Due to the movement and complexity of the carbon market, traditional monoscale forecasting approaches often fail to capture its nonstationary and nonlinear properties and accurately describe its moving tendencies. In this study, a multiscale ensemble forecasting model integrating empirical mode decomposition (EMD), genetic algorithm (GA) and artificial neural network (ANN) is proposed to foreca...

This work investigates the application of the Ensemble Empirical Mode Decomposition (EEMD) and the timefrequency techniques for treatment of the electromyography (EMG) signal. The EMG signals are usually corrupted by artifacts that hide useful information then the extraction of high-resolution EMG signals from recordings contaminated with back ground noise becomes an important problem. The Ense...

Arterial blood pressure is not only an important index that must be measured in routine physical examination but also a key monitoring parameter of the cardiovascular system cardiac surgery, drug testing, and intensive care. To improve measurement accuracy continuous pressure, this paper uses photoplethysmography (PPG) signals to estimate diastolic systolic based on ensemble empirical mode deco...

With the increasing demand of power industry for load forecasting, improving accuracy forecasting has become increasingly important. In this paper, we propose an ultra short-term method based on similar day clustering and EEMD (Ensemble Empirical Mode Decomposition). detail, K-means algorithm was utilized to divide historical data into different clusters. Through EEMD, each cluster were decompo...

According to a recently conducted survey on surgical complication mortality rate, 47% of such cases are due anesthetics overdose. This indicates that there is an urgent need moderate the level anesthesia. Recently deep learning (DL) methods have played major role in estimating depth Anesthesia (DOA) patients and has essential control anesthesia In this paper, Electroencephalography (...