Feng Hai-Lin, Fang Yi-Ming, Xiang Xuan-Qi, Li Jian, Li Guan-Hui
School of Information Engineering, Zhejiang A & F University, Zhejiang, Lin'an 311300, China.
ScientificWorldJournal. 2012;2012:353081. doi: 10.1100/2012/353081. Epub 2012 Nov 20.
Ensemble empirical mode decomposition (EEMD) has been recently used to recover a signal from observed noisy data. Typically this is performed by partial reconstruction or thresholding operation. In this paper we describe an efficient noise reduction method. EEMD is used to decompose a signal into several intrinsic mode functions (IMFs). The time intervals between two adjacent zero-crossings within the IMF, called instantaneous half period (IHP), are used as a criterion to detect and classify the noise oscillations. The undesirable waveforms with a larger IHP are set to zero. Furthermore, the optimum threshold in this approach can be derived from the signal itself using the consecutive mean square error (CMSE). The method is fully data driven, and it requires no prior knowledge of the target signals. This method can be verified with the simulative program by using Matlab. The denoising results are proper. In comparison with other EEMD based methods, it is concluded that the means adopted in this paper is suitable to preprocess the stress wave signals in the wood nondestructive testing.
总体经验模态分解（EEMD）最近已被用于从观测到的噪声数据中恢复信号。通常，这是通过部分重构或阈值操作来实现的。在本文中，我们描述了一种有效的降噪方法。EEMD用于将信号分解为几个固有模态函数（IMF）。IMF内两个相邻过零点之间的时间间隔，称为瞬时半周期（IHP），用作检测和分类噪声振荡的标准。具有较大IHP的不良波形被设置为零。此外，该方法中的最佳阈值可以使用连续均方误差（CMSE）从信号本身推导得出。该方法完全由数据驱动，并且不需要目标信号的先验知识。该方法可以通过使用Matlab的模拟程序进行验证。去噪结果是合适的。与其他基于EEMD的方法相比，可以得出本文所采用的方法适用于木材无损检测中应力波信号的预处理。
