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2024

QUIC(K) communication for GPU Virtualization in Edge Computing

Lübben, R., Gaikwad, N. B., Stefanović, Č., & Kosta, S. (2024). QUIC(K) communication for GPU Virtualization in Edge Computing. In International Conference on Wireless and Mobile Computing, Networking and Communications. Paris: IEEE. (Original work published Oktober 2024)
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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

Conference Paper

Ralf Lübben

Nikhil Gaikwad

Čedomir Stefanović

Sokol Kosta
The Use of Machine Learning in Modeling Video Quality Assessment

Klink, J., Łuczyński, M., & Uhl, T. (2024). The Use of Machine Learning in Modeling Video Quality Assessment. In 32nd International Conference on Software, Telecommunications and Computer Networks (S. 6). Split-Bol/Croatia: IEEE Xplore. http://doi.org/10.23919/SoftCOM62040.2024.10722023 (Original work published September 2024)
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Uhl, Tadeus

Forschungsfeld

Data Science und Künstliche Intelligenz

Fachbereich

Fachbereich 3: Information und Kommunikation
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2024

Conference Paper

Janusz Klink

Michał Łuczyński

Tadeus Uhl
Automatically generated state observers – toolchain and validation with real measurements

Geisler, J. (2024). Automatically generated state observers – toolchain and validation with real measurements. In Torque 2024. Florenz: IOP Publishing. http://doi.org/10.1088/1742-6596/2767/3/032003 (Original work published Juni 2024)

Abstract

State estimation of wind turbines is the basis for many advanced control strategies and analysis applications such as condition monitoring, fault diagnosis, and much more. It depends on the application and its goals, which states of the real turbine are relevant. Therefore, estimators with different levels of detail and sets of states suitable for the problem at hand are needed. This paper describes a toolchain that generates custom-tailored code for an extended Kalman filter. The aim is to create computationally highly efficient estimators for a wide range of applications. Automatic generation of source code allows for agile experimentation and reduces the risk of time-consuming implementation errors. The new contributions are the selection and combination of all parts of the toolchain, the extension of the integration algorithm to include the Jacobian of the transition function, and the adaptation of an efficient covariance auto-tuning algorithm. Four different models are validated in a three-stage process, including measurement data from a real turbine.
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Geisler, Jens

Profilbereich

Erneuerbare Energien und nachhaltige Entwicklung

Forschungsfeld

Wind

Data Science und Künstliche Intelligenz

Fachbereich

Fachbereich 2: Energy and Life Science
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2024

Conference Paper

Jens Geisler
Enhancing Spatial Problem-Solving Through Data-Driven Methods: A Practical Approach Utilizing Immersive Technologies and Multimodal Physical Tracking

Bendig, H. (2024). Enhancing Spatial Problem-Solving Through Data-Driven Methods: A Practical Approach Utilizing Immersive Technologies and Multimodal Physical Tracking. In Companion Proceedings of the 29th International Conference on Intelligent User Interfaces (S. 150–152). Greenville, SC, USA: Association for Computing Machinery. http://doi.org/10.1145/3640544.3645244 (Original work published März 2024)

Abstract

This project aims to explore how data-driven methods can be used to enhance humans’ problem-solving skills in spatial tasks and how they can be integrated into immersive tutoring systems. The goal is to provide an enhanced, state-dependent approach that is also applicable outside the laboratory, within real-life scenarios. Digital tutoring and learning systems are typically expert systems and follow a predefined training routine. Ongoing research concentrates on adaptive methods within affective- and cognition-aware systems, yet there is comparatively less investigation into recognizing and adapting problem-solving strategies. Spatial skill have been shown to be especially important for STEM (Science, Technology, Engineering, Mathematics) disciplines in general. Due to their design, immersive systems are particularly suitable for training of spatial skills and are thus frequently utilized for this.
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Bendig, Henner

Abteilung/Einrichtung

CIVU – Center for Interaction, Visualization and Usability

UsabilityLab

Profilbereich

Informatik und Gestaltung

Forschungsfeld

Human-Computer-Interaction

Data Science und Künstliche Intelligenz

Fachbereich

Fachbereich 3: Information und Kommunikation
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2024

Conference Paper

Henner Bendig

Data-driven Methods

Immersive Technologies

Spatial Skills

biocybernetic control loop
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
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

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
TAILING: Tail Distribution Forecasting of Packet Delays using Quantile Regression Neural Networks

Lübben, R., & Rizk, A. (2023). TAILING: Tail Distribution Forecasting of Packet Delays using Quantile Regression Neural Networks. In IEEE International Conference on Communications (ICC) (S. 7). (Original work published Mai 2023)
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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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2023

Conference Paper

Ralf Lübben

Amr Rizk

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2024

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