Relevant modes selection method based on Spearman correlation coefficient for laser signal denoising using empirical mode decomposition

Duan, Yabo; Song, Chengtian

doi:10.1007/s10043-016-0275-x

Cited by 33 publications

(16 citation statements)

References 19 publications

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“…Step1. The relationship between search terms and infectious disease was determined using the Pearson Correlation Coefficient(PCC) [34], Spearman Correlation Coefficient (SCC) [35], and Kendall Correlation Coefficient(KCC) [36].…”

Section: Methodsmentioning

confidence: 99%

Using Partial Least Squares Regression to Fit Small Data of H7N9 Incidence Based on the Baidu Index

Gan

Tan

et al. 2020

IEEE Access

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The internet search data will help the disease control department to estimate the disease in advance. The H7N9 epidemic that occurred in Guangxi Province was used as an example to demonstrate its association with Baidu search data. At first,16 search terms which have high correlation with H7N9 disease were selected by expert determination and calculation. At the same time, the number of disease cases were downloaded from the website of Guangxi CDC. The partial least square regression was choosed to estimate after comparing the regression models for the number of epidemic cases is very less than baidu searches data. To filter independent variables, cross validation and variable importance in projection were applied. The results show that: 1.the proposed method is suitable for fitting the data of H7N9 disease with few samples, and the fitting degree is perfect. 2.it will help to screen out the important searching index which are more relate to H7N9 epidemic by using cross validation and variable import in project. 3.compared with the PCA methods, the proposed method presented great advantages in performance index, especially with the help of cross validation and variable importance in projection. INDEX TERMS Partial least squares regression, H7N9, Baidu index, variable importance in projection.

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Section: Methodsmentioning

confidence: 99%

Using Partial Least Squares Regression to Fit Small Data of H7N9 Incidence Based on the Baidu Index

Gan

Tan

et al. 2020

IEEE Access

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“…Therefore, it is essential to select the fault-relevant ISCs, which contain rich fault information. A selection method for relevant intrinsic mode functions (IMFs) using spearman correlation coefficient was proposed in the literature [35]. Recently, ZHENG et al proposed a quantified IMFs selection method based on complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) and achieved a promising results [36].…”

Section: Proposed Feature Extraction Methodsmentioning

confidence: 99%

“…Considering that Spearman Correlation Coefficient is superior to the commonly-used Pearson correlation coefficient, it is used as a criterion to discriminate the ISCs, which carry more relevant information with the original signal. For two variables X = {x i } and Y = {y i } with the data length N , they can be converted into ranks g(x i ) and g(y i ) respectively, then the Spearman Correlation Coefficient is defined as [35]:…”

Section: Proposed Feature Extraction Methodsmentioning

confidence: 99%

A Novel Fault Detection Scheme Using Improved Inherent Multiscale Fuzzy Entropy With Partly Ensemble Local Characteristic-Scale Decomposition

2020

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At present, the multiscale fuzzy entropy has been verified to be an excellent measure of the complexity for dynamic time series. However, when using to short-time time series collected in practical application, the conventional multiscale fuzzy entropy may result in undefined or unreliable value. In this work, improved multiscale fuzzy entropy, named moving-average based multiscale fuzzy entropy (MA_MFE), is presented at first to potentially characterize the complexity of short-term time series. The MA_MFE algorithm can successfully produce more template vectors to overcome the problem of shortening the samples in the procedure of the existing approaches. The analysis experiments for both white noise signal and 1/f noise signal are made and the results show MA_MFE method is more effective for the short-term datasets. Then, a novel fault detection scheme has been developed. After using non-local mean approach to reduce background noise, the non-stationary vibration signals are decomposed into several intrinsic scale components (ISCs) by a newly developed time-frequency signal analysis method-partly ensemble local characteristic-scale decomposition (PELCD); The ISCs with higher correlation coefficients are used to reconstruct into a new signal and the inherent MA_MFEs are extracted to quantify the complexity of the collected vibration signal. At last, the multiSVM and improved variable predictive model based class discrimination (VPMCD) are employed as small-sample classifiers to achieve fault detection. Two experiments have been conducted, which include both rolling bearing as vital component in rotating machinery and a piston pump as typical reciprocation machinery in hydraulic system. The comparison results show that the proposed fault detection scheme is more effective and reliable and suitable for real-time online fault detection. INDEX TERMS Multiscale analysis, moving-average, partly ensemble local characteristic-scale decomposition, fault detection. YOUXIN LUO has been the Vice President, a Professor, and the master Supervisor of Mechanical Engineering College, Hunan University of Arts and Science, since 2004. As an expertise in mechanical engineering, his main research works focus on grey theory and its application to parameter optimization, digital signal processing, and fault diagnosis for construction machine. His scientific research articles have been published in

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“…where p is the number of layers. The degree of similarity between x(t) and h(t) needs to be expressed by calculating a specific correlation coefficient: [21].…”

Section: A Identification Methods Of the Correlation Componentsmentioning

confidence: 99%

Noise Reduction of LiDAR Signal via Local Mean Decomposition Combined With Improved Thresholding Method

Zhang

Chang

et al. 2020

IEEE Access

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The echo signal of light detection and ranging (LiDAR) system is easily disturbed by various noises under the influence of intense background light. The signal to noise ratio (SNR) decreases with increasing distance, which seriously affects the retrieval accuracy of the LiDAR system. This paper combines local mean decomposition and an improved thresholding method (LMD-ITM) to process the Lidar signal with a lot of noise, so as to avoid the loss of useful information. In this research, the correlation coefficient and energy entropy are used to distinguish between the relevant and irrelevant components decomposed by the local mean decomposition. Subsequently, the improved thresholding method is applied to process the irrelevant components. It can extract the effective signals which are difficult to be separated from the high-frequency signals. Finally, all components are reconstructed to get the denoising signal. The experimental results demonstrate that this method can effectively avoid the short-range migration, suppress the long-range noise, and adapt to different weather conditions. Compared with the previous denoising method, the proposed method increases the SNR by about 15%. INDEX TERMS LiDAR signal denoising, local mean decomposition, correlation coefficient, thresholding method.

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Relevant modes selection method based on Spearman correlation coefficient for laser signal denoising using empirical mode decomposition

Cited by 33 publications

References 19 publications

Using Partial Least Squares Regression to Fit Small Data of H7N9 Incidence Based on the Baidu Index

Using Partial Least Squares Regression to Fit Small Data of H7N9 Incidence Based on the Baidu Index

A Novel Fault Detection Scheme Using Improved Inherent Multiscale Fuzzy Entropy With Partly Ensemble Local Characteristic-Scale Decomposition

Noise Reduction of LiDAR Signal via Local Mean Decomposition Combined With Improved Thresholding Method

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