scholarly journals Adaptive Vibrarthographic Signal Denoising via Ant Colony Optimization Using Dynamic Denoising Filter Parameters

2021 ◽  
Vol 12 (1) ◽  
pp. 01-15
Author(s):  
Rui Gong ◽  
Kazunori Hase ◽  
Hajime Ohtsu ◽  
Susumu Ota
Keyword(s):  
Ant Colony Optimization ◽  
Signal To Noise Ratio ◽  
Ensemble Empirical Mode Decomposition ◽  
Ant Colony ◽  
Denoising Method ◽  
Mode Decomposition ◽  
Snr Improvement ◽  
Mean Square Difference ◽  
Dynamic Filter

This study proposes an ant colony optimization (ACO) denoising method with dynamic filter parameters. The proposed method is developed based on ensemble empirical mode decomposition (EEMD), and aims to improve the quality of vibrarthographic (VAG) signals. It mixes the original VAG signals with different white noise amplitudes, and adopts a hybrid technology that combines EEMD with a Savitzky-Golay (SG) filter containing the dynamic parameters optimized by ACO. The results show that the proposed method provides a higher peak signal-to-noise ratio (PSNR) and a smaller root-mean-square difference than the regular methods. The SNR improvement for the VAG signals of normal knees can reach 13 dB while maintaining the original signal structure, and the SNR improvement for the VAG signals of abnormal knees can reach 20 dB. The method proposed in this study can improve the quality of nonstationary VAG signals.

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

Electronics ◽  
2019 ◽  
Vol 8 (6) ◽  
pp. 597 ◽  
Author(s):  
Guohui Li ◽  
Zhichao Yang ◽  
Hong Yang
Keyword(s):  
Signal To Noise Ratio ◽  
Noise Signal ◽  
Ensemble Empirical Mode Decomposition ◽  
Measurement Technology ◽  
Signal Denoising ◽  
Frequency Noise ◽  
Denoising Method ◽  
Radiated Noise ◽  
Mode Decomposition ◽  
Adaptive Noise

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.

scholarly journals Denoising the ECG Signal Using Ensemble Empirical Mode Decomposition

2021 ◽  
Vol 11 (5) ◽  
pp. 7536-7541
Author(s):  
W. Mohguen ◽  
S. Bouguezel
Keyword(s):  
Empirical Mode Decomposition ◽  
Additive White Gaussian Noise ◽  
Ensemble Empirical Mode Decomposition ◽  
Ecg Signal ◽  
Mean Square ◽  
Intrinsic Mode Functions ◽  
Denoising Method ◽  
Mode Decomposition ◽  
Mean Square Difference ◽  
Thresholding Function

In this paper, a novel electrocardiogram (ECG) denoising method based on the Ensemble Empirical Mode Decomposition (EEMD) is proposed by introducing a modified customized thresholding function. The basic principle of this method is to decompose the noisy ECG signal into a series of Intrinsic Mode Functions (IMFs) using the EEMD algorithm. Moreover, a modified customized thresholding function was adopted for reducing the noise from the ECG signal and preserve the QRS complexes. The denoised signal was reconstructed using all thresholded IMFs. Real ECG signals having different Additive White Gaussian Noise (AWGN) levels were employed from the MIT-BIH database to evaluate the performance of the proposed method. For this purpose, output SNR (SNRout), Mean Square Error (MSE), and Percentage Root mean square Difference (PRD) parameters were used at different input SNRs (SNRin). The simulation results showed that the proposed method provided significant improvements over existing denoising methods.

Empirical Mode Decomposition (EMD) Based Denoising Method for Heart Sound Signal and Its Performance Analysis

2016 ◽  
Vol 6 (5) ◽  
pp. 2197 ◽  
Author(s):  
Amy Hamidah Salman ◽  
Nur Ahmadi ◽  
Richard Mengko ◽  
Armein Z. R. Langi ◽  
Tati L. R. Mengko
Keyword(s):  
Root Mean Square ◽  
Empirical Mode Decomposition ◽  
Heart Sound ◽  
Signal To Noise Ratio ◽  
Sound Signal ◽  
Mean Square ◽  
Denoising Method ◽  
Mode Decomposition ◽  
Mean Square Difference ◽  
Heart Sound Signal

<p>In this paper, a denoising method for heart sound signal based on empirical mode decomposition (EMD) is proposed. To evaluate the performance of the proposed method, extensive simulations are performed using synthetic normal and abnormal heart sound data corrupted with white, colored, exponential and alpha-stable noise under different SNR input values. The performance is evaluated in terms of signal-to-noise ratio (SNR), root mean square error (RMSE), and percent root mean square difference (PRD), and compared with wavelet transform (WT) and total variation (TV) denoising methods. The simulation results show that the proposed method outperforms two other methods in removing three types of noises.</p>

scholarly journals Empirical Mode Decomposition (EMD) Based Denoising Method for Heart Sound Signal and Its Performance Analysis

2016 ◽  
Vol 6 (5) ◽  
pp. 2197 ◽  
Author(s):  
Amy Hamidah Salman ◽  
Nur Ahmadi ◽  
Richard Mengko ◽  
Armein Z. R. Langi ◽  
Tati L. R. Mengko
Keyword(s):  
Root Mean Square ◽  
Empirical Mode Decomposition ◽  
Heart Sound ◽  
Signal To Noise Ratio ◽  
Sound Signal ◽  
Mean Square ◽  
Denoising Method ◽  
Mode Decomposition ◽  
Mean Square Difference ◽  
Heart Sound Signal

<p>In this paper, a denoising method for heart sound signal based on empirical mode decomposition (EMD) is proposed. To evaluate the performance of the proposed method, extensive simulations are performed using synthetic normal and abnormal heart sound data corrupted with white, colored, exponential and alpha-stable noise under different SNR input values. The performance is evaluated in terms of signal-to-noise ratio (SNR), root mean square error (RMSE), and percent root mean square difference (PRD), and compared with wavelet transform (WT) and total variation (TV) denoising methods. The simulation results show that the proposed method outperforms two other methods in removing three types of noises.</p>

Denoising of the Base Vibration Signal Based on EEMD

2011 ◽  
Vol 143-144 ◽  
pp. 689-693 ◽  
Author(s):  
X.J. Li ◽  
K. Wang ◽  
G.B. Wang ◽  
Q. Li
Keyword(s):  
Vibration Signal ◽  
Ensemble Empirical Mode Decomposition ◽  
Decomposition Level ◽  
Denoising Method ◽  
Mode Decomposition ◽  
Empirical Mode Decomposition Method ◽  
The Common ◽  
Eemd Method ◽  
Selection Of ◽  
Better Than

Vibration signals of rotating machinery on the base are very weak and always buried in noisy noise; the common denoising methods have become powerless. It presents an ensemble empirical mode decomposition method (EEMD) that is used to denoise for the base vibration signal, which not only to overcome the problem of mode mixing, but also to avoid the selection of wavelet basis function and decomposition level of the problem. Experimental results of simulation and measured data show that EEMD method can effectively reduce the base vibration signal noise, which is better than the wavelet and EMD denoising method.

TUNINGS OF PARAMETERS AND PHEROMONE UPDATE STRATEGY IN ANT COLONY OPTIMIZATION

2010 ◽  
Vol 09 (01) ◽  
pp. 73-83
Author(s):  
A. TAMILARASI
Keyword(s):  
Ant Colony Optimization ◽  
Job Shop ◽  
Job Shop Scheduling ◽  
Parameter Tuning ◽  
Ant Colony ◽  
Shop Floor ◽  
Benchmark Problems ◽  
Ant Colony System ◽  
Update Strategy

Scheduling is considered to be a major task to improve the shop-floor productivity. The job shop problem is under this category and is combinatorial in nature. Research on optimization of job shop problem is one of the most significant and promising areas of optimization. This paper presents an application of the Ant Colony Optimization meta heuristic to job shop problem. The main characteristics of this model are positive feedback and distributed computation. The settings of parameter values have more influence in solving instances of job shop problem. An algorithm is introduced to improve the basic ant colony system by using a pheromone updating strategy and also to analyze the quality of the solution for different values of the parameters. In this paper, we present statistical analysis for parameter tuning and we compare the quality of obtained solutions by the proposed method with the competing algorithms given in the literature for well known benchmark problems in job shop scheduling.

scholarly journals Bolt Detection Signal Analysis Method Based on ICEEMD

2018 ◽  
Vol 2018 ◽  
pp. 1-10 ◽  
Author(s):  
Chunhui Guo ◽  
Zhan Zhang ◽  
Xin Xie ◽  
Zhengyu Yang
Keyword(s):  
Signal Analysis ◽  
Approximate Entropy ◽  
Ensemble Empirical Mode Decomposition ◽  
Analysis Method ◽  
Détection Signal ◽  
Mode Function ◽  
Mode Decomposition ◽  
Signal Analysis Method ◽  
Detection Signal

The construction quality of the bolt is directly related to the safety of the project, and, as such, it must be tested. In this paper, the improved complete ensemble empirical mode decomposition (ICEEMD) method is introduced to the bolt detection signal analysis. The ICEEMD is used in order to decompose the anchor detection signal according to the approximate entropy of each intrinsic mode function (IMF). The noise of the IMFs is eliminated by the wavelet soft threshold denoising technique. Based on the approximate entropy and the wavelet denoising principle, the ICEEMD-De anchor signal analysis method is proposed. From the analysis of the vibration analog signal, as well as the bolt detection signal, the result shows that the ICEEMD-De method is capable of correctly separating the different IMFs under noisy conditions and also that the IMF can effectively identify the reflection signal of the end of the bolt.

Parallel Ant Colony Optimization Algorithm Based on Multiplicate Pheromon Declining

2012 ◽  
Vol 433-440 ◽  
pp. 3577-3583
Author(s):  
Yan Zhang ◽  
Hao Wang ◽  
Yong Hua Zhang ◽  
Yun Chen ◽  
Xu Li
Keyword(s):  
Ant Colony Optimization ◽  
Optimization Algorithm ◽  
Ant Colony Algorithm ◽  
Optimal Path ◽  
Ant Colony ◽  
Ant Colony Optimization Algorithm ◽  
Computing Power ◽  
Slow Convergence ◽  
The Impact

To overcome the defect of the classical ant colony algorithm’s slow convergence speed, and its vulnerability to local optimization, the authors propose Parallel Ant Colony Optimization Algorithm Based on Multiplicate Pheromon Declining to solve Traveling Salesman Problem according to the characteristics of natural ant colony multi-group and pheromone updating features of ant colony algorithm, combined with OpenMP parallel programming idea. The new algorithm combines three different pheromone updating methods to make a new declining pheromone updating method. It effectively reduces the impact of pheromone on the non-optimal path in the ants parade loop to subsequent ants and improves the parade quality of subsequent ants. It makes full use of multi-core CPU's computing power and improves the efficiency significantly. The new algorithm is compared with ACO through experiments. The results show that the new algorithm has faster convergence rate and better ability of global optimization than ACO.

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