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Speicherung von Wasserstoff
No full textOpen-Access-Veröffentlichung unter der Lizenz CC BY-NC-ND 4.0 (https://creativecommons.org/licenses/by-nc-nd/4.0/)Vo
Spectroscopic characterization of Pr,Zn:LT and Pr,Zr:LT ridge waveguides, fabricated by high temperature in-diffusion and diamond-blade dicing
No full textNear-surface doping of lithium tantalate crystals with trivalent praseodymium ions (Pr³⁺) with concentrations up to 0.5 at.% was performed using high-temperature in-diffusion. In these substrates, planar waveguides were subsequently fabricated by in-diffusion of thin Zn or Zr metal films. Doping with the latter metals not only increases the refractive index, but also enhances the crystal’s resistance to photorefractive damage, in the short visible wavelength range. In the final step of fabrication, ridge waveguides with near-rectangular cross sections were fabricated by diamond-blade dicing, allowing for propagation of visible light. Propagation losses of about 0.3 dB/cm and optical damage thresholds up to several hundreds of milliwatt were achieved for blue light at 405 nm wavelength. The Pr³⁺-doped waveguides were further optically characterized using absorption and fluorescence spectroscopy, and the lifetime of potential upper laser levels was measured. Combination of spectroscopic properties of Pr³⁺-doped lithium tantalate and its high resistance to photorefractive damage due to Zn (Zr) co-doping, makes these LT ridge waveguides what we believe to be a promising novel platform for compact integrated active devices in the visible spectral range.Vo
Challenges and opportunities in developing INN-based control systems for modular drones
No full textAs drone technology evolves, modular drones are increasingly central, offering rapid adaptability through the interchange of sensors, motors, and structural battery modules. However, this flexibility also introduces complex control challenges that traditional Proportional-Integral-Derivative (PID) controllers often struggle to address, particularly under dynamic reconfigurations and nonlinear responses. In this paper, we propose a novel approach integrating Invertible Neural Networks (INNs) and Reinforcement Learning (RL) to enhance adaptability and effectiveness in modular drone control. INNs facilitate precise, reversible command mapping via bijective transformations, ensuring robust handling of changing drone weight, geometry, and functionality. When combined with RL, these networks further enable real-time optimization of flight performance, dynamically responding to shifts in operational conditions. We outline a comprehensive research agenda employing the PX4 simulation framework to benchmark INN- and RL-based methods against standard PID controllers, focusing on improved response times, reduced error rates, and better system resilience. The anticipated findings aim to substantiate the potential of these advanced control systems – particularly in conjunction with emerging structural battery designs – to significantly expand the capabilities and operational scope of next-generation unmanned aerial vehicle (UAVs) in real-world applications.Vo
Responsibilities, opportunities and challenges of school entry examinations in Germany
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Division of labor in CPS anomaly detection
No full textAnomaly detection in multivariate time series is critical for ensuring the reliability of cyber-physical systems (CPS). We propose a two-stage framework that combines advanced anomaly detection models with large language models (LLMs) to provide robust detection and interpretable explanations. In the first stage, a self-supervised ensemble of temporal and spatiotemporal models identifies anomalies based on reconstruction errors. In the second stage, LLMs generate natural language explanations for these anomalies, making results accessible to domain experts.
To address LLM limitations such as hallucination and instruction adherence, we design structured prompts that provide focused context, anomaly details, and clear guidelines. This framework emphasizes a division of labor between detection models, LLMs, data scientists, and users. We validate the approach using data from a search-and-rescue cruiser, showcasing its ability to detect diverse anomalies and provide interpretable outputs. This work bridges advanced machine learning with practical CPS applications, offering a path towards a user-friendly approach to anomaly detection.Vo
Towards adaptive traffic signal control through foundation models and reinforcement learning
No full textTraffic Signal Control (TSC) is pivotal for managing urban traffic flow and enhancing intersection safety. Traditional TSC systems are rule-based and tailored to specific intersections, requiring substantial training and resources, which restricts their flexibility. This paper proposes a novel adaptive, scalable solution utilizing Foundation Models (FM) and Reinforcement Learning (RL), designed to handle diverse urban intersections efficiently without extensive retraining. The approach leverages advanced neural network architectures, including attention mechanisms, to improve generalization capabilities across different intersection topologies. A safety control mechanism aligned with traffic regulations ensures the safe operation of traffic signals, significantly enhancing the system’s reliability. By systematically classifying intersection types, the method tailors the control strategies to specific traffic scenarios, further reducing implementation times and expertise requirements. This FM- and RL-based approach not only reduces resource demands but also promises more efficient traffic flow and improved safety in various urban settings.Vo
Numerical investigation of soil plugging in clay with the CEL method and effective contact stresses
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Structure-preserving discontinuous Galerkin approximation of a hyperbolic-parabolic system
No full textThis work is licenced under the Creative Commons licence CC BY 4.0 (https://creativecommons.org/licenses/by/4.0/).We study the numerical approximation of a coupled hyperbolic-parabolic system by a family of discontinuous Galerkin (DG) space-time finite element methods. The model is rewritten as a first-order evolutionary problem that is treated by a unified abstract solution theory. For the discretization in space, generalizations of the distribution gradient and divergence operators on broken polynomial spaces are defined. Since their skew-selfadjointness is perturbed by boundary surface integrals, adjustments are introduced such that the skew-selfadjointness of the discrete counterpart of the total system’s first-order differential operator in space is recovered. Well-posedness of the fully discrete problem and error estimates for the DG approximation in space and time are proved.Vo