整合 MIAT 方法論與大型語言模型於系統設計：以指紋影像增強系統為案例研究

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姓名

潘彥廷(Yen-Ting Pan) 查詢紙本館藏

畢業系所

資訊工程學系在職專班

論文名稱

整合 MIAT 方法論與大型語言模型於系統設計：以指紋影像增強系統為案例研究
(Integration of MIAT Methodology and Large Language Models for System Design：A Case Study of Fingerprint Enhancement System)

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至系統瀏覽論文 (2030-2-1以後開放)

摘要(中)

本論文探討了一個整合MIAT 方法論與大型語言模型(LLM)的創新系統設計方法，並選擇指紋影像增強系統作為實證案例。研究採用 MIAT 方法論進行系統設計，其特色在於強調系統架構的層級化模組設計，並透過離散事件模型進行演算法建模，進而產生具備可維護性與延展性的系統程式。研究中運用 LLM 技術，自動生成各個離散事件狀態對應的 Action 程式碼。在系統實作方面，我們建構了一個模組化的指紋影像增強系統，其中包含了前處理、Gabor 濾波與 Median 濾波等功能模組。系統效能評估採用 FVC2004 指紋資料庫，針對不同濾波方法進行對比實驗，評估系統在影像品質改善程度與執行效能兩個面向的表現。測試結果證實了系統在模組化架構下具備良好的濾波技術切換彈性，成功實現了可重構性，同時驗證了各個功能模組的獨立性與可靠度性。實驗結果證實了 LLM 在自動化程式生成可以無縫結合 MIAT 方法論的開發流程，在提升系統開發時程與程式品質方面的表現出很大的優勢。本研究不僅為指紋影像處理領域提供創新解決方案，其提出的整合式系統設計方法論也可延伸應用至其他需要高度模組化與彈性設計的資訊系統開發。

摘要(英)

This thesis presents an innovative system design approach that integrates MIAT methodology with Large Language Models (LLM), using a fingerprint image enhancement system as a validation case study. The research employs MIAT methodology for system design, emphasizing hierarchical modular architecture design and algorithmic modeling through discrete event models to generate maintainable and extensible system code. The study innovatively utilizes LLM technology to automatically generate action code corresponding to each discrete event state.
In terms of system implementation, we constructed a modular fingerprint image enhancement system comprising preprocessing, Gabor filtering, and Median filtering modules. System performance evaluation was conducted using the FVC2004 fingerprint database, comparing different filtering methods to assess both image quality improvement and execution performance. Test results confirmed that the system achieved excellent flexibility in switching filtering techniques under its modular architecture, successfully realizing reconfigurability while validating the independence and reliability of each functional module. Experimental results demonstrated that LLM′s automated code generation could seamlessly integrate with the MIAT methodology development process, showing significant advantages in improving system development timeline and code quality. This research not only provides an innovative solution for fingerprint image processing but also presents an integrated system design methodology that can be extended to other information system developments requiring high modularity and flexible design.

關鍵字(中)

★ 指紋影像增強
★ MIAT 方法論
★ 離散事件建模
★ 高階合成
★ 大型語言模型

關鍵字(英)

論文目次

中文摘要 i
Abstract ii
目錄 iv
圖目錄 vi
表目錄 viii
第一章、緒論 1
1.1 研究背景 1
1.2 研究動機與目的 2
1.3 研究架構 3
第二章、指紋影像增強方法 4
2.1 指紋特徵 4
2.2 指紋影像增強方法與技術 6
2.2.1 正規化（Normalization） 8
2.2.2 二值化技術（Binarization） 10
2.2.3 直方圖均值化(Histogram Equalization) 12
2.2.4 傅立葉轉換方法（Fourier transform） 14
2.2.5 Gabor 濾波器 15
2.2.6 Median 濾波器 17
2.3 指紋影像增強效果評估指標 NFIQ2 20
第三章、可重構指紋影像增強架構設計 21
3.1 MIAT 方法論 21
3.2 應用大型語言模型於 Grafcet 架構生成之實驗 22
3.3 可重構指紋影像增強架構設計 23
3.3.1 指紋影像增強模組(A1) 24
3.3.2 指紋預處理模組(A11) 25
3.3.3 脊密度影像處理模組(A14) 26
3.3.4 細紋連接與分岔影像處理模組(A15) 27
3.4 指紋影像增強離散事件建模 28
3.4.1 指紋影像增強離散事件建模 30
3.4.2 指紋預處理離散事件建模 31
3.4.3 脊密度影像離散事件建模 32
3.4.4 細紋影像處理離散事件建模 33
第四章、可重構指紋影像增強實驗 34
4.1 實驗環境與資料庫 34
4.2 應用大型語言模型於 Grafcet 架構生成之實驗 36
4.2.1 Claude3.5 生成指紋影像增強 Grafcet 系統架構之步驟 36
4.2.2 Claude3.5 生成過程中的除錯策略 40
4.3 可重構指紋影像增強架構實驗 41
4.3.1 Median Filter 實驗 41
4.3.2 Gabor Filter 實驗 43
4.3.3 實驗結果比較 46
4.4 實驗結果討論 50
第五章、結論與未來展望 51
5.1 結論 51
5.2 研究方向 51
參考文獻 53
附錄一可重構指紋增強 Grafcet 程式架構 59
附錄二 Claude 生成 Grafcet 對話流程 71
附錄三實驗結果紀錄 75

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指導教授

陳慶瀚(Ching-Han Chen)

審核日期

2025-1-21

推文