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            Computational, Label, and Data Efficiency in Deep Learning for Sparse 3D Data 

            Li, Lanxiao (2024)
            Deep learning is widely applied to sparse 3D data to perform challenging tasks, e.g., 3D object detection and semantic segmentation. However, the high performance of deep learning comes with high costs, including computational ...
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            Entwicklung und Charakterisierung von Siliziumnitrid-/Siliziumkarbidkompositen für den Einsatz in tribologisch hochbeanspruchten Gleitsystemen 

            Zielonka, Paul S. (2023)
            In this work, silicon nitride/silicon carbide composites are manufactured and characterized for tribologically highly stressed applications. The characterization of the composite materials shows the influence of the silicon ...
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            Multi-Physics Modelling towards the Stabilization of PAN Fibers with Microwaves Based on Experimental Data 

            Hofele, Julia (2024)
            High energy demand is one reason for high costs of carbon fibers. One option to decrease them is to use microwave heating instead of conventional heating. In this work, steps towards a microwave assisted process during the ...
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            OCM 2021 - Optical Characterization of Materials 

            Beyerer, Jürgen; Längle, Thomas (2021)
            The state of the art in the optical characterization of materials is advancing rapidly. New insights have been gained into the theoretical foundations of this research and exciting developments have been made in practice, ...
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            Adaptive Umweltmodellierung für kognitive Systeme in offener Welt durch dynamische Konzepte und quantitative Modellbewertung 

            Kuwertz, Achim Christian (2022)
            In this work, an approach for adaptive world modeling is proposed. World models for cognitive systems often employ predefined domain models, which may become insufficient when encountering unforeseen entities. The presented ...
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              This project received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 871069.

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