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系統識別號 U0026-0608201918042700
論文名稱(中文) 總體經驗模態分解法的自動化程序
論文名稱(英文) Automated program of Ensemble Empirical Mode Decomposition
校院名稱 成功大學
系所名稱(中) 航空太空工程學系
系所名稱(英) Department of Aeronautics & Astronautics
學年度 107
學期 2
出版年 107
研究生(中文) 王晟懋
研究生(英文) Sheng-Mao Wang
學號 P46054456
學位類別 碩士
語文別 中文
論文頁數 97頁
口試委員 指導教授-苗君易
口試委員-呂宗行
口試委員-王大中
口試委員-蔡原祥
口試委員-葉思沂
中文關鍵字 總體經驗模態分解法  風能  非穩定時間序列資料  CUDA 
英文關鍵字 Ensemble empirical mode decomposition (EEMD)  Wind energy  Unsteady time series data  CUDA 
學科別分類
中文摘要 本研究因總體經驗模態分解法運算時間過長,且時間無法估計,因此針對該方法嘗試進行加速,分別利用MATLAB與Python進行撰寫,以探討不同的程式語言運算的特性,並開發圖形化使用者介面方便使用,且執行過程也能自動化接續運算與分類輸出結果。
範例資料來源為美國Case Western Reserve University校內風機所量測到的風速資料,因資料為近幾年來的資料,直到現在也持續的紀錄新資料,因此資料數量龐大且持續增長中,縮短運算時間也成為現階段必須進行的工作,為了縮短時間,本研究不僅欲使用圖形處理器來嘗試縮短運算過程所需時間,也找到逼近趨勢並且改變演算法來縮短約65%的運算時間。
英文摘要 The ensemble empirical mode decomposition (EEMD) method is applied for wind data analysis in the current research. However, calculations could take a very long time. Therefore, an attempt is made to accelerate the calculations. MATLAB and Python are used to explore the characteristics of different programming language operations, and a user-friendly graphical interface is also developed, and the execution process will be operated automatically and continuously.
The wind data analyzed were collected by the wind turbines located on campus of Case Western Reserve University in the United States. The wind data have been collecting since 2012 and the amount of data keeps growing. Thus, reducing the analyzing time is important. This study not only wants to use the graphics processor to try to shorten the time required for the operation process, but also finds the approximation trend in EEMD and refines the algorithm to shorten the operation time by about 65%.
論文目次 摘要 I
Abstract II
誌謝 III
目錄 XIII
圖目錄 XVI
符號索引 XVIII
第一章 緒論 1
1.1前言 1
1.2研究目的與動機 2
1.3文獻回顧 3
1.3.1風的特性與應用 3
1.3.2非平穩時間序列訊號分析應用 4
1.3.3巨量資料 6
第二章 研究設備與開發環境 8
2.1研究設備 8
2.1.1 中央處理器(AMD Ryzen 7 2700X) 8
2.1.2 圖形處理器(NVIDIA GeForce GTX1060 6G) 8
2.2系統開發環境 9
2.3研究軟體與函式庫 10
2.3.1 MATLAB 2019a 10
2.3.2 ANACONDA Navigator 1.9.6 10
2.3.3 Spyder 3.3.2 11
2.3.4 NumPy 1.15.4 11
2.3.5 CuPy-cuda101 5.4.0 11
2.3.6 SciPy 1.1.0 12
第三章 研究方法與訊號處理 13
3.1經驗模態分解法(Empirical Mode Decomposition, EMD) 13
3.2總體經驗模態分解法(Ensemble EMD, EEMD) 15
3.3改良總體經驗模態分解法(Modified EEMD, MEEMD) 17
3.4並行總體經驗模態分解(Parallel Implementation of EEMD, PEEMD) 18
3.5希爾伯特轉換(Hilbert transform, HT) 18
3.6希爾伯特-黃轉換(Hilbert-Huang Transform, HHT) 19
3.7 研究方法 20
第四章 研究結果與討論 23
4.1風速資料 23
4.2研究項目說明 25
4.3使用Python與MATLAB的速度差異 26
4.4不同CPU運算速度的差異 27
4.5加入GPU運算速度的差異 28
4.6研究討論 30
4.7運算過程的逼近趨勢 35
第五章 結論與未來建議 40
5.1結論 40
5.2未來工作與建議 41
參考文獻 43
附錄A 風機技術規格 47
附錄B 甘特圖與研究步驟 48
附錄C EMD/EEMD/MEEMD輸出結果比較 49
附錄D Open source 52
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