@inproceedings{MaurerSejdijaSander2024, author = {Maurer, Florian and Sejdija, Jonathan and Sander, Volker}, title = {Decentralized energy data storages through an Open Energy Database Server}, doi = {10.5281/zenodo.10607895}, pages = {5 Seiten}, year = {2024}, abstract = {In the research domain of energy informatics, the importance of open datais rising rapidly. This can be seen as various new public datasets are created andpublished. Unfortunately, in many cases, the data is not available under a permissivelicense corresponding to the FAIR principles, often lacking accessibility or reusability.Furthermore, the source format often differs from the desired data format or does notmeet the demands to be queried in an efficient way. To solve this on a small scale atoolbox for ETL-processes is provided to create a local energy data server with openaccess data from different valuable sources in a structured format. So while the sourcesitself do not fully comply with the FAIR principles, the provided unique toolbox allows foran efficient processing of the data as if the FAIR principles would be met. The energydata server currently includes information of power systems, weather data, networkfrequency data, European energy and gas data for demand and generation and more.However, a solution to the core problem - missing alignment to the FAIR principles - isstill needed for the National Research Data Infrastructure.}, language = {en} } @inproceedings{MaurerNitschKochemsetal.2024, author = {Maurer, Florian and Nitsch, Felix and Kochems, Johannes and Schimeczek, Christoph and Sander, Volker and Lehnhoff, Sebastian}, title = {Know your tools - a comparison of two open agent-based energy market models}, series = {2024 20th International Conference on the European Energy Market (EEM)}, booktitle = {2024 20th International Conference on the European Energy Market (EEM)}, publisher = {IEEE}, address = {New York, NY}, doi = {10.1109/EEM60825.2024.10609021}, pages = {8 Seiten}, year = {2024}, abstract = {Due to the transition to renewable energies, electricity markets need to be made fit for purpose. To enable the comparison of different energy market designs, modeling tools covering market actors and their heterogeneous behavior are needed. Agent-based models are ideally suited for this task. Such models can be used to simulate and analyze changes to market design or market mechanisms and their impact on market dynamics. In this paper, we conduct an evaluation and comparison of two actively developed open-source energy market simulation models. The two models, namely AMIRIS and ASSUME, are both designed to simulate future energy markets using an agent-based approach. The assessment encompasses modelling features and techniques, model performance, as well as a comparison of model results, which can serve as a blueprint for future comparative studies of simulation models. The main comparison dataset includes data of Germany in 2019 and simulates the Day-Ahead market and participating actors as individual agents. Both models are comparable close to the benchmark dataset with a MAE between 5.6 and 6.4 €/MWh while also modeling the actual dispatch realistically.}, language = {en} } @incollection{KraftKohlMeinecke2024, author = {Kraft, Bodo and Kohl, Philipp and Meinecke, Matthias}, title = {Analyse und Nachverfolgung von Projektzielen durch Einsatz von Natural Language Processing}, series = {KI in der Projektwirtschaft : was ver{\"a}ndert sich durch KI im Projektmanagement?}, booktitle = {KI in der Projektwirtschaft : was ver{\"a}ndert sich durch KI im Projektmanagement?}, editor = {Bernert, Christian and Scheurer, Steffen and Wehnes, Harald}, publisher = {UVK Verlag}, isbn = {978-3-3811-1132-9 (Online)}, doi = {10.24053/9783381111329}, pages = {157 -- 167}, year = {2024}, language = {de} } @article{KohlKraemerFohryetal.2024, author = {Kohl, Philipp and Kr{\"a}mer, Yoka and Fohry, Claudia and Kraft, Bodo}, title = {Scoping review of active learning strategies and their evaluation environments for entity recognition tasks}, series = {Deep learning theory and applications}, journal = {Deep learning theory and applications}, editor = {Fred, Ana and Hadjali, Allel and Gusikhin, Oleg and Sansone, Carlo}, publisher = {Springer}, address = {Cham}, isbn = {978-3-031-66694-0 (online ISBN)}, doi = {10.1007/978-3-031-66694-0_6}, pages = {84 -- 106}, year = {2024}, abstract = {We conducted a scoping review for active learning in the domain of natural language processing (NLP), which we summarize in accordance with the PRISMA-ScR guidelines as follows: Objective: Identify active learning strategies that were proposed for entity recognition and their evaluation environments (datasets, metrics, hardware, execution time). Design: We used Scopus and ACM as our search engines. We compared the results with two literature surveys to assess the search quality. We included peer-reviewed English publications introducing or comparing active learning strategies for entity recognition. Results: We analyzed 62 relevant papers and identified 106 active learning strategies. We grouped them into three categories: exploitation-based (60x), exploration-based (14x), and hybrid strategies (32x). We found that all studies used the F1-score as an evaluation metric. Information about hardware (6x) and execution time (13x) was only occasionally included. The 62 papers used 57 different datasets to evaluate their respective strategies. Most datasets contained newspaper articles or biomedical/medical data. Our analysis revealed that 26 out of 57 datasets are publicly accessible. Conclusion: Numerous active learning strategies have been identified, along with significant open questions that still need to be addressed. Researchers and practitioners face difficulties when making data-driven decisions about which active learning strategy to adopt. Conducting comprehensive empirical comparisons using the evaluation environment proposed in this study could help establish best practices in the domain.}, language = {en} } @article{KarschuckPoghossianSeretal.2024, author = {Karschuck, Tobias and Poghossian, Arshak and Ser, Joey and Tsokolakyan, Astghik and Achtsnicht, Stefan and Wagner, Patrick and Sch{\"o}ning, Michael Josef}, title = {Capacitive model of enzyme-modified field-effect biosensors: Impact of enzyme coverage}, series = {Sensors and Actuators B: Chemical}, volume = {408}, journal = {Sensors and Actuators B: Chemical}, publisher = {Elsevier}, address = {Amsterdam}, issn = {0925-4005 (Print)}, doi = {10.1016/j.snb.2024.135530}, pages = {12 Seiten}, year = {2024}, abstract = {Electrolyte-insulator-semiconductor capacitors (EISCAP) belong to field-effect sensors having an attractive transducer architecture for constructing various biochemical sensors. In this study, a capacitive model of enzyme-modified EISCAPs has been developed and the impact of the surface coverage of immobilized enzymes on its capacitance-voltage and constant-capacitance characteristics was studied theoretically and experimentally. The used multicell arrangement enables a multiplexed electrochemical characterization of up to sixteen EISCAPs. Different enzyme coverages have been achieved by means of parallel electrical connection of bare and enzyme-covered single EISCAPs in diverse combinations. As predicted by the model, with increasing the enzyme coverage, both the shift of capacitance-voltage curves and the amplitude of the constant-capacitance signal increase, resulting in an enhancement of analyte sensitivity of the EISCAP biosensor. In addition, the capability of the multicell arrangement with multi-enzyme covered EISCAPs for sequentially detecting multianalytes (penicillin and urea) utilizing the enzymes penicillinase and urease has been experimentally demonstrated and discussed.}, language = {en} } @inproceedings{KahraBreussKleefeldetal.2024, author = {Kahra, Marvin and Breuß, Michael and Kleefeld, Andreas and Welk, Martin}, title = {An Approach to Colour Morphological Supremum Formation Using the LogSumExp Approximation}, series = {Discrete Geometry and Mathematical Morphology}, booktitle = {Discrete Geometry and Mathematical Morphology}, editor = {Brunetti, Sara and Frosini, Andrea and Rinaldi, Simone}, publisher = {Springer}, address = {Cham}, isbn = {978-3-031-57793-2}, doi = {10.1007/978-3-031-57793-2_25}, pages = {325 -- 337}, year = {2024}, abstract = {Mathematical morphology is a part of image processing that has proven to be fruitful for numerous applications. Two main operations in mathematical morphology are dilation and erosion. These are based on the construction of a supremum or infimum with respect to an order over the tonal range in a certain section of the image. The tonal ordering can easily be realised in grey-scale morphology, and some morphological methods have been proposed for colour morphology. However, all of these have certain limitations. In this paper we present a novel approach to colour morphology extending upon previous work in the field based on the Loewner order. We propose to consider an approximation of the supremum by means of a log-sum exponentiation introduced by Maslov. We apply this to the embedding of an RGB image in a field of symmetric 2x2 matrices. In this way we obtain nearly isotropic matrices representing colours and the structural advantage of transitivity. In numerical experiments we highlight some remarkable properties of the proposed approach.}, language = {en} } @unpublished{GriegerMehrkanoonBialonski2024, author = {Grieger, Niklas and Mehrkanoon, Siamak and Bialonski, Stephan}, title = {Preprint: Data-efficient sleep staging with synthetic time series pretraining}, series = {arXiv}, journal = {arXiv}, pages = {10 Seiten}, year = {2024}, abstract = {Analyzing electroencephalographic (EEG) time series can be challenging, especially with deep neural networks, due to the large variability among human subjects and often small datasets. To address these challenges, various strategies, such as self-supervised learning, have been suggested, but they typically rely on extensive empirical datasets. Inspired by recent advances in computer vision, we propose a pretraining task termed "frequency pretraining" to pretrain a neural network for sleep staging by predicting the frequency content of randomly generated synthetic time series. Our experiments demonstrate that our method surpasses fully supervised learning in scenarios with limited data and few subjects, and matches its performance in regimes with many subjects. Furthermore, our results underline the relevance of frequency information for sleep stage scoring, while also demonstrating that deep neural networks utilize information beyond frequencies to enhance sleep staging performance, which is consistent with previous research. We anticipate that our approach will be advantageous across a broad spectrum of applications where EEG data is limited or derived from a small number of subjects, including the domain of brain-computer interfaces.}, language = {en} } @article{EngelmannSimsekShalabyetal.2024, author = {Engelmann, Ulrich M. and Simsek, Beril and Shalaby, Ahmed and Krause, Hans-Joachim}, title = {Key contributors to signal generation in frequency mixing magnetic detection (FMMD): an in silico study}, series = {Sensors}, volume = {24}, journal = {Sensors}, number = {6}, publisher = {MDPI}, address = {Basel}, issn = {1424-8220}, doi = {10.3390/s24061945}, pages = {Artikel 1945}, year = {2024}, abstract = {Frequency mixing magnetic detection (FMMD) is a sensitive and selective technique to detect magnetic nanoparticles (MNPs) serving as probes for binding biological targets. Its principle relies on the nonlinear magnetic relaxation dynamics of a particle ensemble interacting with a dual frequency external magnetic field. In order to increase its sensitivity, lower its limit of detection and overall improve its applicability in biosensing, matching combinations of external field parameters and internal particle properties are being sought to advance FMMD. In this study, we systematically probe the aforementioned interaction with coupled N{\´e}el-Brownian dynamic relaxation simulations to examine how key MNP properties as well as applied field parameters affect the frequency mixing signal generation. It is found that the core size of MNPs dominates their nonlinear magnetic response, with the strongest contributions from the largest particles. The drive field amplitude dominates the shape of the field-dependent response, whereas effective anisotropy and hydrodynamic size of the particles only weakly influence the signal generation in FMMD. For tailoring the MNP properties and parameters of the setup towards optimal FMMD signal generation, our findings suggest choosing large particles of core sizes dc > 25 nm nm with narrow size distributions (σ < 0.1) to minimize the required drive field amplitude. This allows potential improvements of FMMD as a stand-alone application, as well as advances in magnetic particle imaging, hyperthermia and magnetic immunoassays.}, language = {en} } @book{ElsaesserKlebinggatKuhnetal.2024, author = {Elsaesser, Evelyn and Klebinggat, Michael and Kuhn, Wilfried and Michielsens, Constant and Pauels, Willibert and Popkes, Enno E. and Schneider, Elke and Laack, Walter van and Warven, Rinus van}, title = {Schnittstelle Tod - Ist die Menschheit zu retten ohne Vertrauen auf ein Danach}, editor = {Laack, Walter van}, publisher = {van Laack Buchverlag}, address = {Aachen}, isbn = {978-3-936624-58-8}, pages = {156 Seiten}, year = {2024}, language = {de} } @article{ClausnitzerKleefeld2024, author = {Clausnitzer, Julian and Kleefeld, Andreas}, title = {A spectral Galerkin exponential Euler time-stepping scheme for parabolic SPDEs on two-dimensional domains with a C² boundary}, series = {Discrete and Continuous Dynamical Systems - Series B}, volume = {29}, journal = {Discrete and Continuous Dynamical Systems - Series B}, number = {4}, publisher = {AIMS}, address = {Springfield}, issn = {1531-3492}, doi = {10.3934/dcdsb.2023148}, pages = {1624 -- 1651}, year = {2024}, abstract = {We consider the numerical approximation of second-order semi-linear parabolic stochastic partial differential equations interpreted in the mild sense which we solve on general two-dimensional domains with a C² boundary with homogeneous Dirichlet boundary conditions. The equations are driven by Gaussian additive noise, and several Lipschitz-like conditions are imposed on the nonlinear function. We discretize in space with a spectral Galerkin method and in time using an explicit Euler-like scheme. For irregular shapes, the necessary Dirichlet eigenvalues and eigenfunctions are obtained from a boundary integral equation method. This yields a nonlinear eigenvalue problem, which is discretized using a boundary element collocation method and is solved with the Beyn contour integral algorithm. We present an error analysis as well as numerical results on an exemplary asymmetric shape, and point out limitations of the approach.}, language = {en} }