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。

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%.

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