Offenburg University of Applied Sciences
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Formal Modeling and Verification of Generic Credential Management Processes for Industrial Cyber–Physical Systems
No full textIndustrial cyber-physical systems (ICPS) face rising cyberattacks, requiring secure credential management also in resource-constrained embedded systems. Standards specifying field level communication of ICPS (e.g., PROFINET or OPC UA) define protocol-specific credential management processes, yet lack formal security verification. We propose a generic model capturing initial security onboarding and automated credential provisioning. Using ProVerif, an automatic symbolic protocol verifier, we formalize certificate-based authentication under a Dolev-Yao adversary, verifying private key secrecy, component authentication, and mutual authentication with the operator domain. Robustness checks confirm resilience against key leakage and highlight the vulnerabilities of the trust on first use concept proposed by the standards. Our model offers the first formal guarantees for secure credential management in ICPS
Verbesserte viskoplastische Berechnungskonzepte für kriechermüdungsbeanspruchte Kraftwerkskomponenten aus Grade 92
Get PDFSchwankungen bei der Nutzung von Wind- und Sonnenenergie zur Stromerzeugung und des dadurch notwendigen Residuallastbetriebs von bestehenden Kraftwerken führen zur Kriechermüdung der eingesetzten Stähle. Zur verbesserten Bewertung und Modellierung der Kriechermüdungsverformung des ferritisch-martensitischen Stahls Grade 92 werden Versuche bei betriebsähnlichen Belastungen durchgeführt und darauf basierend viskoplastische Werkstoffmodelle und Berechnungskonzepte abgeleitet. Mit einem 2-Dehnraten Modell und einem neuen Modellierungsansatz ist eine gute Beschreibung von Ermüdungsversuchen, Kriechversuchen und den Versuchen mit betriebsähnlichen Belastungen möglich. Das Modell wird in Berechnungskonzepte für Kraftwerkskomponenten integriert und es werden Vergleichsrechnungen zu den Berechnungskonzepten durchgeführt
Creep-Fatigue Deformation of the Ferritic-Martensitic Steel P92: Non-unified Viscoplasticity Modeling of Service-Like Material Tests
No full textThe ferritic-martensitic steel P92 is used for thick-walled components in thermal power plants. Besides continuous operation, the components are exposed to cyclic loadings so that creep-fatigue deformation is relevant for the assessment of the components in finite-element calculations. In this work, a non-unified viscoplasticity model is developed to describe the creep-fatigue deformation behavior of the P92 steel. For the determination of the relevant model parameters of the material model, results of service-like material tests are employed that mimic the material loading conditions in thick-walled components. A good description of the service-like material tests is achieved with the model
Kriterien beim Laborgerätekauf: Wann sich der Modellwechsel lohnt
No full textIrgendwann erreicht jedes Laborgerät sein Ende. Dann stellt sich die Frage: Einfach „das gleiche Gerät“ nochmal kaufen? Oder die Gelegenheit nutzen und ein komplett anderes Fabrikat anschaffen? Eine Entscheidung zwischen Gewohnheit, Kosten und Innovationspotenzial. Dieser Praxisleitfaden für Laboranten und Führungskräfte soll helfen
Untersuchung der Kunststoff-Metall-Haftung bei der additiven Fertigung hybrider Spritzgussformen
Get PDFZur Herstellung hybrider Spritzgussformen wird die Haftung verschiedener 3D-Druck-Filamente auf Metallmodulen untersucht. Mit einem modifizierten FFF-Drucker und roboterbasierter Abzugsprüfung wird die Verbindung bewertet. PA12+CF15 zeigte die besten mechanischen Eigenschaften und haftet am besten in Kombination mit Metallkleber auf Polychloropren Basis. Zukünftig soll das Verfahren auf ein robotergestütztes Mehrachs-System übertragen und um einen mechanischen Haftungsmechanismus ergänzt werden, um temperaturbedingte Haftkraftverluste auszugleichen
Überlegungen zum Einfluss generativer KI auf die Zukunft der Lehre
No full textDie Auswirkungen der KI-Revolution auf die Lehre in Schule und Hochschulen sind derzeit noch nicht wirklich zu überblicken. Einige grundlegende Trends in der Gestaltung von Prüfungen, der Auswahl von Inhalten und den benötigten Lehrmethoden sind aber bereits erkennbar und sollen in diesem Essay kurz angerissen werden
PhysicsGen: Can Generative Models Learn from Images to Predict Complex Physical Relations?
No full textThe image-to-image translation abilities of generative learning models have recently made significant progress in the estimation of complex (steered) mappings between image distributions. While appearance based tasks like image in-painting or style transfer have been studied at length, we propose to investigate the potential of generative models in the context of physical simulations. Providing a dataset of 300k image-pairs and baseline evaluations for three different physical simulation tasks, we propose a benchmark to investigate the following research questions: i) are generative models able to learn complex physical relations from input-output image pairs? ii) what speedups can be achieved by replacing differential equation based simulations? While baseline evaluations of different current models show the potential for high speedups (ii), these results also show strong limitations toward the physical correctness (i). This underlines the need for new methods to enforce physical correctness
How Much Training Data Is Enough? An Empirical Study on Load Forecasting for a Distribution Transformer in Germany
No full textThe increasing digitalization of distribution grids and the gradual deployment of smart monitoring infrastructure are beginning to generate valuable data streams at the lowvoltage level. These advances enable data-driven forecasting and the integration of flexibility to enhance grid operation. However, since monitoring equipment has only recently been installed, historical data availability remains limited. This raises a central question: how much training history is necessary to achieve reliable day-ahead load forecasts that can support flexibility use in distribution grids? This paper investigates this issue by systematically examining the influence of training dataset length on forecasting performance for a low-voltage transformer in Germany. A broad set of machine learning models, including linear approaches, tree-based ensembles, and deep learning methods, was evaluated under a day-ahead forecasting framework. The results show that tree-based ensembles consistently outperform other models, with CatBoost delivering the most accurate and stable forecasts across all training horizons. Yet, when the training data spans only up to three months, simple naive baselines remain highly competitive due to their zero training cost. From the fourth month onward, machine learning models increasingly leverage longer training histories to decisively surpass naive methods. These findings provide practical guidance for distribution system operators: employ naive averages in the early stages of monitoring and transition to advanced ensemble methods once at least four months of data are available
Understanding Users’ Acceptance and Adoption of Voice-Driven Systems
No full textThis study investigates the main factors influencing the acceptance and adoption of voice-driven systems. To this end, the Technology Acceptance Model was extended by incorporating the concepts of perceived security, perceived response quality, and perceived comprehensibility of the system. Our model is validated with a path analysis using the SEM module in JASP. Data were gathered through a standardized paper-and-pencil survey from 146 respondents, all under 35 years old and owners of voice-enabled devices. Findings reveal that perceived comprehensibility of the system enhances the perceived ease of use of voice-driven system. Furthermore, perceived response quality, perceived security, and perceived ease of use positively affect the perceived usefulness of voice-driven systems. No support was found for a positive effect of perceived security on the intention to use voice-driven systems
