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2026

Physik - Ausgewählte Grundkonzepte für Natur- und Ingenieurwissenschaften

Vest, A., & Voigt, A. (2026). Physik - Ausgewählte Grundkonzepte für Natur- und Ingenieurwissenschaften (S. 436). Hamburg: Books on Demand GmbH. Abgerufen von https://buchshop.bod.de/physik-alexander-voigt-9783819275401
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Vest, Anja

Fachbereich

Fachbereich 2: Energy and Life Science
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2026

Book

Anja Vest

Alexander Voigt

Physik

2025

Artificial intelligence predicts c-KIT exon 11 genotype by phenotype in canine cutaneous mast cell tumors: Can human observers learn it?

Puget, C., Ganz, J., Bertram, C. A., Conrad, T., Baeblich, M., Voss, A., … Klopfleisch, R. (2025). Artificial intelligence predicts c-KIT exon 11 genotype by phenotype in canine cutaneous mast cell tumors: Can human observers learn it? Veterinary Pathology, 03009858251380284. http://doi.org/10.1177/03009858251380284 (Original work published 2026)
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Aubreville, Marc

Abteilung/Einrichtung

FLAIR - Flensburg Artificial Intelligence Research

CIVU – Center for Interaction, Visualization and Usability

Profilbereich

Gesundheit und Life Sciences

Fachbereich

Fachbereich 3: Information und Kommunikation
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2025

Journal Article

Chloé Puget

Jonathan Ganz

Christof Bertram

Thomas Conrad

Malte Baeblich

Anne Voss

Katharina Landmann

Alexander Haake

Andreas Spree

Svenja Hartung

Leonore Aeschlimann

Sara Soto

Simone De Brot

Martina Dettwiler

Heike Aupperle-Lellbach

Pompei Bolfa

Alexander Bartel

Matti Kiupel

Katharina Breininger

Marc Aubreville

Robert Klopfleisch
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 2026)
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Schlipf, David

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Fachbereich 2: Energy and Life Science
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2025

Miscellaneous

David Schlipf
"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 2026)
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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)

Abstract

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
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), (4/2025), 4. Abgerufen von https://www.hlb.de/die-neue-hochschule# (Original work published August 2025)

Abstract

Dieser Beitrag diskutiert das Problem, automatisierte und zugleich realitätsnahe digitale Prüfungen im Fach Buchhaltung mit Moodle abzuhalten. Es werden verschiedene Behelfslösungen erläutert und ein möglicher Lösungsansatz skizziert.
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Tausch-Nebel, Lasse
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2025

Journal Article

Lasse Tausch-Nebel

Hendrik van der Sluis
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
GPU Virtualization and Remoting Service for AI Acceleration at the Edge

Gaikwad, N., Lübben, R., & Kosta, S. (2025). GPU Virtualization and Remoting Service for AI Acceleration at the Edge. In Summer School on Cyber-Physical Systems and Internet-of-Things. (Original work published Juli 2025)
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KI-Anwendungszentrum (KIAZ)

Abteilung/Einrichtung

FLAIR - Flensburg Artificial Intelligence Research

Forschungsfeld

Data Science und Künstliche Intelligenz

Fachbereich

Fachbereich 4: Wirtschaft
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2025

Conference Paper

Nikhil Gaikwad

Ralf Lübben

Sokol Kosta
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)

Abstract

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)

Abstract

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

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2026

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