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2024

TorqTwin—An open-source reference multibody modeling framework for wind turbine structural dynamics

Guo, F. ., Gao, Z. ., & Schlipf, D. . (2024). TorqTwin—An open-source reference multibody modeling framework for wind turbine structural dynamics. Renewable Energy, 235, 121268. http://doi.org/10.1016/j.renene.2024.121268
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Schlipf, David
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2024

Journal Article

Feng Guo

Zhen Gao

David Schlipf
Nuclear pleomorphism in canine cutaneous mast cell tumors: Comparison of reproducibility and prognostic relevance between estimates, manual morphometry, and algorithmic morphometry

Haghofer, A. ., Parlak, E. ., Bartel, A. ., Donovan, T. ., Assenmacher, C.-A. ., Bolfa, P. ., … Bertram, C. . (2024). Nuclear pleomorphism in canine cutaneous mast cell tumors: Comparison of reproducibility and prognostic relevance between estimates, manual morphometry, and algorithmic morphometry. Veterinary Pathology, 03009858241295399. http://doi.org/10.1177/03009858241295399

Abstract

Variation in nuclear size and shape is an important criterion of malignancy for many tumor types; however, categorical estimates by pathologists have poor reproducibility. Measurements of nuclear characteristics can improve reproducibility, but current manual methods are time-consuming. The aim of this study was to explore the limitations of estimates and develop alternative morphometric solutions for canine cutaneous mast cell tumors (ccMCTs). We assessed the following nuclear evaluation methods for accuracy, reproducibility, and prognostic utility: (1) anisokaryosis estimates by 11 pathologists; (2) gold standard manual morphometry of at least 100 nuclei; (3) practicable manual morphometry with stratified sampling of 12 nuclei by 9 pathologists; and (4) automated morphometry using deep learning–based segmentation. The study included 96 ccMCTs with available outcome information. Inter-rater reproducibility of anisokaryosis estimates was low (k = 0.226), whereas it was good (intraclass correlation = 0.654) for practicable morphometry of the standard deviation (SD) of nuclear size. As compared with gold standard manual morphometry (area under the ROC curve [AUC] = 0.839, 95% confidence interval [CI] = 0.701–0.977), the prognostic value (tumor-specific survival) of SDs of nuclear area for practicable manual morphometry and automated morphometry were high with an AUC of 0.868 (95% CI = 0.737–0.991) and 0.943 (95% CI = 0.889–0.996), respectively. This study supports the use of manual morphometry with stratified sampling of 12 nuclei and algorithmic morphometry to overcome the poor reproducibility of estimates. Further studies are needed to validate our findings, determine inter-algorithmic reproducibility and algorithmic robustness, and explore tumor heterogeneity of nuclear features in entire tumor sections.
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Aubreville, Marc

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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

Andreas Haghofer

Eda Parlak

Alexander Bartel

Taryn Donovan

Charles-Antoine Assenmacher

Pompei Bolfa

Michael Dark

Andrea Fuchs-Baumgartinger

Andrea Klang

Kathrin Jäger

Robert Klopfleisch

Sophie Merz

Barbara Richter

Yvonne Schulman

Hannah Janout

Jonathan Ganz

Josef Scharinger

Marc Aubreville

Stephan Winkler

Matti Kiupel

Christof Bertram
Influence of Compressive Stresses on Core Losses and Magnetization Demand of Electrical Steel Sheets and Press Fitted Stator Cores

Regnet, M. ., Pérez, A. V., Dietz, A. ., Loehlein, B. ., & Reinlein, M. . (2024). Influence of Compressive Stresses on Core Losses and Magnetization Demand of Electrical Steel Sheets and Press Fitted Stator Cores. In 14th International Electric Drives Production Conference (EDPC).
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2024

Conference Paper

Martin Regnet

Alejandro Pérez

Armin Dietz

Bernd Loehlein

Michael Reinlein
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

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Fachbereich 3: Information und Kommunikation
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2024

Journal Article

Jonas Ammeling

Marc Aubreville

Alexis Fritz

Angelika Kießig

Sebastian Krügel

Matthias Uhl

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 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
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

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David Schlipf

Feng Guo

Steffen Raach

Frank Lemmer
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

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Feng Guo

Frank Lemmer

David Schlipf

Steffen Raach

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2024

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