Thesis - In-Su Han, "A Method for Searching Ship Regulations Based on Generative AI Considering the Intent of User’s Query", M.Sc. Thesis, Seoul National University, 2025.02.26

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첨부 '1'	석사학위논문_한인수.pdf,

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Abstract	Various international organizations and classification societies have established extensive regulations to enhance the safety and reliability of ships. These regulations are continuously revised and updated to keep pace with technological advancements, the increasing size of ships, and the tightening of environmental regulations in the shipbuilding and maritime industry. However, the complex formats, including formulas and tables, and the high level of expertise required to comprehend these regulations pose significant challenges for users seeking to retrieve and accurately interpret the necessary information quickly. This study proposed a generative AI-based search method for ship regulations utilizing Retrieval-Augmented Generation (RAG) to address these challenges. The proposed method comprises a Question-and-Answer (Q&A) system that retrieves relevant information from a large-scale database and generates reliable answers tailored to the intent of the user’s query. To achieve this, we built ship regulations into a database optimized for searchability and incorporated hierarchical structures to improve search accuracy. Additionally, a domain adaptation technique was applied to a general-purpose language model, enabling the Q&A system to clearly understand the specialized terminology and context within the domain of ship regulations. The Q&A system developed in this study supports users in quickly navigating ship regulations, facilitating clear comprehension and practical application of the information. The proposed method was applied to the “Classification Technical Rules” regulations of the Korean Register, demonstrating its capability to promptly and accurately retrieve rules related to user queries within the regulations and generate reliable answers.
Publication Date	2025-02-26

Abstract

Various international organizations and classification societies have established extensive regulations to enhance the safety and reliability of ships. These regulations are continuously revised and updated to keep pace with technological advancements, the increasing size of ships, and the tightening of environmental regulations in the shipbuilding and maritime industry. However, the complex formats, including formulas and tables, and the high level of expertise required to comprehend these regulations pose significant challenges for users seeking to retrieve and accurately interpret the necessary information quickly.
This study proposed a generative AI-based search method for ship regulations utilizing Retrieval-Augmented Generation (RAG) to address these challenges. The proposed method comprises a Question-and-Answer (Q&A) system that retrieves relevant information from a large-scale database and generates reliable answers tailored to the intent of the user’s query. To achieve this, we built ship regulations into a database optimized for searchability and incorporated hierarchical structures to improve search accuracy. Additionally, a domain adaptation technique was applied to a general-purpose language model, enabling the Q&A system to clearly understand the specialized terminology and context within the domain of ship regulations.
The Q&A system developed in this study supports users in quickly navigating ship regulations, facilitating clear comprehension and practical application of the information. The proposed method was applied to the “Classification Technical Rules” regulations of the Korean Register, demonstrating its capability to promptly and accurately retrieve rules related to user queries within the regulations and generate reliable answers.

Publication Date

2025-02-26

In-Su Han, "A Method for Searching Ship Regulations Based on Generative AI Considering the Intent of User’s Query", M.Sc. Thesis, Seoul National University, 2025.02.26

List of Articles
번호	분류	제목	Publication Date
29	M.Sc. Thesis	Yun-Sik Kim, "An Improved Method for Detection and Tracking of Maritime Obstacles Based on Multiple Sensor Fusion", M.Sc. Thesis, Seoul National University, 2026.02.25	2026-02-25
28	M.Sc. Thesis	Seong-Won Choi, "An Improved Method for Ship Collision Avoidance Based on Deep Reinforcement Learning Using Attention Module and Mixture-of-Experts", M.Sc. Thesis, Seoul National University, 2026.02.25	2026-02-25
27	Ph.D. Thesis	Do-Hyun Chun, "A Method for Collision Avoidance of a Ship Based on Reinforcement Learning in Complex Maritime Situations", Ph.D. Thesis, Seoul National University, 2024.02.29	2024-02-29
26	M.Sc. Thesis	Dong-Gyu Park, "An Optimization Method of Initial Principal Particulars of Small Naval Ships Considering Design Requirements", M.Sc. Thesis, Seoul National University, 2025.02.26	2025-02-26
»	M.Sc. Thesis	In-Su Han, "A Method for Searching Ship Regulations Based on Generative AI Considering the Intent of User’s Query", M.Sc. Thesis, Seoul National University, 2025.02.26	2025-02-26
24	M.Sc. Thesis	Ha-Yun Kim, "An Improved Method for Tracking of Maritime Obstacles Using Sensor Data", M.Sc. Thesis, Seoul National University, 2024.02.26	2024-02-26
23	Ph.D. Thesis	Jisang Ha, "Optimal Arrangement Method of Equipment and Pipes in the Engine Room of a Ship", Ph.D. Thesis, Seoul National University, 2023.08.29	2023-08-29
22	M.Sc. Thesis	Dong-Geun Jeong, "A Method for Hierarchical Route Planning of Small ships in Coastal Area Using Quadtree Chart", M.Sc. Thesis, Seoul National University, 2023.02.24	2023-02-24
21	M.Sc. Thesis	Jeong-Ho Park, "A Method for Detection and Tracking of Maritime Obstacles Based on Multi-Video", M.Sc. Thesis, Seoul National University, 2023.02.24	2023-02-24
20	M.Sc. Thesis	Hamin Song, "Optimization of Crew Manning Considering Operation Scenarios of a Naval Ship", M.Sc. Thesis, Seoul National University, 2023.02.24	2023-02-24
19	M.Sc. Thesis	Yeongmin Jo, "A Method for the Path Tracking of Surrounding Ships Using Multiple Sensors", M.Sc. Thesis, Seoul National University, 2022.02.25	2022-02-25
18	M.Sc. Thesis	Won-Jae Lee, "Image-based Object Detection and Tracking Method for the Awareness around the Ship", M.Sc. Thesis, Seoul National University, 2021.02.26	2021-02-26
17	M.Sc. Thesis	June-Beom Lee, "Development of Prediction Models of Ship Power and Ocean Environmental Data Based on Deep Learning", M.Sc. Thesis, Seoul National University, 2021.02.26	2021-02-26
16	Ph.D. Thesis	Hye-Won Lee, "Wire Rope Contact Model and Crane Control Method for the Advanced Simulation and Automation of Block Erection", Ph.D. Thesis, Seoul National University, 2020.02.26	2020-02-26
15	Ph.D. Thesis	Ki-Su Kim, "Assessment Method of the Fitness of Initial Arrangement Design of a Naval Ship", Ph.D. Thesis, Seoul National University, 2019.08.29	2019-08-29
14	M.Sc. Thesis	Sung-Woo Park, "Data Mining Method for Offshore Structures based on Big Data Technology", M.Sc. Thesis, Seoul National University, 2019.02.26	2019-02-26
13	Ph.D. Thesis	Luman Zhao, "Simulation Method to Support Autonomous Navigation and Installation Operation of an Offshore Support Vessel", Ph.D. Thesis, Seoul National University, 2019.02.26	2019-02-01
12	M.Sc. Thesis	Joo-Pil Lee, "Design of Wreck Removal Considering Safety and Economy", M.Sc. Thesis, Seoul National University, 2019.02.26	2019-02-26
11	Ph.D. Thesis	Seung-Ho Ham, "Integrated Simulation Method Based on Multibody Dynamics for Production Design Verification in Ships and Offshore Structures", Ph.D. Thesis, Seoul National University, 2018.08.29	2018-08-29
10	M.Sc. Thesis	Sang-Hyun Lee, "Integrated Method for Layout Design of LNG FPSO Based on Optimization Technique and Expert System", M.Sc. Thesis, Seoul National University, 2018.02.26	2018-02-26

번호

분류

제목

Publication Date

M.Sc. Thesis

Yun-Sik Kim, "An Improved Method for Detection and Tracking of Maritime Obstacles Based on Multiple Sensor Fusion", M.Sc. Thesis, Seoul National University, 2026.02.25 file