David M. McCourt: The end of engagement. America’s China and Russia experts and U.S. strategy since 1989

HSU-O

Division of labor in CPS anomaly detection

Anomaly 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

[Kommentierung] Art. 28

VoR8., neubearbeitete Auflag

Use of a qualitative system model for machine learning of the flight behavior of a multicopter

Die Anwendung von maschinellem Lernen gewinnt in zahlreichen Bereichen zunehmend an Bedeutung. Reinforcement Learning ermöglicht es, Steuerungsstrategien direkt aus den Interaktionen eines Agenten mit seiner Umgebung zu erlernen – komplexe Systeme können so ohne explizite numerische Modellgleichungen angelernt werden. In diesem Beitrag steht das eigenständige Erlernen eines Flugverhaltens unterstützt durch ein qualitatives Systemmodell im Fokus. In einer virtuellen Umgebung lernt ein Multi-Rotor-UAS-Agent eigenständig einen Zielpunkt anzufliegen und zeigt dabei wie das Verwenden eines modellbasierten Ansatzes des Reinforcement Learnings das Lernverhalten gegenüber einem klassischen modellfreien Ansatz beschleunigen kann. Um dies zu erreichen wird mittels dem logischen Modell der Aktionsraum des Agenten eingeschränkt, sodass das Training mit weniger Schritten erfolgreich abgeschlossen werden kann. Durch den direkten Vergleich der Flugperformance der verschiedenen untersuchten Ansätze wird der Vorteil des modellbasierten Ansatzes zusätzlich verdeutlicht. Das zugrunde liegende qualitative Modell kann mit einfachem Systemvorwissen hergeleitet werden, ohne dass eine numerische Parametrisierung des Systems notwendig wird. Gegenüber eines klassischem numerischen Systemmodells wird ein deutlich geringerer Aufwand für die Implementierung aufgebracht. Anschließend werden die Möglichkeiten des Einsatzes von qualitativen Systemmodellen zur Optimierung von ML-Verfahren in einem Ausblick erläutert.Vo

Accelerated development of multi-component alloys in discrete design space using Bayesian multi-objective optimisation

This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/).Bayesian optimisation (BO) protocols grounded in active learning (AL) principles have gained significant recognition for their ability to efficiently optimize black-box objective functions. This capability is critical for advancing autonomous and high-throughput materials design and discovery processes. However, the application of these protocols in materials science, particularly in the design of novel alloys with multiple targeted properties, remains constrained by computational complexity and the absence of reliable and robust acquisition functions for multiobjective optimisation. Recent advancements have demonstrated that expected hypervolume-based geometrical acquisition functions outperform other multiobjective optimisation algorithms, such as Thompson Sampling Efficient Multiobjective optimisation and pareto efficient global optimisation (parEGO), in both performance and speed. This study evaluates several leading multiobjective BO acquisition functions–namely, parallel expected hypervolume improvement (qEHVI), noisy qEHVI, parallel parEGO, and parallel noisy parEGO (qNparEGO)–in optimizing the physical properties of multi-component alloys. Our findings highlight the superior performance of the qEHVI acquisition function in identifying the optimal Pareto front across 1-, 2-, and 3-objective aluminum alloy optimisation problems, all within a constrained evaluation budget and reasonable computational cost. Furthermore, we explore the impact of various surrogate model optimisation methods from both computational cost and efficiency perspectives. Finally, we demonstrate the effectiveness of a pool-based AL protocol in expediting the discovery process by executing multiple computational and experimental campaigns in each iteration. This approach is particularly advantageous for deployment in massively parallel high-throughput synthesis facilities and advanced computing architectures.Vo

China’s debt reckoning

Beitrag im Portal East Asia ForumVo

Optimal pressure approximation for the nonstationary Stokes problem by a variational method in time with post-processing

This work is licensed under the Creative Commons Attribution 4.0 International License (https://creativecommons.org/licenses/by/4.0/).We provide an error analysis for the solution of the nonstationary Stokes problem by a variational method in space and time. We use finite elements of higher order for the approximation in space and a Galerkin-Petrov method with first order polynomials for the approximation in time. We require global continuity of the discrete velocity trajectory in time, while allowing the discrete pressure trajectory to be discontinuous at the endpoints of the time intervals. We show existence and uniqueness of the discrete velocity solution, characterize the set of all discrete pressure solutions and prove an optimal second order estimate in time for the pressure error in the midpoints of the time intervals. The key result and innovation is the construction of approximations to the pressure trajectory by means of post-processing together with the proof of optimal order error estimates. We propose two variants for a post-processed pressure within the set of pressure solutions based on collocation techniques or interpolation. Both variants guarantee that the pressure error measured in the L2-norm converges with optimal second order in time and optimal order in space. For the discrete velocity solution, we prove error estimates of optimal order in time and space. We present some numerical tests to support our theoretical results.SMU

Impedance measurement of active systems with pulsed multitone signals from 300 kHz to 60 MHz

A dynamical impedance analysis of complex electrical aerospace systems during operation can provide detailed information for efforts to further enhance system stability and reliability. As conventional single-tone impedance measurements are relatively slow compared to possible system state changes, a pulsed method using a synthesised stochastic multitone signal is proposed. This method is validated for a passive permanent magnet synchronous motor through a result comparison with an RF I-V impedance measurement. Then it is applied to an active DC- DC converter to highlight the detection of impedance changes due to the switching of the involved transistors, impossible to detect with conventional impedance measurement methods. It will be discussed how this method can be further adapted to overcome currently existing practical limitations and how it can be optimised for other electrical systems of interest.Vo

Alltagspraktische Aneignung digitaler Kompetenzen (2022)

Vo

Optimal order FEM for dynamic poroelasticity: error analysis for equal order elements

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