| Type: | Package |
| Title: | Spearman Variational Mode Decomposition |
| Version: | 0.1.0 |
| Author: | Dr. Himadri Shekhar Roy [aut, cre], Dr. Ranjit Kumar Paul [aut], Dr. Chiranjit Mazumder [aut], Dr. Kamalika Nath [aut], Dr. Prakash Kumar [aut] |
| Maintainer: | Dr. Himadri Shekhar Roy <himadriiasri@gmail.com> |
| Description: | In practice, it is difficult to determine the number of decomposition modes, K, for Variational Mode Decomposition (VMD). To overcome this issue, this study offers Spearman Variational Mode Decomposition (SVMD), a method that uses the Spearman correlation coefficient to calculate the ideal mode number. Unlike the Pearson correlation coefficient, which only returns a perfect value when X and Y are linearly connected, the Spearman correlation can be calculated without knowing the probability distributions of X and Y. The Spearman correlation coefficient, also called Spearman's rank correlation coefficient, is a subset of a wider correlation coefficient. As VMD decomposes a signal, the Spearman correlation coefficient between the reconstructed and original sequences rises as the mode number K increases. Once the signal has been fully decomposed, subsequent increases in K cause the correlation to gradually level off. When the correlation reaches a specific level, VMD is said to have adequately decomposed the signal. Numerous experiments revealed that a threshold of 0.997 produces the best denoising effect, so the threshold is set at 0.997. This package has been developed using concept of Yang et al. (2021)<doi:10.1016/j.aej.2021.01.055>. |
| License: | GPL-3 |
| Encoding: | UTF-8 |
| Imports: | VMDecomp, stats |
| NeedsCompilation: | no |
| RoxygenNote: | 7.3.1 |
| Packaged: | 2024-09-14 19:20:00 UTC; Admin |
| Repository: | CRAN |
| Date/Publication: | 2024-09-16 14:30:06 UTC |
Spearman Variational Mode Decomposition
Description
Optimal number of modes of Variational Mode Decomposition (VMD) using Spearman's rank correlation coefficient
Usage
sVMD(
series,
alpha = 2000,
tau = 0,
DC = FALSE,
init = 1,
tol = 1e-07,
threshold = 0.997,
max_modes = 10,
verbose = FALSE
)
Arguments
series |
The input time series signal to be decomposed. |
alpha |
The balancing parameter of the data-fidelity constraint. Default is 2000. |
tau |
Time-step of the dual ascent (pick 0 for noise-slack). Default is 0. |
DC |
If TRUE, the first mode is put and kept at DC (0 frequency). Default is FALSE. |
init |
Mode initialization (1 = all omegas start at 0). Default is 1. |
tol |
Convergence tolerance criterion. Default is 1e-7. |
threshold |
The correlation coefficient threshold to determine the optimal number of modes. Default is 0.997. |
max_modes |
The maximum number of modes to consider. Default is 10. |
verbose |
Logical, if TRUE, prints detailed messages about the decomposition process. |
Value
Returns a list containing the optimal number of modes, reconstructed signal, and additional outputs from the VMD process:
-
optimal_K: The optimal number of modes. -
reconstructed_signal: The reconstructed signal from the selected modes. -
imfs: Intrinsic Mode Functions (IMFs) obtained from SVMD. -
u_hat: Estimated envelopes of the modes. -
omega: Frequencies of the modes.
References
Yang, H., Cheng, Y., and Li, G. (2021). A denoising method for ship radiated noise based on Spearman variational mode decomposition, spatial-dependence recurrence sample entropy, improved wavelet threshold denoising, and Savitzky-Golay filter. Alexandria Engineering Journal, 60(3), 3379-3400
Examples
{
# Example data generation:
# Set the number of observations
N <- 300
# Set a random seed for reproducibility
set.seed(123)
# Generate random uniform values
rand_unif <- runif(n = N, min = 0, max = 1.0)
# Create the components of the time series
sig1 <- 6 * rand_unif
sig2 <- sin(8 * pi * rand_unif) # Using sine function
sig3 <- 0.5 * sin(40 * pi * rand_unif) # Using sine function
# Combine the components to form the final signal
signal <- sig1 + sig2 + sig3
# Apply the sVMD function to the signal
result <- sVMD(signal)
}