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2024

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
Data-Driven Analysis of Physical and Mental Rotation Strategies

Wetzel, S., & Bertel, S. (2024). Data-Driven Analysis of Physical and Mental Rotation Strategies. CogSci 2025 conference. Rotterdam: Cognitive Science Society.

Abstract

Studying physical rotation (i.e., rotation tasks during which figures can be physically rotated, such as through gestures) can offer insights also into problem solving processes at work during mental rotation. We present a novel method for behavioral pattern analysis which we applied to data from 2,999 physical rotation tasks gathered in-class from 50 secondary school students. The method uses normalized, resampled, time-dependent data on angular offsets between figures over time and agglomerative, correlation-based clustering. Each cluster represents a distinct behavioral pattern and its respective prototype a problem solving strategy. Results indicate that multiple strategies were employed: The dominant strategy matches the classical model of mental rotation, in which angular offsets between figures are decreased over time. For the secondary strategy, angular offsets were actually increased. A subsequent analysis shows that the secondary strategy was more frequently used for symmetric figures, possibly indicating problems with correctly matching segments across figures.
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Bertel, Sven

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Kompetenzen für eine digitale Welt: partizipative Entwicklung von Apps zum Training räumlicher Fähigkeiten im Unterricht

Abteilung/Einrichtung

CIVU – Center for Interaction, Visualization and Usability

Profilbereich

Informatik und Gestaltung

Forschungsfeld

Human-Computer-Interaction

Fachbereich

Fachbereich 3: Information und Kommunikation
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2024

Conference Proceedings

Stefanie Wetzel

Sven Bertel

2023

Improving the Quality of Wind Field Reconstruction Techniques for Lidar-assisted Control of Wind Turbines

Afanasieva, N., & Schlipf, D. (2023). Improving the Quality of Wind Field Reconstruction Techniques for Lidar-assisted Control of Wind Turbines. In 19thEAWE PhD Seminar on Wind Energy. http://doi.org/10.5281/zenodo.8335186 (Original work published 2025)
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Schlipf, David
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2023

Conference Paper

Nadiia Afanasieva

David Schlipf
The performance of two control strategies for floating wind turbines: lidar-assisted feedforward and multi-variable feedback

Guo, F., Schlipf, D., Lemmer, F., Raach, S., Özinan, U., Adam, R., & Choisnet, T. (2023). The performance of two control strategies for floating wind turbines: lidar-assisted feedforward and multi-variable feedback. In Journal of Physics: Conference Series (Bd. 2626, S. 012005). http://doi.org/10.1088/1742-6596/2626/1/012005 (Original work published 2025)
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Schlipf, David
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2023

Conference Paper

Feng Guo

David Schlipf

Frank Lemmer

Steffen Raach

Umut Özinan

Raphaël Adam

Thomas Choisnet
A Tutorial on Lidar-Assisted Control for Floating Offshore Wind Turbines

Schlipf, D., Guo, F., Raach, S., & Lemmer, F. (2023). A Tutorial on Lidar-Assisted Control for Floating Offshore Wind Turbines. In American Control Conference. San Diego, CA, USA. http://doi.org/10.23919/ACC55779.2023.10156419 (Original work published 2025)
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Schlipf, David
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2023

Conference Paper

David Schlipf

Feng Guo

Steffen Raach

Frank Lemmer
A data-driven approach to the design and implementation of retrofit lidar assisted control systems

Pitter, M., Slinger, C., Guo, F., Schlipf, D., Raach, S., & White, S. (2023). A data-driven approach to the design and implementation of retrofit lidar assisted control systems. http://doi.org/10.5281/zenodo.8037351 (Original work published 2025)
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Schlipf, David
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2023

Miscellaneous

Mark Pitter

Chris Slinger

Feng Guo

David Schlipf

Steffen Raach

Steven White
Feedforward control for a 15-MW wind turbine using a spinner-mounted single-beam lidar

Fu, W., Guo, F., Schlipf, D., & Peña, A. (2023). Feedforward control for a 15-MW wind turbine using a spinner-mounted single-beam lidar. http://doi.org/10.5281/zenodo.8033374 (Original work published 2025)
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Schlipf, David
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2023

Miscellaneous

Wei Fu

Feng Guo

David Schlipf

Alfredo Peña
Recommended Practices on Lidar-assisted Control of Floating Wind Turbines

Guo, F., Lemmer, F., Schlipf, D., & Raach, S. (2023). Recommended Practices on Lidar-assisted Control of Floating Wind Turbines. http://doi.org/10.5281/zenodo.8031738 (Original work published 2025)
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Schlipf, David
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2023

Miscellaneous

Feng Guo

Frank Lemmer

David Schlipf

Steffen Raach
Lessons learned from the VAMOS project in collaboration with the FLOATEOLE project

Özinan, U., Cheng, P. W., Netzband, S., Schulz, C. W., Abdel-Maksoud, M., Raach, S., … Neumann, T. (2023). Lessons learned from the VAMOS project in collaboration with the FLOATEOLE project. http://doi.org/10.5281/zenodo.10410945 (Original work published 2025)
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Schlipf, David
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2023

Miscellaneous

Umut Özinan

Po Cheng

Stefan Netzband

Christian Schulz

Moustafa Abdel-Maksoud

Steffen Raach

David Schlipf

Sandrine Aubrun

William Bruch

Boris Conan

Yves Perignon

Jean-Marc Rousset

Raphaël Adam

Mike Ludde

Thomas Neumann

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2024

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