@inproceedings{HopfenspergerDaubnerHerrmannetal., author = {Hopfensperger, Bernhard and Daubner, Andreas and Herrmann, Fabian and Hopkins, Andrew and Mellor, Phil}, title = {Investigation of Shifted PWM Methods for a Dual Three-Phase System to Reduce Capacitor RMS Current}, series = {PCIM Europe Digital Days 2020: International Exhibition and Conference for Power Electronics, Intelligent Motion, Renewable Energy and Energy Management: 07.-08.07.2020, Online [proceedings]}, booktitle = {PCIM Europe Digital Days 2020: International Exhibition and Conference for Power Electronics, Intelligent Motion, Renewable Energy and Energy Management: 07.-08.07.2020, Online [proceedings]}, publisher = {VDE-Verlag}, address = {Berlin, Offenbach}, isbn = {978-3-8007-5245-4}, pages = {124 -- 131}, abstract = {Mild hybrid automotive topologies containing a 48V high power (>15 kW) electric drive system demand a high integration of power electronics and electrical machine. A multi-phase motor winding topology helps to keep the per-phase operating currents to a reasonable level. Close integration and multi-phase system have led to a drive system with dual 3-phase systems supplied by a common 48V DC-link, which allows to shift PWM patterns for reduction of DC-link capacitor ripple current and size. This paper derives some basic rules for combinations of common PWM methods for dual 3-phase systems without magnetic cross-coupling. Experimental measurements verify simulated results.}, subject = {Elektroantrieb}, language = {en} } @article{HerrmannEnglTrubjansky, author = {Herrmann, Frank and Engl, Fabian and Trubjansky, Philipp}, title = {Ein funktionaler Vergleich der SAP Analytics Cloud und Microsoft Power BI zur Verwendung im Bereich People Analytics bei Vitesco Technologies}, series = {Anwendungen und Konzepte der Wirtschaftsinformatik}, journal = {Anwendungen und Konzepte der Wirtschaftsinformatik}, number = {15}, publisher = {Hochschule Luzern}, issn = {2296-4592}, doi = {10.26034/lu.akwi.2022.3337}, pages = {8 -- 21}, abstract = {Aufgrund leistungsbedingter Einschr{\"a}nkungen durch die aktuelle Business-Intelligence-Software PowerBI vergleicht die People-Analytics-Abteilung von Vitesco Technologies diese mit der Alternativsoftware SAP Analytics Cloud. Daf{\"u}r wurden zun{\"a}chst aktuelle Herausforderungen im People-Analytics-Umfeld identifiziert und basierend darauf Vergleichskriterien erarbeitet. Als Vergleichsmodell kommt das Kano-Modell zum Einsatz. Die durchgef{\"u}hrte Evaluation favorisiert aus funktionaler Sicht einen Umstieg auf die SAP Analytics Cloud, identifiziert allerdings eine Reihe an Herausforderungen, die einen sofortigen Wechsel einschr{\"a}nken. Zu diesen geh{\"o}ren sowohl die Verf{\"u}gbarkeit als auch die Qualit{\"a}t der HR-Daten.}, language = {de} } @article{EnglHerrmann, author = {Engl, Fabian and Herrmann, Frank}, title = {A Machine Learning based Approach on Employee Attrition Prediction with an Emphasize on predicting Leaving Reasons}, series = {Anwendungen und Konzepte der Wirtschaftsinformatik}, journal = {Anwendungen und Konzepte der Wirtschaftsinformatik}, number = {18}, publisher = {AKWI}, issn = {2296-4592}, doi = {10.26034/lu.akwi.2023.4488}, pages = {30 -- 40}, abstract = {Using Vitesco Technologies as an example, this article examines whether machine learning models are suitable for detecting employee attrition at an early stage, with the aim of uncovering underlying reasons for leaving. Nine different machine learning algorithms were examined: K-nearest-neighbors, Naive Bayes, logistic regression, a support vector machine, a neural network, a random forest, adaptive boosting, and two gradient boosting models. A three-way-holdout validation method was implemented to assess the quality of the results and measure both the f-score and the degree of model generalization. Initially, it was found that tree-based methods are best suited for classifying employees. A multiclass classification approach showed that under certain conditions it is even possible to predict the underlying leaving reasons.}, language = {en} }