scholarly journals A New Approach of 3D Lightning Location Based on Pearson Correlation Combined with Empirical Mode Decomposition

2021 ◽  
Vol 13 (19) ◽  
pp. 3883
Author(s):  
Yanhui Wang ◽  
Yingchang Min ◽  
Yali Liu ◽  
Guo Zhao
Keyword(s):  
Empirical Mode Decomposition ◽  
Feature Matching ◽  
Pearson Correlation ◽  
Correlation Method ◽  
New Method ◽  
Lightning Flash ◽  
Mode Decomposition ◽  
Lightning Channel ◽  
Lightning Discharges ◽  
Developmental Characteristics

To improve the accuracy of pulse matching and the mapping quality of lightning discharges, the Pearson correlation method combined with empirical mode decomposition (EMD) is introduced for discharge electric field pulse matching. This paper uses the new method to locate the lightning channels of an intra-cloud (IC) lightning flash and a cloud-to-ground (CG) lightning flash and analyzes the location results for the two lightning flashes. The results show that this method has a good performance in lightning location. Compared with the pulse-peak feature matching method, the positioning results of the new method are significantly improved, which is mainly due to the much larger number of positioning points (matched pulses). The number of located radiation sources has increased by nearly a factor of seven, which can significantly improve the continuity of the lightning channel and clearly distinguish the developmental characteristics. In the CG flash, there were three negative recoil streamers in the positive leader channel. After the three negative recoil streamers were finished, taking approximately 1 ms, 12 ms, and 2 ms, respectively, the negative leader channel underwent a K-process. The three negative recoil streamers are not connected to the K-processes in the negative leader channel. We think that the three negative recoil streamers may have triggered the three K-processes, respectively.

EEMD-Based Speaker Emotional Analysis for Speech Signal

2011 ◽  
Vol 121-126 ◽  
pp. 815-819 ◽  
Author(s):  
Yu Qiang Qin ◽  
Xue Ying Zhang
Keyword(s):  
Emotion Recognition ◽  
Empirical Mode Decomposition ◽  
Speech Signal ◽  
Ensemble Empirical Mode Decomposition ◽  
New Method ◽  
Speech Signals ◽  
Emotional Speech ◽  
Mode Decomposition ◽  
Two Parameters ◽  
Mode Mixing

Ensemble empirical mode decomposition(EEMD) is a newly developed method aimed at eliminating mode mixing present in the original empirical mode decomposition (EMD). To evaluate the performance of this new method, this paper investigates the effect of two parameters pertinent to EEMD: the emotional envelop and the number of emotional ensemble trials. At the same time, the proposed technique has been utilized for four kinds of emotional(angry、happy、sad and neutral) speech signals, and compute the number of each emotional ensemble trials. We obtain an emotional envelope by transforming the IMFe of emotional speech signals, and obtain a new method of emotion recognition according to different emotional envelop and emotional ensemble trials.

scholarly journals Spatiotemporal empirical mode decomposition of resting-state fMRI signals: application to global signal regression

2019 ◽  
Author(s):  
Narges Moradi ◽  
Mehdy Dousty ◽  
Roberto C. Sotero
Keyword(s):  
Functional Connectivity ◽  
Empirical Mode Decomposition ◽  
Resting State ◽  
Large Scale ◽  
Pearson Correlation ◽  
Brain Regions ◽  
Connectivity Matrix ◽  
Correlation Pattern ◽  
Mode Decomposition ◽  
Global Signal

AbstractResting-state functional connectivity MRI (rs-fcMRI) is a common method for mapping functional brain networks. However, estimation of these networks is affected by the presence of a common global systemic noise, or global signal (GS). Previous studies have shown that the common preprocessing steps of removing the GS may create spurious correlations between brain regions. In this paper, we decompose fMRI signals into 5 spatial and 3 temporal intrinsic mode functions (SIMF and TIMF, respectively) by means of the empirical mode decomposition (EMD), which is an adaptive data-driven method widely used to analyze nonlinear and nonstationary phenomena. For each SIMF, brain connectivity matrices were computed by means of the Pearson correlation between TIMFs of different brain areas. Thus, instead of a single connectivity matrix, we obtained 5 × 3 = 15 functional connectivity matrices. Given the high value obtained for large-scale topological measures such as transitivity, in the low spatial maps (SIMF3, SIMF4, and SIMF5), our results suggest that these maps can be considered as spatial global signal masks. Thus, the spatiotemporal EMD of fMRI signals automatically regressed out the GS, although, interestingly, the removed noisy component was voxel-specific. We compared the performance of our method with the conventional GS regression and to the results when the GS was not removed. While the correlation pattern identified by the other methods suffers from a low level of precision, our approach demonstrated a high level of accuracy in extracting the correct correlation between different brain regions.

Purification of Axis Trace by Ensemble Empirical Mode Decomposition

2013 ◽  
Vol 791-793 ◽  
pp. 1006-1009
Author(s):  
Jia Xing Zhu ◽  
Wen Bin Zhang ◽  
Ya Song Pu ◽  
Yan Jie Zhou
Keyword(s):  
Prior Knowledge ◽  
Empirical Mode Decomposition ◽  
Simple Algorithm ◽  
Ensemble Empirical Mode Decomposition ◽  
Rotating Machinery ◽  
New Method ◽  
Intrinsic Mode Functions ◽  
Mode Function ◽  
Mode Decomposition ◽  
Novel Method

Aiming at the purification of axis trace, a novel method was proposed by using ensemble empirical mode decomposition (EEMD). Ensemble empirical mode decomposition decomposed a complicated signal into a collection of intrinsic mode functions (IMFs). Then according to prior knowledge of rotating machinery, chose intrinsic mode function components and reconstructed the signal. Finally the purification of axis trace was obtained. Simulation and practical results show the advantage of ensemble empirical mode decomposition. This method also has simple algorithm and high calculating speed; it provides a new method for purification of axis trace.

scholarly journals Noise Reduction Method of Underwater Acoustic Signals Based on Uniform Phase Empirical Mode Decomposition, Amplitude-Aware Permutation Entropy, and Pearson Correlation Coefficient

Entropy ◽  
2018 ◽  
Vol 20 (12) ◽  
pp. 918 ◽  
Author(s):  
Guohui Li ◽  
Zhichao Yang ◽  
Hong Yang
Keyword(s):  
Noise Reduction ◽  
Empirical Mode Decomposition ◽  
High Frequency ◽  
Reduction Method ◽  
Pearson Correlation ◽  
Low Frequency ◽  
Acoustic Signals ◽  
Permutation Entropy ◽  
Underwater Acoustic ◽  
Mode Decomposition

Noise reduction of underwater acoustic signals is of great significance in the fields of military and ocean exploration. Based on the adaptive decomposition characteristic of uniform phase empirical mode decomposition (UPEMD), a noise reduction method for underwater acoustic signals is proposed, which combines amplitude-aware permutation entropy (AAPE) and Pearson correlation coefficient (PCC). UPEMD is a recently proposed improved empirical mode decomposition (EMD) algorithm that alleviates the mode splitting and residual noise effects of EMD. AAPE is a tool to quantify the information content of nonlinear time series. Unlike permutation entropy (PE), AAPE can reflect the amplitude information on time series. Firstly, the original signal is decomposed into a series of intrinsic mode functions (IMFs) by UPEMD. The AAPE of each IMF is calculated. The modes are separated into high-frequency IMFs and low-frequency IMFs, and all low-frequency IMFs are determined as useful IMFs (UIMFs). Then, the PCC between the high-frequency IMF with the smallest AAPE and the original signal is calculated. If PCC is greater than the threshold, the IMF is also determined as a UIMF. Finally, all UIMFs are reconstructed and the denoised signal is obtained. Chaotic signals with different signal-to-noise ratios (SNRs) are used for denoising experiments. Compared with EMD and extreme-point symmetric mode decomposition (ESMD), the proposed method has higher SNR and smaller root mean square error (RMSE). The proposed method is applied to noise reduction of real underwater acoustic signals. The results show that the method can further eliminate noise and the chaotic attractors are smoother and clearer.

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