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Forschung zu Strahlenschäden mit dem Stratosphärenballon-Experiment ASTRABAX
No full textDas für die Jahre 2024 bis 2026 geförderte Projekt ASTRABAX („Aschaffenburger Stratosphärenballon- Experiment“) ist als multimodale Plattform der Material- und Biowissenschaften für Untersuchungen bei extremen Strahlenbelastungen in großer Höhe konzipiert. Dabei ist der UV-C-Spektralbereich von besonderem Interesse und wird mit Miniatur-UV-Spektrometern untersucht. Die Plattform des Ballonexperimentes umfasst eine gemeinsame Strahlungsdosimetrie, eine Stromquelle für die Bordelektronik und Abschirmungen für mehrere Bestrahlungskombinationen. Biologische Zellen werden Kombinationen aus Partikel-, Röntgen- und UV-Strahlung ausgesetzt. Nach dem Flug werden mögliche Veränderungen in der räumlichen Chromatinorganisation mittels hochauflösender Mikroskopie untersucht. Auswirkungen von Kombinationen aus hoch- und niederenergetischer Strahlung sind bisher nicht ausreichend erforscht und beschrieben. Zusätzlich werden auch für Satelliten relevante Materialproben bestrahlt. Untersuchungen unter solchen Bedingungen sind realistisch und relevant für Flüge in der Stratosphäre, für Raumflüge sowie für vergleichbare Expositionen bei anderen Objekten des Sonnensystems oder Exoplaneten-Habitaten.Poste
A New Cascaded Multilevel Inverter for Modular Structure and Reduced Passive Components
No full textIn high-power applications, achieving adequate power quality in power converter design is accomplished by utilizing multilevel inverters instead of using two-level and three-level inverters. The device generates a sinusoidal output voltage, which results in reduced total harmonic distortion and lower voltage stress on the switches and leads to lower electromagnetic interference, making it suitable for use in renewable energy applications. However, to illustrate the advantages mentioned above, a significant number of switching devices and DC sources are necessary while raising the voltage levels. This article proposes an asymmetrical voltage generation method, which operates in a ratio of 1:5 and generates 25 levels using 11 power switches. The topology is modular in structure, and each module has a lower component count, which significantly reduces the overall cost. The proposed topology is capable of generating negative output voltage levels without the use of an H-bridge configuration, where only three switches are used to generate any voltage levels. The functionality of the developed module is amended by fixing different voltage values in DC sources. This article also presents a comprehensive examination of the circuit and the functioning of various voltage levels. The advantages of the proposed inverter have been demonstrated by comparative research with the currently existing MLI topologies. Ultimately, both the simulation and experimental findings validated the practical capabilities
Femtosecond reductive Laser Sintering under multiple focus conditions for rapid production of conductive copper layers
No full textOnline-Befragung zur aktuellen Situation der Praxisanleiter*innen und Herausforderungen in Deutschland
No full textFrom Data to Innovation
No full textData can be the source of innovation. Data can help to analyse the
constitution of the people involved in the innovation process and to give them
recommendations along the innovation process to increase their innovation
performance, productivity in individual and group work and to optimise the
quality of the results. The article proposes a long-term study in which the
innovation potential of young adults in secondary schools, universities and
companies is continuously recorded. The aim is to develop a chatbot that
supports individuals in transforming their innovation potential into innovation
performance, so that innovations develop that transform society. The aim is to
improve the quality of innovation outcomes and the productivity of individuals
involved in solving complex problems
Exploring Argument Mining and Bayesian Networks for Assessing Topics for City Project Proposals
No full textThe digital transformation of cities inspired the city administration of Aschaffenburg, Germany, to apply artificial intelligence to reduce the significant amount of manual administrative effort needed to evaluate citizens’ ideas for potential future projects. This paper introduces a methodology that combines argument mining with Bayesian networks to evaluate the relative eligibility of city project proposals. The methodology involves two main steps: (1) clustering arguments extracted from public information available on the Internet, and (2) assessing and comparing selected urban issues, planning topics, and citizens’ ideas that have been widely discussed to measure public interest in potential candidate projects.
The results of the clustering are fed into a Bayesian network, along with scores for several evaluation criteria, to generate a relative eligibility score. The framework was applied to three candidate projects, resulting in the selection of one of them, while the other two were rejected with a given explanation. The latter motivates the decision and provides transparency to all parties involved in the decision process. The methodology is applicable
to other cities after adjustments of criteria
A visitor experiment on astronomical X-ray optics for the "Deutsches Röntgen-Museum"
No full textThe “Deutsches Röntgenmuseum” in Remscheid is the unique institution worldwide that researches and documents the life and work of the first Nobel Prize winner, Wilhelm Conrad Röntgen, and the effects of his discovery of X-rays. As an integral part of the museum, the RöLab laboratory offers visitors the opportunity to gain practical experience in X-rays, optics and technology through own experiments. As part of a joint development project, students from Aschaffenburg University of Applied Sciences are currently designing a visitor experiment on astronomical X-ray optics for the museum laboratory. An optical set-up with visible light illustrate the focusing principle of X-ray telescopes, e.g. for wide-angle optics based on the lobster-eye principle. Display boards explain the optics of various types of telescopes with accompanying text and corresponding illustrations. Modern X-ray observatories such as CHANDRA, XMM-Newton and eROSITA are presented clearly. Once implemented, the new visitor experiment inside the “Deutsches Röntgenmuseum” intend to inspire young researchers for the fascinating world of X-ray optics and X-ray astronomy
Machine Learning Based Early Rejection of Low Performance Cells in Li Ion Battery Production
No full textLithium-ion battery cell production is conducted through a multistep production process which suffers from a notable scrap rate. Machine learning (ML) based process monitoring provides solutions to mitigate the impact of substantial scrap rates by repeated multifactorial quality predictions (virtual quality gates) along the process line. This enables an early rejection of battery cells which are unlikely to reach required specifications, avoids further waste of resources at later process steps and simplifies recycling of rejected cells. A hierarchical architecture is used to apply ML algorithms first for process-adapted feature extraction which is guided by a priori knowledge on typical production anomalies. In a second step, these features are correlated with end-of-line quality control data using explainable ML methods. The resulting predictions may lead to pass or fail of a battery cell, or -in the context of flexible production- may also trigger adjustments of later process steps to compensate for detected deficiencies. An example ML based quality control concept is illustrated for a pilot battery cell production line
Synthetic demand data generation for individual electricity consumers: Inpainting
No full textIn this contribution we deal with the problem of producing “reasonable” data, when considering recorded energy consumption data, which are at certain sections incomplete and/or erroneous. This task is important, when energy providers employ prediction models for expected energy consumption, which are based on past recorded consumption data, which then of course should be reliable and valid. In a related contribution Yilmaz (2022), GAN-based methods for producing such “artificial data” have been investigated. In this contribution, we describe an alternative and complementary method based on signal inpainting, which has been successfully applied to audio processing Lieb and Stark (2018). After giving a short overview of the theory of proximity-based convex optimization, we describe and adapt an iterative inpainting scheme to our problem. The usefulness of this approach is demonstrated by analyzing real-world-data provided by a German energy supplier