05 Fakultät Informatik, Elektrotechnik und Informationstechnik
Permanent URI for this collectionhttps://elib.uni-stuttgart.de/handle/11682/6
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Item Open Access Development of an Euler-Lagrangian framework for point-particle tracking to enable efficient multiscale simulations of complex flows(2023) Kschidock, HelenaIn this work, we implement, test, and validate an Euler-Lagrangian point-particle tracking framework for the commercial aerodynamics and aeroacoustics simulation tool ultraFluidX, which is based on the Lattice Boltzmann Method and optimized for GPUs. Our framework successfully simulates one-way and two-way coupled particle-laden flows based on drag forces and gravitation. Trilinear interpolation is used for determining the fluid's macroscopic properties at the particle position. Object and domain boundary conditions are implemented using a planar surface approximation. The whole particle framework is run within three dedicated GPU kernels, and data is only copied back to the CPU upon output. We show validation for the velocity interpolation, gravitational acceleration, back-coupling forces and boundary conditions, and test runtimes and memory requirements. We also propose the next steps required to make the particle framework ready for use in engineering applications.Item Open Access Improving usability of gaze and voice based text entry systems(2023) Sengupta, Korok; Staab, Steffen (Prof. Dr.)Item Open Access Hochperformante Auflösung kleiner Referenzen in verteilten Systemen(2023) Waimer, JoelBereits mit dem Aufkommen erster Filesharing-Systeme wurde die Entwicklung effektiver Verfahren zum Auffinden von mittels global eindeutiger Referenzen bezeichneter Datenobjekte in Peer-to-Peer-Systemen intensiv diskutiert und vorangetrieben, mit dem Ergebnis zahlreicher konkret ausgearbeiteter Lösungsansätze. Unbeachtet geblieben ist dabei jedoch der, für Filesharing-Systeme nicht lohnenswerte, für kleinere verteilte Datenspeichersysteme aber durchaus vorteilhafte Einsatz kleiner Referenzen, in der, aufgrund neuer Fortschritte im Bereich der Speicherdichte, damit einhergehenden dünnen Besetzung dieser kleinen Adressräume, durch welche allerdings die den Verfahren zueigenen Garantien bezüglich der benötigten Anzahl an Schritten zur Auflösung einer Referenz innerhalb des Systems stark verzerrt werden und sich die je Auflösung nötige Laufzeit vergrößert. Diese Arbeit beleuchtet zunächst die Grundlagen der mit der Auflösung von Datenreferenzen in verteilten Speichersystemen einhergehenden Problematiken, beschreibt die beiden Verfahren Chord [SMK+01] und Koorde [KK03] und misst anschließend deren Leistungsfähigkeit in dünn besetzten Adressräumen, unter der Verwendung kleiner Referenzen; mit den Messungen kann schließlich die Vermutung eines negativen Einflusses der dünnen Besetztheit des Adressraums auf die benötigte Laufzeit je Auflösung bestätigt werden. Eingegangen wurde hierbei auch auf mögliche Gegenmaßnahmen zur Verbesserung der Leistungsfähigkeit der beiden Verfahren, wobei hier die Verbindung der beiden untersuchten Verfahren mit einer Abwandlung des beim Distance-Halving-Netzwerk [NW03] eingesetzten Initialisierungsverfahrens zu einer nahezu gleichmäßigen Aufteilung des Adressraumes auf die einzelnen Knoten hier großes Potential besitzt, da so einer Entartung der Pfadlänge je Auflösung entgegengewirkt werden kann; zudem zeigten sich in den Messungen stark ungünstige Auswirkungen einer naiven, iterativen Implementierung des Chord-Verfahrens gegenüber Koorde.Item Open Access Benchmarking pre-trained language models for schema-agnostic entity resolution(2024) Bothmann, JanData integration is a process in which data from different sources are brought together to create a unified picture of the data. A vital aspect of this integration is Entity Resolution, which tries to identify elements that correspond to the same entity across multiple datasets. The complexity of ER tasks can vary significantly, as data exhibits different characteristics and levels of structuredness, which can influence the difficulty of the task. In this thesis, we evaluate how current state-of-the-art Entity Resolution systems perform when dealing with semi-structured data. To do this, several semi-structured ER benchmarks covering data from various domains were created for evaluation. Additionally, to explore how different data characteristics or other influencing factors impact the performance of matching systems, we developed the Benchmark Creator. This tool allows us and other users to generate benchmarks where data exhibits specific characteristics that may influence the complexity of the ER task. We used Ditto, Sudowoodo and the GPT4o-mini model to evaluate performance on the newly created benchmarks. Our evaluation reveals that Ditto and the GPT4o-mini model can effectively perform schema-agnostic ER on semi-structured data.Item Open Access Anonymisierung von Daten : von der Literatur zum Automobilbereich(2023) Herkommer, JanDie Datenanonymisierung im Automobilbereich gewinnt immer mehr an Bedeutung. Jedoch gibt es kaum Literatur und Ansätze, die sich mit der Anonymisierung von Automobildaten beschäftigen. In dieser Arbeit werden deshalb mit Hilfe einer strukturierten Literaturrecherche die aktuell verbreitetsten Verfahren und Anwendungsbereiche erörtert und die wichtigsten Erkenntnisse der Recherche zusammengefasst. So werden bei den analysierten Paper der Anwendungsbereich, die Methodik sowie der zu anonymisierende Datentyp ermittelt. DesWeiteren werden die Metriken zum Vergleich von unterschiedlichen Ansätzen betrachtet. Mit Hilfe dieser Erkenntnisse wird im Anschluss auf die Anonymisierung von Fahrzeugdaten anhand verschiedener Anwendungsfälle eingegangen und Herausforderungen und Lösungsansätze skizziert. Zuletzt wird beispielhaft ein Ansatz zur Anonymisierung von Routen implementiert, um mit Hilfe eines GPS-Sensors aufgezeichnete Fahrzeugrouten zu anonymisieren. Dabei werden zusätzliche Probleme wie der Umgang mit Messungenauigkeiten und Messfehlern sowie die tatsächlichen Auswirkungen von reduzierter Datennutzbarkeit verdeutlicht.Item Open Access Flexible and efficient data mapping for simulation of coupled problems(2026) Schneider, David; Uekermann, Benjamin (Jun.-Prof. Dr.)Multi-physics simulations model various physical phenomena and their interactions. Examples include climate models or the simulation of fusion reactors. Modeling more physical phenomena in the same simulation often provides new insights. This poses significant challenges for the underlying methods and the simulation software itself. Decomposing a multi-physics simulation into its parts is an effective way to tame the inherent complexity. The coupling library preCICE allows for such partitioned simulations, coupling simulation models without access to their internal numerics. While preCICE is well-suited for conventional, mesh-based surface couplings, its applicability breaks down in alternative scenarios due to restrictive data-mapping algorithms. These algorithms apply spatial mapping operators to statically defined coupling meshes and are generally not designed for large problems. As a result, they hinder the flexible and efficient simulation of relevant applications, including volumetric couplings, high-order couplings, and mesh-particle couplings. To overcome these limitations, this work focuses on four main aspects: first, developing a scalable partition-of-unity radial-basis-function interpolation customized for coupled problems; second, implementing data-parallel kernel methods on CPUs and GPUs to ensure cross-platform efficiency; third, enabling immediate access to meshes received from coupling partners for user-defined mapping operators; fourth, computing a mapping operator just-in-time on temporary coordinates for the seamless coupling of meshless solvers. Taken together, these concepts enable plug-and-play integration of diverse numerical models in multi-physics simulations. Large-scale volumetric couplings are now feasible and efficient, breaking the traditional accuracy-efficiency trade-off. Multi-physics couplings can exploit spatial high-order convergence rates of existing models for high-fidelity simulations, while preserving full black-box compatibility. The simulation of fluid-particle couplings can be modularized and leverage already-existing models for both the simulation of the mesh-based fluid and the particles. In the end, the robust implementation, the gained efficiency, and the flexibility significantly extend the applicability of preCICE and benefit its vibrant user community. Beyond preCICE, the presented concepts provide generally applicable building blocks for scalable, modular multi-physics coupling.Item Open Access Time-sensitive converged networks : a comprehensive architecture approach(2023) Hellmanns, David; Rothermel, Kurt (Prof. Dr. rer. nat.)Item Open Access Wearable-based study on the influence of musical micro-breaks on daily stress recovery(2025) Baumann, RalfThis master thesis focuses on the influence of listening to music during Micro-Breaks on stress reduction. The goal was to gain further information regarding effective stress reduction using Micro-Breaks at work. While the existing studies analyzed either the felt stress under normal conditions or the objective stress under clinical conditions, this study collected data (via measurements and questionnaires) for both the felt stress and the objective stress under normal conditions. To collect the measurements and the answers in the questionnaires, an Android app and corresponding Wear OS app were installed on smartphones and Samsung Galaxy Watch4 devices respectively. This study examined the question whether Micro-Breaks with listening to music can reduce the objective stress and the felt stress more than Micro-Breaks without listening to music. The objective stress was examined using the heart rate and the heart rate variability (SDNN and RMSSD). The raw data were checked, filtered (for example to include only cases where there were values for before, after and during the Micro-Break, deleting "0" values) and then analyzed. The questionnaires were analyzed for changes in the felt stress by taking the answers in the questionnaires and comparing them. The results found in this study were that the values of the heart rate variability (both of the RMSSD and of the SDNN) suggested that the stress level increased during Micro-Breaks with listening to music, while the heart rate decreased and thereby indicated a stress reduction. For the Micro-Breaks without listening to music, the heart rate variability suggested that the stress had slightly increased in the case of the SDNN and slightly decreased in the case of the RMSSD, while the increased heart rate suggested that the stress level had increased. As for the felt stress, the values suggested a slightly better stress reduction during the Micro-Breaks with listening to music than during those without listening to music.Item Open Access Research data management in simulation science : infrastructure, tools, and applications(2024) Flemisch, Bernd; Hermann, Sibylle; Herschel, Melanie; Pflüger, Dirk; Pleiss, Jürgen; Range, Jan; Roy, Sarbani; Takamoto, Makoto; Uekermann, BenjaminResearch Data Management (RDM) has gained significant traction in recent years, being essential to allowing research data to be, e.g., findable, accessible, interoperable, and reproducible (FAIR), thereby fostering collaboration or accelerating scientific findings. We present solutions for RDM developed within the DFG-Funded Cluster of Excellence EXC2075 Data-Integrated Simulation Science (SimTech). After an introduction to the scientific context and challenges faced by simulation scientists, we outline the general data management infrastructure and present tools that address these challenges. Exemplary domain applications demonstrate the use and benefits of the proposed data management software solutions. These are complemented by additional measures for enablement and dissemination to foster the adoption of these techniques.Item Open Access Differential privacy by sampling(2023) Kuksina, OlenaCollection and storage of immense volumes of data has become commonplace in today's digital age, making the protection of personal data increasingly important. Private data often includes sensitive information about an individual, and is gathered by medical and financial institutions, research and social science organisations, government, etc., taking full advantage of data-driven analytics and knowledge-based decision-making to improve products and services, enterprise statistical analysis, comprehensive studies of demographic trends, and many others. The disclosure or sharing of such information among different parties could infringe on privacy. This information can be used for malicious purposes, such as identity theft, scams or targeted advertising. This work examines the field of privacy-preserving data publishing. Quality of published data significantly affects not only understanding and processing strategy, but the accuracy of data analysis as well as consequently the interpretation and decisions derived from the data. In order to meet this challenge, synthetic anonymization techniques, such as k-anonymity and its enhanced algorithms, are applied. However, they are based on the background knowledge of the adversary. A semantic model, or differential privacy, is a more rigorous mathematical notion of privacy assurance that operates under no assumptions. Nevertheless, differential privacy applies to the subsequent phase, namely privacy preserving data mining, query answering and aggregate statistics. In the scope of this work, a subsampling anonymization algorithm DP-anonym providing k-anonymity with integrated differential privacy mechanisms, such as Laplace mechanism and exponential mechanisms, is elaborated. The algorithm provides synthetic and semantic privacy, combining the best of the two areas of private data exploration. According to experimental results, the proposed DP-anonym algorithm provides better data utility when compared to standard anonymization algorithms among general data utility metrics. It also provides more precise answers to typical database queries as it uses multidimensional generalization approach. In contrast to standard methods, DP-anonym achieves (epsilon, delta)-differential privacy, which guarantees the privacy of published anonymized data more efficiently.