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系統識別號 U0026-2908201620330700
論文名稱(中文) 改良經驗模態分解之探討
論文名稱(英文) The Study of Improved Empirical Mode Decomposition
校院名稱 成功大學
系所名稱(中) 資訊工程學系
系所名稱(英) Institute of Computer Science and Information Engineering
學年度 104
學期 2
出版年 105
研究生(中文) 楊子徵
研究生(英文) Tzu-Cheng Yang
學號 P76034622
學位類別 碩士
語文別 英文
論文頁數 62頁
口試委員 指導教授-郭淑美
口試委員-蔡聖鴻
口試委員-謝孫源
口試委員-洪金車
口試委員-龔旭陽
中文關鍵字 經驗模態分解  混模現象  二維混模現象  本質模態函數 
英文關鍵字 Empirical mode decomposition  Mode mixing phenomenon  2D mode mixing phenomenon  Intrinsic mode function 
學科別分類
中文摘要 在傳統經驗模態分解的分解過程中,容易受到間歇性訊號的影響而產生混模現象,造成分解出的本質模態函數失去真正的物理意義。本文提出了,能快速且穩定地改善混模現象。所提出的改良型經驗模態分解藉由訊號中每一個波的時間尺度進行分配,能直接得到改善混模現象的結果。此外,本文亦對現有文獻中較少探討的二維影像資料的混模現象進行探討,申明二維混模現象的定義與二維混模現象的檢視方法。
英文摘要 In the sifting process of the traditional empirical mode decomposition (EMD), intermittence causes mode mixing phenomenon. The intrinsic mode function (IMF) with the mode mixing phenomenon loses its original real physical meaning. In the current study, an improved EMD based on time scale allocation method has been proposed to improve the decomposition of the mode mixing phenomenon fast and stably. Additionally, the 2D version of our method has been extended to improve the decomposition of the mode mixing phenomenon in the 2D image data. Experimental results show that the improved EMD not only improves the decomposition of the mode mixing phenomenon correctly regardless for 1D signal or 2D image, but also exhibits great performance in quality and computation time. Furthermore, this thesis discusses the mode mixing phenomenon for 2D image data directly rather than extending the definition form 1D to 2D.
論文目次 摘要 I
Abstract II
誌謝 III
Table of Contents IV
List of Acronyms VI
List of Talbes VII
List of Figures VIII
Chapter 1 Introduction 1
Chapter 2 Background 4
2.1 1D Algorithm 4
2.2 2D Algorithm 11
Chapter 3 Mode Mixing Phenomenon 14
3.1 1D Mode Mixing Phenomenon 14
3.2 2D Mode Mixing Phenomenon 16
Chapter 4 Proposed Method 22
4.1 The Judgement Method Based on the Time Scale 24
4.2 The Improved EMD with TSA Step 27
4.3 Multi-dimensional TSAEMD (MTSAEMD) 29
Chapter 5 Experimental Results and Comparisons 30
5.1 1D Artificial Signals 30
5.2 2D Standard Test Images 48
Chapter 6 Conclusions 58
References 60
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