@inproceedings{WittigRuettersBragard2024,
  author    = {Wittig, M. and R{\"u}tters, Ren{\´e} and Bragard, Michael},
  title     = {Application of RL in control systems using the example of a rotatory inverted pendulum},
  series = {Tagungsband AALE 2024 : Fit f{\"u}r die Zukunft: praktische L{\"o}sungen f{\"u}r die industrielle Automation},
  booktitle = {Tagungsband AALE 2024 : Fit f{\"u}r die Zukunft: praktische L{\"o}sungen f{\"u}r die industrielle Automation},
  editor    = {Reiff-Stephan, J{\"o}rg and J{\"a}kel, Jens and Schwarz, Andr{\´e}},
  publisher = {le-tex publishing services GmbH},
  address   = {Leipzig},
  isbn      = {978-3-910103-02-3},
  doi       = {10.33968/2024.53},
  pages     = {241 -- 248},
  year      = {2024},
  abstract  = {In this paper, the use of reinforcement learning (RL) in control systems is investigated using a rotatory inverted pendulum as an example. The control behavior of an RL controller is compared to that of traditional LQR and MPC controllers. This is done by evaluating their behavior under optimal conditions, their disturbance behavior, their robustness and their development process. All the investigated controllers are developed using MATLAB and the Simulink simulation environment and later deployed to a real pendulum model powered by a Raspberry Pi. The RL algorithm used is Proximal Policy Optimization (PPO). The LQR controller exhibits an easy development process, an average to good control behavior and average to good robustness. A linear MPC controller could show excellent results under optimal operating conditions. However, when subjected to disturbances or deviations from the equilibrium point, it showed poor performance and sometimes instable behavior. Employing a nonlinear MPC Controller in real time was not possible due to the high computational effort involved. The RL controller exhibits by far the most versatile and robust control behavior. When operated in the simulation environment, it achieved a high control accuracy. When employed in the real system, however, it only shows average accuracy and a significantly greater performance loss compared to the simulation than the traditional controllers. With MATLAB, it is not yet possible to directly post-train the RL controller on the Raspberry Pi, which is an obstacle to the practical application of RL in a prototyping or teaching setting. Nevertheless, RL in general proves to be a flexible and powerful control method, which is well suited for complex or nonlinear systems where traditional controllers struggle.},
  language  = {en}
}
@misc{FrauenrathNiendorf2012,
  author    = {Frauenrath, Tobias and Niendorf, Thoralf},
  title     = {MRT-Vorrichtung und Verfahren zum Betreiben einer MRT-Vorrichtung},
  year      = {2012},
  abstract  = {A magnetic resonance tomography (MRT) apparatus (1) for the examination of a body (14) comprises parameter acquisition devices (13) for the acquisition of cardiovascular parameters of the body (14) and a control device (15) in communication with the parameter acquisition devices (13) for synchronizing the imaging, wherein the control device (15) is adapted to analyse the data of at least two parameter acquisition devices (13) and to output a control signal based on the analysis.},
  language  = {de}
}
@article{SchulteTiggesFoersterNikolovskietal.2022,
  author    = {Schulte-Tigges, Joschua and F{\"o}rster, Marco and Nikolovski, Gjorgji and Reke, Michael and Ferrein, Alexander and Kaszner, Daniel and Matheis, Dominik and Walter, Thomas},
  title     = {Benchmarking of various LiDAR sensors for use in self-driving vehicles in real-world environments},
  series = {Sensors},
  volume    = {22},
  journal   = {Sensors},
  number    = {19},
  publisher = {MDPI},
  address   = {Basel},
  issn      = {1424-8220},
  doi       = {10.3390/s22197146},
  pages     = {20 Seiten},
  year      = {2022},
  abstract  = {Abstract In this paper, we report on our benchmark results of the LiDAR sensors Livox Horizon, Robosense M1, Blickfeld Cube, Blickfeld Cube Range, Velodyne Velarray H800, and Innoviz Pro. The idea was to test the sensors in different typical scenarios that were defined with real-world use cases in mind, in order to find a sensor that meet the requirements of self-driving vehicles. For this, we defined static and dynamic benchmark scenarios. In the static scenarios, both LiDAR and the detection target do not move during the measurement. In dynamic scenarios, the LiDAR sensor was mounted on the vehicle which was driving toward the detection target. We tested all mentioned LiDAR sensors in both scenarios, show the results regarding the detection accuracy of the targets, and discuss their usefulness for deployment in self-driving cars.},
  language  = {en}
}
@article{HeinrichsUttingFrauenrathetal.2009,
  author    = {Heinrichs, Uwe and Utting, Jane F. and Frauenrath, Tobias and Hezel, Fabian and Krombach, Gabriele A. and Hodenius, Michael A. J. and Kozerke, Sebastian and Niendorf, Thoralf},
  title     = {Myocardial T2 mapping free of distortion using susceptibility-weighted fast spin-echo imaging: A feasibility study at 1.5 T and 3.0 T},
  series = {Magnetic Resonance in Medicine},
  volume    = {62},
  journal   = {Magnetic Resonance in Medicine},
  number    = {3},
  publisher = {Wiley-Liss},
  address   = {New York},
  issn      = {1522-2594},
  doi       = {10.1002/mrm.22054},
  pages     = {822 -- 828},
  year      = {2009},
  abstract  = {This study demonstrates the feasibility of applying free-breathing, cardiac-gated, susceptibility-weighted fast spin-echo imaging together with black blood preparation and navigator-gated respiratory motion compensation for anatomically accurate T₂ mapping of the heart. First, T₂ maps are presented for oil phantoms without and with respiratory motion emulation (T₂ = (22.1 ± 1.7) ms at 1.5 T and T₂ = (22.65 ± 0.89) ms at 3.0 T). T₂ relaxometry of a ferrofluid revealed relaxivities of R2 = (477.9 ± 17) mM⁻¹s⁻¹ and R2 = (449.6 ± 13) mM⁻¹s⁻¹ for UFLARE and multiecho gradient-echo imaging at 1.5 T. For inferoseptal myocardial regions mean T₂ values of 29.9 ± 6.6 ms (1.5 T) and 22.3 ± 4.8 ms (3.0 T) were estimated. For posterior myocardial areas close to the vena cava T₂-values of 24.0 ± 6.4 ms (1.5 T) and 15.4 ± 1.8 ms (3.0 T) were observed. The merits and limitations of the proposed approach are discussed and its implications for cardiac and vascular T₂-mapping are considered.},
  language  = {en}
}
@misc{BragardHueningKowalewski2023,
  author    = {Bragard, Michael and H{\"u}ning, Felix and Kowalewski, Paul},
  title     = {Vorrichtung zur Relativlagenbestimmung [Offenlegungschrift]},
  year      = {2023},
  abstract  = {Die Erfindung betrifft eine Vorrichtung zur Bestimmung einer Relativlage zwischen einem feststehenden Teil und einem zu demselben in eine Bewegungsrichtung bewegbaren beweglichen Teil, wobei der feststehende Teil mit einem Wiegandsensor versehen ist, wobei der Wiegandsensor zwischen zwei gegenpolig zueinander ausgebildeten Permanentmagneten angeordnet ist und dass der bewegliche Teil eine Mehrzahl von beabstandet zueinander angeordneten Magnetisierungsstegen aus einem magnetisch leitenden Material aufweist, die in der Bewegungsrichtung zumindest eine gleich große Erstreckung aufweisen wie der Permanentmagnet, dass ein Abstand zwischen benachbarten Magnetisierungsstegen derart gew{\"a}hlt ist, dass in einer ersten Relativlage ein erster Permanentmagnet von einem der Magnetisierungsstege {\"u}berdeckt ist und ein zweiter Permanentmagnet nicht von einem der Magnetisierungsstege {\"u}berdeckt ist.},
  language  = {de}
}
@techreport{HoffmannUllrich2024,
  type      = {Working Paper},
  author    = {Hoffmann, Sarah and Ullrich, Anna Valentine},
  title     = {30 Minuten FDM f{\"u}r HAW. Ein Informationsformat f{\"u}r Forschende an HAW in NRW},
  doi       = {10.5281/zenodo.12569282},
  pages     = {1 Seite},
  year      = {2024},
  abstract  = {Wie kann man das Thema Forschungsdatenmanagement (FDM) konkret und anwendbar f{\"u}r Forschende gestalten, die bisher noch wenig Kontakt damit hatten? Auf diese Frage gibt das Konzept „30 Minuten FDM f{\"u}r HAW. Ein Informationsformat f{\"u}r Forschende an HAW in NRW" eine Antwort. Es entstand als Projektarbeit im Zertifikatskurs Forschungsdatenmanagement 2023/24},
  language  = {de}
}
@book{Heuermann2023,
  author    = {Heuermann, Holger},
  title     = {Mikrowellentechnik : Feldsimulation, nichtlineare Schaltungstechnik, Komponenten und Subsysteme, Plasmatechnik, Antennen und Ausbreitung},
  edition   = {2. Auflage},
  publisher = {Springer Vieweg},
  address   = {Wiesbaden},
  isbn      = {978-3-658-41286-9},
  doi       = {10.1007/978-3-658-41287-6},
  pages     = {XVI, 394 Seiten},
  year      = {2023},
  abstract  = {Das Lehrbuch behandelt alle Aspekte, die den aktuellen Stand der GHz-Technik betreffen. Das Buch behandelt die verschiedenen numerischen Feldsimulationsverfahren. Mit vielen modernen Themen.},
  language  = {de}
}
@article{OrzadaFiedlerBitzetal.2020,
  author    = {Orzada, Stephan and Fiedler, Thomas M. and Bitz, Andreas and Ladd, Mark E. and Quick, Harald H.},
  title     = {Local SAR compression with overestimation control to reduce maximum relative SAR overestimation and improve multi-channel RF array performance},
  series = {Magnetic Resonance Materials in Physics, Biology and Medicine},
  journal   = {Magnetic Resonance Materials in Physics, Biology and Medicine},
  number    = {34 (2021)},
  publisher = {Springer},
  address   = {Heidelberg},
  isbn      = {1352-8661},
  doi       = {10.1007/s10334-020-00890-0},
  pages     = {153 -- 164},
  year      = {2020},
  abstract  = {Objective In local SAR compression algorithms, the overestimation is generally not linearly dependent on actual local SAR. This can lead to large relative overestimation at low actual SAR values, unnecessarily constraining transmit array performance. Method Two strategies are proposed to reduce maximum relative overestimation for a given number of VOPs. The first strategy uses an overestimation matrix that roughly approximates actual local SAR; the second strategy uses a small set of pre-calculated VOPs as the overestimation term for the compression. Result Comparison with a previous method shows that for a given maximum relative overestimation the number of VOPs can be reduced by around 20\% at the cost of a higher absolute overestimation at high actual local SAR values. Conclusion The proposed strategies outperform a previously published strategy and can improve the SAR compression where maximum relative overestimation constrains the performance of parallel transmission.},
  language  = {en}
}
@article{FiedlerLaddBitz2017,
  author    = {Fiedler, Thomas M. and Ladd, Mark E. and Bitz, Andreas},
  title     = {SAR Simulations \& Safety},
  series = {NeuroImage},
  journal   = {NeuroImage},
  number    = {Epub ahead of print},
  publisher = {Elsevier},
  address   = {Amsterdam},
  issn      = {1053-8119},
  doi       = {10.1016/j.neuroimage.2017.03.035},
  year      = {2017},
  language  = {en}
}
@article{SchifferFerreinLakemeyer2012,
  author    = {Schiffer, Stefan and Ferrein, Alexander and Lakemeyer, Gerhard},
  title     = {Caesar: an intelligent domestic service robot},
  series = {Intelligent service robotics},
  volume    = {5},
  journal   = {Intelligent service robotics},
  number    = {4},
  publisher = {Springer},
  address   = {Berlin},
  issn      = {1861-2776},
  doi       = {10.1007/s11370-012-0118-y},
  pages     = {259 -- 276},
  year      = {2012},
  abstract  = {In this paper we present CAESAR, an intelligent domestic service robot. In domestic settings for service robots complex tasks have to be accomplished. Those tasks benefit from deliberation, from robust action execution and from flexible methods for human-robot interaction that account for qualitative notions used in natural language as well as human fallibility. Our robot CAESAR deploys AI techniques on several levels of its system architecture. On the low-level side, system modules for localization or navigation make, for instance, use of path-planning methods, heuristic search, and Bayesian filters. For face recognition and human-machine interaction, random trees and well-known methods from natural language processing are deployed. For deliberation, we use the robot programming and plan language READYLOG, which was developed for the high-level control of agents and robots; it allows combining programming the behaviour using planning to find a course of action. READYLOG is a variant of the robot programming language Golog. We extended READYLOG to be able to cope with qualitative notions of space frequently used by humans, such as "near" and "far". This facilitates human-robot interaction by bridging the gap between human natural language and the numerical values needed by the robot. Further, we use READYLOG to increase the flexible interpretation of human commands with decision-theoretic planning. We give an overview of the different methods deployed in CAESAR and show the applicability of a system equipped with these AI techniques in domestic service robotics},
  language  = {en}
}