@inbook{55635, author = {{Hartung, Olaf}}, booktitle = {{Geschichtskulturen im digitalen Wandel?}}, editor = {{Hartung, Olaf and Krebs, Alexandra and Meyer-Hamme, Johannes}}, isbn = {{978-3-7344-1629-3}}, issn = {{1435-7658}}, pages = {{9--32}}, publisher = {{Wochenschau Verlag}}, title = {{{Geschichtskulturen im digitalen Wandel? – Zu den Gründen und Zielen dieses Bandes}}}, year = {{2024}}, } @phdthesis{67220, abstract = {{

Angesichts der wachsenden Komplexität technischer Systeme stehen Entwicklungsabteilungen besonders in den frühen Entwicklungsphasen vor Herausforderungen, die im Umgang mit großen, unstrukturierten Lösungsräumen liegen. Dies erfordert Technologien zur systematischen Lösungsraumexploration, um eine Vielzahl an Entwürfen automatisiert in Bezug auf Nutzungsszenarien zu generieren und die Entscheidungsfindung hinsichtlich optimaler Lösungen algorithmisch zu gestalten. Diese Arbeit stellt eine generativ parametrische Entwicklungsumgebung zur Automatisierung der Lösungsraumexploration mittels numerischer Inferenzbildung für technische Systeme vor. Die Entwicklungsumgebung umfasst drei wesentliche Komponenten: Ein generativ parametrisches Geometriemodell, ein Analysewerkzeug für funktionale Simulationen und ein Synthesewerkzeug zur Inferenzbildung. Um den Einsatz von funktionalen und geometrischen Modellen für eine numerische Optimierung zu ermöglichen, wird eine Modellierungsstrategie eingeführt, die eine Beschreibung von technischen Systemen mit normierten Parametern zulässt. Das Vorgehen für den Aufbau derartiger Entwicklungsumgebungen wird in einem methodischen Rahmenwerk festgehalten und am Beispiel einer Teemaschine demonstriert.

}}, author = {{Altun, Osman}}, isbn = {{9783959009621}}, publisher = {{TEWISS – Technik und Wissen GmbH}}, title = {{{Generativ parametrische Entwicklung von technischen Systemen}}}, doi = {{10.51202/9783959009621}}, year = {{2024}}, } @article{57143, author = {{Bürgel, Christoph and Siepmann, Dirk}}, journal = {{Actes du Congrès Mondial de Linguistique française 2024 - 9ème Congrès Mondial de Linguistique Française}}, title = {{{Une nouvelle grammaire universitaire du français à l’intention des étudiants allophones : la Grammatik des gesprochenen und geschriebenen Französisch }}}, year = {{2024}}, } @book{58215, editor = {{Bürgel, Christoph and Heiderich, Jens and Koch, Corinna and Schlaak, Claudia and Visser, Judith}}, title = {{{ Zeitschrift für Romanische Sprachen und ihre Didaktik}}}, volume = {{Heft 18,1}}, year = {{2024}}, } @book{65622, editor = {{Bürgel, Christoph and Heiderich, Jens and Koch, Corinna and Schlaak, Claudia and Visser, Judith}}, title = {{{Zeitschrift für Romanische Sprachen und ihre Didaktik}}}, volume = {{18,2}}, year = {{2024}}, } @inbook{58137, author = {{Scharlau, Ingrid}}, booktitle = {{Wissenschaftsdidaktik als kritische Kommunikationsanalyse: Ein Sammelband zur Weiterführung eines Gedankens von Ludwig Huber}}, editor = {{Scharlau, Ingrid and Jenert, Tobias}}, isbn = {{9783847432296}}, keywords = {{Unterrichtsfach Psychologie, Psychologiedidaktik}}, publisher = {{Verlag Barbara Budrich}}, title = {{{Eine wissenschaftsdidaktische Analyse psychologischer Forschungsartikel und Lehrbücher}}}, doi = {{10.2307/jj.18255581.8}}, year = {{2024}}, } @misc{67245, abstract = {{This data set consist of experimental data collected during 17 run-to-failure experiments on ball bearings subjected to time-varying load and speed conditions. No defect was initiated in the bearings before the experiments. A detailed description file is enclosed.}}, author = {{Aimiyekagbon, Osarenren}}, keywords = {{Prognostics, Non-Stationary conditions, RUL, Run to failure data, Ball bearing, Varying conditions, Accelerated life tests, bearings}}, publisher = {{Zenodo}}, title = {{{Run-to-failure data set of ball bearings subjected to time-varying load and speed conditions}}}, doi = {{10.5281/ZENODO.10805042}}, year = {{2024}}, } @inbook{46578, abstract = {{Multiobjective optimization plays an increasingly important role in modern applications, where several criteria are often of equal importance. The task in multiobjective optimization and multiobjective optimal control is therefore to compute the set of optimal compromises (the Pareto set) between the conflicting objectives. The advances in algorithms and the increasing interest in Pareto-optimal solutions have led to a wide range of new applications related to optimal and feedback control - potentially with non-smoothness both on the level of the objectives or in the system dynamics. This results in new challenges such as dealing with expensive models (e.g., governed by partial differential equations (PDEs)) and developing dedicated algorithms handling the non-smoothness. Since in contrast to single-objective optimization, the Pareto set generally consists of an infinite number of solutions, the computational effort can quickly become challenging, which is particularly problematic when the objectives are costly to evaluate or when a solution has to be presented very quickly. This article gives an overview of recent developments in the field of multiobjective optimization of non-smooth PDE-constrained problems. In particular we report on the advances achieved within Project 2 "Multiobjective Optimization of Non-Smooth PDE-Constrained Problems - Switches, State Constraints and Model Order Reduction" of the DFG Priority Programm 1962 "Non-smooth and Complementarity-based Distributed Parameter Systems: Simulation and Hierarchical Optimization".}}, author = {{Bernreuther, Marco and Dellnitz, Michael and Gebken, Bennet and Müller, Georg and Peitz, Sebastian and Sonntag, Konstantin and Volkwein, Stefan}}, booktitle = {{DFG Priority Programme 1962}}, publisher = {{Birkhäuser}}, title = {{{Multiobjective Optimization of Non-Smooth PDE-Constrained Problems}}}, year = {{2024}}, } @inproceedings{42160, abstract = {{The goal of this paper is to make a strong point for the usage of dynamical models when using reinforcement learning (RL) for feedback control of dynamical systems governed by partial differential equations (PDEs). To breach the gap between the immense promises we see in RL and the applicability in complex engineering systems, the main challenges are the massive requirements in terms of the training data, as well as the lack of performance guarantees. We present a solution for the first issue using a data-driven surrogate model in the form of a convolutional LSTM with actuation. We demonstrate that learning an actuated model in parallel to training the RL agent significantly reduces the total amount of required data sampled from the real system. Furthermore, we show that iteratively updating the model is of major importance to avoid biases in the RL training. Detailed ablation studies reveal the most important ingredients of the modeling process. We use the chaotic Kuramoto-Sivashinsky equation do demonstarte our findings.}}, author = {{Werner, Stefan and Peitz, Sebastian}}, booktitle = {{European Control Conference (ECC)}}, pages = {{2958--2964}}, publisher = {{IEEE}}, title = {{{Numerical evidence for sample efficiency of model-based over model-free reinforcement learning control of partial differential equations}}}, doi = {{10.23919/ECC64448.2024.10590945}}, year = {{2024}}, } @inproceedings{48502, abstract = {{The prediction of photon echoes is an important technique for gaining an understanding of optical quantum systems. However, this requires a large number of simulations with varying parameters and/or input pulses, which renders numerical studies expensive. This article investigates how we can use data-driven surrogate models based on the Koopman operator to accelerate this process. In order to be successful, we require a model that is accurate over a large number of time steps. To this end, we employ a bilinear Koopman model using extended dynamic mode decomposition and simulate the optical Bloch equations for an ensemble of inhomogeneously broadened two-level systems. Such systems are well suited to describe the excitation of excitonic resonances in semiconductor nanostructures, for example, ensembles of semiconductor quantum dots. We perform a detailed study on the required number of system simulations such that the resulting data-driven Koopman model is sufficiently accurate for a wide range of parameter settings. We analyze the L2 error and the relative error of the photon echo peak and investigate how the control positions relate to the stabilization. After proper training, the dynamics of the quantum ensemble can be predicted accurately and numerically very efficiently by our methods.}}, author = {{Peitz, Sebastian and Hunstig, Anna and Rose, Hendrik and Meier, Torsten}}, booktitle = {{IEEE 63rd Conference on Decision and Control (CDC)}}, pages = {{7964--7969}}, title = {{{Accelerating the analysis of optical quantum systems using the Koopman operator}}}, doi = {{10.1109/CDC56724.2024.10886589}}, year = {{2024}}, } @inproceedings{55431, author = {{Gräßler, Iris and Özcan, Deniz}}, booktitle = {{Automation 2024}}, isbn = {{9783181024379}}, pages = {{363–374}}, publisher = {{VDI Verlag}}, title = {{{Anwendungspotenziale von Künstlicher Intelligenz im Model-Based Systems Engineering für automatisierte Systeme}}}, doi = {{10.51202/9783181024379-363}}, volume = {{2437}}, year = {{2024}}, } @inproceedings{54540, abstract = {{System models are used to merge relevant engineering artefacts and relationships. Therefore, a high model quality must be ensured. Currently, there is no method for defining company-specific metrics to assess system model quality. In a six-step research approach, a method is defined: (1) literature analysis on quality assessment approaches, (2) derivation of success factors, (3) evaluation of approaches, (4) development of a method, (5) application in automotive industry, and (6) evaluation. The method supports system engineers to derive tailored metrics to objectively assess the model quality.}}, author = {{Gräßler, Iris and Wiechel, Dominik and Özcan, Deniz and Taplick, Patrick}}, booktitle = {{Proceedings of the Design Society}}, issn = {{2732-527X}}, pages = {{2545--2554}}, publisher = {{Cambridge University Press (CUP)}}, title = {{{Tailored metrics for assessing the quality of MBSE models}}}, doi = {{10.1017/pds.2024.257}}, volume = {{4}}, year = {{2024}}, } @unpublished{67320, author = {{Berkemeier, Manuel and Sonntag, Konstantin and Peitz, Sebastian}}, booktitle = {{Optimizaton Online}}, keywords = {{own, own-preprint}}, title = {{{New Nonlinear Conjugate Gradient Methods with Guaranteed Descent for Multi-Objective Optimization}}}, year = {{2024}}, } @inproceedings{66172, author = {{Malcher, Jannik and Biebert, Daniel and Chen, Kuan-Hsun and Buschjäger, Sebastian and Hakert, Christian and Chen, Jian-Jia}}, booktitle = {{Proceedings of the 25th ACM SIGPLAN/SIGBED International Conference on Languages, Compilers, and Tools for Embedded Systems}}, publisher = {{ACM}}, title = {{{Language-Based Deployment Optimization for Random Forests (Invited Paper)}}}, doi = {{10.1145/3652032.3659366}}, year = {{2024}}, } @inproceedings{66170, author = {{Smit, Tijmen T. and Forlin, Bruno Endres and Chen, Kuan-Hsun and Souvatzoglou, Ioanna and Psarakis, Mihalis and Ottavi, Marco}}, booktitle = {{2024 IEEE International Symposium on Defect and Fault Tolerance in VLSI and Nanotechnology Systems (DFT)}}, publisher = {{IEEE}}, title = {{{An Enhanced Fault Injection Framework for FPGA-Based Soft-Cores}}}, doi = {{10.1109/dft63277.2024.10753564}}, year = {{2024}}, } @inproceedings{66169, author = {{Hakert, Christian and Chen, Kuan-Hsun and Chen, Jian-Jia}}, booktitle = {{2024 Design, Automation & Test in Europe Conference & Exhibition (DATE)}}, publisher = {{IEEE}}, title = {{{FLInt: Exploiting Floating Point Enabled Integer Arithmetic for Efficient Random Forest Inference}}}, doi = {{10.23919/date58400.2024.10546851}}, year = {{2024}}, } @inproceedings{66190, author = {{Henkel, Jorg and Siddhu, Lokesh and Bauer, Lars and Teich, Jurgen and Wildermann, Stefan and Tahoori, Mehdi and Mayahinia, Mahta and Castrillon, Jeronimo and Khan, Asif Ali and Farzaneh, Hamid and Lima, Joao Paulo C. De and Chen, Jian-Jia and Hakert, Christian and Chen, Kuan-Hsun and Yang, Chia-Lin and Cheng, Hsiang-Yun}}, booktitle = {{Proceedings of the International Conference on Compilers, Architecture, and Synthesis for Embedded Systems}}, publisher = {{ACM}}, title = {{{Special Session - Non-Volatile Memories: Challenges and Opportunities for Embedded System Architectures with Focus on Machine Learning Applications}}}, doi = {{10.1145/3607889.3609088}}, year = {{2024}}, } @inbook{67555, author = {{Herbst et al., Bianca}}, booktitle = {{Musik erleben – erforschen – vermitteln. Begegnungen mit Thomas Krettenauer }}, editor = {{Kristin Sander-Steinert, Heiner Gembris}}, publisher = {{LIT-Verlag}}, title = {{{Evaluation des Zertifikatsprogramms BaSiMusiG. Beitrag zur Entwicklung des musikalischen Selbstkonzepts, der Motivation musikpädagogischen Handelns und der musizier- sowie musikunterrichtsbezogenen Kompetenzen von (zukünftigen) Grundschullehrenden ohne Musikstudium}}}, volume = {{20}}, year = {{2024}}, } @misc{67557, author = {{Herbst, Bianca Maria}}, publisher = {{Schott Music}}, title = {{{Musik, Spiel und Tanz mit Kindern von 0 bis 6}}}, volume = {{2}}, year = {{2024}}, } @inproceedings{34083, abstract = {{In the context of language learning, feedback comment generation is the task of generating hints or explanatory notes for learner texts that help understand why a part of text is erroneous. This paper presents our approach to the Feedback Comment Generation Shared Task, collocated with the 16th International Natural Language Generation Conference (INLG 2023). The approach augments the generation of feedback comments by a self-supervised identification of feedback types in a multitask-learning setting. Within the shared task, other approaches performed more effective, yet the combined modeling of feedback type classification and feedback comment generation is superior to performing feedback generation only.}}, author = {{Stahl, Maja and Wachsmuth, Henning}}, booktitle = {{Proceedings of the 16th International Natural Language Generation Conference}}, title = {{{Identifying Feedback Types to Augment Feedback Comment Generation}}}, year = {{2023}}, }