Comparative Research on Noise Reduction of Transient Electromagnetic Signals Based on Empirical Mode Decomposition and Variational Mode Decomposition

Radio Science ◽  
2021 ◽  
Author(s):  
Huiru Wei ◽  
Tingye Qi ◽  
Guorui Feng ◽  
Haina Jiang
Keyword(s):  
Noise Reduction ◽  
Empirical Mode Decomposition ◽  
Comparative Research ◽  
Variational Mode Decomposition ◽  
Transient Electromagnetic ◽  
Mode Decomposition ◽  
Electromagnetic Signals

scholarly journals Application of variational mode decomposition to seismic random noise reduction

2017 ◽  
Vol 14 (4) ◽  
pp. 888-898 ◽  
Author(s):  
Wei Liu ◽  
Siyuan Cao ◽  
Zhiming Wang
Keyword(s):  
Noise Reduction ◽  
Empirical Mode Decomposition ◽  
Random Noise ◽  
Computational Cost ◽  
Superior Performance ◽  
Variational Mode Decomposition ◽  
Intrinsic Mode Functions ◽  
Mode Decomposition ◽  
Reconstruction Performance ◽  
Non Stationary Signal

Abstract We have proposed a new denoising method for the simultaneous noise reduction and preservation of seismic signals based on variational mode decomposition (VMD). VMD is a recently developed adaptive signal decomposition method and an advance in non-stationary signal analysis. It solves the mode-mixing and non-optimal reconstruction performance problems of empirical mode decomposition that have existed for a long time. By using VMD, a multi-component signal can be non-recursively decomposed into a series of quasi-orthogonal intrinsic mode functions (IMFs), each of which has a relatively local frequency range. Meanwhile, the signal will focus on a smaller number of obtained IMFs after decomposition, and thus the denoised result is able to be obtained by reconstructing these signal-dominant IMFs. Synthetic examples are given to demonstrate the effectiveness of the proposed approach and comparison is made with the complete ensemble empirical mode decomposition, which demonstrates that the VMD algorithm has lower computational cost and better random noise elimination performance. The application of on field seismic data further illustrates the superior performance of our method in both random noise attenuation and the recovery of seismic events.

Noise reduction based on empirical mode decomposition for full waveforms data of GaoFen-7 laser altimetry

2020 ◽  
Vol 49 (11) ◽  
pp. 20200261-20200261
Author(s):  
刘向锋 Xiangfeng Liu ◽  
黄庚华 Genghua Huang ◽  
张志杰 Zhijie Zhang ◽  
王凤香 Fengxiang Wang ◽  
舒嵘 Rong Shu
Keyword(s):  
Noise Reduction ◽  
Empirical Mode Decomposition ◽  
Laser Altimetry ◽  
Mode Decomposition ◽  
Full Waveforms

BP-AdaBoost Algorithm Based on Variational Mode Decomposition Optimized by Envelope Entropy for Diagnosing the Working Conditions of a Slideway Seedling-Picking Mechanism

2021 ◽  
Vol 37 (4) ◽  
pp. 665-675
Author(s):  
Zhitao He ◽  
Haiyang Zhang ◽  
Jun Wang ◽  
Xin Jin ◽  
Song Gao ◽  
...  
Keyword(s):  
Empirical Mode Decomposition ◽  
Working Conditions ◽  
Working Condition ◽  
Recognition Accuracy ◽  
Variational Mode Decomposition ◽  
Adaboost Algorithm ◽  
Layer Number ◽  
Entropy Minimization ◽  
Mode Decomposition ◽  
Penalty Factor

Highlights A method of monitoring the working conditions of a slideway seedling-picking mechanism based on variational mode decomposition (VMD), envelope entropy, and energy entropy is proposed. Based on the criterion of envelope entropy minimization, the combination of the decomposition layer number and penalty factor in VMD is optimized to yield a satisfactory decomposition effect of the analyzed vibration signal. The BP-AdaBoost algorithm is used to improve the working condition classification performance for the slideway seedling-picking mechanism. The working-condition identification effect with the proposed method are compared with those through EMD-based, LMD-based, and EEMD-based methods. Abstract . The slideway seedling-picking mechanism is a type of rotating machinery. This study proposes a novel method of identifying the working conditions of slideway seedling-picking mechanisms for early fault diagnosis by utilizing a back-propagation adaptive boosting (BP-AdaBoost) algorithm based on variational mode decomposition (VMD) optimized by the envelope entropy. The experimental results demonstrate that the proposed method can effectively verify the four working conditions (normal state, slideway failure, cam failure, and spring failure). The overall recognition accuracy reaches 90.0% under the optimal combination of the decomposition layer number K and penalty factor a in VMD determined through the envelope entropy minimization criterion. Classification comparisons with empirical mode decomposition (EMD), local mean decomposition (LMD) and ensemble empirical mode decomposition (EEMD) integrated into the BP-AdaBoost algorithm indicate that the overall recognition accuracy of the proposed method is 18.1%, 16.9%, and 15.6% higher than the accuracies of the three conventional methods, respectively. Compared with the K-means, support vector machine (SVM) algorithms, BP-AdaBoost algorithm demonstrates a more dependable capability for identifying the working conditions. This study provides a useful reference for monitoring the working conditions of slideway seedling-picking mechanisms. Keywords: BP-AdaBoost algorithm, Energy entropy, Envelope entropy, Slideway seedling-picking mechanism, Variational mode decomposition, Working conditions.

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.

Operation feature extraction of flood discharge structure based on improved variational mode decomposition and variance dedication rate

2019 ◽  
Vol 26 (3-4) ◽  
pp. 229-240
Author(s):  
Jianwei Zhang ◽  
Ge Hou ◽  
Han Wang ◽  
Yu Zhao ◽  
Jinlin Huang
Keyword(s):  
Feature Extraction ◽  
Empirical Mode Decomposition ◽  
Variational Mode Decomposition ◽  
Denoising Method ◽  
Flood Discharge ◽  
Mode Decomposition ◽  
Empirical Wavelet Transform ◽  
Discharge Structure ◽  
Operation Characteristic ◽  
Rate Method

Operation feature extraction of flood discharge structures under ambient excitation has attracted increasing attention in recent years. However, the vibration signal of flood discharge structures is a nonstationary random signal with low signal-to-noise ratio, which is mixed with lots of low-frequency water flow noise and high-frequency white noise. It is difficult to excavate the hidden vibration characteristic information accurately. To solve the problem, we propose a novel denoising method called improved variational mode decomposition. As an improved method of variational mode decomposition, improved variational mode decomposition can effectively determine the decomposition mode number of variational mode decomposition by using the mutual information method. Furthermore, improved variational mode decomposition is combined with a variance dedication rate to extract the overall operation characteristic information of the structure. In order to evaluate the applicability and effectiveness of the proposed improved variational mode decomposition–variance dedication rate method, we compare the denoising results of simulation signals produced by an improved variational mode decomposition–variance dedication rate with those produced by digital filter, wavelet threshold, empirical mode decomposition, empirical wavelet transform, complete ensemble empirical mode decomposition with adaptive noise, and improved variational mode decomposition methods and find a better performance of the improved variational mode decomposition–variance dedication rate method. In addition, the proposed method is applied to the Three Gorges Dam, and the results show that the improved variational mode decomposition–variance dedication rate method can effectively remove heavy background noises and extract the operation characteristic information of the flood discharge structure, which contributes to health monitoring and damage identification of the flood discharge structure.

scholarly journals Random Noise Reduction in Seismic Data by Using Bidimensional Empirical Mode Decomposition and Shearlet Transform

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 71374-71386 ◽  
Author(s):  
Wen-Long Hou ◽  
Rui-Sheng Jia ◽  
Hong-Mei Sun ◽  
Xing-Li Zhang ◽  
Meng-Di Deng ◽  
...  
Keyword(s):  
Noise Reduction ◽  
Empirical Mode Decomposition ◽  
Seismic Data ◽  
Random Noise ◽  
Shearlet Transform ◽  
Mode Decomposition ◽  
Bidimensional Empirical Mode Decomposition

Applications of variational mode decomposition in seismic time-frequency analysis

Geophysics ◽  
2016 ◽  
Vol 81 (5) ◽  
pp. V365-V378 ◽  
Author(s):  
Wei Liu ◽  
Siyuan Cao ◽  
Yangkang Chen
Keyword(s):  
Empirical Mode Decomposition ◽  
Frequency Analysis ◽  
Synthetic Data ◽  
Ensemble Empirical Mode Decomposition ◽  
Variational Mode Decomposition ◽  
Intrinsic Mode Functions ◽  
Decomposition Approach ◽  
Time Frequency Analysis ◽  
Time Frequency ◽  
Mode Decomposition

We have introduced a novel time-frequency decomposition approach for analyzing seismic data. This method is inspired by the newly developed variational mode decomposition (VMD). The principle of VMD is to look for an ensemble of modes with their respective center frequencies, such that the modes collectively reproduce the input signal and each mode is smooth after demodulation into baseband. The advantage of VMD is that there is no residual noise in the modes and it can further decrease redundant modes compared with the complete ensemble empirical mode decomposition (CEEMD) and improved CEEMD (ICEEMD). Moreover, VMD is an adaptive signal decomposition technique, which can nonrecursively decompose a multicomponent signal into several quasi-orthogonal intrinsic mode functions. This new tool, in contrast to empirical mode decomposition (EMD) and its variations, such as EEMD, CEEMD, and ICEEMD, is based on a solid mathematical foundation and can obtain a time-frequency representation that is less sensitive to noise. Two tests on synthetic data showed the effectiveness of our VMD-based time-frequency analysis method. Application on field data showed the potential of the proposed approach in highlighting geologic characteristics and stratigraphic information effectively. All the performances of the VMD-based approach were compared with those from the CEEMD- and ICEEMD-based approaches.

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