降噪
噪音(视频)
希尔伯特-黄变换
还原(数学)
光纤
噪声测量
光纤传感器
计算机科学
电子工程
材料科学
声学
电信
物理
工程类
数学
人工智能
白噪声
图像(数学)
几何学
作者
Zhen Pan,Biao Xu,Wenjia Chen,Dian Fan,Xianghan Meng,Mengfan Peng,Ciming Zhou
标识
DOI:10.1109/jlt.2024.3489953
摘要
Complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) addresses the incomplete decomposition problem in ensemble empirical mode decomposition (EEMD) during the ensemble averaging process, but the residual noise in intrinsic mode function (IMF) severely affect the decomposition results and the efficiency of the algorithm. To address the shortcomings of CEEMDAN, improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN) algorithm is presented. This method introduces pairs of Gaussian white noise with opposite amplitudes to assist in the decomposition of original signal. The final IMF is the ensemble of both the IMFs with positive and negative noises, where the introduced noise can be effectively neutralized. Simulation and experimental results indicate that ICEEMDAN has advantages in improving the efficiency and decomposition accuracy of the algorithm. The proposed approach was applied to signal denoising in fiber optic sensing, and the analysis results verified the effectiveness and superiority of the method.
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