臺灣博碩士論文加值系統

本篇論文提出以基於可調控式模糊證據理論架構(weighted fuzzy Dempster-Shafer framework)及其應用於多模式訊號整合(multi-modal information integration)。傳統圖形識別任務上，常以單一訊號或分類演算法作為系統決策之依據，然而，單一決策源易受複雜環境與量測困難度所影響，降低系統之準確度，因此為有效提升系統性能，多模式訊號整合模型為此提供有效之途徑，訊號的整合除可獲得不同證據源之共通性，並可同時兼備不同訊號對環境之獨特性，證據理論(Dempster-Shafer evidence theory) 在證據融合的便利性及對於不確定性的量化處理，促使其在訊號整合中被廣泛使用。本篇論文提出可調控式模糊證據理論整合架構。此架構以模糊邏輯機制收集不同訊號源之決策資訊及不確定性，利用動態調控機制對不同決策並加以整合，同時採用直譯式機率分配法，解決過去於證據理論上，基礎機率(basic probability)易受環境擾動之問題，此分配法可有效量化不同訊號源之類間不確定性(inter-class uncertainty)；量化之不確定性資訊，將進一步對不同決策源做信心度加權，有效提高系統決策可靠度；此外，透過網狀訓練演算法(grid search algorithm)來改善系統性能。所提出之多模式訊號整合架構透過動態系統模擬，證明優於現行其他方法，並進一步整合腦波及眼動訊號，將其融合之決策應用於駕駛輔助腦機介面(brain-computer interface)，實驗結果顯示能在駕駛環境中有效應用。本論文所提出之架構具有動態調節機制及不確定性監控等優點，能有效率融合不同證據，為系統的辨識度提供大幅度的增加，在訊號整合及圖形識別領域極具實用性，相較於傳統識別方法，提供更彈性化的選擇與使用。

This study proposes an architecture based on a weighted fuzzy Dempster-Shafer framework (WFDSF), which can adjust weights associated with inconsistent evidences obtained from different classification approaches, to realize a fusion system for integrating multi-modal information. The Dempster-Shafer theory of evidence enables us to integrate heterogeneous information coming from multiple sources to reap collaborative inferences for a given problem. To conquer various uncertainties associated with the collected information, it assigns beliefs and plausibilities to possible hypotheses of each decision-maker and uses a combination rule to fuse multi-modal information. For information fusion, an important step in Dempster-Shafer aggregation is to find an appropriate basic probability assignment scheme for allocating support to each possible hypothesis/class, which still remains an arduous unsolved problem. Here we propose a mathematical structure to aggregate weighted evidences extracted from two different types of approaches: fuzzy Naïve Bayes and nearest mean classification rule. Further, an intuitionistic belief assignment is employed to deal with uncertainties between hypotheses/classes. Finally, twelve benchmark problems from UCI machine learning repository for classification are employed to validate the proposed WFDSF based scheme. In addition, an application of WFDSF to practical brain-computer-interface problem involving multi-modal data fusion is demonstrated in this study. Experimental results show that the WFDSF is superior to several existing methods.

Chinese Abstract i
English Abstract ii
Acknowledgement iii
Table of Contents iv
List of Tables vii
List of Figures viii

Chapter 1 INTRODUCTION 1
1.1 Background 1
1.2 Statement of the Problem 3
1.3 Aim of the Study 6
1.4 Organization of Dissertation 8

Chapter 2 BACKGROUND ON DEMPSTER-SHAFER THEORY OF EVIDENCE 9
2.1 Frame of Discernment 9
2.2 Mass Functions, Focal Elements and Basic Probability Assignment 10
2.3 Belief and Plausibility Measure Functions 10
2.4 Dempster’s Rule of Belief Combination 12
2.5 Pignistic Probability Transformation 13

Chapter 3 WEIGHTED FUZZY DEMPSTER-SHAFER FRAMEWORK 15
3.1 Generative-type Approach - Fuzzy Naïve Bayes 15
3.2 Discriminative-type Approach - Nearest Mean Classification Rule 17
3.3 Procedure of the WFDSF 19

Chapter 4 EXPERIMENTS - BENCHMARK DATA 28
4.1 Computation of Basic Probabilities 28
4.2 Evaluation Methods 36
4.3 Evaluation Results 38

Chapter 5 EXPERIMENT - REAL PHYSIOLOGICAL DATA 45
5.1 Experimental setup 45
5.1.1 Virtual Reality-Based Experimental Highway Environment 46
5.1.2 Driving Fatigue Paradigm 48
5.1.3 Participants 51
5.1.4 Physiological Signal Recording 52
5.1.5 Visual Information Measurements 53
5.2 Physiological signal processing and categorization 54
5.2.1 EEG Preprocessing and Segmentation 54
5.2.2 Power Spectrum Analysis and Feature Extraction 57
5.2.3 Eye Movement Analysis and Feature Extraction 57
5.3 Evaluation Results 58

Chapter 6 CONCLUSION 60

References 62

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