Informatik/Technik

Dauerhafte URI für die Sektionhttps://epub.uni-luebeck.de/handle/zhb_hl/4

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    Advanced sensor fusion methods with applications to localization and navigation
    (2025-03-18) Fetzer, Toni
    We use sensors to track how many steps we take during the day or how well we sleep. Sensor fusion methods are used to draw these conclusions. A particularly difficult application is indoor localization, i.e. finding a person’s position within a building. This is mainly due to the many degrees of freedom of human movement and the physical properties of sensors inside buildings. Suitable approaches for sensor fusion for the purpose of self-localization using a smartphone are the subject of this thesis. To best address the complexity of this problem, a non-linear and non-Gaussian distributed state space must be assumed. For the required position estimation, we therefore focus on the class of particle filters and build a novel generic filter framework on top of it. The special feature of this framework is the modular approach and the low requirements towards the sensor and movement models. In this work, we investigate models for Wi-Fi and Bluetooth RSSI measurements using radio propagation models, the relatively new standard Wi-Fi FTM, which is explicitly designed for localization purposes, the barometer to determine floor changes as accurately as possible, and activity recognition to find out what the pedestrian is doing, e.g., ascending stairs. The human motion is then modeled in a movement model using IMU data. Here we propose two approaches: a regular tessellated grid graph and an irregular tessellated navigation mesh. From these we formulate our proposal for an indoor localization system (ILS). However, some fundamental problems of the particle filter lead to critical errors. These can be a multi- modal density to be estimated, unbalanced sensor models or the so-called sample impoverish- ment. Compensation, or in the best case elimination, of these errors by advanced sensor fusion methods is the main contribution of this thesis. The most important approach in this context is our adaptation of an interacting multiple modal particle filter (IMMPF) to the requirements of indoor localization. This results in a completely new approach to the formulation of an ILS. Using quality metrics, it is possible to dynamically switch between arbitrarily formulated par- ticle filters running in parallel. Furthermore, we explicitly propose several approaches from the field of particle distribution optimization (PDO) to avoid the sample impoverishment problem. In particular, the support filter approach (SFA), which is also based on the IMMPF principle, leads to excellent position estimates even under the most difficult conditions, as extensive ex- periments show.
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    Integrating humans and artificial intelligence in diagnostic tasks
    (2025) Schrills, Tim Philipp Peter
    This dissertation investigates the integration of humans and artificial intelligence (AI) in diagnostic tasks, focusing on user experience and interaction in explainable AI (XAI) systems. Central to this research is the development of the Subjective Information Processing Awareness (SIPA) concept, which deal with user experience in automated information processing. The work addresses the increasing reliance on AI for automating information processing in critical domains such as healthcare, where transparency and human oversight may be enabled through explainable systems. Drawing on theories of human-automation interaction, this research develops and validates a model of integrated human-AI information processing. Four empirical studies explore automation-related user experience in different contexts: digital contact tracing, automated insulin delivery, AI-supported pattern recognition, and AI-based diagnosis. The findings highlight the psychological impacts of AI explanations on trust, situation awareness, and decision-making. Based on empirical findings, this dissertation discusses the concept of diagnosticity as a central metric for successful human-AI integration and proposes a framework for designing XAI systems that enhance user experience by aligning with human information processing. The dissertation concludes with practical guidelines for developing human-centered AI systems, emphasizing the importance of SIPA, user awareness, system transparency, and maintaining human control in automated diagnostic processes.
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    Enabling research data management for non IT-professionals
    (2025-02-16) Schiff, Simon
    In almost all academic fields, results are derived from found evidence such as objects to be digitized, case studies, observations, experiments, or research data. Ideally, results are linked to its evidence to ease data governance and reproducibility of results, and publicly stored in a research data repository to be themselves linked as evidence for new results. This linking has created a huge mesh of data over the years. Searching for information, deciding whether found information is relevant, and then using relevant information for producing results costs a lot of time in such a mesh of data. Due to the fact that a high investment of time is associated with high costs, funding agencies such as the German Research Foundation (Deutsche Forschungsgemeinschaft; DFG) or the Federal Ministry of Education and Research (Bundesministerium für Bildung und Forschung; BMBF) demand a data management plan (DMP). A DMP is designed to reduce the costs of projects submitted to a funding agency and to avoid future costs when data repositories are to be reused. Nevertheless, a DMP is often not fully implemented because it is too costly, which in the long run leads to a mesh of data. In this thesis, we identify problems and present solutions usable by non-IT-experts to spend less time on solving problems that arise at implementing a DMP at each project’s repository and coping with a huge mesh of data across many repositories. According to our observations, humanities scholars produce research data that are meant to be printed later or uploaded at a repository. Potential problems that arise at a repository, independent of other repositories, to be solved are manifold. Data to be printed is encoded with a markup language for illustration purposes and not machine interpretable formatted. We not only show that such formatted data can be structured with a parser to be interpreted by machines, but also what possibilities open up from the structured data. Structured data is automatically combined, linked, transformed into other formats, and visualized on the web. Visualized data can be cited and annotated to help others assess relevance. Once, problems are solved at each repository, we show how we cope with data linked across repositories. This is achieved by designing a human-aware information retrieval (IR) agent, that can search in a mesh of data for relevant information. We discuss in what way the interaction of a user with such an IR agent can be optimized with human-aware collaborative planning strategies.
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    Weak convergence of the Milstein scheme for semi-linear parabolic stochastic evolution equations
    (2025) Kastner, Felix
    The numerical analysis of the Milstein scheme for stochastic ordinary differential equations (SDEs) is relatively well understood. It converges with both strong and weak order one. However, much less is known about the Milstein scheme and its variants when applied to stochastic partial differential equations or more general stochastic evolution equations. This thesis focuses on the weak convergence of the Milstein scheme in the latter setting. We prove that, similar to the SDE case, it also achieves an order of almost one — specifically, an order of 1 − ε for all ε > 0. More concretely, we work in the semigroup framework introduced by Da Prato and Zabczyk and examine the approximation of mild solutions of equations of semi-linear parabolic type. In addition, we allow the drift coefficient of the evolution equation to take values in certain distribution spaces associated to the dominating linear operator. In that case, the order of convergence depends on the regularity of the coefficients and tends to zero as the regularity decreases. The proof employs elements of the mild stochastic calculus recently introduced by Da Prato, Jentzen and Röckner (Trans. Amer. Math. Soc., 372(6), 2019) and crucially depends on recent results on the regularity of solutions to the associated infinite-dimensional Kolmogorov backward equation by Andersson, Hefter, Jentzen and Kurniawan (Potential Anal., 50(3), 2019). It is based on work by Jentzen and Kurniawan investigating Euler-type schemes (Found. Comput. Math., 21(2), 2021).
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    On Markov decision processes with the stochastic differential Bellman Equation
    (2025) Cakir, Merve Nur
    Stochastic differential equations play an important role in capturing the dynamics of complex systems, where uncertainty prevails in the form of noise. In complex systems noise is abundant, but its exact behaviour is unknown. However, noise can be simulated with stochastic processes. Stochastic calculi, such as the Itˆo formula, provide tools for navigating these systems. In this work, the adaptation of the Bellman equation, a cornerstone of dynamic programming, to the realm of stochastic differential equations is explored, facilitating the modeling of decision problems subject to noise. Value iteration and Q-learning, two well-known solution methods in machine learning, are extended to stochastic algorithms in order to approximate the solution for Markov decision processes with uncertainties modeled by the stochastic differential Bellman equation. These stochastic algorithms enable a realistic approach to modeling and solving decision problems in stochastic environments efficiently. The stochastic value iteration is applied when the environment is fully known, while the stochastic Q-learning extends its utility even in cases where transition probabilities remain unknown. Through theoretical analyses and case studies, these algorithms demonstrate their efficacy and applicability, delivering meaningful results. Additionally, the stochastic Q-learning achieves superior rewards compared to the deterministic algorithm, indicating its ability to optimize decision processes in stochastic environments more effectively by exploring more states. Finally, the stochastic differential Bellman equation is formulated as a system of ordinary equations, providing an alternative solution. For this, the concept of the random dynamical system is explored, of which a stochastic differential equation is an example.
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    Conceptual orthospaces
    (2024) Leemhuis, Mena
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    Advancing ultrasound image guidance
    (2024) Wulff, Daniel
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    E-nets as novel deep networks
    (2024) Grüning, Philipp
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    Augmented reality applications to facilitate vascular diagnostics and intervention
    (2023) Freiherr von Haxthausen, Felix Andreas
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    Functional lifting
    (2024) Bednarski, Danielle
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    Mathematical models of cooperation among heterogeneous individuals
    (2024) Couto, Marta Gomes da Cunha
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