As an alternative method of empirical mode decomposition (EMD), the empirical Wavelet
transform (EWT) method was proposed to realize the signal decomposition by constructing …

Temporal data produced by industrial, human, and natural phenomena typically contain
deterministic and stochastic influences, being the first ideally modelled using Dynamical …

Significant wave height prediction for the following hours is a necessity for the planning and
operation of wave energy devices. For a site-specific and short-term prediction, classical …

Real-world data streams may change their behaviors along time, what is referred to as
concept drift. By detecting those changes, researchers obtain relevant information about the …

This study addresses the challenges posed by the strong noise and nonstationary
characteristics of vibration signals to enhance the efficiency and accuracy of rolling-bearing …

This paper deals with the time-frequency characteristic analysis for track geometry
irregularities using field data recorded by a comprehensive track inspection train. The …

Accurate forecasting for the crude oil price is important for government agencies, investors,
and researchers. To cope with this issue, in this paper, a new paradigm is designed for the …

Compared with thresholding methods based on the traditional wavelet transform (WT),
empirical wavelet transform (EWT) has been demonstrated to outperform in terms of noise …

Livestock production efficiency is essential to improve the world food chain in terms of
making meat available to more people and reducing producer costs, while supporting …

This paper presents a new approach for denoising Partial Discharge (PD) signals using a
hybrid algorithm combining the adaptive decomposition technique with Entropy measures …