physics-informed parameterized temperature scaling for neural network uncertainty calibration}, series = {International Journal of Computer Vision (IJCV)}, journal = {International Journal of Computer Vision (IJCV)}, publisher = {arXiv}, doi = {10.48550/arXiv.2412.13695}, url = {http://nbn-resolving.de/urn:nbn:de:hbz:due62-opus-50120}, year = {2024}, abstract = {'A trustworthy representation of uncertainty is desirable and should be considered as a key feature of any machine learning method' (Huellermeier and Waegeman, 2021). This conclusion of Huellermeier et al. underpins the importance of calibrated uncertainties. Since AI-based algorithms are heavily impacted by dataset shifts, the automotive industry needs to safeguard its system against all possible contingencies. One important but often neglected dataset shift is caused by optical aberrations induced by the windshield. For the verification of the perception system performance, requirements on the AI performance need to be translated into optical metrics by a bijective mapping (Braun, 2023). Given this bijective mapping it is evident that the optical system characteristics add additional information about the magnitude of the dataset shift. As a consequence, we propose to incorporate a physical inductive bias into the neural network calibration architecture to enhance the robustness and the trustworthiness of the AI target application, which we demonstrate by using a semantic segmentation task as an example. By utilizing the Zernike coefficient vector of the optical system as a physical prior we can significantly reduce the mean expected calibration error in case of optical aberrations. As a result, we pave the way for a trustworthy uncertainty representation and for a holistic verification strategy of the perception chain.}, language = {en} } @unpublished{WolfBalajiBraunetal.2024, author = {Wolf, Dominik Werner and Balaji, Prasannavenkatesh and Braun, Alexander and Ulrich, Markus}, title = {Decoupling of neural network calibration measures}, series = {German Conference on Pattern Recognition (GCPR) 2024}, journal = {German Conference on Pattern Recognition (GCPR) 2024}, publisher = {arXiv}, doi = {10.48550/arXiv.2406.02411}, url = {http://nbn-resolving.de/urn:nbn:de:hbz:due62-opus-50104}, year = {2024}, abstract = {A lot of effort is currently invested in safeguarding autonomous driving systems, which heavily rely on deep neural networks for computer vision. We investigate the coupling of different neural network calibration measures with a special focus on the Area Under the Sparsification Error curve (AUSE) metric. We elaborate on the well-known inconsistency in determining optimal calibration using the Expected Calibration Error (ECE) and we demonstrate similar issues for the AUSE, the Uncertainty Calibration Score (UCS), as well as the Uncertainty Calibration Error (UCE). We conclude that the current methodologies leave a degree of freedom, which prevents a unique model calibration for the homologation of safety-critical functionalities. Furthermore, we propose the AUSE as an indirect measure for the residual uncertainty, which is irreducible for a fixed network architecture and is driven by the stochasticity in the underlying data generation process (aleatoric contribution) as well as the limitation in the hypothesis space (epistemic contribution).}, language = {en} } @unpublished{PtasczynskiBlaettermannGrebetal.2023, author = {Ptasczynski, Lena Esther and Bl{\"a}ttermann, Patrick and Greb, Fabian and Sterzer, Philipp and Steffens, Jochen}, title = {The vibe of musical and social reward: Listening to beat-based music acts as a surrogate for socioemotional support during the Covid-19 pandemic}, series = {Research Square}, journal = {Research Square}, edition = {v1}, publisher = {Research Square}, doi = {10.21203/rs.3.rs-2529116/v1}, pages = {34}, year = {2023}, abstract = {Music listening is subjectively efficient in surrogating socioemotional needs during the Covid-19 pandemic, although underlying mechanisms remain unclear. We here challenge the generalizability of music-based coping by testing whether the insufficient satisfaction of distinct needs during the first lockdown entails enhanced listening to beat-based music with medium complexity (BBMMC) stimulating the cortico-striatal reward circuit and triggering oxytocinergic propagation. A Quasi-Bayesian mediation analysis on open-source data revealed that, across 11 European countries, more severe social distancing measures evoke a decline in experienced social provision (SP), in turn provoking BBMMC preferences. The latter was best predicted by the SP dimensions of social reward and attachment, suggesting that the partial overlap in neurophysiological processing at dopaminergic and oxytocinergic sites enables BBMMC to specifically alleviate these socioemotional needs, which has important implications for the development of mental health interventions. Finally, a stress-alleviating effect of BBMMC was found pointing towards HPA-activity modulation through music listening.}, subject = {Psychoakustik}, language = {en} } @unpublished{DeckertSchomakerAlbiez2025, author = {Deckert, Carsten and Schomaker, Rahel and Albiez, Philipp}, title = {Social Capital and National Innovativeness - The Influence of Trust and Tolerance on the National Capacity to Innovate}, series = {Qeios}, journal = {Qeios}, publisher = {Qeios}, issn = {2632-3834}, doi = {10.32388/646CQI}, year = {2025}, abstract = {While national innovativeness is of pivotal relevance for economic development, so far, relatively little attention has been paid to the social drivers of innovativeness. Thus, the role of social norms and values as drivers of innovativeness is somewhat blurry. Tackling this gap, the article at hand reflects the concepts of social capital, trust, and tolerance, before a model of social capital and innovativeness is developed and tested empirically, followed by the presentation and discussion of the results and a brief conclusion.}, subject = {Innovationsf{\"a}higkeit}, language = {en} } @unpublished{YuwonoSchwungSchwung2024, author = {Yuwono, Steve and Schwung, Dorothea and Schwung, Andreas}, title = {Distributed Stackelberg Strategies in State-based Potential Games for Autonomous Decentralized Learning Manufacturing Systems}, publisher = {arXiv}, doi = {10.48550/arXiv.2408.06397}, pages = {14}, year = {2024}, abstract = {This article describes a novel game structure for autonomously optimizing decentralized manufacturing systems with multi-objective optimization challenges, namely Distributed Stackelberg Strategies in State-Based Potential Games (DS2-SbPG). DS2-SbPG integrates potential games and Stackelberg games, which improves the cooperative trade-off capabilities of potential games and the multi-objective optimization handling by Stackelberg games. Notably, all training procedures remain conducted in a fully distributed manner. DS2-SbPG offers a promising solution to finding optimal trade-offs between objectives by eliminating the complexities of setting up combined objective optimization functions for individual players in self- learning domains, particularly in real-world industrial settings with diverse and numerous objectives between the sub-systems. We further prove that DS2-SbPG constitutes a dynamic potential game that results in corresponding converge guarantees. Experimental validation conducted on a laboratory-scale testbed highlights the efficacy of DS2-SbPG and its two variants, such as DS2-SbPG for single-leader-follower and Stack DS2-SbPG for multi-leader-follower. The results show significant reductions in power consumption and improvements in overall performance, which signals the potential of DS2-SbPG in real-world applications.}, subject = {Maschinelles Lernen}, language = {en} } @unpublished{YuwonoSchwungSchwung2024, author = {Yuwono, Steve and Schwung, Dorothea and Schwung, Andreas}, title = {Transfer learning of state-based potential games for process optimization in decentralized manufacturing systems}, series = {arXiv}, journal = {arXiv}, publisher = {arXiv}, doi = {10.48550/arXiv.2408.05992}, pages = {48}, year = {2024}, abstract = {This paper presents a novel transfer learning approach in state-based potential games (TL-SbPGs) for enhancing distributed self-optimization in manufacturing systems. The approach focuses on the practical relevant industrial setting where sharing and transferring gained knowledge among similar-behaved players improves the self-learning mechanism in large-scale systems. With TL-SbPGs, the gained knowledge can be reused by other players to optimize their policies, thereby improving the learning outcomes of the players and accelerating the learning process. To accomplish this goal, we develop transfer learning concepts and similarity criteria for players, which offer two distinct settings: (a) predefined similarities between players and (b) dynamically inferred similarities between players during training. We formally prove the applicability of the SbPG framework in transfer learning. Additionally, we introduce an efficient method to determine the optimal timing and weighting of the transfer learning procedure during the training phase. Through experiments on a laboratory-scale testbed, we demonstrate that TL-SbPGs significantly boost production efficiency while reducing power consumption of the production schedules while also outperforming native SbPGs.}, subject = {Maschinelles Lernen}, language = {en} } @unpublished{deJongeJoasTruongetal.2024, author = {de Jonge, Niek F. and Joas, David and Truong, Lem-Joe and van der Hooft, Justin J.J. and Huber, Florian}, title = {Reliable cross-ion mode chemical similarity prediction between MS2 spectra}, series = {biorxiv}, journal = {biorxiv}, publisher = {Cold Spring Harbor Laboratory}, doi = {10.1101/2024.03.25.586580}, year = {2024}, abstract = {Mass spectrometry is commonly used to characterize metabolites in untargeted metabolomics. This can be done in positive and negative ionization mode, a choice typically guided by the fraction of metabolites a researcher is interested in. During analysis, mass spectral comparisons are widely used to enable annotation through reference libraries and to facilitate data organization through networking. However, until now, such comparisons between mass spectra were restricted to mass spectra of the same ionization mode, as the two modes generally result in very distinct fragmentation spectra. To overcome this barrier, here, we have implemented a machine learning model that can predict chemical similarity between spectra of different ionization modes. Hence, our new MS2DeepScore 2.0 model facilitates the seamless integration of positive and negative ionization mode mass spectra into one analysis pipeline. This creates entirely new options for data exploration, such as mass spectral library searching of negative ion mode spectra in positive ion mode libraries or cross-ionization mode molecular networking. Furthermore, to improve the reliability of predictions and better cope with unseen data, we have implemented a method to estimate the quality of prediction. This will help to avoid false predictions on spectra with low information content or spectra that substantially differ from the training data. We anticipate that the MS2DeepScore 2.0 model will extend our current capabilities in organizing and annotating untargeted metabolomics profiles.}, subject = {Massenspektrometrie}, language = {en} } @unpublished{SulaimanSaberHasanetal.2024, author = {Sulaiman, Mohammed Abdulqader and Saber, Hindren Ali and Hasan, Hasan Fahmi and Benim, Ali Cemal}, title = {Experimental and Numerical Investigation of Novel Dew-Point Evaporative Cooler with Shell and Tube Design}, series = {SSRN}, journal = {SSRN}, publisher = {Elsevier}, doi = {10.2139/ssrn.4980477}, pages = {38}, year = {2024}, abstract = {Implementing a neoteric practical design for dew-point evaporative cooler (DPEC) rather than the widely utilized flat plate and corrugated plate has been challenging since the first invention. Thus far, no study has been dedicated to design and experimentally implement a shell and tube concept for DPEC with an innovative configuration that can contribute in mass production, globalization, and effortless maintenance of such a high performance cooling machine. Therefore, in this paper, the shell and tube concept has been employed as a core design for DPEC that has been reinforced by a practical approaches. The practical approaches include a dedicated novel design for the proposed system and utilizing super cheap materials for the system's construction. Both of the aforementioned approaches provide solutions for overcoming the aforementioned issues. In the meanwhile, the superiority of the proposed DPEC has been approved by comparing it to the flat plate DPEC. During the experimentations, the prototype was able to reduce the ambient temperature by up to 34.1°C (from 53°C to 18.9°C), besides, it could achieve a dew-point effectiveness of 99.6\% and wet-bulb effectiveness of 135\%.}, subject = {Numerische Str{\"o}mungssimulation}, language = {en} } @unpublished{YuwonoLoeppenbergSchwungetal.2024, author = {Yuwono, Steve and L{\"o}ppenberg, Marlon and Schwung, Dorothea and Schwung, Andreas}, title = {Gradient-based Learning in State-based Potential Games for Self-Learning Production Systems}, series = {arxiv}, journal = {arxiv}, publisher = {arxiv}, doi = {10.48550/arXiv.2406.10015}, year = {2024}, abstract = {In this paper, we introduce novel gradient-based optimization methods for state-based potential games (SbPGs) within self-learning distributed production systems. SbPGs are recognised for their efficacy in enabling self-optimizing distributed multi-agent systems and offer a proven convergence guarantee, which facilitates collaborative player efforts towards global objectives. Our study strives to replace conventional ad-hoc random exploration-based learning in SbPGs with contemporary gradient-based approaches, which aim for faster convergence and smoother exploration dynamics, thereby shortening training duration while upholding the efficacy of SbPGs. Moreover, we propose three distinct variants for estimating the objective function of gradient-based learning, each developed to suit the unique characteristics of the systems under consideration. To validate our methodology, we apply it to a laboratory testbed, namely Bulk Good Laboratory Plant, which represents a smart and flexible distributed multi-agent production system. The incorporation of gradient-based learning in SbPGs reduces training times and achieves more optimal policies than its baseline.}, subject = {Maschinelles Lernen}, language = {en} } @unpublished{SchroederEberhardtBelavadietal.2023, author = {Schr{\"o}der, Kay and Eberhardt, Wiebke and Belavadi, Poornima and Ajdadilish, Batoul and van Haften, Nanette and Overes, Ed and Brouns, Taryn and Calero Valdez, Andr{\´e}}, title = {Telling stories with data - A systematic review}, publisher = {arXiv}, doi = {10.48550/arXiv.2312.01164}, pages = {25}, year = {2023}, abstract = {The exponential growth of data has outpaced human ability to process information, necessitating innovative approaches for effective human-data interaction. To transform raw data into meaningful insights, storytelling, and visualization have emerged as powerful techniques for communicating complex information to decision makers. This article offers a comprehensive, systematic review of the utilization of storytelling in visualizations. It organizes the existing literature into distinct categories, encompassing frameworks, data and visualization types, application domains, narrative structures, outcome measurements, and design principles. By providing a well-structured overview of this rapidly evolving field, the article serves as a valuable guide for educators, researchers, and practitioners seeking to harness the power of storytelling in data visualization.}, subject = {Mensch-Maschine-Kommunikation}, language = {en} } @unpublished{PriesmannMuenchRidhaetal.2021, author = {Priesmann, Jan and M{\"u}nch, Justin and Ridha, Elias and Spiegel, Thomas and Reich, Marius and Adam, Mario and Nolting, Lars and Praktiknjo, Aaron}, title = {Artificial Intelligence and Design of Experiments for Assessing Security of Electricity Supply: A Review and Strategic Outlook}, publisher = {arXiv}, doi = {https://doi.org/10.48550/arXiv.2112.04889}, year = {2021}, abstract = {Assessing the effects of the energy transition and liberalization of energy markets on resource adequacy is an increasingly important and demanding task. The rising complexity in energy systems requires adequate methods for energy system modeling leading to increased computational requirements. Furthermore, with complexity, uncertainty increases likewise calling for probabilistic assessments and scenario analyses. To adequately and efficiently address these various requirements, new methods from the field of data science are needed to accelerate current methods. With our systematic literature review, we want to close the gap between the three disciplines (1) assessment of security of electricity supply, (2) artificial intelligence, and (3) design of experiments. For this, we conduct a large-scale quantitative review on selected fields of application and methods and make a synthesis that relates the different disciplines to each other. Among other findings, we identify metamodeling of complex security of electricity supply models using AI methods and applications of AI-based methods for forecasts of storage dispatch and (non-)availabilities as promising fields of application that have not sufficiently been covered, yet. We end with deriving a new methodological pipeline for adequately and efficiently addressing the present and upcoming challenges in the assessment of security of electricity supply.}, language = {en} } @unpublished{GeerkensSieberichsBraunetal.2023, author = {Geerkens, Simon and Sieberichs, Christian and Braun, Alexander and Waschulzik, Thomas}, title = {QI2 -- an Interactive Tool for Data Quality Assurance}, publisher = {arXiv}, year = {2023}, abstract = {The importance of high data quality is increasing with the growing impact and distribution of ML systems and big data. Also the planned AI Act from the European commission defines challenging legal requirements for data quality especially for the market introduction of safety relevant ML systems. In this paper we introduce a novel approach that supports the data quality assurance process of multiple data quality aspects. This approach enables the verification of quantitative data quality requirements. The concept and benefits are introduced and explained on small example data sets. How the method is applied is demonstrated on the well known MNIST data set based an handwritten digits.}, language = {en} } @unpublished{SieberichsGeerkensBraunetal.2023, author = {Sieberichs, Christian and Geerkens, Simon and Braun, Alexander and Waschulzik, Thomas}, title = {ECS -- an Interactive Tool for Data Quality Assurance}, publisher = {arXiv}, doi = {10.48550/arXiv.2307.04368}, year = {2023}, abstract = {With the increasing capabilities of machine learning systems and their potential use in safety-critical systems, ensuring high-quality data is becoming increasingly important. In this paper we present a novel approach for the assurance of data quality. For this purpose, the mathematical basics are first discussed and the approach is presented using multiple examples. This results in the detection of data points with potentially harmful properties for the use in safety-critical systems.}, language = {en} }