Localization of steel strand damage based on Empirical Mode Decomposition algorithm

2020 ◽  
Author(s):  
Yongju Chu ◽  
Ke He ◽  
Haiyan Wang ◽  
Xiaopeng Lv
Keyword(s):  
Empirical Mode Decomposition ◽  
Decomposition Algorithm ◽  
Mode Decomposition ◽  
Steel Strand

Empirical Mode Decomposition Algorithm Research & Application of Mie Lidar Atmospheric Backscattering Signal

2009 ◽  
Vol 36 (5) ◽  
pp. 1068-1074 ◽  
Author(s):  
郑发泰 Zheng Fatai ◽  
华灯鑫 Hua Dengxin ◽  
周阿维 Zhou Awei
Keyword(s):  
Empirical Mode Decomposition ◽  
Decomposition Algorithm ◽  
Research Application ◽  
Mode Decomposition ◽  
Mie Lidar

An Empirical Mode Decomposition Algorithm Based on Cross Validation and Its Application to Lidar Return Signal De-Noising

2014 ◽  
Vol 41 (10) ◽  
pp. 1014001
Author(s):  
王欢雪 Wang Huanxue ◽  
刘建国 Liu Jianguo ◽  
张天舒 Zhang Tianshu ◽  
董云升 Dong Yunsheng
Keyword(s):  
Empirical Mode Decomposition ◽  
Cross Validation ◽  
Decomposition Algorithm ◽  
Mode Decomposition ◽  
Lidar Return

BANDWIDTH EMPIRICAL MODE DECOMPOSITION AND ITS APPLICATION

2008 ◽  
Vol 06 (06) ◽  
pp. 777-798 ◽  
Author(s):  
QIWEI XIE ◽  
BO XUAN ◽  
SILONG PENG ◽  
JIANPING LI ◽  
WEIXUAN XU ◽  
...  
Keyword(s):  
Empirical Mode Decomposition ◽  
Wavelet Decomposition ◽  
Electricity Consumption ◽  
Decomposition Algorithm ◽  
Intrinsic Mode Functions ◽  
Fourier Decomposition ◽  
Remarkable Effect ◽  
Mode Decomposition ◽  
Consumption Data ◽  
Key Points

There are some methods to decompose a signal into different components such as: Fourier decomposition and wavelet decomposition. But they have limitations in some aspects. Recently, there is a new signal decomposition algorithm called the Empirical Mode Decomposition (EMD) Algorithm which provides a powerful tool for adaptive multiscale analysis of nonstationary signals. Recent works have demonstrated that EMD has remarkable effect in time series decomposition, but EMD also has several problems such as scale mixture and convergence property. This paper proposes two key points to design Bandwidth EMD to improve on the empirical mode decomposition algorithm. By analyzing the simulated and actual signals, it is confirmed that the Intrinsic Mode Functions (IMFs) obtained by the bandwidth criterion can approach the real components and reflect the intrinsic information of the analyzed signal. In this paper, we use Bandwidth EMD to decompose electricity consumption data into cycles and trend which help us recognize the structure rule of the electricity consumption series.

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