Congratulations to Ning Houchun, Zhang Xiaobing, and Zhang Jiahong on successfully passing the thesis defense for their Master's degrees at Tsinghua University!
Ning Houchun's thesis is titled "Research on attitude monitoring and optimization technology of offshore floating wind turbines based on digital twins." This study investigates real-time monitoring and analysis techniques for the attitude of offshore floating wind turbines, with a particular focus on accurately capturing the posture of the column. Real-time data collection and cleansing are performed using the Internet of Things and data cleansing technology, while a digital twin platform enables three-dimensional dynamic visualization of the operational state. Additionally, finite element and non-linear regression methods are employed to analyze column deformations and derive precise mathematical models for capturing the deformation characteristics under various wind and wave conditions.
Zhang Xiaobing's thesis is titled "Highway Engineering Safety Management Based on Knowledge Graph and Data Template." This research innovatively explores the field of highway engineering safety management using techniques such as ontology, knowledge graph, and data templates. By dividing data dimensions, constructing semantic models, and establishing a standardized knowledge repository, deep extraction and management of knowledge in the engineering domain are achieved. Ultimately, a safety management template is designed to transform ontology into a user-friendly format, assisting managers in accurately identifying operational scenarios and acquiring necessary knowledge.
Zhang Jiahong's thesis is titled "Code-Based Knowledge Extraction and Application in Subway Engineering." This study employs deep learning, large language models, ontology, and knowledge graph techniques to systematically analyze, extract, and manage knowledge in the field of subway engineering. Through the analysis of 143 engineering specification documents, a knowledge extraction method is developed to automatically identify entity elements and construct a knowledge graph of subway engineering specifications. Furthermore, an enhanced model architecture that integrates knowledge graph and large language models is proposed, along with the development of a platform with knowledge navigation, management, and question-answering capabilities.