整合MIAT方法論與大型語言模型於系統設計：以指紋比對系統為案例研究

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姓名

饒珮以(Pei-Yi Jao) 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：以指紋比對系統為案例研究
(Integration of MIAT Methodology and Large Language Models for System Design：A Case Study of Fingerprint Matching System)

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[文章引用]

[完整記錄]

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至系統瀏覽論文 (2030-2-1以後開放)

摘要(中)

本研究以指紋特徵比對系統為驗證案例，探討結合 MIAT 方法論系統設計與大型語言模型的系統設計和高階合成方法。MIAT 方法論著重在系統的階層式模組化的架構設計，以及演算法的離散事件建模，最後合成可維護、可擴展的系統程式碼。在方法論的最後階段，我們引進大型語言模型生成每個離散事件狀態所驅動所需要的程式碼。為了驗證這個方法，我們設計了指紋特徵比對系統的階層模組，包括旋轉排除模組、線段方向排除模組及特徵比對模組。實驗部分，基於FVC2004 指紋資料集進行系統性能測試，透過不同比對演算法如Minutia 與FLANN 的比較，驗證了系統在準確性與運行效率上的表現。實驗結果顯示在模組化設計下，各模組的獨立性和可靠性得到了充分驗證。大型語言模型在程式碼生成中的應用，提升了開發效率並降低人為錯誤的風險。此研究不僅對於指紋辨識技術的進一步發展具有啟發性，也提供了一種創新的系統設計方法，適用於其他高複雜度的軟體開發領域。

摘要(英)

This research uses a fingerprint feature matching system as a validation case to explore system design and high-level synthesis methods that combine MIAT methodology with Large Language Models (LLMs). The MIAT methodology focuses on hierarchical modular architecture design and discrete event modeling of algorithms, ultimately synthesizing maintainable and scalable system code. In the final stage of the methodology, we introduce LLMs to generate the necessary code driven by each discrete event state. To validate this approach, we designed hierarchical modules for the fingerprint feature matching system, including rotation elimination module, segment direction elimination module, and feature matching module. For experimentation, we conducted system performance testing based on the FVC2004 fingerprint dataset, comparing different matching algorithms such as Minutia and FLANN to validate the system′s performance in terms of accuracy and operational efficiency. The experimental results demonstrated that under the modular design, the independence and reliability of each module were thoroughly validated. The application of LLMs in code generation improved development efficiency and reduced the risk of human error. This research not only provides insights for further development of fingerprint recognition technology but also offers an innovative system design method applicable to other complex software development domains.

關鍵字(中)

★ MIAT 方法論
★ 指紋比對
★ 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 vii
表目錄 ix
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、指紋比對技術回顧 4
2.1 指紋特徵 4
2.1.1 全域特徵 4
2.1.2 局部特徵 5
2.1.3 次級特徵 6
2.2 指紋比對演算法 8
2.2.1 Image correlation 演算法比對 9
2.2.2 Phase 演算法比對 12
2.2.3 Skeleton 演算法比對 14
2.2.4 Minutia 演算法比對 17
2.2.5 FLANN 演算法比對 22
第三章、可重構指紋特徵比對系統設計 25
3.1 MIAT方法論 25
3.1.1 IDEF0 階層式模組化設計 26
3.1.2 Grafcet 離散事件建模 27
3.2 LLM輔助生成 Grafcet action程式碼 29
3.3 可重構指紋特徵比對系統架構設計 31
3.2.1 指紋比對模組(A4) 32
3.2.2 線段旋轉排除模組(A41) 33
3.2.3 線段方向排除模組(A42) 34
3.2.4 特徵點比對模組(A43) 35
3.4 離散事件建模 36
3.3.1 指紋特徵比對離散事件模型 37
3.3.2 線段旋轉排除離散事件模型 38
3.3.3 線段方向排除離散事件模型 39
3.3.4 特徵點比對離散模型 40
第四章、可重構指紋特徵比對實驗 41
4.1 實驗環境 41
4.2 資料描述 41
4.3 基於LLM 的Grafcet框架生成方法：實驗與應用分析 43
4.3.1 LLM 模型 43
4.3.2 Grafcet Action 生成流程 43
4.3.3 生成結果驗證 45
4.4 可重構指紋特徵比對驗證 49
4.4.1 指紋特徵點擷取 49
4.4.2 Minutia 比對實驗 51
4.4.3 FLANN 比對實驗 54
4.5 結果比較 57
4.6 實驗結果分析 60
第五章、結論 61
5.1 結論 61
5.2 未來展望 62
參考文獻 63
附錄一生成Grafcet 對話流程 72
附錄二可重構指紋比對 Grafcet Action 75

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指導教授

陳慶瀚(Ching-Han Chen)

審核日期

2025-1-21

推文