Analysis of Vibration Signals Based on Machine Learning for Crack Detection in a Low-Power Wind Turbine.

Currently, renewable energies, including wind energy, have been experiencing significant growth. Wind energy is transformed into electric energy through the use of wind turbines (WTs), which are located outdoors, making them susceptible to harsh weather conditions. These conditions can cause different types of damage to WTs, degrading their lifetime and efficiency, and, consequently, raising their operating costs.

Low-Density EEG for Neural Activity Reconstruction Using Multivariate Empirical Mode Decomposition.

Several approaches can be used to estimate neural activity. The main differences between them concern the information used and its sensitivity to high noise levels. Empirical mode decomposition (EMD) has been recently applied to electroencephalography EEG-based neural activity reconstruction to provide time-frequency information to improve the estimation of neural activity.

Localization of Active Brain Sources From EEG Signals Using Empirical Mode Decomposition: A Comparative Study.

The localization of active brain sources from Electroencephalogram (EEG) is a useful method in clinical applications, such as the study of localized epilepsy, evoked-related-potentials, and attention deficit/hyperactivity disorder. The distributed-source model is a common method to estimate neural activity in the brain. The location and amplitude of each active source are estimated by solving the inverse problem by regularization or using Bayesian methods with spatio-temporal constraints.