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研究生:陳思予
研究生(外文):Szu-Yu Chen
論文名稱:改良式本質模態分解法在訊號處理之應用
論文名稱(外文):Reformed Intrinsic Mode Decomposition and its Application for Signal Processing
指導教授:吳順德吳順德引用關係
指導教授(外文):Shuen-De Wu
學位類別:碩士
校院名稱:國立臺灣師範大學
系所名稱:機電科技研究所
學門:工程學門
學類:機械工程學類
論文種類:學術論文
論文出版年:2009
畢業學年度:97
語文別:中文
論文頁數:85
中文關鍵詞:改良式本質模態分解法經驗模態分解法片段式線性非穩態訊號雜訊濾除
外文關鍵詞:reformed intrinsic mode decompositionempirical mode decompositionpiecewise linearnon-stationary signalssignal de-noising
相關次數:
  • 被引用被引用:3
  • 點閱點閱:422
  • 評分評分:
  • 下載下載:51
  • 收藏至我的研究室書目清單書目收藏:0
訊號處理在科學以及工程領域上皆是很重要的課題。自然界的訊號,大多都是非穩態(時變)、非線性過程,故往往得到的訊息包含了雜訊的部分。傳統的傅立葉轉換,其處理的訊號限制為:線性、穩態過程,所以並不能處理大部分的訊號特性。經驗模態分解法(Empirical Mode Decomposition, EMD)對於非線性、非穩態訊號提供一種多尺度、適應性的解析方式,這個方法大大改善了上述的限制。本論文針對多位學者提出此演算法三大課題的改進方式:停止準則、包絡線與邊界效應,作歸納與比較。另外,簡單介紹有關EMD基底的正交性條件、分解上的限制以及基底重建問題。針對文獻提到片段式線性訊號的研究,啟發了本論文對改良式本質模態分解法(Reformed Intrinsic Mode Decomposition, RIMD)的靈感,而片段式線性之概念最大的特點是可以得到較快速的分解法。本研究利用此概念求得之中值點建立出包絡線均值,並對真實訊號之應用上提出以下的方法:以立方雲線聯結中值點;以原始訊號的比例大小聯結中值點,找出包絡線均值。改良式本質模態分解法不但能大大降低演算法的計算量,並且減少在邊界效應極值點選取上之考量,這將會使得訊號在篩選程序中之平穩性以及對稱性大幅的提升。最後,以模擬以及測試訊號透過改良式本質模態分解法拆解出之結果,進行試驗結果之分析與討論。
Signal processing is very important for science and engineering researches. Real world signals are often noisy, non-stationary, and obtained from nonlinear systems. However, the majority of signal processing algorithms proposed in the literature such as Fourier transform are better suited for analyzing the linear stationary signals with weak noise. Empirical Mode Decomposition (EMD) provides a powerful tool for adaptive multi-scale analysis of nonlinear and non-stationary signals. In this thesis, the proposed improvement way of three main topics on the algorithm, stopping criterion, envelope and boundary effect, were summarized and compared. In addition, we make a brief introduction involving orthogonality condition of basis functions, the limitation of decomposition capacity and reconstruction issue of basis functions. It inspired us to propose the Reformed Intrinsic Mode Decomposition (RIMD) by the study of piecewise linear signals in the literature. The best feature of piecewise linear processing is to obtain the faster decomposition efficiency. In this study, we utilize this notion to get middle points and then establish the mean envelope, and propose following methods for the application of real signals: connecting middle points by cubic spline, connecting middle points by the propotion of the original signal, and finding the mean envelope. RIMD is not only reducing the computational cost but also decreasing the selection of extrema for the boundary effect. It will make signals smoother and more symmetric in the sifting process. At last, the results of decomposition using RIMD for the simulated and testing signals were analysized and discussed.
誌謝 I
摘要 II
ABSTRACT III
目錄 IV
圖目錄 VI
表目錄 IX
第一章 序論 1
1.1 研究背景 1
1.2 研究動機 3
1.3 研究目標 4
1.4 論文架構 4
第二章 經驗模態分解法 6
2.1 演算法 6
2.1.1 本質模態函數 7
2.1.2 篩選程序 9
2.2 間歇性雜訊問題 10
2.2.1 間歇性準則 11
2.2.2 整體經驗模態分解法 11
2.3 經驗模態分解法之相關問題 12
2.3.1 分解上的限制 12
2.3.2 正交條件 13
2.4 經驗模態分解法之雜訊濾除比較 14
第三章 評估與規劃經驗模態分解法之演算法 17
3.1 主要議題 17
3.1.1 停止準則 17
3.1.2 包絡線 22
3.1.3 邊界效應 28
3.2 本質模態函數之相關研究 33
3.2.1 最佳訊號重建 33
3.2.2 本值模態熵 34
3.3 改良式本質模態分解法之構想 34
第四章 改良式本質模態分解法之架構與分析結果 38
4.1 改良式本質模態分解法 38
4.1.1 演算法 38
4.1.2 極值點選取與邊界點處理 41
4.2 模擬訊號分析 43
4.2.1 平穩訊號 43
4.2.2 非平穩訊號 47
4.2.3 小結 57
4.3 測試訊號分析 58
4.3.1 大棕蝙蝠音訊檔 58
4.3.2 基準線飄移心電訊號 61
4.3.3 MIT-BIH波形資料庫檔案 66
4.3.4 小結 70
第五章 結論 71
5.1 本論文之貢獻 71
5.2 未來展望 71
參考文獻 72
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