Publikationen

2024

• Green hydrogen production with offshore wind at Colombian Caribbean Sea: Analysis for Atlántico Department s coast

  Bermejo, E., Berrío, J., Llain, J., Rincón, A., Schlipf, D., & Zambrano, H. (2024). Green hydrogen production with offshore wind at Colombian Caribbean Sea: Analysis for Atlántico Department s coast. http://doi.org/10.5281/zenodo.10912206

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    • Schlipf, David

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  • 2024
  • Miscellaneous
  • • Edwin Bermejo
    • Julián Berrío
    • Jose Llain
    • Andrés. Rincón
    • David Schlipf
    • Habib Zambrano

• Investigation of asymmetric SVM switching patterns with minor flux loops in inverter fed induction motors

  Königs, M., & Löhlein, B. (2024). Investigation of asymmetric SVM switching patterns with minor flux loops in inverter fed induction motors. Research gate, open access. http://doi.org/http://dx.doi.org/10.13140/RG.2.2.21923.26407

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  • 2024
  • Journal Article
  • • Mike Königs
    • Bernd Löhlein

• Problem Solving Strategies in Physical and Mental Rotation

  Wetzel, S., & Bertel, S. (2024). Problem Solving Strategies in Physical and Mental Rotation. Spatial Cognition Conference 2024. Dublin.

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    • Bertel, Sven

  • Forschungsprojekt

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

  • Abteilung/Einrichtung

    • CIVU – Center for Interaction, Visualization and Usability

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    • Informatik und Gestaltung

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    • Human-Computer-Interaction

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    • Fachbereich 3: Information und Kommunikation

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  • 2024
  • Conference Proceedings
  • • Stefanie Wetzel
    • Sven Bertel

• Impact of motions on floating wind turbine power production

  Garcia-Sagrado, A., Schlipf, D., Brovia, S. P., Burstein, J., & Yoshinaga, T. (2024). Impact of motions on floating wind turbine power production. In Journal of Physics: Conference Series. http://doi.org/10.1088/1742-6596/2767/6/062034

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    • Schlipf, David

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  • 2024
  • Conference Paper
  • • Ana Garcia-Sagrado
    • David Schlipf
    • Santiago Brovia
    • Justin Burstein
    • Tsuyoshi Yoshinaga

• Why state-of-the-art analytical models for eddy current losses in PM of PMSM are insufficient for variable speed motors

  Königs, M., & Löhlein, B. (2024). Why state-of-the-art analytical models for eddy current losses in PM of PMSM are insufficient for variable speed motors. e+i Elektrotechnik und Informationstechnik, 141. http://doi.org/10.1007/s00502-024-01207-y

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  • 2024
  • Journal Article
  • • Mike Königs
    • Bernd Löhlein

• 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

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  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

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    • Informatik und Gestaltung

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    • Data Science und Künstliche Intelligenz

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    • 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.

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  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

  • Forschungsprojekt

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

  • Abteilung/Einrichtung

    • CIVU – Center for Interaction, Visualization and Usability

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    • Informatik und Gestaltung

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    • Human-Computer-Interaction

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    • Fachbereich 3: Information und Kommunikation

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  • 2024
  • Conference Proceedings
  • • Stefanie Wetzel
    • Sven Bertel

• A federated learning approach to QoS forecasting in cellular vehicular communications: Approaches and empirical evidence

  Baganal-Krishna, N., Lübben, R., Liotou, E., Katsaros, K. V., & Rizk, A. (2024). A federated learning approach to QoS forecasting in cellular vehicular communications: Approaches and empirical evidence. Computer Networks, 242, 110239. http://doi.org/https://doi.org/10.1016/j.comnet.2024.110239

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  QoS forecasting for cellular vehicular communications allows cooperative, connected and automated mobility applications to tailor their behavior to the expected communication conditions on the road. In a nutshell, vehicles may, for example, execute cooperative maneuvers if the communication quality of service is only above a certain quantitative level whereas if not they revert to the individual autonomous mode. In this paper, we propose and show empirical methods for estimating packet-based QoS metrics obtained from 5G network measurements with a direct application to vehicular applications. As many distributed vehicular applications possess strict QoS requirements, we focus here on bounding packet-based statistical QoS quantiles, specifically for latency and loss. Our approach is based on training regression neural networks in a federated learning fashion and show that it can obtain predictions on par with centralized training without the vehicles needing to transmit raw measurement data. In contrast to QoS prediction using physical layer information, we briefly discuss the embedding of such much simpler application-level service within the 5G architecture. We also validate our approach through recovering classical closed-form delay quantiles that are obtained from analytical models of simple queueing systems. We show that our approach goes beyond these simple models in that it provides quantile estimates for the complex scenario of cellular vehicle communications and under different application traffic patterns including empirical data traffic traces as well as 5G testbed measurements.

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    • Lübben, Ralf

  • Abteilung/Einrichtung

    • FLAIR - Flensburg Artificial Intelligence Research

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    • Data Science und Künstliche Intelligenz

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    • Fachbereich 4: Wirtschaft

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  • 2024
  • Journal Article
  • • Nehal Baganal-Krishna
    • Ralf Lübben
    • Eirini Liotou
    • Konstantinos Katsaros
    • Amr Rizk
  • • Predictive QoS
    • Vehicular communications
    • Federated learning
    • 5G measurements

• 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

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  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