Design of sEMG Acquisition Circuit and Its Adaptive EEMD Denosing Research

The Surface electromyography (sEMG) signal is a kind of electrical signal which generated by human muscles during contraction. It is prone to being affected by noise because of its small amplitude, so it is necessary to remove the noise in its original signal with an appropriate algorithm. Based on the traditional signal denoising indicators, a new complex indicator r has been proposed in this paper which combines three different indicator parameters, that is, Signal to Noise Ratio (SNR), correlation coefficient (R), and standard error (SE). At the same time, an adaptive ensemble empirical mode decomposition (EEMD) method named AIO-EEMD which based on the proposed indicator is represented later. To verify the effective of the proposed algorithm, an electromyography signal acquisition circuit is designed firstly for collecting the original sEMG signal. Then, the denosing performance from the designed method is been compared with empirical mode decomposition (EMD) method and wavelet transform noise reduction method, respectively. The experiment results shown that the designed algorithm can not only automatically get the numbers of the reconstructed signal numbers, but also obtain the best reduction performance.

Ensemble Empirical Mode Decomposition Based Deep Learning Model for Short-Term Wind Power Forecasting

In the last few years, wind power forecasting has established itself as an essential tool in the energy industry due to the increase of wind power penetration in the electric grid. This paper presents a wind power forecasting method based on ensemble empirical mode decomposition (EEMD) and deep learning. EEMD is employed to decompose wind power time series data into several intrinsic mode functions and a residual component. Afterwards, every intrinsic mode function is trained by means of a CNN-LSTM architecture. Finally, wind power forecast is obtained by adding the prediction of every component. Compared to the benchmark model, the proposed approach provides more accurate predictions for several time horizons. Furthermore, prediction intervals are modelled using quantile regression.