||Automated program of Ensemble Empirical Mode Decomposition
||Department of Aeronautics & Astronautics
Ensemble empirical mode decomposition (EEMD)
Unsteady time series data
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。
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%.
第一章 緒論 1
第二章 研究設備與開發環境 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
2.3.1 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
第五章 結論與未來建議 40
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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