Publikationen

2025

• First Evaluation of the IEA Wind Task 52 LAC Summer Games 2024

  Schlipf, D. . (2025). First Evaluation of the IEA Wind Task 52 LAC Summer Games 2024. Zenodo. http://doi.org/10.5281/zenodo.14778978 (Original work published 2025)

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    • Schlipf, David

  • Fachbereich

    • Fachbereich 2: Energy and Life Science

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  • 2025
  • Miscellaneous
  • • David Schlipf

• Digitale Prüfungen im Fach Buchhaltung – Entwicklung eines Moodle-Fragetyps

  Tausch-Nebel, L. ., & van der Sluis, H. . (2025). Digitale Prüfungen im Fach Buchhaltung – Entwicklung eines Moodle-Fragetyps. Die Neue Hochschule (DNH), (erscheint in 4/2025), 4. (Original work published August 2025)

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    • Tausch-Nebel, Lasse

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  • 2025
  • Journal Article
  • • Lasse Tausch-Nebel
    • Hendrik van der Sluis

• "When Two Wrongs Don t Make a Right" - Examining Confirmation Bias and the Role of Time Pressure During Human-AI Collaboration in Computational Pathology

  Rosbach, E. ., Ammeling, J. ., Krügel, S. ., Kießig, A. ., Fritz, A. ., Ganz, J. ., … Aubreville, M. . (2025). "When Two Wrongs Don t Make a Right" - Examining Confirmation Bias and the Role of Time Pressure During Human-AI Collaboration in Computational Pathology. In Proceedings of the 2025 CHI Conference on Human Factors in Computing Systems (S. 1–18). Yokohama Japan: ACM. http://doi.org/10.1145/3706598.3713319 (Original work published 2025)

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    • Aubreville, Marc

  • Abteilung/Einrichtung

    • CIVU – Center for Interaction, Visualization and Usability
    • FLAIR - Flensburg Artificial Intelligence Research

  • Profilbereich

    • Gesundheit und Life Sciences

  • Forschungsfeld

    • Data Science und Künstliche Intelligenz

  • Fachbereich

    • Fachbereich 3: Information und Kommunikation

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  • 2025
  • Conference Paper
  • • Emely Rosbach
    • Jonas Ammeling
    • Sebastian Krügel
    • Angelika Kießig
    • Alexis Fritz
    • Jonathan Ganz
    • Chloé Puget
    • Taryn Donovan
    • Andrea Klang
    • Maximilian Köller
    • Pompei Bolfa
    • Marco Tecilla
    • Daniela Denk
    • Matti Kiupel
    • Georgios Paraschou
    • Mun Kok
    • Alexander Haake
    • Ronald De Krijger
    • Andreas Sonnen
    • Tanit Kasantikul
    • Gerry Dorrestein
    • Rebecca Smedley
    • Nikolas Stathonikos
    • Matthias Uhl
    • Christof Bertram
    • Andreas Riener
    • Marc Aubreville

• Automation Bias in AI-assisted Medical Decision-making under Time Pressure in Computational Pathology

  Rosbach, E. ., Ganz, J. ., Ammeling, J. ., Riener, A. ., & Aubreville, M. . (2025). Automation Bias in AI-assisted Medical Decision-making under Time Pressure in Computational Pathology. Bildverarbeitung für die Medizin 2025. Wiesbaden: Springer Fachmedien Wiesbaden. (Original work published 2025)

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  Artificial intelligence (AI)-based clinical decision support systems (CDSS) promise to enhance diagnostic accuracy and efficiency in computational pathology. However, human-AI collaboration might introduce automation bias, where users uncritically follow automated cues. This bias may worsen when time pressure strains practitioners’ cognitive resources. We quantified automation bias by measuring the adoption of negative system consultations and examined the role of time pressure in a web-based experiment, where trained pathology experts (n=28) estimated tumor cell percentages. Our results indicate that while AI integration led to a statistically significant increase in overall performance, it also resulted in a 7% automation bias rate, where initially correct evaluations were overturned by erroneous AI advice. Conversely, time pressure did not exacerbate automation bias occurrence, but appeared to increase its severity, evidenced by heightened reliance on the system’s negative consultations and subsequent performance decline. These findings highlight potential risks of AI use in healthcare. The final dataset (including a table with image patch details), participant demographics, and source code are available at: https://github.com/emelyrosbach/AB-TP.git

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    • Aubreville, Marc

  • Abteilung/Einrichtung

    • CIVU – Center for Interaction, Visualization and Usability
    • FLAIR - Flensburg Artificial Intelligence Research

  • Profilbereich

    • Gesundheit und Life Sciences

  • Forschungsfeld

    • Data Science und Künstliche Intelligenz

  • Fachbereich

    • Fachbereich 3: Information und Kommunikation

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  • 2025
  • Conference Proceedings
  • • Emely Rosbach
    • Jonathan Ganz
    • Jonas Ammeling
    • Andreas Riener
    • Marc Aubreville

• Reporting guidelines for manuscripts that use artificial intelligence–based automated image analysis in Veterinary Pathology

  Bertram, C. A., Schutten, M. ., Ressel, L. ., Breininger, K. ., Webster, J. D., & Aubreville, M. . (2025). Reporting guidelines for manuscripts that use artificial intelligence–based automated image analysis in Veterinary Pathology. Veterinary Pathology, 62, 615–617. http://doi.org/10.1177/03009858251344320 (Original work published August 2025)

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    • Aubreville, Marc

  • Forschungsprojekt

    • Robust and Accurate Multi-Tumor, Multi-Species, Multi-Laboratory and Multi-Scanner Mitosis Detection with Large-Scale Datasets and Artificial Intelligence

  • Abteilung/Einrichtung

    • FLAIR - Flensburg Artificial Intelligence Research
    • CIVU – Center for Interaction, Visualization and Usability

  • Profilbereich

    • Gesundheit und Life Sciences

  • Forschungsfeld

    • Data Science und Künstliche Intelligenz

  • Fachbereich

    • Fachbereich 3: Information und Kommunikation

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  • 2025
  • Journal Article
  • • Christof Bertram
    • Melissa Schutten
    • Lorenzo Ressel
    • Katharina Breininger
    • Joshua Webster
    • Marc Aubreville

• An Experimental Wind Farm Emulator with a Dual Variable-Speed Drive Configuration

  Gruman, N. ., Moses, P. ., & Jauch, C. . (2025). An Experimental Wind Farm Emulator with a Dual Variable-Speed Drive Configuration. In 2025 Intermountain Engineering, Technology and Computing (IETC) (S. 6). Orem, UT, USA: IEEE. http://doi.org/10.1109/IETC64455.2025.11039473 (Original work published Juni 2025)

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  The rise of wind power in bulk electric systems creates a growing need to model, understand, and characterize responses to various sources of disruption. Despite the numerous advantages of computer models, physical emulators have enormous benefits in characterizing dynamic magnetic electromechanical phenomena which are unexpected or difficult for models to process accurately. The development of an experimental apparatus for physical wind farm emulation is described. The control and operation of an induction motor-generator setup for mimicking wind generator dynamics are demonstrated. Recommendations for further application of this emulator are also given.

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    • Jauch, Clemens

  • Abteilung/Einrichtung

    • Wind Energy Technology Institute

  • Profilbereich

    • Erneuerbare Energien und nachhaltige Entwicklung

  • Forschungsfeld

    • Wind
    • Netzintegration

  • Fachbereich

    • Fachbereich 2: Energy and Life Science

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  • 2025
  • Conference Paper
  • • Noah Gruman
    • Paul Moses
    • Prof. Dr. Clemens Jauch
  • • Emulator
    • grid connection
    • induction machine
    • inverter, renewables
    • wind energy

• State estimation using LSTMs trained on synthetic data and validated with real measurements

  Geisler, J. . (2025). State estimation using LSTMs trained on synthetic data and validated with real measurements. Namtes: WESC 2025. http://doi.org/10.5281/zenodo.15830516 (Original work published Juni 2025)

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  This study represents a novel contribution by demonstrating that LSTM-based estimators can replace traditional state estimation pipelines in wind turbine applications. By eliminating the need for detailed modeling and manual tuning, this approach opens up new opportunities for data-driven estimation in renewable energy systems.

  Unlike conventional estimators based on linear or nonlinear physical models, the proposed method trains LSTM networks to learn the mapping from inputs and outputs to internal state estimates using only simulation data. Such data can be generated in large volumes with complete access to all internal states, leveraging high-fidelity turbine simulation tools. The trained LSTM functions as a complete estimator, replacing both the predictor and corrector stages typically found in Kalman filter structures.

  The approach is evaluated across a variety of estimation tasks, from rotor effective wind speed (REWS) to more complex quantities such as blade deflections and wind shear coefficients. Results demonstrate that LSTMs trained entirely on simulation data can generalize well to real-world measurements, achieving estimation quality comparable to that of EKFs. Moreover, the method proves flexible and scalable to different input-output combinations without extensive manual modeling.

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    • Geisler, Jens

  • Forschungsprojekt

    • Adaptive Betriebsstrategien für bestehende Windenergieanlagen

  • Profilbereich

    • Erneuerbare Energien und nachhaltige Entwicklung

  • Forschungsfeld

    • Wind

  • Fachbereich

    • Fachbereich 2: Energy and Life Science

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  • 2025
  • Miscellaneous
  • • Jens Geisler
  • • state estimation
    • observer
    • kalman filter
    • neural network
    • LSTM
    • high fidelity model
    • real measurements

• Minimum CO2 Saving Requirement for the Hydraulic-Pneumatic Flywheel System in the Rotor of a Wind Turbine

  Goga, A. ., Jauch, C. ., Alhrshy, L. ., Gagel, A. ., & Kloft, P. . (2025). Minimum CO2 Saving Requirement for the Hydraulic-Pneumatic Flywheel System in the Rotor of a Wind Turbine. In NEIS Conference 2024 (S. 184–191). Hamburg: VDE Verlag GmbH. http://doi.org/10.30420/566464025 (Original work published März 2025)

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  Researchers in the wind sector continuously aim to lower mechanical and structural loads on wind turbines to reduce the amount of material used in their components. One such innovative concept that is being investigated here is the integration of a flywheel system into rotor blades. In contrast to its intended aim of reducing the material usage and thereby CO2 emissions, the flywheel system in its implementation requires additional materials, which increases the carbon footprint considerably. Hence, to achieve a net CO2 saving, the CO2 expenditure of the additional components needs to be saved by the system as a minimum. The purpose of this work is to quantify the rise in CO2 emissions, caused by the installation of the flywheel system, relative to the original design. Thereby, the flywheel system’s minimal performance requirement for an exemplary wind turbine is defined.

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    • Goga, Abhinay
    • Jauch, Clemens
    • Alhrshy, Laurence

  • Forschungsprojekt

    • Ressourceneffizienz in der Windenergie

  • Abteilung/Einrichtung

    • Wind Energy Technology Institute

  • Profilbereich

    • Erneuerbare Energien und nachhaltige Entwicklung

  • Forschungsfeld

    • Wind

  • Fachbereich

    • Fachbereich 2: Energy and Life Science

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  • 2025
  • Conference Paper
  • • Abhinay Goga
    • Prof. Dr. Clemens Jauch
    • Laurence Alhrshy
    • Andreas Gagel
    • Peter Kloft
  • • Wind Turbine
    • Variable inertia flywheel
    • Increase In CO2 Emissions
    • Construction Materials

• On the road to interactive LLM-based systematic mapping studies

  Petersen, K. ., & Gerken, J. M. (2025). On the road to interactive LLM-based systematic mapping studies. Information and Software Technology, 178. (Original work published Februar 2025)

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    • Petersen, Kai
    • Gerken, Jan

  • Abteilung/Einrichtung

    • FLAIR - Flensburg Artificial Intelligence Research

  • Fachbereich

    • Fachbereich 4: Wirtschaft

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  • 2025
  • Journal Article
  • • Kai Petersen
    • Jan Gerken
  • • Large language models
    • Systematic mapping studies
    • GPT

• Joint Frequency Stabilisation in Future 100% Renewable Electric Power Systems

  Reese, L. ., Rettig, A. ., Jauch, C. ., Domin, R. J., & Karshüning, T. . (2025). Joint Frequency Stabilisation in Future 100% Renewable Electric Power Systems. Energies, 18, 17. http://doi.org/10.3390/en18020418 (Original work published Januar 2025)

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  Due to the energy transition, the future electric power system will face further challenges that affect the functionality of the electricity grid and therefore the security of supply. For this reason, this article examines the future frequency stabilisation in a 100% renewable electric power system. A focus is set on the provision of inertia and frequency containment reserve. Today, the frequency stabilisation in most power systems is based on synchronous generators. By using grid-forming frequency converters, a large potential of alternative frequency stabilisation reserves can be tapped. Consequently, frequency stabilisation is not a problem of existing capacities but whether and how these are utilised. Therefore, in this paper, a collaborative approach to realise frequency stabilisation is proposed. By distributing the required inertia and frequency containment reserve across all technologies that are able to provide it, the relative contribution of each individual provider is low. To cover the need for frequency containment reserve, each capable technology would have to provide less than 1% of its rated power. The inertia demand can be covered by the available capacities at a coverage ratio of 171% (excluding wind power) to 217% (all capacities). As a result, it is proposed that provision of frequency stabilisation is made mandatory for all capable technologies. The joint provision distributes the burden of frequency stabilisation across many participants and hence increases redundancy. It ensures the stability of future electricity grids, and at the same time, it reduces the technological and economic effort. The findings are presented for the example of the German electricity grid.

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    • Jauch, Clemens

  • Forschungsprojekt

    • Inno!Nord: Innovationslabor: Speicher zur Nutzung erneuerbarer Energien im echten Norden

  • Abteilung/Einrichtung

    • Wind Energy Technology Institute

  • Profilbereich

    • Erneuerbare Energien und nachhaltige Entwicklung

  • Fachbereich

    • Fachbereich 2: Energy and Life Science

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  • 2025
  • Journal Article
  • • Lisanne Reese
    • Arne Rettig
    • Prof. Dr. Clemens Jauch
    • Richard Domin
    • Tom Karshüning
  • • electric power system
    • energy transition
    • frequency containment reserve
    • frequency stabilisation
    • inertia
    • renewable energy
    • synchronous stability