The goal of this thesis is to design, implement, and evaluate a sensor-based maintenance bot that uses Large Language Models (LLMs) to support predictive maintenance and decision-making. The bot should be capable of retrieving, analyzing, and reasoning over time series sensor data as well as unstructured maintenance-related documentation (e.g., technical manuals, incident reports). The result ...
In modern manufacturing, data plays a crucial role in optimizing processes, enhancing efficiency, and enabling interoperability across different systems. However, data models in manufacturing are often heterogeneous, proprietary, and lack standardization, making data sharing and integration challenging. The FAIR principles – Findability, Accessibility, Interoperability, and Reusability – provide a structured framework to improve data ...
The goal of this thesis is to investigate and define stakeholder engagement and involvement within the lifecycle of sensor-based MLOps. While MLOps principles streamline the deployment and maintenance of machine learning (ML) models, ensuring proper stakeholder involvement remains a challenge. Different stakeholders must collaborate effectively across various lifecycle stages to ensure model reliability, fairness, and ...
The proliferation of misinformation and fake news on social media platforms poses a significant challenge in today’s digital age. Traditional automated fake news detection systems often struggle with the complexity of the task, lacking the ability to provide detailed explanations and interpret nuanced contextual information.
This thesis explores the enhancement of fake news detection using Multi-Agent ...
AutoML automates machine learning pipelines, making model training accessible without deep expertise. Recent advancements use LLM-based agents to optimize pipeline steps, but existing solutions often require large-scale models with high computational costs. Smaller, open-source models provide a more accessible alternative, especially when combined with domain-specific pre-trained models. However, integrating these models into AutoML via agent ...
Das Ziel dieser Bachelorarbeit “OGRETA – Ontologie-gestützter Rahmen für das Entwicklungstracking von Arbeitskompetenzen” ist die Überführung des GRETA-Kompetenzmodells in eine Ontologie, die als Grundlage für ein Empfehlungssystem dienen kann. Das GRETA-Modell definiert die beruflichen Kompetenzen von Lehrkräften in der Erwachsenenbildung unabhängig von Fachgebiet, Erfahrung oder Beschäftigungsart. Durch die Strukturierung dieses Modells als Ontologie soll die ...
Sensor data is often unstructured and while available datasets show some clear use cases, for example calculating energy consumption over time, relationships between measurements can often go unnoticed without a thorough examination of the data. While exploratory data analysis can reveal connections, without a clear analytical direction the results may be limited to general information, ...
With the increasing popularity of personalized tourism experiences, recommendation systems play a crucial role in helping travelers discover destinations, activities, and itineraries that match their preferences. Traditional recommendation models often rely on collaborative filtering or content-based filtering, which may struggle with cold-start issues, lack of contextual awareness, and limited adaptability to dynamic tourism trends.
Knowledge graphs ...
Radiology report generation is a critical task in medical imaging analysis, where accurate and comprehensive descriptions of medical scans (such as X-ray, CT, or MRI) are required for diagnosis and treatment planning. Vision-language models (VLMs) have recently gained attention for automating this process by generating textual reports from medical images. However, standard VLMs often suffer ...
With the increasing complexity of industrial control systems (ICS) in smart grids, the risk of cyber-attacks is also rising. To enhance the security and resilience of these systems, new approaches are needed for detecting and mitigating cyber incidents. This thesis develops a decision support system (DSS) designed to assess and recommend effective countermeasures against cyber ...
The aim of this thesis is to design, implement, and evaluate a machine learning-based system for detecting chatter in thin-walled workpieces during machining processes. By leveraging MLOps principles, the system will automate the data pipeline from sensor data acquisition to model deployment, ensuring a scalable and efficient workflow. Additionally, the integration of the system within ...
Aim of the Thesis:
The aim of this bachelor thesis is to design, implement, and evaluate a blockchain-based escrow mechanism to support secure and transparent payment handling for data challenges in the Blockchain4DataMarketPlace. The proposed system will automate fund management, support dispute resolution processes, and provide transparent documentation of transactions, ensuring fairness and trust between Data ...
The growing adoption of electric vehicles (EVs) is reshaping power grids by introducing cybersecurity challenges that could be exploited to target both the EV and the power grid. This thesis focuses on vulnerabilities associated with EV charging and the effects of cyberattacks on grid stability and resilience. Of particular interest is the Open Charge Point ...
Exploring the potential of Solid Pods for the cultural sector offers an exciting opportunity to address challenges in data management, privacy, and interoperability. Solid Pods, a technology framework designed to enable individuals to store and control their data, promises transformative applications in the cultural sector. This thesis investigates how Solid Pods can be applied to ...
Conveying information in a tour traditionally either requires a real human or an audio recording. However, the human guide might not always be available and the quality in the tours varies. With audio recordings, extra care has to be taken to convey to the user which element is currently being talked about since there are ...
Learning content by heart can be facilitated by the method of Loci. In this mnemonic technique, the learner converts pieces of information into mental imagery. The imagined representations are then anchored in a location. If the learner then traverses a path through this location, the information can be remembered by recalling the mental imagery. However, ...
The emergence of large language models (LLMs), along with recent advances in mixed reality (MR) and virtual reality (VR), enable new opportunities for applying virtual agents in education. These simulated humans can imitate real-life situations and interactions with native speakers, which leads to an immersive and engaging learning experience. Especially in VR, interactions can be ...
Large Language Models (LLM) can be applied to transform a natural language (NL)-based text input query into a NL-based text answer. A common use case are personal assistants, e.g., for learning activities. In such teaching contexts they can process knowledge recorded in plain text documents, create summarizations, or teach knowledge according to a curriculum. However, ...
The aim of this thesis is to evaluate and extend a developing ontology of explainable data principles, an ongoing work aimed at establishing a structured framework for Data Explainability in AI systems. The current version of this ontology is in its early stages, primarily focused on defining key principles of Data Explainability and exploring their ...
The aim of this thesis is to extend the existing latency analysis of a psychomotor feedback engine within our existing MLOps pipeline . Building upon preliminary latency estimations, this thesis will focus on systematically evaluating each processing step in the pipeline, assessing both theoretical and practical contributions to the overall latency and throughput. By ...
This project aims to develop a methodology for the systematic selection and implementation of identifier systems in manufacturing, with a focus on ultrashort-pulsed (UKP) laser systems. By creating a robust identification framework tailored to manufacturing environments, the project will enhance data traceability, interoperability, and reusability within data-driven feedback loops, particularly in highly automated settings. The ...
This master’s thesis builds on the Psychomotor Feedback Engine (PFE) and the IMPECT framework , aiming to improve the graphical user interface that teachers use to implement rules and feedback elements in psychomotor learning.
This thesis aims to address those usability issues by redesigning the teacher interface and integrating the IMPECT framework to ...
