In this paper, we propose a multiscale dependence-based methodology to analyze the dependence structure and to estimate the downside portfolio risk measures in the energy markets. More specifically, under this methodology, we formulate a new bivariate Empirical Mode Decomposition (EMD) copula based approach to analyze and model the multiscale dependence structure in the energy markets. The prop...

The measurement of Very Low Frequency Electromagnetic (VLF-EM) is important in many different applications, i.e, environmental, archeological, geotechnical studies, etc. In recent years, improving and enhancing VLF-EM data containing complex numbers (bivariate) was presented by several authors in order to produce reliable models, generally using univariate empirical mode decomposition (EMD). Ap...

Misalignment is an important cause for the early failure of large doubly-fed wind turbines (DFWT). For the non-stationary characteristics of the signals in the transmission system of DFWT and the reality that it is difficult to obtain a large number of fault samples, Solidworks and Adams are used to simulate the different operating conditions of the transmission system of the DFWT to obtain the...

Arrhythmia is one kind of diseases that gives rise to the death and possibly forms the immedicable danger. The most common cardiac arrhythmia is the ventricular premature beat. The main purpose of this study is to develop an efficient arrhythmia detection algorithm based on Empirical Mode Decomposition (EMD). This algorithm requires the following stages: band-pass Butterworth filters, Empirical...

As an important part of power system planning and the basis of economic operation of power systems, the main work of power load forecasting is to predict the time distribution and spatial distribution of future power loads. The accuracy of load forecasting will directly influence the reliability of the power system. In this paper, a novel short-term Empirical Mode Decomposition-Grey Relational ...

Modal parameters of power transformer winding are closely related to transformer manufacturing and detection technology of winding deformation based on vibration analysis method. Aimed at identifying the modal parameters of transformer winding accurately, a modal experiment is designed and made on a real 10 kV power transformer. An improved Empirical Mode Decomposition (EMD) algorithm is propos...

Epilepsy is a global disease with considerable incidence due to recurrent unprovoked seizures. These seizures can be noninvasively diagnosed using electroencephalogram (EEG), a measure of neuronal electrical activity in brain recorded along scalp. EEG is highly nonlinear, nonstationary and non-Gaussian in nature. Nonlinear adaptive models such as empirical mode decomposition (EMD) provide intui...

The Hilbert–Huang transform (HHT) is an empirically based data-analysis method. Its basis of expansion is adaptive, so that it can produce physically meaningful representations of data from nonlinear and non-stationary processes. The advantage of being adaptive has a price: the difficulty of laying a firm theoretical foundation. This chapter is an introduction to the basic method, which is foll...

The recorded electroencephalography (EEG) signals are usually contaminated by electrooculography (EOG) artifacts. In this project, the multivariate empirical mode decomposition (MEMD)method will be proposed to remove EOG artifacts (EOAs) from multichannel EEG signals. Firstly, the EEG signals will be decomposed by the MEMD into multiple multivariate intrinsic mode functions (MIMFs). The EOG-rel...

Efficient detection of ventricular fibrillation is very important for clinical purposes as it is the most serious cardiac rhythm disturbance that can be life threatening. This paper presents a new method for detection of Ventricular fibrillation by discriminating it with Ventricular tachycardia using empirical mode decomposition (EMD) and Approximate Entropy. First Intrinsic mode functions (IMF...