TY - CONF AU - Holst, Stefan AU - Kampmann, Matthias AU - Sprenger, Alexander AU - Reimer, Jan Dennis AU - Hellebrand, Sybille AU - Wunderlich, Hans-Joachim AU - Weng, Xiaoqing ID - 19421 T2 - IEEE International Test Conference (ITC'20), November 2020 TI - Logic Fault Diagnosis of Hidden Delay Defects ER - TY - CHAP AU - Peckhaus, Volker ED - Beiderbeck, Friedrich ED - Li, Wenchao ED - Waldhoff, Stephan ID - 17638 T2 - Gottfried Wilhelm Leibniz. Rezeption, Forschung, Ausblick TI - Logik ER - TY - CONF AU - Aßmuth, Verena AU - Teutenberg, Dominik AU - Meschut, Gerson AU - Philipp, Jens AU - Stammen, Elisabeth AU - Dilger, Klaus ID - 20652 T2 - 20. Kolloquium Gemeinsame Forschung in der Klebtechnik TI - Lokales Konzept zur Auslegung von elastischen Klebverbindungen (LoKAl) ER - TY - CONF AB - This paper presents the results of static short-term and long-term tensile tests for beta-nucleated joined polypropylene samples by the hot plate welding process. In the present study different dimensionless joining displacements are accounted for. The results show that high short-term tensile strength does not directly transfer to high long-term tensile strength. The morphology of the weld seam in the joined samples is examined by means of transmitted and reflected light microscopy. For the dimensionless joining displacements of 0.75 and 0.95, stretched spherulites are obtained. X-Ray diffraction can be used as a tool for qualitative and quantitative analysis and eventually for differentiation of samples of various joining displacements. AU - Wübbeke, Andrea AU - Schöppner, Volker AU - Paul, André AU - Tiemann, Michael AU - Austermeier, Laura AU - Fitze, Marcus AU - Chen, Mingie AU - Jakob, Fabian AU - Heim, Hans-Peter AU - Wu, Tao AU - Niendorf, Thomas AU - Röhricht, Marie-Luise AU - Schmidt, Michael ID - 42892 T2 - SPE ANTEC 2020: The Virtual Edition 5 TI - Long- and Short-Term Tensile Strength and Morphology of Joined Beta-Nucleated Polypropylene Parts ER - TY - CONF AU - Schöppner, Volker AU - Wübbeke, Andrea AU - Paul, A. AU - Tiemann, M. AU - Austermeier, Laura AU - Fitze, M. AU - Chen, M. AU - Jakob, F. AU - Heim, H.-P- AU - Wu, T. AU - Niendorf, T. AU - Röhricht, M.-L. AU - Schmidt, M. ID - 24472 T2 - ANTEC20 TI - LONG- AND SHORT-TERM TENSILE STRENGTH AND MORPHOLOGY OF JOINED BETA-NUCLEATED POLYPROPYLENE PARTS ER - TY - JOUR AU - Gappa, Monika AU - Filipiak‐Pittroff, Birgit AU - Libuda, Lars AU - Berg, Andrea AU - Koletzko, Sibylle AU - Bauer, Carl‐Peter AU - Heinrich, Joachim AU - Schikowski, Tamara AU - Berdel, Dietrich AU - Standl, Marie ID - 26526 JF - Allergy SN - 0105-4538 TI - Long‐term effects of hydrolyzed formulae on atopic diseases in the GINI study ER - TY - GEN AU - Weber, Jutta AU - Pentenrieder, Annelie ED - Heßler, Martina ED - Liggieri, Kevin ID - 37507 T2 - Technikanthropologie. Handbuch für Wissenschaft und Studium TI - Lucy Suchman ER - TY - CHAP AU - Willeke, Stephanie ED - Eke, Norbert Otto ID - 31732 T2 - Vormärz-Handbuch TI - Ludwig Tieck ER - TY - CHAP AU - Diedrich, Alena ED - Eke, Norbert Otto ID - 49832 T2 - Handbuch Vormärz TI - Lyrik im Vormärz ER - TY - CONF AB - Network function virtualization (NFV) proposes to replace physical middleboxes with more flexible virtual network functions (VNFs). To dynamically adjust to everchanging traffic demands, VNFs have to be instantiated and their allocated resources have to be adjusted on demand. Deciding the amount of allocated resources is non-trivial. Existing optimization approaches often assume fixed resource requirements for each VNF instance. However, this can easily lead to either waste of resources or bad service quality if too many or too few resources are allocated. To solve this problem, we train machine learning models on real VNF data, containing measurements of performance and resource requirements. For each VNF, the trained models can then accurately predict the required resources to handle a certain traffic load. We integrate these machine learning models into an algorithm for joint VNF scaling and placement and evaluate their impact on resulting VNF placements. Our evaluation based on real-world data shows that using suitable machine learning models effectively avoids over- and underallocation of resources, leading to up to 12 times lower resource consumption and better service quality with up to 4.5 times lower total delay than using standard fixed resource allocation. AU - Schneider, Stefan Balthasar AU - Satheeschandran, Narayanan Puthenpurayil AU - Peuster, Manuel AU - Karl, Holger ID - 16219 T2 - IEEE Conference on Network Softwarization (NetSoft) TI - Machine Learning for Dynamic Resource Allocation in Network Function Virtualization ER -