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AITwin: a uniform digital twin interface for artificial intelligence applications
No full textThis chapter is licensed under the terms of the Creative Commons Attribution 4.0 International License (https://creativecommons.org/licenses/by/4.0/).Cyber-physical systems that integrate machine learning (ML)-based services and methods from the broader field of Artificial Intelligence (AI) rely on a virtual representation of the underlying real physical system. Unfortunately, depending on respective solution approaches, usually similar but rarely the same virtual representation of the physical system is required. Thus, two solutions for the same problem might use different virtual representations. Informed Machine Learning is one technique to integrate expert knowledge into AI applications. It uses techniques to combine an often proprietary and expert-defined virtual representation with data from a real cyber-physical system. But methods for Informed ML have a much higher demand on the virtual representation than, for example, traditional distance-based methods in Machine Learning. Informed ML requires domain specific knowledge, which needs to be represented in some standardized Digital Twin as its virtual representation. Practitioners benefit through some categorization indicating which Digital Twin can be used to acquire a unique virtual representation of a cyber-physical system. Especially, by using a common standardized application programming interface (API). In short: a standardized Digital Twin is needed for AI-based solutions. In this chapter, such an API for Digital Twins for AI solutions is presented and different levels of complexity for Digital Twins are defined. The suggested API is considered as an AI reference model and is verified by using it on several simulated and real examples from the process and manufacturing industries. Additionally, it is compared against currently ongoing research projects.Vo
Imputing missing multi-sensor data in the healthcare domain: a systematic review
No full textThis is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).Chronic diseases, especially diabetes, are burdens for the patient since lifelong management is required, and comorbidities can occur as a consequence of insufficient prevention. Hypoglycemia, a medical condition encountered by diabetic individuals, can result in severe symptoms if untreated, necessitating prompt preventive actions. Continuous health monitoring based on data collected with wearables can enable the early prediction of extreme blood glucose states. However, integrating and using data acquired from various sensors is challenging, especially when it comes to maintaining the quality and quantity of data due to inherent noise and missing values. To this end, the review discusses dataset constraints and highlights the temporal behaviour of prominent features in predicting hypoglycemia. It outlines a framework of preprocessing techniques that could be adopted to mitigate dataset limitations. A thorough analysis of the imputation procedures employed in the reviewed studies is conducted. In addition, machine learning imputation techniques applied in other healthcare domains are studied to investigate if they could be adopted to close the longer gaps of missing values in the datasets involved in the prediction of hypoglycemia. Based on a comprehensive evaluation of imputation techniques, a paradigm, Impute-Paradigm, is proposed and validated through a case study, enabling imputation tailored to variable duration time gaps. After analysing the reviewed studies, we recommend studying the rate of change of individual features and conclude that different time gaps of separate features should be treated with multiple imputation techniques.Vo
Gedankensplitter zu temporalen, digitalen, virtuellen und hybriden Phänomenen von Lehren und Lernen
No full textDieser Beitrag bietet eine Sammlung zahlreicher Phänomene von Zeit und Raum in der digitalen Bildung. Daraus entstehen erste Deutungsversuche und Implikationen, indem sowohl theoretische als auch praktische Perspektiven einbezogen, reflektiert und diskutiert werden. Digitalisierung und Digitalität beeinflussen Bildungsprozesse tiefgreifend und prägen die Wahrnehmung von Zeit und Raum nochmals neu – so unsere Ausgangsannahme. Bildung verstehen wir als ein komplexes Zusammenspiel von räumlichen, zeitlichen und anderen Dimensionen, wobei digitale Technologien scheinbar reibungslose Lernumgebungen schaffen, die zeitliche wie physische Grenzen überwinden. Gleichzeitig bleiben Lernprozesse eng an subjektive und soziale Konstruktionen von Zeit und Raum geknüpft.Unsere Analysen zu Zeiterfahrungen Lernender wie Lehrender zeigen, dass traditionelle Konzepte zunehmend hinterfragt und durch relationale Ansätze neu gedacht werden müssen. Unsere Beobachtungen verdeutlichen Herausforderungen, die für eine differenzierte Betrachtung digitaler Lehr- und Lernpraktiken anhand ihrer zeitlichen sowie räumlichen Relationen plädieren und zu einem erneuten Nachdenken über Lern- und Bildungsprozesse im digitalen Zeitalter einladen.Vo
A comparative characterization of the creep behavior of short-fiber reinforced composites based on the prony series and fractional derivative-based creep models
No full textThis work examines dynamic models for describing the viscoelastic behaviour of short-fibre reinforced plastics in tensile tests. The creep behaviour of reinforced PBT GF30 compared to unreinforced PBT GF0 is investigated on the basis of experimental data. Two different modelling approaches are compared: a generalised Maxwell model based on the prony series and a model with fractional derivatives. The experimental data show that glass fibres significantly reduce the deformation under constant load, as they stiffen the polymer matrix and inhibit creep deformation. Parameters can be determined for both models using machine learning methods. However, the Prony-Maxwell based model requires three parameters to accurately represent the data, whereas the fractional model only requires two parameters. The results clearly show the advantages of fractional model for the description of the long time series behaviour: on the one hand, fewer parameters are required and on the other hand, additional knowledge can be gained through the interpretation of the parameters obtained. The experimental data as well a the open-source software developed to learn the model is published alongside this work.HSU-draf
[Kommentierung] § 28 Verfahren bei externen Meldungen
No full textOnline-Ausgabe, laufende AktualisierungenStand: 01.08.2025Statt Seitenzahlen: Randnummern 0-93Vo
Investigation of the degradation of 1200V SiC-MOSFETs stressed by different gamma radiation dose
No full textThis paper presents the degradation of 1200 V Siliconcarbide (SiC) MOSFETs in regard to the influence of different gamma radiation doses (γ-radiation). The impacts of four distinct γ-radiation doses (300 Gy, 200 Gy, 100 Gy and 50 Gy) were examined. The analysis was conducted using four different MOSFETs from several manufacturers, all of them in the TO263-7 package. They belong to the same voltage class with similar on-state resistance but varying technologies. The primary indicators for degradation analysed in this study are the threshold VGS(th) and the breakdown voltage VDSS(BR).Vo
Datenreport zum Forschungsprojekt Politisch-Administrative Elite (PAE) zu Bundesoberbehörden
No full textDie PAE-Umfrage zu den Bundesoberbehörden ist Teil des Forschungsprojekts „Politisch-Administrative Elite“ (PAE). Dieses knüpft an die Comparative Elite Study (CES) der 1970er und 1980er Jahre an (Aberbach et al., 1981; Mayntz & Derlien, 1989). Seit 2005 wird die Umfrage unter dem Namen PAE als Online-Survey durchgeführt (Ebinger & Jochheim, 2009; Schwanke & Ebinger, 2006). Zunächst war die Zielgruppe auf das Leitungspersonal in Bundesministerien fokussiert. Seit 2013 wird im Rahmen von PAE auch das Leitungspersonal in Landesministerien, Ressortforschungseinrichtungen und Bundesoberbehörden befragt. Die PAE-Umfrage wird regelmäßig kurz vor der Bundestagswahl durchgeführt und liefert seit vielen Jahren wertvolle empirische Daten. Die Befragten teilen ihre persönlichen Wahrnehmungen und Erfahrungen, nicht die „Sicht des Hauses” (Beneke et al., 2023).Vo