@inproceedings{HaslbeckRauchBrueckletal., author = {Haslbeck, Matthias and Rauch, Johannes and Br{\"u}ckl, Oliver and B{\"a}smann, Rainer and G{\"u}nther, Andreas and Rietsche, Hansj{\"o}rg and Tempelmeier, Achim}, title = {Blindleistungsmanagement in Mittelspannungsnetzen}, series = {Zuk{\"u}nftige Stromnetze 2019, 30.-31.Jan.2019, Berlin}, booktitle = {Zuk{\"u}nftige Stromnetze 2019, 30.-31.Jan.2019, Berlin}, pages = {170 -- 182}, abstract = {Die Energiewende f{\"u}hrt zu neuen Herausforderungen f{\"u}r Verteilungsnetzbetreiber hinsichtlich der Erbringung von Systemdienstleistungen, der Integrationsf{\"a}higkeit weiterer Erzeugungsanlagen und Lasten sowie bei der Gew{\"a}hrleistung einer hohen Versorgungssicherheit. Die Deckung der steigenden Blindleistungsbedarfe seitens der Netzbetriebsmittel, Verbraucher und Erzeuger gewinnt durch den Wegfall der Großkraftwerke f{\"u}r Netzbetreiber zunehmend an Bedeutung.Das abgeschlossene und vom BMWi gef{\"o}rderte Projekt SyNErgie (Laufzeit von 03/2015 bis 05/2018) besch{\"a}ftigt sich mit der Entwicklung von Blindleistungsmanagementsystemen f{\"u}r Mittelspannungsnetze (MS-Netze). Ziel dabeiist es, das bisher ungenutzte, freie Blindleistungspotenzial betrieblicher Kompensationsanlagen und dezentraler Erzeugungsanlagen (allg.: Q-Quellen) zu nutzen, um die Blindleistungs{\"a}nderungsf{\"a}higkeit 1 eines Verteilungsnetzes zu erh{\"o}hen. Diese Ver{\"o}ffentlichung stellt ausgew{\"a}hlte Einzelergebnisse und Erfahrungen des Projektes vor, welche u. a. {\"u}ber zahlreiche Messungen inMS-Netzen bei Firmen mit Anschlusspunkt in der MS-Ebene, Netzsimulationen und mathematische Modelle abgeleitet wurden.}, language = {de} } @article{KnoedlerBaecherKaukeNavarroetal., author = {Knoedler, Leonard and Baecher, Helena and Kauke-Navarro, Martin and Prantl, Lukas and Machens, Hans-G{\"u}nther and Scheuermann, Philipp and Palm, Christoph and Baumann, Raphael and Kehrer, Andreas and Panayi, Adriana C. and Knoedler, Samuel}, title = {Towards a Reliable and Rapid Automated Grading System in Facial Palsy Patients: Facial Palsy Surgery Meets Computer Science}, series = {Journal of Clinical Medicine}, volume = {11}, journal = {Journal of Clinical Medicine}, number = {17}, publisher = {MDPI}, address = {Basel}, doi = {10.3390/jcm11174998}, abstract = {Background: Reliable, time- and cost-effective, and clinician-friendly diagnostic tools are cornerstones in facial palsy (FP) patient management. Different automated FP grading systems have been developed but revealed persisting downsides such as insufficient accuracy and cost-intensive hardware. We aimed to overcome these barriers and programmed an automated grading system for FP patients utilizing the House and Brackmann scale (HBS). Methods: Image datasets of 86 patients seen at the Department of Plastic, Hand, and Reconstructive Surgery at the University Hospital Regensburg, Germany, between June 2017 and May 2021, were used to train the neural network and evaluate its accuracy. Nine facial poses per patient were analyzed by the algorithm. Results: The algorithm showed an accuracy of 100\%. Oversampling did not result in altered outcomes, while the direct form displayed superior accuracy levels when compared to the modular classification form (n = 86; 100\% vs. 99\%). The Early Fusion technique was linked to improved accuracy outcomes in comparison to the Late Fusion and sequential method (n = 86; 100\% vs. 96\% vs. 97\%). Conclusions: Our automated FP grading system combines high-level accuracy with cost- and time-effectiveness. Our algorithm may accelerate the grading process in FP patients and facilitate the FP surgeon's workflow.}, language = {en} } @article{ThorScherzingerSpecht, author = {Thor, Andreas and Scherzinger, Stefanie and Specht, G{\"u}nther}, title = {Editorial}, series = {Datenbank-Spektrum}, volume = {14}, journal = {Datenbank-Spektrum}, number = {2}, publisher = {Springer}, doi = {10.1007/s13222-014-0162-1}, pages = {81 -- 84}, language = {de} }