Publikationsserver der Ostbayerischen Technischen Hochschule Regensburg
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Was lange währt ist auch gut: Langzeitverhalten von ZFSV
No full textVollständige Prozessbegleitung alternativer bindemittelversetzter Bettungsmaterialien im Fernwärmeleitungsbau über Eignungsprüfung, Qualitätskontrolle, Stati
Experimenal method for studying electron beams in gaseous environments using a CMOS image sensor
No full textDetecting and analyzing electron beams in atmospheric conditions remains a significant challenge due to scattering and absorption of electrons by gas molecules. In this work, a novel approach using a CMOS image sensor for real-time electron beam detection after its transmission through air is presented. The setup enables visualization of beam divergence, emission profiles, and dynamic behavior under atmospheric pressure. Presented results demonstrate the potential of CMOS image sensor for electron beam analysis in gaseous environment
Aspekte der sozialen Nachhaltigkeit am Beispiel des Einsatzes von KI. Ergebnisse einer Befragung von zivilgesellschaftlichen Organisationen in der Grenzregion im Bereich Umweltschutz, Sozial- und Gesundheitswesen
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Application of a transformer encoder for the prediction of intra-abdominal pressure
No full textIntra-abdominal pressure is a significant physiological parameter influencing spinal stability and pelvic floor health. This study investigates the potential of a transformer encoder model to predict IAP using motion capture data and musculoskeletal modeling. Data from 211 subjects performing walking, fast walking, and running were used to train a transformer encoder. The model showed promising results with an overall Mean Absolute Percentage Error of 13.5% and a Pearson correlation coefficient of 0.85. Predictions for fast walking and running proved to be more challenging compared to walking, which was attributed to the greater variability and complexity of faster movements
Forschungsprojekt "EVEKT "Erhöhung der Verbraucherpartizipation an der Energiewende und datenbasierte Mehrwertdienste" — Datensatz [Data set]
No full textUm Leistungsschwankungen im Stromnetz durch fluktuierende erneuerbare Energien auszugleichen, sollen in Deutschland intelligente Messtechnologien in Privathaushalten verbaut werden. Diese gelten als zentraler Bestandteil sog. Smart-Grids (intelligente Stromnetze), die Stromnachfrage und -angebot steuern, um Stromnetzstabilität zu gewährleisten. Die Werte, die bei der Messung des Stromverbrauchs in Privathaushalten erhoben werden, können an die Verbraucher*innen mithilfe von Apps oder anderer Smart-Meter-Plattformen (datenbasierte Mehrwertdienste) rückgemeldet werden. Vor diesem Hintergrund wurde 2023 eine Online-Befragung der Wohnbevölkerung in Deutschland durchgeführt (n=2.027). Untersucht wurden Bekanntheit, Nutzungsbereitschaft und Akzeptanz intelligenter Stromzähler bzw. Smart-Meter sowie die Nutzungsintention bei Anwendungsszenarien und Datenschutzbedenken
Performance assessment of a green hydrogen-based household energy system supported by a battery storage at different resolutions of the electrical load profile
No full textHydrogen deems quite suitable for medium- and long-term energy storage of surplus renewable electricity. Nowadays, all-in-one solutions consisting of an electrolyzer, a compressor, pressurized hydrogen storage tanks, a fuel cell (FC) and the necessary peripheral components are available for single family houses. This work presents a comparative assessment of the system’s key performance indicators in a household system with three market available FCs with the nominal powers of 0.8, 1.4 and 7.8 kW. The design tool developed for the assessment of the hybrid energy system along with two energy management system configurations are introduced. An electrochemical and thermal model widely applied in the literature is used to model the FCs, which is validated against the available experimental data in the literature for all three FCs. The influence of the FCs’ nominal power, their power dynamic operation range, the use of a battery storage with different capacities and the load profile’s resolution have been technically assessed regarding the system’s self-sufficiency (SS), the FC’s efficiency, full-load operating hours, and number of on/off cycles. It turned out that, the system performance is strongly dependent on the nominal power of the FC and its power dynamic operation range. If no battery-storage is applied, the complete grid independence is not possible, and a high resolution of the load profile is indispensable in the assessment of the system design. A hybrid energy system comprising the 1.4 kW FC, a PV system of 10 kW peak power and a battery of 15 kW storage capacity showed a degree of SS of 98%. The number of the full-load operating hours and on/off cycles of that FC amount to 768 and 116 cycles, respectively. Such promising results are referred to the high dynamic operation range of the battery, and its high discharge power capacity, which makes it more suitable to cover a remarkably higher fraction of the load deficit, if compared to a system without a battery
OpenMIBOOD's classification models for the MIDOG, PhaKIR, and OASIS-3 benchmarks [Data set]
No full textThese models are provided for evaluating post-hoc out-of-distribution methods on the three OpenMIBOOD benchmarks: MIDOG, PhaKIR, and OASIS-3.
When using these models, make sure to give appropriate credit and cite the OpenMIBOOD publication
EXPERT SURVEYS TO REAL TIME ADAPTATION OF LEARNING PATHS
No full textLearning management systems rely on adaptive algorithms that use learner preferences to personalize the instructional content in form of learning paths. However, these preferences are uncertain in nature, and change over time. The present solutions are either static or purely data-driven missing the dynamic adaption to changes in the preferences and infusion of pedagogical nuances respectively.
This paper introduces an extended variant of Nestor, our Bayesian network engine that models personality traits, learning styles, and learning strategies. This extension overlays a lightweight rule-based mechanism whose “secret recipe’’ lies in the infusion of expert-derived weights adapting learning paths dynamically whenever a learner selects new material in Moodle.
To parameterise these rules, we conducted a structured survey with 12 hand-picked professors and researchers in educational science. Each expert responded to 4 demographic items and 12 item that are distributed across algorithm-overview, scenario-based, and example-based categories, thereby supplying the nuanced weightings that result the personalised recommendations.
This hybrid system (Nestor plus the expert-infused rule layer) operated during the winter term of 2025. 18 students completed an end-of-term questionnaire. Although their learning gains were not recorded, the majority of respondents reported positive or neutral experiences with the dynamically adapted learning paths.
The {Future work} will compare three engines:
(i) the present dynamic, expert-infused rule layer on top of the static Bayesian network,
(ii) purely data-driven machine-learning models that neglect expert weighting, and
(iii) the original static-adaptation Bayesian network without rules.
Analyses of log files, intermediate satisfaction surveys, and pre/post term surveys will clarify whether this on-the-fly adaptation and pedagogical nuance lead to measurable learning benefits