TY - CHAP AU - Biehler, Rolf AU - Frischemeier, Daniel AU - Gould, Ronald AU - Pfannkuch, Maxine ED - Pepin, Birgit ED - Gueudet, Ghislaine ED - Choppin, Jeffrey ID - 48042 T2 - Handbook of Digital Resources in Mathematics Education TI - Impacts of Digitalization on Content and Goals of Statistics Education ER - TY - JOUR AU - Humpert, Lynn AU - Wäschle, Moritz AU - Horstmeyer, Sarah AU - Anacker, Harald AU - Dumitrescu, Roman AU - Albers, Albert ID - 48051 JF - Procedia CIRP KW - General Medicine SN - 2212-8271 TI - Stakeholder-oriented Elaboration of Artificial Intelligence use cases using the example of Special-Purpose engineering VL - 119 ER - TY - JOUR AU - Winkel, Fabian AU - Wallscheid, Oliver AU - Scholz, Peter AU - Böcker, Joachim ID - 48059 JF - IEEE Open Journal of the Industrial Electronics Society KW - Electrical and Electronic Engineering KW - Industrial and Manufacturing Engineering KW - Control and Systems Engineering SN - 2644-1284 TI - Pseudo-Labeling Machine Learning Algorithm for Predictive Maintenance of Relays ER - TY - JOUR AU - Winkel, Fabian AU - Deuse-Kleinsteuber, Johannes AU - Böcker, Joachim ID - 48058 JF - IEEE Transactions on Reliability KW - Electrical and Electronic Engineering KW - Safety KW - Risk KW - Reliability and Quality SN - 0018-9529 TI - Run-to-Failure Relay Dataset for Predictive Maintenance Research With Machine Learning ER - TY - CONF AB - Artificial benchmark functions are commonly used in optimization research because of their ability to rapidly evaluate potential solutions, making them a preferred substitute for real-world problems. However, these benchmark functions have faced criticism for their limited resemblance to real-world problems. In response, recent research has focused on automatically generating new benchmark functions for areas where established test suites are inadequate. These approaches have limitations, such as the difficulty of generating new benchmark functions that exhibit exploratory landscape analysis (ELA) features beyond those of existing benchmarks.The objective of this work is to develop a method for generating benchmark functions for single-objective continuous optimization with user-specified structural properties. Specifically, we aim to demonstrate a proof of concept for a method that uses an ELA feature vector to specify these properties in advance. To achieve this, we begin by generating a random sample of decision space variables and objective values. We then adjust the objective values using CMA-ES until the corresponding features of our new problem match the predefined ELA features within a specified threshold. By iteratively transforming the landscape in this way, we ensure that the resulting function exhibits the desired properties. To create the final function, we use the resulting point cloud as training data for a simple neural network that produces a function exhibiting the target ELA features. We demonstrate the effectiveness of this approach by replicating the existing functions of the well-known BBOB suite and creating new functions with ELA feature values that are not present in BBOB. AU - Prager, Raphael Patrick AU - Dietrich, Konstantin AU - Schneider, Lennart AU - Schäpermeier, Lennart AU - Bischl, Bernd AU - Kerschke, Pascal AU - Trautmann, Heike AU - Mersmann, Olaf ID - 47522 KW - Benchmarking KW - Instance Generator KW - Black-Box Continuous Optimization KW - Exploratory Landscape Analysis KW - Neural Networks SN - 9798400702020 T2 - Proceedings of the 17th ACM/SIGEVO Conference on Foundations of Genetic Algorithms TI - Neural Networks as Black-Box Benchmark Functions Optimized for Exploratory Landscape Features ER - TY - CONF AB - Exploratory landscape analysis (ELA) in single-objective black-box optimization relies on a comprehensive and large set of numerical features characterizing problem instances. Those foster problem understanding and serve as basis for constructing automated algorithm selection models choosing the best suited algorithm for a problem at hand based on the aforementioned features computed prior to optimization. This work specifically points to the sensitivity of a substantial proportion of these features to absolute objective values, i.e., we observe a lack of shift and scale invariance. We show that this unfortunately induces bias within automated algorithm selection models, an overfitting to specific benchmark problem sets used for training and thereby hinders generalization capabilities to unseen problems. We tackle these issues by presenting an appropriate objective normalization to be used prior to ELA feature computation and empirically illustrate the respective effectiveness focusing on the BBOB benchmark set. AU - Prager, Raphael Patrick AU - Trautmann, Heike ED - Correia, João ED - Smith, Stephen ED - Qaddoura, Raneem ID - 46297 SN - 978-3-031-30229-9 T2 - Applications of Evolutionary Computation TI - Nullifying the Inherent Bias of Non-invariant Exploratory Landscape Analysis Features ER - TY - CONF AB - The design and choice of benchmark suites are ongoing topics of discussion in the multi-objective optimization community. Some suites provide a good understanding of their Pareto sets and fronts, such as the well-known DTLZ and ZDT problems. However, they lack diversity in their landscape properties and do not provide a mechanism for creating multiple distinct problem instances. Other suites, like bi-objective BBOB, possess diverse and challenging landscape properties, but their optima are not well understood and can only be approximated empirically without any guarantees. AU - Schäpermeier, Lennart AU - Kerschke, Pascal AU - Grimme, Christian AU - Trautmann, Heike ED - Emmerich, Michael ED - Deutz, André ED - Wang, Hao ED - Kononova, Anna V. ED - Naujoks, Boris ED - Li, Ke ED - Miettinen, Kaisa ED - Yevseyeva, Iryna ID - 46298 SN - 978-3-031-27250-9 T2 - Evolutionary Multi-Criterion Optimization TI - Peak-A-Boo! Generating Multi-objective Multiple Peaks Benchmark Problems with Precise Pareto Sets ER - TY - JOUR AB - The herein proposed Python package pflacco provides a set of numerical features to characterize single-objective continuous and constrained optimization problems. Thereby, pflacco addresses two major challenges in the area optimization. Firstly, it provides the means to develop an understanding of a given problem instance, which is crucial for designing, selecting, or configuring optimization algorithms in general. Secondly, these numerical features can be utilized in the research streams of automated algorithm selection and configuration. While the majority of these landscape features is already available in the R package flacco, our Python implementation offers these tools to an even wider audience and thereby promotes research interests and novel avenues in the area of optimization. AU - Prager, Raphael Patrick AU - Trautmann, Heike ID - 46299 JF - Evolutionary Computation SN - 1063-6560 TI - Pflacco: Feature-Based Landscape Analysis of Continuous and Constrained Optimization Problems in Python ER - TY - CHAP AU - Leiss, Dominik AU - Gerlach, Kerstin AU - Wessel, Lena AU - Schmidt-Thieme, Barbara ID - 48319 SN - 9783662666036 T2 - Handbuch der Mathematikdidaktik TI - Sprache und Mathematiklernen ER - TY - CONF AU - Habernal, Ivan AU - Mireshghallah, Fatemehsadat AU - Thaine, Patricia AU - Ghanavati, Sepideh AU - Feyisetan, Oluwaseyi ID - 48289 T2 - Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics: Tutorial Abstracts TI - Privacy-Preserving Natural Language Processing ER - TY - CONF AU - Matzken, Cleo AU - Eger, Steffen AU - Habernal, Ivan ID - 48288 T2 - Findings of the Association for Computational Linguistics: ACL 2023 TI - Trade-Offs Between Fairness and Privacy in Language Modeling ER - TY - CONF AU - Mouhammad, Nina AU - Daxenberger, Johannes AU - Schiller, Benjamin AU - Habernal, Ivan ID - 48291 T2 - Proceedings of the 17th Linguistic Annotation Workshop (LAW-XVII) TI - Crowdsourcing on Sensitive Data with Privacy-Preserving Text Rewriting ER - TY - CONF AU - Yin, Ying AU - Habernal, Ivan ID - 48296 T2 - Proceedings of the Natural Legal Language Processing Workshop 2022 TI - Privacy-Preserving Models for Legal Natural Language Processing ER - TY - CONF AB - Star-connected cascaded H-bridge Converters require large DC-link capacitors to buffer the second-order harmonic voltage ripple. First, it is analytically proven that the DC-link voltage ripple is proportional to the apparent converter power and does not depend on the power factor for nominal operation with sinusoidal reference arm voltages and currents. A third-harmonic zero-sequence voltage injection with an optimal amplitude and phase angle transforms the 2nd harmonic to a 4th harmonic DC-link voltage ripple. This reduces the voltage ripple by exactly 50% for all power factors at steady-state at balanced conditions. However, this requires 54% additional modules for unity power factor operation and even 100% for pure reactive power operation to account for the increased reference arm voltages due to the large amplitude of the optimal third-harmonic injection. If not enough modules are available, an adaptive discontinuous PWM is utilized to still minimize the voltage ripple for the given number of modules and power factor. With a very limited number of modules (modulation index is 1.15), the proposed method still reduces the DC-link voltage ripple by 24.4% for unity power factor operation. It requires the same number of modules as the commonly utilized 3rd harmonic injection with 1/6 of the grid voltage amplitude and achieves superior results. Simulations of a 10 kV/1 MVA system confirm the analysis. AU - Unruh, Roland AU - Böcker, Joachim AU - Schafmeister, Frank ID - 48352 KW - Cascaded H-Bridge KW - Solid-State Transformer KW - Capacitor voltage ripple KW - Zero sequence voltage KW - Third harmonic injection SN - 979-8-3503-1678-0 T2 - 2023 25th European Conference on Power Electronics and Applications (EPE'23 ECCE Europe) TI - An Optimized Third-Harmonic Injection Reduces DC-Link Voltage Ripple in Cascaded H-Bridge Converters up to 50% for all Power Factors ER - TY - CONF AU - Pena, Mario AU - Meyer, Michael AU - Wallscheid, Oliver AU - Böcker, Joachim ID - 48093 T2 - 2023 IEEE International Electric Machines and Drives Conference (IEMDC) TI - Fade-Over Strategy for use of Model Predictive Direct Self-Control with Field-Oriented Control ER - TY - GEN AB - Bei dem betrachteten Speicherproblem werden Daten mit verschiedenen Zugriffswahrscheinlichkeiten auf Speicher mit verschiedenen Bandbreiten und Kapazitäten aufgeteilt, dabei sind Replikate erlaubt. Es wird die nach Zugriffswahrscheinlichkeit gewichtete kleinste Bandbreite der Daten maximiert. Wir zeigen, dass sowohl das diskrete Speicherproblem, bei dem die Bandbreite der Speicher jeweils gleichmäßig auf die dort abgelegten Daten aufgeteilt wird, als auch das kontinuierliche Speicherproblem, bei dem die Bandbreite der Speicher beliebig auf abgelegte Daten verteilt werden darf, NP-schwer ist. Es können also, wenn P ̸ = NP, keine effizienten Algorithmen für eine optimale Lösung existieren. Stattdessen zeigen wir jeweils einen 1/2-Approximationsalgorithmus. AU - Decking, Leo ID - 48430 TI - Zuweisung verteilter Speicher unter Maximierung der minimalen gewichteten Bandbreite ER - TY - CHAP AU - Kortmeyer, Jörg AU - Biehler, Rolf ED - Dreyfus, T. ED - Gonzalez-Martin, A. S. ED - Nardi, E. ED - Monaghan, J. ED - Thompson, P. W. ID - 48480 T2 - The Learning and Teaching of Calculus Across Disciplines – Proceedings of the Second Calculus Conference TI - The use of integrals for accumulation and mean values in basic electrical engineering courses ER - TY - CONF AU - Gburrek, Tobias AU - Schmalenstroeer, Joerg AU - Haeb-Umbach, Reinhold ID - 48269 T2 - European Signal Processing Conference (EUSIPCO) TI - On the Integration of Sampling Rate Synchronization and Acoustic Beamforming ER - TY - CONF AB - Low-code development platforms (LCDPs) recently sparked interest in both academia and industry, promising to speed up software development and make it accessible to users with little or no programming experience. Thus, the mass-development of software applications that are custom-made to the tasks, skills, and preferences of end users is potentially enabled. Although different LCDPs have been analysed with respect to their functionality and applied to exemplary case studies in recent work, there is a shortage of experience reports in which LCDPs are used to digitize business processes in small and medium manufacturing enterprises. In this paper, we therefore summarize our experience from supporting industry partners to identify business processes that are suitable for being implemented with low-code technologies and to select an LCDP that meets the requirements of the business process while aligning with the overall digitization strategy of the respective company. We also present the opportunities and challenges of the low-code approach as perceived by industry partners. In summary, the low-code approach should be seen as an essential factor for the digitization of business processes in small and medium manufacturing companies. AU - Weidmann, Nils AU - Kirchhoff, Jonas AU - Sauer, Stefan ID - 48368 TI - Digitizing Processes in Manufacturing Companies via Low-Code Software (to appear) ER - TY - CONF AU - Bogere, Paul AU - Temmen, Katrin AU - Bode, Henrik ID - 48499 T2 - 2023 IEEE Global Engineering Education Conference (EDUCON) TI - Knowledge Transfer Concepts for Microgrids Sustainability - The Case of East Africa ER -