Denoising and Feature Extraction Algorithms Using NPE Combined with VMD and Their Applications in Ship-Radiated Noise

Li, Yuxing; Li, Yaan; Xiao, Chunxia; Yu, Jing

doi:10.3390/sym9110256

Cited by 84 publications

(46 citation statements)

References 31 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…, NPE reaches the smallest value 0, this means that the distance to white noise is 0. Li et al [21] have proved that NPE is more stable than PE, and NPE has strong noise recognition ability.…”

Section: Npementioning

confidence: 99%

“…Therefore, there is no need to use white Gaussian noise as an auxiliary for residual signal, the EMD decomposition can be directly performed. In order to solve the above problems, we introduce the new permutation entropy (NPE) [20] to detect the complexity of signal, and propose a modified CEEMDAN (MCEEMDAN) algorithm.A growing number of scholars are focusing on developing Shannon entropy algorithms, and these algorithms are employed in underwater acoustic processing, such as permutation entropy (PE) [5,21], sample entropy (SE) [22], and differential symbolic entropy (DSE) [23], etc. Although SE is robust to noise, its computational efficiency is low.…”

mentioning

confidence: 99%

“…A growing number of scholars are focusing on developing Shannon entropy algorithms, and these algorithms are employed in underwater acoustic processing, such as permutation entropy (PE) [5,21], sample entropy (SE) [22], and differential symbolic entropy (DSE) [23], etc. Although SE is robust to noise, its computational efficiency is low.…”

mentioning

confidence: 99%

See 2 more Smart Citations

A Denoising Method of Ship Radiated Noise Signal Based on Modified CEEMDAN, Dispersion Entropy, and Interval Thresholding

Yang

2019

Electronics

View full text Add to dashboard Cite

Due to the non-linear and non-stationary characteristics of ship radiated noise (SR-N) signal, the traditional linear and frequency-domain denoising methods cannot be used for such signals. In this paper, an SR-N signal denoising method based on modified complete ensemble empirical mode decomposition (EMD) with adaptive noise (CEEMDAN), dispersion entropy (DE), and interval thresholding is proposed. The proposed denoising method has the following advantages: (1) as an improved version of CEEMDAN, modified CEEMDAN (MCEEMDAN) combines the advantages of EMD and CEEMDAN, and it is more reliable than CEEMDAN and has less consuming time; (2) as a fast complexity measurement technology, DE can effectively identify the type of intrinsic mode function (IMF); and (3) interval thresholding is used for SR-N signal denoising, which avoids loss of amplitude information compared with traditional denoising methods. Firstly, the original signal is decomposed into a series of IMFs using MCEEMDAN. According to the DE value of IMF, the modes are divided into three types: noise IMF, noise-dominated IMF and pure IMF. After noise IMFs are removed, the noise-dominated IMFs are denoised using interval thresholding. Finally, the pure IMF and the processed noise-dominated IMFs are reconstructed to obtain the final denoised signal. The denoising experiments with the Chen's chaotic system show that the proposed method has a higher signal-to-noise ratio (SNR) than the other three methods. Applying the proposed method to denoise the real SR-N signal, the topological structure of chaotic attractor can be recovered clearly. It is proved that the proposed method can effectively suppress the high-frequency noise of SR-N signal.Empirical mode decomposition (EMD) [8] is an adaptive signal decomposition method, which can decompose the signal into a series of intrinsic mode functions (IMFs). Therefore, EMD provides a new idea for nonlinear and non-stationary signal processing, but EMD has problems such as the mode mixing and residual noise. In order to alleviate the above problems, some noise-assisted versions have been proposed, such as ensemble EMD (EEMD) [9], complementary EEMD (CEEMD) [10] and complete EEMD with adaptive noise (CEEMDAN) [11]. These optimized EMD algorithms are widely used in various fields, especially in medicine [12,13], fault diagnosis [14,15], chaotic signal processing [16], wind power prediction [17] and so on.CEEMDAN can overcome the mode mixing of EMD and reduce the reconstruction error of EEMD. However, each time an IMF is extracted, white Gaussian noise is added multiple times in the signal, which increases the algorithm complexity. In fact, in the decomposition process, the high-frequency intermittent components and noises that cause the mode mixing are usually decomposed first [18]. After separating these components, the extreme values of the remaining signal are evenly distributed [19]. Therefore, there is no need to use white Gaussian noise as an auxiliary for residual signal, the EMD decomposition can be directly perf...

show abstract

Section: Npementioning

confidence: 99%

mentioning

confidence: 99%

See 1 more Smart Citation

A Denoising Method of Ship Radiated Noise Signal Based on Modified CEEMDAN, Dispersion Entropy, and Interval Thresholding

Yang

2019

Electronics

View full text Add to dashboard Cite

show abstract

“…Due to the complexity of ocean ambient noise and the time-varying of underwater acoustic channels, feature extraction of underwater acoustic signals has always been a difficult problem in the area of underwater acoustic signal processing [1,2]. In order to solve this problem, some feature extraction approaches for underwater acoustic signals have been proposed, including a time domain analysis, a spectral analysis, a time-frequency analysis, a high-order statistics analysis and a complexity analysis.…”

Section: Introductionmentioning

confidence: 99%

A Hybrid Energy Feature Extraction Approach for Ship-Radiated Noise Based on CEEMDAN Combined with Energy Difference and Energy Entropy

Chen

2019

Processes

Self Cite

View full text Add to dashboard Cite

Influenced by the complexity of ocean environmental noise and the time-varying of underwater acoustic channels, feature extraction of underwater acoustic signals has always been a difficult challenge. To solve this dilemma, this paper introduces a hybrid energy feature extraction approach for ship-radiated noise (S-RN) based on complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) combined with energy difference (ED) and energy entropy (EE). This approach, named CEEMDAN-ED-EE, has two main advantages: (i) compared with empirical mode decomposition (EMD) and ensemble EMD (EEMD), CEEMDAN has better decomposition performance by overcoming mode mixing, and the intrinsic mode function (IMF) obtained by CEEMDAN is beneficial to feature extraction; (ii) the classification performance of the single energy feature has some limitations, nevertheless, the proposed hybrid energy feature extraction approach has a better classification performance. In this paper, we first decompose three types of S-RN into sub-signals, named intrinsic mode functions (IMFs). Then, we obtain the features of energy difference and energy entropy based on IMFs, named CEEMDAN-ED and CEEMDAN-EE, respectively. Finally, we compare the recognition rate for three sorts of S-RN by using the following three energy feature extraction approaches, which are CEEMDAN-ED, CEEMDAN-EE and CEEMDAN-ED-EE. The experimental results prove the effectivity and the high recognition rate of the proposed approach.

show abstract

“…Its number of components is also less than that of EMD and EEMD, and it shows better noise robustness. VMD has been widely used in many fields, such as prediction of stock price [25], short-term load [26] and solar irradiation [27], fault diagnosis [28,29], feature extraction [30,31], and so on. In order to improve the prediction accuracy, the hybrid model combined with a single model has been widely used in the field of prediction.…”

Section: Introductionmentioning

confidence: 99%

A Hybrid Model for Monthly Precipitation Time Series Forecasting Based on Variational Mode Decomposition with Extreme Learning Machine

Yang

2018

Information

View full text Add to dashboard Cite

Abstract:The matter of success in forecasting precipitation is of great significance to flood control and drought relief, and water resources planning and management. For the nonlinear problem in forecasting precipitation time series, a hybrid prediction model based on variational mode decomposition (VMD) coupled with extreme learning machine (ELM) is proposed to reduce the difficulty in modeling monthly precipitation forecasting and improve the prediction accuracy. The monthly precipitation data in the past 60 years from Yan'an City and Huashan Mountain, Shaanxi Province, are used as cases to test this new hybrid model. First, the nonstationary monthly precipitation time series are decomposed into several relatively stable intrinsic mode functions (IMFs) by using VMD. Then, an ELM prediction model is established for each IMF. Next, the predicted values of these components are accumulated to obtain the final prediction results. Finally, three predictive indicators are adopted to measure the prediction accuracy of the proposed hybrid model, back propagation (BP) neural network, Elman neural network (Elman), ELM, and EMD-ELM models: mean absolute error (MAE), root mean squared error (RMSE), and mean absolute percentage error (MAPE). The experimental simulation results show that the proposed hybrid model has higher prediction accuracy and can be used to predict the monthly precipitation time series.

show abstract

Denoising and Feature Extraction Algorithms Using NPE Combined with VMD and Their Applications in Ship-Radiated Noise

Cited by 84 publications

References 31 publications

A Denoising Method of Ship Radiated Noise Signal Based on Modified CEEMDAN, Dispersion Entropy, and Interval Thresholding

A Denoising Method of Ship Radiated Noise Signal Based on Modified CEEMDAN, Dispersion Entropy, and Interval Thresholding

A Hybrid Energy Feature Extraction Approach for Ship-Radiated Noise Based on CEEMDAN Combined with Energy Difference and Energy Entropy

A Hybrid Model for Monthly Precipitation Time Series Forecasting Based on Variational Mode Decomposition with Extreme Learning Machine

Contact Info

Product

Resources

About