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Inband-pumped, high-power thulium-doped fiber amplifiers for an ultrafast pulsed operation
No full text4427044282We investigate the influence of the pump wavelength on the high-power amplification of large-mode area, thulium-doped fibers which are suitable for an ultrashort pulsed operation in the 2 µm wavelength region. By pumping a standard, commercially available photonic crystal fiber in an amplifier configuration at 1692 nm, a slope efficiency of 80 % at an average output power of 60 W could be shown. With the help of simulations we investigate the effect of cross-relaxations on the efficiency and the thermal behavior. We extend our investigations to a rod-type, large-pitch fiber with very large mode area, which is exceptionally suited for high-energy ultrafast operation. Pumping at 1692 nm leads to a slope efficiency of 74 % with a average output power of 67 W, instead of the 38 % slope efficiency obtained when pumping at 793 nm. These results pave the way to highly efficient 2 µm fiber-based CPA systems.302
Additive manufacturing: Towards a digital twin of UD tapes for automated fibre placement
No full text232414
Influence Of Powder Characteristics On Material Properties In Laser Powder Bed Fusion Of Cucr1zr
No full textPowder bed fusion of metals using laser beam (PBF-LB/M) creates a new platform for the fabrication of complex geometries from high-performance materials. In this regard, the high strength and thermal conductivity of CuCr1Zr lead to great interest in the aerospace and nuclear industries. By understanding the influence of powder properties, the quality of those critical components can be optimized. In this context, the present work investigates the effects of powder properties in PBF-LB/M of CuCr1Zr. For this reason, powders from different suppliers were characterized and processed. Subsequently, the achieved component density, hardness, and electrical conductivity were systematically investigated and discussed to define basic property profiles depending on the powder material
ROBUST communication platform – A decentralized, distributed communication platform for the earthquake early warning system ROBUST
No full text822836Strong earthquakes of great intensity pose a severe threat to human life and property. Earthquake early warning systems are designed to give people in endangered areas valuable seconds to save their lives and property. The basis of an efficient warning system is a communication infrastructure that provides high-speed and reliable communication between the components of the warning system. This paper presents the distributed, decentralized communication platform for the ROBUST project. It discusses the key challenges and requirements such as resilience, real-time capability and target group-specific information distribution that are placed on such a communication platform. In addition, it presents the conception of the communication platform, which is based on a subscriber procedure between autonomous, decentralized peers (nodes), in order to be able to realize the requirements. Finally, it details the technical implementation, practical realization, and evaluation of the communication platform
Self-supervised Few-Shot Learning for Semantic Segmentation: An Annotation-Free Approach
No full text159171Few-shot semantic segmentation (FSS) offers immense potential in the field of medical image analysis, enabling accurate object segmentation with limited training data. However, existing FSS techniques heavily rely on annotated semantic classes, rendering them unsuitable for medical images due to the scarcity of annotations. To address this challenge, multiple contributions are proposed: First, inspired by spectral decomposition methods, the problem of image decomposition is reframed as a graph partitioning task. The eigenvectors of the Laplacian matrix, derived from the feature affinity matrix of self-supervised networks, are analyzed to estimate the distribution of the objects of interest from the support images. Secondly, we propose a novel self-supervised FSS framework that does not rely on any annotation. Instead, it adaptively estimates the query mask by leveraging the eigenvectors obtained from the support images. This approach eliminates the need for manual annotation, making it particularly suitable for medical images with limited annotated data. Thirdly, to further enhance the decoding of the query image based on the information provided by the support image, we introduce a multi-scale large kernel attention module. By selectively emphasizing relevant features and details, this module improves the segmentation process and contributes to better object delineation. Evaluations on both natural and medical image datasets demonstrate the efficiency and effectiveness of our method. Moreover, the proposed approach is characterized by its generality and model-agnostic nature, allowing for seamless integration with various deep architectures. The code is publicly available at GitHub
Aktuelle Entwicklungen des Rechtsrahmens der Cybersicherheit und Privatheit
No full textDer Sammelband zum Rechtsrahmen der Cybersicherheit und Privatheit bietet eine umfassende Sammlung von Beiträgen aus den Rechtswissenschaften. Die Themen reichen von datenschutzrechtlichen Herausforderungen der Nutzung neuer Technologien, über die angemessene Umsetzung neuer Rechtsakte im Kontext Privatheit und Cybersicherheit bis hin zu aktuellen rechtlichen Entwicklungen in der Cybersicherheitsforschung
Minimum Directed Information: A Design Principle for Compliant Robots
No full text1195311960A robot's dynamics - especially the degree and location of compliance - can significantly affect performance and control complexity. Passive dynamics can be designed with good regions of attraction or limit cycles for a specific task, but achieving flexibility on a range of tasks requires co-design of control. This paper takes an information perspective: the robot dynamics should reduce the amount of information required for a controller to achieve a threshold of performance in a range of tasks. Towards this goal, an iterative method is proposed to minimize the directed information from state to control on discrete-time nonlinear systems. iLQG is used to find a controller and value of information, then the design parameters of the dynamics (e.g. stiffness of end-effector or joint) are optimized to reduce directed information while maintaining a minimum bound on performance. The approach is validated in simulation, on a two-mass system in contact with an uncertain wall position and a high-DOF door opening task, and shown to improve noise robustness and reduce time variance of control gains