Publikationen

2025

• 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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  • Personenseiten

    • Petersen, Kai
    • Gerken, Jan Michael

  • 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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  • Personenseiten

    • 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

• REACH: A Card-based Approach to Guide Research Engagement and Science Communication Methods

  Amidi, M., Horstmann, A. C., Mertineit, J. A., & Wallbaum, T. (2025). REACH: A Card-based Approach to Guide Research Engagement and Science Communication Methods. In Proceedings of the 2025 Mensch und Computer 2025 (S. 694–699).

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  • Personenseiten

    • Amidi, Maryam
    • Wallbaum, Torben

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  • 2025
  • Conference Paper
  • • Maryam Amidi
    • Aike Horstmann
    • Jana Mertineit
    • Torben Wallbaum

• Chromosome Mask-Conditioned Generative Inpainting for Atypical Mitosis Classification

  Banerjee, S., Weiss, V., Conrad, T., Donovan, T. A., Ammeling, J., Fick, R., … Aubreville, M. (2025). Chromosome Mask-Conditioned Generative Inpainting for Atypical Mitosis Classification. In Proceedings of the MICCAI Workshop on Computational Pathology (Bd. 316, S. 266–277). PMLR.

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    • Banerjee, Sweta
    • Aubreville, Marc

  • Forschungsprojekt

    • (mu)ROMI: 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

  • Profilbereich

    • Informatik und Gestaltung

  • Forschungsfeld

    • Data Science und Künstliche Intelligenz

  • Fachbereich

    • Fachbereich 3: Information und Kommunikation

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  • 2025
  • Conference Paper
  • • Sweta Banerjee
    • Viktoria Weiss
    • Thomas Conrad
    • Taryn Donovan
    • Jonas Ammeling
    • Rutger Fick
    • Jonas Utz
    • Robert Klopfleisch
    • Christopher Kaltenecker
    • Christof Bertram
    • Katharina Breininger
    • Marc Aubreville

• Comprehensive Dataset of Coarse Tumor Annotations for The Cancer Genome Atlas Breast Invasive Carcinoma

  Banerjee, S., Bertram, C. A., Ammeling, J., Weiss, V., Conrad, T., Klopfleisch, R., … Aubreville, M. (2025). Comprehensive Dataset of Coarse Tumor Annotations for The Cancer Genome Atlas Breast Invasive Carcinoma. In Bildverarbeitung für die Medizin 2025 (S. 260–265). Wiesbaden: Springer Fachmedien Wiesbaden. http://doi.org/10.1007/978-3-658-47422-5_56

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  Automated tumor segmentation of histologic images is crucial for the development of computer-assisted diagnostic workflows aiming at accurate prognostication. We present a dataset of coarse annotations of over 1,000 highresolution breast tumor images from The cancer genome atlas breast invasive carcinoma (TCGA-BRCA) repository, each annotated with binary segmentation masks that delineate coarse tumor areas. Additionally, a subset of 20 images includes fine-grained annotations, providing precise delineation of tumor boundaries beyond the broader outlines used in coarse annotations. Initial evaluations using U-Net and DeepLabv3 models show promising segmentation performance. On a held-out, coarsely annotated test set, U-Net achieved an average intersection over union (IoU) score of 0.795, while DeepLabv3 scored 0.783. On the finely annotated subset of this test set, U-Net reached an average IoU of 0.746, with DeepLabv3 slightly outperforming at 0.765. The public availability of this dataset aims to support research in automated tumor analysis, advancing diagnostic workflows and thereby ultimately improving breast cancer management.

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    • Banerjee, Sweta
    • Aubreville, Marc

  • Forschungsprojekt

    • (mu)ROMI: Robust and Accurate Multi-Tumor, Multi-Species, Multi-Laboratory and Multi-Scanner Mitosis Detection with Large-Scale Datasets and Artificial Intelligence

  • 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
  • • Sweta Banerjee
    • Christof Bertram
    • Jonas Ammeling
    • Viktoria Weiss
    • Thomas Conrad
    • Robert Klopfleisch
    • Christopher Kaltenecker
    • Katharina Breininger
    • Marc Aubreville

• MeasureTip: Towards a Finger-Worn Measurement Device

  Olberding, S., & Wallbaum, T. (2025). MeasureTip: Towards a Finger-Worn Measurement Device. In Proceedings of the 2025 Mensch und Computer 2025 (S. 673–677).

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    • Wallbaum, Torben

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  • 2025
  • Conference Paper
  • • Simon Olberding
    • Torben Wallbaum

• Vortrag: Extradimensionen und mikroskopische Schwarze Löcher

  Vest, A. (2025). Vortrag: Extradimensionen und mikroskopische Schwarze Löcher.

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    • Vest, Anja

  • Fachbereich

    • Fachbereich 2: Energy and Life Science

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  • 2025
  • Miscellaneous
  • • Anja Vest

• Investigation of Class Separability within Object Detection Models in Histopathology

  Ammeling, J., Ganz, J., Wilm, F., Breininger, K., & Aubreville, M. (2025). Investigation of Class Separability within Object Detection Models in Histopathology. IEEE Transactions on Medical Imaging, 1–1. http://doi.org/10.1109/TMI.2025.3560134

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    • Aubreville, Marc

  • Forschungsprojekt

    • (mu)ROMI: 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

  • Profilbereich

    • Informatik und Gestaltung

  • Forschungsfeld

    • Data Science und Künstliche Intelligenz

  • Fachbereich

    • Fachbereich 3: Information und Kommunikation

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  • 2025
  • Journal Article
  • • Jonas Ammeling
    • Jonathan Ganz
    • Frauke Wilm
    • Katharina Breininger
    • Marc Aubreville
  • • Feature extraction
    • Object detection
    • Measurement
    • Adaptation models
    • histopathology
    • Training
    • Complexity theory
    • YOLO
    • Location awareness
    • Data mining
    • object detection
    • latent space discrimination

• Characterization of irradiated mucosa using confocal laser endomicroscopy in the upper aerodigestive tract

  Thesing, L., Sievert, M., Panuganti, B., Aubreville, M., Meyer, T., Müller-Diesing, F., … Goncalves, M. (2025). Characterization of irradiated mucosa using confocal laser endomicroscopy in the upper aerodigestive tract. European Archives of Oto-Rhino-Laryngology, 282, 2507–2514.

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  • Personenseiten

    • Aubreville, Marc

  • Forschungsprojekt

    • Optical Biopsy of Sinonasal Tumors using Confocal Laser Endomicroscopy: A Clinical and Deep Learning-based Assessment and Visualization

  • Abteilung/Einrichtung

    • FLAIR - Flensburg Artificial Intelligence Research

  • Forschungsfeld

    • Data Science und Künstliche Intelligenz

  • Fachbereich

    • Fachbereich 3: Information und Kommunikation

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  • 2025
  • Journal Article
  • • Lisa Thesing
    • Matti Sievert
    • Bharat Panuganti
    • Marc Aubreville
    • Till Meyer
    • Flurin Müller-Diesing
    • Agmal Scherzad
    • Stephan Hackenberg
    • Miguel Goncalves