Yuchen Xia

Forschungsschwerpunkt (Research Focus):

• Large Language Models for Specialized Tasks in Industrial Automation
• Intelligent Autonomous Systems based on Large Language Model Agents and Digital Twins.
• Semantics in Digital Twins

Beschreibung (Description):

LLMs exhibit remarkable capabilities in interpreting complex texts and utilizing collective human knowledge. These models exhibit impressive proficiency in comprehending and analyzing semantics in natural language, while also displaying advanced abilities in reasoning and problem-solving.

By combining automation systems, digital twins and large language models, my research seeks to develop more intelligent and efficient systems. On one hand, automation systems are enhanced with digital twins and LLMs to unlock the potential of data- and AI-driven smart factories. On the other hand, LLMs can interact with physical environments by having an embodiment in reality through the established infrastructure that consists of automation systems and digital twins. This approach equips an artificial “brain” with mechatronic “hands” and “eyes” for more intelligent interaction. By combining these interdisciplinary technologies, my objective is to develop innovative technology stacks that enhance the efficiency and intelligence of smart factories.

Forschungsportal (Research Portal):

Google Scholar: https://scholar.google.de/citations?user=hi1srxkAAAAJ

Research Gate: https://www.researchgate.net/profile/Yuchen-Xia-4

LinkedIn: https://www.linkedin.com/in/xiayuchen/

My academic voyage started at the esteemed Wuhan University in China (2011), where I earned my first Bachelor’s Degree in Mechanical Design and Automation (2017). During this period, I also participated in a dual-degree program that took me to the University of Stuttgart. Here, I completed the full study program in Automotive and Engine Technology, earning my second Bachelor’s Degree in Germany (2017).

For my master’s studies, I delved into the specialized fields of Mechatronics and Autonomous Driving. I earned my master’s degree from the University of Stuttgart in 2019 and felt compelled to continue my academic journey, leading me to pursue a doctoral degree.

In 2020, I proposed my doctoral research, which received financial backing from the Stiftung der Deutschen Wirtschaft, which aims to supporting entrepreneurship. This project was conducted at the Institute of Industrial Automation and Software Engineering (IAS) and under the affiliation of the Graduate School of Excellence in Advanced Manufacturing Engineering (GSaME) at the University of Stuttgart. Since then, my focus has shifted to the semantic analysis of information models. My exploratory research journey has led me to the fields of Neural Language Models and Deep Learning. My expertise lies in Large Language Models and their applications in industrial automation. Driven by an entrepreneurial spirit, I am focused on developing research products that offer both economic and social value.

Supervised Students’ Research Projects:

• Automating Safety and Risk Management with Large Language Models Agents [Master Thesis, 2024]
• Fine-Tuning of Large Language Models for Enhanced Semantic Interpretation of Microservices in Automation Systems [Research Project, 2024]
• Transforming Vehicle User Manuals into Interactive AI Chatbot Powered by Large Language Model [Research Project, 2024]
• Synthetic training data creation for supervised fine-tuning of large language models for autonomous production planning and control [Master Thesis, 2024]
• Integrating Large Language Model Agents with Embedded Systems for Smart Oven Control [Master Thesis, 2024]
• Survey on Large Language Models for Applications in Industrial Automation and Software Engineering [Research Project, 2024]
• Large Language Models for OPC UA Server Data Retrieval [Research Project, 2023]
• Generation of Simscape Models Using Large Language Models [Bachelor Thesis, 2023]
• Refining Automation Systems for Enhanced Modular Control [Master Thesis, 2023]
• Interpretability Study of Large Language Models with Probing Techniques [Research Project, 2023]
• Evaluation of Quantized Large Language Models for Semantic Interpretation and Reasoning within Industrial Automation Contexts [Research Project, 2023]
• Prompt optimization with a dual GPT-agent feedback system [Research Project, 2023]
• Automated Test Scenario Generation for Autonomous Driving from Real-World Traffic Accident Reports [Research Project, 2023]
• Investigation of the Explainability of Results Generated by Large Language Models [Research Project, 2023]
• Semantic modeling of machine skills and automated matching between user requests and executable skills by applying neural language models [Master Thesis, 2023]
• Experimental evaluation of neural language models for semantic matching between user requests and executable functions of an automation system [Research Project, 2023]
• Development of an Enterprise-Architect plugin tool for generating a Simulink framework model [Research Project, 2023]
• Development and evaluation of a knowledge management system powered by large language models [Master Thesis, 2023]
• Capability modeling of the production system on the example of a machine tool [Master Thesis, 2022]
• Development of a data parser for extracting information from technical documents with text mining methods [Research Project, 2022]
• Generation of knowledge graph from textual data for describing causal system behaviors on the example of an automated production facility [Master Thesis, 2022]
• Training of general neural language models for automated interpretation of the semantics of the data properties in industrial automation domain [Master Thesis, 2022]
• Activity recognition based on acceleration sensor data for a bottle opener [Research Project, 2022]
• Determination of user behavior during the usage of power tools based on measurement data [Master Thesis, 2022]
• Evaluation of performance of foundation models for semantics-based classification of standardized data properties for measurement data [Research Project, 2022]
• Development of a graph database to manage identifiable concept descriptions for unambiguous semantics of data features in the field of automation engineering [Research Project, 2022]
• Development of a digital twin with a semantic query and command interface for a modular production facility [Research Project, 2021]
• Development of an ontology model for semi-automated data integration of sensor components into production systems [Master Thesis, 2021]