scholarly journals A Data-Driven Noise Reduction Method and Its Application for the Enhancement of Stress Wave Signals

2012 ◽  
Vol 2012 ◽  
pp. 1-7 ◽  
Author(s):  
Hai-Lin Feng ◽  
Yi-Ming Fang ◽  
Xuan-Qi Xiang ◽  
Jian Li ◽  
Guan-Hui Li
Keyword(s):  
Noise Reduction ◽  
Stress Wave ◽  
Reduction Method ◽  
Ensemble Empirical Mode Decomposition ◽  
Data Driven ◽  
Intrinsic Mode Functions ◽  
Time Intervals ◽  
Zero Crossings ◽  
Partial Reconstruction ◽  
Mode Decomposition

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.

Random Noise Reduction Based on Ensemble Empirical Mode Decomposition and Wavelet Threshold Filtering

2012 ◽  
Vol 518-523 ◽  
pp. 3887-3890 ◽  
Author(s):  
Wei Chen ◽  
Shang Xu Wang ◽  
Xiao Yu Chuai ◽  
Zhen Zhang
Keyword(s):  
Noise Reduction ◽  
Empirical Mode Decomposition ◽  
High Frequency ◽  
Reduction Method ◽  
Random Noise ◽  
Low Frequency ◽  
Ensemble Empirical Mode Decomposition ◽  
Intrinsic Mode Functions ◽  
Mode Decomposition ◽  
Eemd Method

This paper presents a random noise reduction method based on ensemble empirical mode decomposition (EEMD) and wavelet threshold filtering. Firstly, we have conducted spectrum analysis and analyzed the frequency band range of effective signals and noise. Secondly, we make use of EEMD method on seismic signals to obtain intrinsic mode functions (IMFs) of each trace. Then, wavelet threshold noise reduction method is used on the high frequency IMFs of each trace to obtain new high frequency IMFs. Finally, reconstruct the desired signal by adding the new high frequency IMFs on the low frequency IMFs and the trend item together. When applying our method on synthetic seismic record and field data we can get good results.

The improved noise reduction method for the vibration signal based on variational mode decomposition

Filomat ◽  
2020 ◽  
Vol 34 (15) ◽  
pp. 4975-4983
Author(s):  
Zhiting Liu ◽  
Yuhua Wang ◽  
Wenwei Zheng ◽  
Yuexia Zhou
Keyword(s):  
Noise Reduction ◽  
Correlation Coefficient ◽  
Reduction Method ◽  
Vibration Signal ◽  
Energy Concentration ◽  
Variational Model ◽  
Original Signal ◽  
Intrinsic Mode Functions ◽  
Model Decomposition ◽  
Mode Decomposition

The variational model decomposition (VMD) has a problem that is dificult to determine the number of intrinsic mode functions (IMF).We use the leaked energy to determine the number of IMFs. And we use the energy concentration rate of the IMF?s autocorrelation function and the correlation coefficient between the IMFs and the original signal, define Q as the ratio of the energy concentration and the correlation coefficient, and use Q to determine the noise IMFs in the IMFs. Then, we filter the noise IMFs and use the remaining IMFs to reconstruct signal to achieve noise reduction. Finally, we use the signal-tonoise ratio (SNR) to compare the noise reduction method proposed in this paper and the Empirical Mode Decomposition (EMD) noise reduction method.

scholarly journals Noise Reduction of Welding Crack AE Signal Based on EMD and Wavelet Packet

Sensors ◽  
2020 ◽  
Vol 20 (3) ◽  
pp. 761 ◽  
Author(s):  
Kuanfang He ◽  
Zixiong Xia ◽  
Yin Si ◽  
Qinghua Lu ◽  
Yanfeng Peng
Keyword(s):  
Noise Reduction ◽  
Basis Function ◽  
Crack Detection ◽  
Reduction Method ◽  
Wavelet Packet ◽  
Welding Process ◽  
Intrinsic Mode Functions ◽  
Mode Decomposition ◽  
Ae Signal ◽  
Adaptive Decomposition

The acoustic emission (AE) signal collected by a sensor in the welding process has an overlapping frequency band and weak characteristics under a complex noise background. It is difficult for the wavelet noise reduction method, with single basis function, to effectively match the different characteristic information of the welding crack AE signal. Taking into account the adaptive decomposition characteristics of Empirical Mode Decomposition (EMD), a novel wavelet packet noise reduction method for welding AE signal was proposed. The welding AE signal was adaptively decomposed into several Intrinsic Mode Functions (IMFs) by the EMD. The effective IMFs were selected by the frequency distribution characteristics of the welding crack AE signal. A wavelet packet, with a specific basis function, was subsequently performed on the effective IMFs, which were reconstructed to be the welding crack AE signal. The simulated and experimental results indicated that the proposed method can effectively achieve noise reduction of the welding crack AE signal, which provided a mean for structure crack detection in the welding process.

scholarly journals Noise Reduction Method of Underwater Acoustic Signals Based on CEEMDAN, Effort-To-Compress Complexity, Refined Composite Multiscale Dispersion Entropy and Wavelet Threshold Denoising

Entropy ◽  
2018 ◽  
Vol 21 (1) ◽  
pp. 11 ◽  
Author(s):  
Guohui Li ◽  
Qianru Guan ◽  
Hong Yang
Keyword(s):  
Noise Reduction ◽  
Empirical Mode Decomposition ◽  
Reduction Method ◽  
Signal To Noise Ratio ◽  
Acoustic Signals ◽  
Ensemble Empirical Mode Decomposition ◽  
Underwater Acoustic ◽  
Real Signal ◽  
Mode Decomposition ◽  
Soft Threshold

Owing to the problems that imperfect decomposition process of empirical mode decomposition (EMD) denoising algorithm and poor self-adaptability, it will be extremely difficult to reduce the noise of signal. In this paper, a noise reduction method of underwater acoustic signal denoising based on complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN), effort-to-compress complexity (ETC), refined composite multiscale dispersion entropy (RCMDE) and wavelet threshold denoising is proposed. Firstly, the original signal is decomposed into several IMFs by CEEMDAN and noise IMFs can be identified according to the ETC of IMFs. Then, calculating the RCMDE of remaining IMFs, these IMFs are divided into three kinds of IMFs by RCMDE, namely noise-dominant IMFs, real signal-dominant IMFs, real IMFs. Finally, noise IMFs are removed, wavelet soft threshold denoising is applied to noise-dominant IMFs and real signal-dominant IMFs. The denoised signal can be obtained by combining the real IMFs with the denoised IMFs after wavelet soft threshold denoising. Chaotic signals with different signal-to-noise ratio (SNR) are used for denoising experiments by comparing with EMD_MSE_WSTD and EEMD_DE_WSTD, it shows that the proposed algorithm has higher SNR and smaller root mean square error (RMSE). In order to further verify the effectiveness of the proposed method, which is applied to noise reduction of real underwater acoustic signals. The results show that the denoised underwater acoustic signals not only eliminate noise interference also restore the topological structure of the chaotic attractors more clearly, which lays a foundation for the further processing of underwater acoustic signals.

Noise Reduction Method for the Ring LaserGyro Signal Based on Ceemdan and the Savitzky–Golay Algorithm

2021 ◽  
pp. 2250005
Author(s):  
Hao Liang ◽  
Xingfa Zhao ◽  
Yu Guo
Keyword(s):  
Noise Reduction ◽  
Ring Laser ◽  
Measurement Accuracy ◽  
Reduction Method ◽  
Ensemble Empirical Mode Decomposition ◽  
Engineering Problem ◽  
Permutation Entropy ◽  
Laser Gyro ◽  
Mode Decomposition ◽  
Ring Laser Gyroscope

The ring laser gyro signal contains complex noise components, affecting the system’s measurement accuracy. It is an engineering problem worthy of study to find an effective method to reduce the noise in the sampling signal and improve the system’s accuracy. In order to reduce various noises of the ring laser gyroscope and improve its measurement accuracy, a noise reduction method combining complete ensemble empirical mode decomposition with adaptive noise and the Savitzky–Golay algorithm is proposed. First, the measured samples are mode decomposed, and the concept of weighted-permutation entropy is introduced to distinguish the noisy modes. Then, the Savitzky–Golay algorithm is used to process the noisy modes, and finally, the signal is reconstructed. Simulated test signal and actual gyro signal are used to test, and compared with the EMD noise reduction method, each indicator is improved. The paper proposes a new noise reduction method for the laser gyro signal, and introduces weighted-permutation entropy to analyze the dividing point. The test data show the effectiveness of the method.

Ensemble Empirical Mode Decomposition Based Deep Learning Model for Short-Term Wind Power Forecasting

2022 ◽  
Author(s):  
J.M. González-Sopeña
Keyword(s):  
Deep Learning ◽  
Wind Power ◽  
Empirical Mode Decomposition ◽  
Time Series Data ◽  
Ensemble Empirical Mode Decomposition ◽  
Series Data ◽  
Intrinsic Mode Functions ◽  
Wind Power Forecasting ◽  
Mode Decomposition ◽  
Power Forecasting

Abstract. In the last few years, wind power forecasting has established itself as an essential tool in the energy industry due to the increase of wind power penetration in the electric grid. This paper presents a wind power forecasting method based on ensemble empirical mode decomposition (EEMD) and deep learning. EEMD is employed to decompose wind power time series data into several intrinsic mode functions and a residual component. Afterwards, every intrinsic mode function is trained by means of a CNN-LSTM architecture. Finally, wind power forecast is obtained by adding the prediction of every component. Compared to the benchmark model, the proposed approach provides more accurate predictions for several time horizons. Furthermore, prediction intervals are modelled using quantile regression.

scholarly journals NOISE REDUCTION METHOD FOR OCT IMAGES BASED ON EMPIRICAL MODE DECOMPOSITION

2013 ◽  
Vol 06 (02) ◽  
pp. 1350009 ◽  
Author(s):  
OLEG O. MYAKININ ◽  
DMITRY V. KORNILIN ◽  
IVAN A. BRATCHENKO ◽  
VALERIY P. ZAKHAROV ◽  
ALEXANDER G. KHRAMOV
Keyword(s):  
Noise Reduction ◽  
Empirical Mode Decomposition ◽  
Noise Suppression ◽  
Reduction Method ◽  
Tissue Structure ◽  
Diagnostic Process ◽  
Important Process ◽  
Post Processing ◽  
Mode Decomposition ◽  
Mesh Implant

In this paper, the new method for OCT images denoizing based on empirical mode decomposition (EMD) is proposed. The noise reduction is a very important process for following operations to analyze and recognition of tissue structure. Our method does not require any additional operations and hardware modifications. The basics of proposed method is described. Quality improvement of noise suppression on example of edge-detection procedure using the classical Canny's algorithm without any additional pre- and post-processing operations is demonstrated. Improvement of raw-segmentation in the automatic diagnostic process between a tissue and a mesh implant is shown.

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