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Anforderungsdefinition zur Anwendung von Chaos Engineering in der industriellen Produktion
No full textChaos Engineering ist ein etabliertes Konzept in der IT-Branche, das zur Überprüfung der Resilienz von Systemen verwendet wird. Dabei werden Chaos-Experimente im System durchgeführt, um potenzielle Schwachstellen aufzudecken und diese zu beheben. In der industriellen Produktion hat Chaos Engineering jedoch noch keine breite Akzeptanz gefunden, obwohl auch hier die Resilienz von hoher Bedeutung ist. In dieser Arbeit wird daher überprüft, ob Chaos Engineering auf die industrielle Produktion übertragbar ist. Dazu wird ein Überblick über die Themenfelder Resilienz, Chaos Engineering und Produktion in der Industrie 4.0 gegeben. Zudem wird der aktuelle Stand der Technik von Chaos Engineering sowohl allgemein als auch spezifisch in der industriellen Produktion beleuchtet. Dabei wird gezeigt, dass die industrielle Produktion alle Voraussetzungen erfüllt, um Chaos Engineering anzuwenden. Anschließend wird der Forschungsfrage nachgegangen, welche Anforderungen erforderlich sind, um Chaos Engineering in der industriellen Produktion anzuwenden. Dafür wird eine Anforderungsdefinition erarbeitet, die eine vollständige Beschreibung der Anforderungen für die Anwendung von Chaos Engineering in der industriellen Produktion liefert. Die Anforderungen werden durch eine systematische Literaturrecherche ermittelt. Die Anforderungsdefinition ist allgemein gehalten, um auf verschiedene Systeme übertragbar zu sein und beinhaltet ein Vorgehensmodell zur Implementierung von Chaos Engineering in der industriellen Produktion
Multi-Matrix Completion: A Novel Framework for Structurally Missing Elements
No full textA common assumption in matrix completion (MC) and tensor completion (TC) is that the missing locations are sampled randomly. However, in real-world scenarios, the unobserved elements are often not arbitrarily located, and may concentrate within entire rows or columns. We refer to this missing mechanism as structural missingness, and traditional MC and TC schemes suffer from drastic degradation under these circumstances. This work addresses the challenge of restoring structural missingness by introducing a novel framework for simultaneously reconstructing multiple matrices, called multi-matrix completion (MMC). In MMC, tri-factorization across matrices captures the correlation between matrices, and Tikhonov regularization on each matrix exploits its correlation. This design enables MMC to efficiently handle both random and structural missingness. In addition, MMC is not affected by the smoothness along matrices which makes it suitable for a wider variety of data compared to Fourier transform based TC methods. The alternating direction method of multipliers is utilized to solve the resultant optimization problem. The global convergence of the algorithm is supported by comprehensive theoretical analyses. We demonstrate the versatility of MMC through extensive experiments in image and video restoration, and showcase its superior performance in comparison to traditional MC and TC methods. The code is available at https://github.com/ShuDun23/MMC
Digitale Regelwerke im Bahnbetrieb: Evaluation der Richtlinie 400 aus Sicht von Fahrdienstleitern
No full textE-Lkw im Vor- und Nachlauf von Umschlagterminals: Auswirkungen auf Terminalprozesse im Kombinierten Straßen-/Schienengüterverkehr
No full textPilotstudie zur Evaluation eines Systems zur adaptiven Höhen- und Neigungsverstellung großer Arbeitsobjekte für simulierte Tätigkeiten aus der Automobiltürmontage
No full textIn der Fahrzeugendmontage dominieren weiterhin manuelle Tätigkeiten, die häufig mit ungünstigen Körperhaltungen verbunden sind, welche mit der Entstehung muskuloskelettaler Erkrankungen (MSE) assoziiert werden. Adaptive Arbeitsstationen, die die Höhe und Neigung großflächiger Arbeitsobjekte automatisiert an individuelle Körpermaße und den Arbeitsschritt anpassen, sollen physische Belastungen reduzieren. Im Rahmen einer Pilotstudie wurde die Wirkung von drei Ausprägungen einer individualisierten Höhen- und Neigungsanpassung gegenüber einer „One-Size-Fits-All“ Positionierung eines großflächigen Arbeitsobjekts auf die körperliche Belastung und Sichtbedingungen während simulierten Tätigkeiten eines Autotürmontageprozesses untersucht.
Die Ergebnisse zeigen, dass eine einmalige Anpassung der Arbeitsobjektposition die Expositionsdauer in kritischen Gelenkwinkelbereichen der Schulter und des Nackens signifikant verringert. Subjektiv bevorzugten die Teilnehmenden die Arbeit und Sichtbedingungen der adaptiven Ausprägungen des Systems gegenüber einer starren „One-Size-Fits-All“ Einstellung
Exploring Algorithmic Management Practices in Healthcare – Use Cases along the Hospital Value Chain
No full textDer Einfluss von Kündigungsverzichtserklärungen auf den Shareholder Value: Evidenz aus Deutschland
No full textGaussian Process-Based Surrogate Models for Optimizing Electrode Configurations in HD-tDCS
No full textHigh-definition transcranial direct current stimulation (HD-tDCS) is a promising noninvasive neurostimulation technique used in therapeutic applications and brain-machine interfaces. It delivers direct current via multiple scalp electrodes, generating targeted electrical fields to modulate specific brain areas. In the context of HD-tDCS, optimizing electrode placements is challenging due to the complexity of brain anatomy and the vast number of possible configurations. While simulation models enable model-based optimization, continuous electrode positioning is generally computationally prohibitive. We propose Gaussian Process (GP)-based framework for optimizing HD-tDCS, allowing continuous prediction of electric field distributions. Unlike traditional leadfield-based methods, which restrict electrode placement, our approach expands the search space for greater precision. We employ a Sparse Gaussian Process (SGP) approximation, optimized using Block-Coordinate Descent and Subset of Data techniques, to efficiently handle large datasets. Results demonstrate that the SGP-based model significantly enhanced focality for superficial and mid-brain regions, achieving performance comparable to leadfield-based methods for deep brain targets. Overall, this framework offers enhanced stimulation precision and flexibility, supporting the advancement of tDCS in research and clinical contexts
Stability of SnSe-based thermoelectric compounds
No full textSnSe compounds are studied as promising candidates for thermoelectric (TE) applications, primarily due to their remarkable achievement of a high ZT value and the relative abundance of their constituent elements. In former studies, a significant disparity in the performance of polycrystalline SnSe compounds has been observed, and the reasons for the non-reproducibility have been investigated. This study focuses on the impact of sintering temperature on the thermoelectric properties of both Br-doped and undoped SnSe materials. Through a targeted synthesis approach, we achieved a ZT value of 1.04 at T = 873 K. The results reveal a critical challenge in controlling the mobility of ions and defects for long-term application of SnSe-based thermoelectric materials. The peak ZT values observed in the initial measurements are not sustainable, as the thermoelectric performance experiences a decline during multiple heating–cooling cycles. This issue is further underscored by extended annealing experiments, which resulted in a substantial ZT decrease of approximately 50%. These outcomes emphasise the need for a comprehensive understanding of the long-term stability of SnSe materials in thermoelectric applications. Additionally, they emphasise the importance of conducting heating–cooling measurements in thermoelectric systems, particularly when aiming to achieve and maintain high ZT values for longer periods
Pressure-induced nano-crystallization and high hardness of optically transparent α-Si3N4 ceramics
No full textTransparent silicon nitride ceramics with good optical and mechanical properties are promising ceramics for scientific and industrial window materials with a long service life. The optical transparency and mechanical strength will be substantially enhanced in dense nano-polycrystalline monoliths. However, the synthesis of nano-polycrystalline alpha-Si3N4 has not been realized due to the limitations of conventional sintering techniques. Here, nano-polycrystalline alpha-Si3N4 was prepared by direct conversion of micron-grain silicon nitride without additives under high-pressure conditions of 5 GPa and a limited temperature range 1650 degrees C-1700 degrees C. The micron-sized grains undergo grain refinement and recrystallization to form uniform nano-grains under high pressure and high temperature. Furthermore, transparent alpha-Si3N4 samples exhibit the highest Vickers hardness of 26.7 GPa, which is far higher than that of specimens with or without additives used in other sintering methods (e.g., SPS, and HP). According to the Hall-Petch and Taylor dislocation hardening effects, the refined nano-grains, coherent grain boundaries, and high dislocation density lead to high hardness. Moreover, the high density, nanoscale grains, and fine grain boundaries are ascribed to the improvement of transparency. Ultrahigh-pressure sintering induces grain refinement, grain coherency, and increased dislocation in silicon nitrides, thus providing a promising method for preparing advanced transparent ceramic windows in the future