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2025

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)

Abstract

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

2024

Prediction of tumor board procedural recommendations using large language models

Aubreville, M. ., Ganz, J. ., Ammeling, J. ., Rosbach, E. ., Gehrke, T. ., Scherzad, A. ., … Goncalves, M. . (2024). Prediction of tumor board procedural recommendations using large language models. European Archives of Oto-Rhino-Laryngology. http://doi.org/10.1007/s00405-024-08947-9 (Original work published 2025)

Abstract

Multidisciplinary tumor boards are meetings where a team of medical specialists, including medical oncologists, radiation oncologists, radiologists, surgeons, and pathologists, collaborate to determine the best treatment plan for cancer patients. While decision-making in this context is logistically and cost-intensive, it has a significant positive effect on overall cancer survival.
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Aubreville, Marc

Abteilung/Einrichtung

FLAIR - Flensburg Artificial Intelligence Research

Profilbereich

Informatik und Gestaltung

Forschungsfeld

Digitalisierung im Gesundheitswesen

Data Science und Künstliche Intelligenz

Fachbereich

Fachbereich 3: Information und Kommunikation
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2024

Journal Article

Marc Aubreville

Jonathan Ganz

Jonas Ammeling

Emely Rosbach

Thomas Gehrke

Agmal Scherzad

Stephan Hackenberg

Miguel Goncalves
Re-identification from histopathology images

Ganz, J. ., Ammeling, J. ., Jabari, S. ., Breininger, K. ., & Aubreville, M. . (2024). Re-identification from histopathology images. Medical Image Analysis, 103335. http://doi.org/10.1016/j.media.2024.103335 (Original work published 2025)
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Aubreville, Marc

Abteilung/Einrichtung

FLAIR - Flensburg Artificial Intelligence Research

Forschungsfeld

Digitalisierung im Gesundheitswesen

Data Science und Künstliche Intelligenz

Fachbereich

Fachbereich 3: Information und Kommunikation
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2024

Journal Article

Jonathan Ganz

Jonas Ammeling

Samir Jabari

Katharina Breininger

Marc Aubreville
Navigating the complexities of a changing world - Interdisciplinary Electives at Flensburg University of Applied Sciences

van der Sluis, H. ., & John, P. . (2024). Navigating the complexities of a changing world - Interdisciplinary Electives at Flensburg University of Applied Sciences. Educational Developments, 25.4, 4. Abgerufen von https://www.seda.ac.uk/seda-publishing/educational-developments/educational-developments-issue-25-4-2024/ (Original work published Dezember 2024)

Abstract

In a competitive Higher Education (HE) landscape, universities face the challenge of developing an organisational culture that supports the needs of its staff and students. The traditional staff development approach has been the attainment of skills through teaching and the completion of certificates of learning. This article challenges that practice and seeks to make the case for a renewed focus on the importance of learning through individual and group coaching practice to be ingrained into universities, shifting from a view of staff development purely as the acquisition of skills through training and teaching and moving towards
engaged learning.
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John, Peter

Abteilung/Einrichtung

Dekanat FB3

Lern- und Sprachenzentrum (LSZ)

Forschungsfeld

Fachkräfte und Vernetzung

Fachbereich

Fachbereich 3: Information und Kommunikation
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2024

Journal Article

Hendrik van der Sluis

Peter John
Hydraulic variable inertia flywheel

Jauch, C. ., Jost, R. ., & Kloft, P. . (2024). Hydraulic variable inertia flywheel. Applied Energy, 360, 16. http://doi.org/https://doi.org/10.1016/j.apenergy.2024.122830 (Original work published Februar 2024)

Abstract

A novel variable inertia flywheel that uses the mass of a rotating hydraulic fluid is proposed in this paper. In contrast to variable inertia flywheels that use solid masses, this flywheel stands out for its simplicity. In contrast to many other common energy storages, it does not require any environmentally harmful, rare or expensive materials. The basic working principle and the equations that cover the hydraulic behaviour, the pneumatic behaviour and the energy from angular momentum are introduced. These equations are applied to the geometry of the proposed flywheel concept, which allows quantifying the pressures that act on the different mechanical flywheel components. These pressures, together with the centrifugal acceleration from rotation, lead to the loads that have to be withstood by the mechanical components. A simple method for quantifying these loads, and for dimensioning the mechanical components, allows deriving the stationary masses and inertias. A parameter study is conducted, in which different parameters of the flywheel are varied in order to find the maxima in energy density and specific energy. The results of this parameter study reveal that the proposed hydraulic variable inertia flywheel is a very simple and safe energy storage that could provide AC power systems with inertia and control power to support their frequency.
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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

Forschungsfeld

Innovation und Wachstum, Nachhaltigkeit

Wind

Netzintegration

Fachbereich

Fachbereich 2: Energy and Life Science
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2024

Journal Article

Prof. Dr. Clemens Jauch

Rebecca Jost

Peter Kloft

Energy storage

Fluid

Flywheel

Kinetic energy

Variable moment of inertia

Variable inertia flywheel
Dominance hierarchy does not influence distances travelled and area utilization in a large group of ponies

Weidling, G. ., Krieter, J. ., Lübben, R. ., & Czycholl, I. . (2024). Dominance hierarchy does not influence distances travelled and area utilization in a large group of ponies. Applied Animal Behaviour Science, 271. http://doi.org/https://doi.org/10.1016/j.applanim.2024.106178 (Original work published Februar 2024)

Abstract

In order to improve existing horse husbandry practices by providing social contact and exercise opportunities, it is necessary to fully understand the behaviour of horses in relation to group housing. Therefore, this study investigated the influence of dominance hierarchy on the locomotor behaviour and area utilization in a large group of horses in a pasture-based housing system in Northern Germany. The movements of 40 ponies with established group affiliations were observed over approximately one month from late August to early October 2022 using GPS technology. Their dominance hierarchy was determined by testing each possible combination in separate pair-feeding tests. For the determination of area utilization, the available space was divided into a total of 8889 quadrants with 3x3 m each. Analysis of daily distances travelled by the horses revealed an average of 5.4 ± 1.2 km/day, with no significant effect of social rank. However, age had a significant effect, with older horses walking shorter distances. Quadrants used per hour, as measure of area utilization, averaged 37.9 ± 6.4, with no effect of social rank but a significant effect of age observed. Heat maps showed full pasture utilization, with lower ranked horses having less access to resources around the watering trough and a greater use of less attractive grazing areas. The study suggests that in this specific setting, social rank did not significantly affect locomotor behaviour or area use. Although the study did not find a correlation between dominance rank and locomotor behaviour or area use, it highlighted the need for further research in different group sizes and housing systems to optimise group housing practices for horses, especially in settings with limited space and resources.
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Lübben, Ralf

Abteilung/Einrichtung

FLAIR - Flensburg Artificial Intelligence Research

Forschungsfeld

Data Science und Künstliche Intelligenz

Fachbereich

Fachbereich 4: Wirtschaft
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2024

Journal Article

Gesa Weidling

Joachim Krieter

Ralf Lübben

Irena Czycholl

Horse

Dominance hierarchy

GPS

Distance

Area utilization

Group housing
An Interdisciplinary Perspective on AI-Supported Decision Making in Medicine

Ammeling, J. ., Aubreville, M. ., Fritz, A. ., Kießig, A. ., Krügel, S. ., & Uhl, M. . (2024). An Interdisciplinary Perspective on AI-Supported Decision Making in Medicine. Technology in Society, 102791.
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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

Informatik und Gestaltung

Forschungsfeld

Data Science und Künstliche Intelligenz

Fachbereich

Fachbereich 3: Information und Kommunikation
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2024

Journal Article

Jonas Ammeling

Marc Aubreville

Alexis Fritz

Angelika Kießig

Sebastian Krügel

Matthias Uhl
Analysis of Turbine Yaw Misalignment Estimated by LIDAR Assuming Homogeneous Flow

Zhang, Z. ., Guo, F. ., Schlipf, D. ., Schito, P. ., & Zasso, A. . (2024). Analysis of Turbine Yaw Misalignment Estimated by LIDAR Assuming Homogeneous Flow. Wind Energy Science Discussions, 2024, 1–21. http://doi.org/10.5194/wes-2023-162
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2024

Journal Article

Z. Zhang

F. Guo

D. Schlipf

P. Schito

A. Zasso
Breast cancer survival prediction using an automated mitosis detection pipeline

Stathonikos, N. ., Aubreville, M. ., de Vries, S. ., Wilm, F. ., Bertram, C. A., Veta, M. ., & van Diest, P. J. (2024). Breast cancer survival prediction using an automated mitosis detection pipeline. The Journal of Pathology: Clinical Research, 10, e70008. http://doi.org/https://doi.org/10.1002/2056-4538.70008

Abstract

Abstract Mitotic count (MC) is the most common measure to assess tumor proliferation in breast cancer patients and is highly predictive of patient outcomes. It is, however, subject to inter- and intraobserver variation and reproducibility challenges that may hamper its clinical utility. In past studies, artificial intelligence (AI)-supported MC has been shown to correlate well with traditional MC on glass slides. Considering the potential of AI to improve reproducibility of MC between pathologists, we undertook the next validation step by evaluating the prognostic value of a fully automatic method to detect and count mitoses on whole slide images using a deep learning model. The model was developed in the context of the Mitosis Domain Generalization Challenge 2021 (MIDOG21) grand challenge and was expanded by a novel automatic area selector method to find the optimal mitotic hotspot and calculate the MC per 2 mm2. We employed this method on a breast cancer cohort with long-term follow-up from the University Medical Centre Utrecht (N = 912) and compared predictive values for overall survival of AI-based MC and light-microscopic MC, previously assessed during routine diagnostics. The MIDOG21 model was prognostically comparable to the original MC from the pathology report in uni- and multivariate survival analysis. In conclusion, a fully automated MC AI algorithm was validated in a large cohort of breast cancer with regard to retained prognostic value compared with traditional light-microscopic MC.
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Aubreville, Marc

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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2024

Journal Article

Nikolas Stathonikos

Marc Aubreville

Sjoerd de Vries

Frauke Wilm

Christof Bertram

Mitko Veta

Paul van Diest

artificial intelligence

machine learning

histopathology

prognosis

mitosis

outcome

deep learning
Information mismatch in PHH3-assisted mitosis annotation leads to interpretation shifts in H&E slide analysis

Ganz, J. ., Marzahl, C. ., Ammeling, J. ., Rosbach, E. ., Richter, B. ., Puget, C. ., … Aubreville, M. . (2024). Information mismatch in PHH3-assisted mitosis annotation leads to interpretation shifts in H&E slide analysis. Scientific Reports, 14, 26273. http://doi.org/10.1038/s41598-024-77244-6

Abstract

Abstract The count of mitotic figures (MFs) observed in hematoxylin and eosin (H&E)-stained slides is an important prognostic marker, as it is a measure for tumor cell proliferation. However, the identification of MFs has a known low inter-rater agreement. In a computer-aided setting, deep learning algorithms can help to mitigate this, but they require large amounts of annotated data for training and validation. Furthermore, label noise introduced during the annotation process may impede the algorithms’ performance. Unlike H&E, where identification of MFs is based mainly on morphological features, the mitosis-specific antibody phospho-histone H3 (PHH3) specifically highlights MFs. Counting MFs on slides stained against PHH3 leads to higher agreement among raters and has therefore recently been used as a ground truth for the annotation of MFs in H&E. However, as PHH3 facilitates the recognition of cells indistinguishable from H&E staining alone, the use of this ground truth could potentially introduce an interpretation shift and even label noise into the H&E-related dataset, impacting model performance. This study analyzes the impact of PHH3-assisted MF annotation on inter-rater reliability and object level agreement through an extensive multi-rater experiment. Subsequently, MF detectors, including a novel dual-stain detector, were evaluated on the resulting datasets to investigate the influence of PHH3-assisted labeling on the models’ performance. We found that the annotators’ object-level agreement significantly increased when using PHH3-assisted labeling (F1: 0.53 to 0.74). However, this enhancement in label consistency did not translate to improved performance for H&E-based detectors, neither during the training phase nor the evaluation phase. Conversely, the dual-stain detector was able to benefit from the higher consistency. This reveals an information mismatch between the H&E and PHH3-stained images as the cause of this effect, which renders PHH3-assisted annotations not well-aligned for use with H&E-based detectors. Based on our findings, we propose an improved PHH3-assisted labeling procedure.
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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

Informatik und Gestaltung

Forschungsfeld

Human-Computer-Interaction

Data Science und Künstliche Intelligenz

Fachbereich

Fachbereich 3: Information und Kommunikation
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2024

Journal Article

Jonathan Ganz

Christian Marzahl

Jonas Ammeling

Emely Rosbach

Barbara Richter

Chloé Puget

Daniela Denk

Elena Demeter

Flaviu Tăbăran

Gabriel Wasinger

Karoline Lipnik

Marco Tecilla

Matthew Valentine

Michael Dark

Niklas Abele

Pompei Bolfa

Ramona Erber

Robert Klopfleisch

Sophie Merz

Taryn Donovan

Samir Jabari

Christof Bertram

Katharina Breininger

Marc Aubreville

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2025

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