sVMD: Spearman Variational Mode Decomposition

In SVMD: Spearman Variational Mode Decomposition

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)
}

SVMD documentation built on Sept. 16, 2024, 5:06 p.m.