Publikationen

2024

• 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

• Green Hydrogen for the Energy Transition in Germany - Potentials, Limits, and Priorities

  Doucet, F., Blohm, M., von Düsterlho, D. J.-E., & Bannert, J. (2024). Green Hydrogen for the Energy Transition in Germany - Potentials, Limits, and Priorities. In 20th International Conference on the European Energy Market (EEM). http://doi.org/https://doi.org/10.1109/EEM60825.2024.10609015

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  • Personenseiten

    • Blohm, Marina

  • Forschungsprojekt

    • NRL Norddeutsches Reallabor

  • Abteilung/Einrichtung

    • Wind Energy Technology Institute

  • Fachbereich

    • Fachbereich 2: Energy and Life Science

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  • 2024
  • Conference Paper
  • • Felix Doucet
    • Marina Blohm
    • Prof. Dr. von Düsterlho
    • Jonas Bannert

• 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

• 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

• 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

• 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

  • 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

• Wasserstoff in der kommunalen Wärmeplanung. Lessons learned aus Schleswig-Holstein

  Blohm, M., Braunger, I., Hoffmann, I., & Semp, J. (2024). Wasserstoff in der kommunalen Wärmeplanung. Lessons learned aus Schleswig-Holstein. Abgerufen von https://coaltransitions.org/publications/wasserstoff-in-der-kommunalen-warmeplanung/

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    • Blohm, Marina

  • Abteilung/Einrichtung

    • Wind Energy Technology Institute

  • Fachbereich

    • Fachbereich 2: Energy and Life Science

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  • 2024
  • Report
  • • Marina Blohm
    • Isabell Braunger
    • Ilka Hoffmann
    • Josephine Semp

• 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

  • 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

• 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

  • Forschungsfeld

    • Data Science und Künstliche Intelligenz

  • Fachbereich

    • 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

• Influence of flexibility options on the German transmission grid — A sector-coupled mid-term scenario

  Büttner, C., Esterl, K., Cußmann, I., Realpe, C. A. E., Amme, J., & Nadal, A. (2024). Influence of flexibility options on the German transmission grid — A sector-coupled mid-term scenario. Renewable and Sustainable Energy Transition, 5, 100082. http://doi.org/https://doi.org/10.1016/j.rset.2024.100082

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  Germany must decarbonise all energy sectors to meet international and national climate goals. This task necessitates linking the electricity with the gas, heat and mobility sectors. On the one hand, sector coupling increases the demand for electrical energy and changes well-known demand patterns requiring updates to the grid infrastructure. On the other hand, the newly coupled sectors offer flexibility options to support the grid infrastructure and reduce expansion needs. This study employs a highly detailed model of the German transmission grid to analyse the impact of sector coupling comprising additional electricity demands and flexibility options on grid and storage expansion needs in the year 2035. The results demonstrate that utilising flexibility options can reduce system costs and lower CO2 emissions. The research adheres to open source and open data principles, with all data and tools being publicly accessible.

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    • eGon

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  • 2024
  • Journal Article
  • • Clara Büttner
    • Katharina Esterl
    • Ilka Cußmann
    • Carlos Realpe
    • Jonathan Amme
    • Amélia Nadal
  • • Energy system modelling
    • Sector coupling
    • Flexibility options
    • Open source
    • Open data