Iris feature extraction and recognition based on Empirical mode decomposition

2010 ◽  
Author(s):  
Shunli Zhang ◽  
Min Han ◽  
Weifeng Sun ◽  
Mingqiang Yang
Keyword(s):  
Feature Extraction ◽  
Empirical Mode Decomposition ◽  
Mode Decomposition ◽  
Iris Feature Extraction

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 Feature Extraction of Ship Radiated Noise Based on Permutation Entropy of the Intrinsic Mode Function with the Highest Energy

2016 ◽  
Author(s):  
Yu-Xing Li ◽  
Ya-An Li ◽  
Zhe Chen ◽  
Xiao Chen
Keyword(s):  
Feature Extraction ◽  
Empirical Mode Decomposition ◽  
Characteristic Parameter ◽  
Intrinsic Mode Function ◽  
Permutation Entropy ◽  
Intrinsic Mode Functions ◽  
Characteristic Parameters ◽  
Radiated Noise ◽  
Mode Function ◽  
Mode Decomposition

In order to solve the problem of feature extraction of underwater acoustic signals in complex ocean environment, a new method for feature extraction from ship radiated noise is presented based on empirical mode decomposition theory and permutation entropy. It analyzes the separability for permutation entropies of the intrinsic mode functions of three types of ship radiated noise signals, and discusses the permutation entropy of the intrinsic mode function with the highest energy. In this study, ship radiated noise signals measured from three types of ships are decomposed into a set of intrinsic mode functions with empirical mode decomposition method. Then, the permutation entropies of all intrinsic mode functions are calculated with appropriate parameters. The permutation entropies are obviously different in the intrinsic mode functions with the highest energy, thus, the permutation entropy of the intrinsic mode function with the highest energy is regarded as a new characteristic parameter to extract the feature of ship radiated noise. After that, the characteristic parameters, namely, the energy difference between high and low frequency, permutation entropy, and multi-scale permutation entropy, are compared with the permutation entropy of the intrinsic mode function with the highest energy. It is discovered that the four characteristic parameters are at the same level for similar ships, however, there are differences in the parameters for different types of ships. The results demonstrate that the permutation entropy of the intrinsic mode function with the highest energy is better in separability as the characteristic parameter than the other three parameters by comparing their fluctuation ranges and the average values of the four characteristic parameters. Hence, the feature of ship radiated noise can be extracted efficiently with the method.

Degradation Feature Extraction of Rolling Bearings Based on Optimal Ensemble Empirical Mode Decomposition and Improved Composite Spectrum Analysis

2018 ◽  
Author(s):  
Fengli Wang ◽  
Hua Chen
Keyword(s):  
Feature Extraction ◽  
Empirical Mode Decomposition ◽  
Single Point ◽  
Rolling Bearing ◽  
Ensemble Empirical Mode Decomposition ◽  
Significant Feature ◽  
Vibration Signals ◽  
Composite Spectrum ◽  
Optimal Ensemble ◽  
Mode Decomposition

Rolling bearing is a key part of turbomachinery. The performance and reliability of the bearing is vital to the safe operation of turbomachinery. Therefore, degradation feature extraction of rolling bearing is important to prevent it from failure. During rolling bearing degradation, machine vibration can increase, and this may be used to predict the degradation. The vibration signals are however complicated and nonlinear, making it difficult to extract degradation features effectively. Here, a novel degradation feature extraction method based on optimal ensemble empirical mode decomposition (EEMD) and improved composite spectrum (CS) analysis is proposed. Firstly, because only a few IMFs are expected to contain the information related to bearing fault, EEMD is utilized to pre-process the vibration signals. An optimization method is designed for adaptively determining the appropriate EEMD parameters for the signal, so that the significant feature components of the faulty bearing can be extracted from the signal and separated from background noise and other irrelevant components to bearing faults. Then, Bayesian information criterion (BIC) and correlation kurtosis (CK) are employed to select the sensitive intrinsic mode function (IMF) components and obtain fault information effectively. Finally, an improved CS analysis algorithm is used to fuse the selected sensitive IMF components, and the CS entropy (CSE) is extracted as degradation feature. Experimental data on the test bearings with single point faults separately at the inner race and rolling element were studied to demonstrate the capabilities of the proposed method. The results show that it can assess the bearing degradation status and has good sensitivity and good consistency to the process of bearing degradation.

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