The mirror extending approach proposed by Zhao and Huang in EMD method is improved in this paper. Mirror extending manner of data is kept unchanged, but the approach for determining envelopes is changed. When the end ...The mirror extending approach proposed by Zhao and Huang in EMD method is improved in this paper. Mirror extending manner of data is kept unchanged, but the approach for determining envelopes is changed. When the end of data is obviously not extremum, the envelope is determined by the first inner extremum and the image value in the mirror, ignoring the value on the end. This improvement eliminates the frequency compression near the end and decreases the error. Meanwhile, tridiagonal equations are used and the calculation speed is much increased. The temporal process curve is more important in reflecting the real physical process and comparable with other phenomena. Frequency mixing in IMFs makes it impossible. A high frequency reconstruction (HFR) approach is proposed to eliminate common frequency mixing and reconstruct an IMF with all high frequency portions. By this approach, the IMFs without frequency mixing are obtained to express significative processes. The high frequency information restored in high frequency IMF can be extracted by general spectrum method. After obtaining IMFs by EMD method, some of the theoretical and technological issues still exist when using the IMFs. The consistency of IMFs with real physical process is discussed in detail. By virtue of the approach proposed in this paper, the EMD method can be widely used in various fields.展开更多
The electromagnetic radiation (EMR) signal collected by monitoring system during coal or rock dynamic disaster may be interferred easily by electromagnetic noises in mines. The noises have a direct influence on the ...The electromagnetic radiation (EMR) signal collected by monitoring system during coal or rock dynamic disaster may be interferred easily by electromagnetic noises in mines. The noises have a direct influence on the recognition and analysis of the EMR signal features during the disaster. With the aim of removing these noises, an ensemble empirical mode decomposition (EEMD) adaptive morphological filter was proposed. From the result of the simulation and the experiment, it is shown that the method can restrain the random noise and white Gaussian noise mixed with EMR signal effectively. The filter is highly useful for improving the robustness of the coal or rock dynamic disaster monitoring system.展开更多
文摘The mirror extending approach proposed by Zhao and Huang in EMD method is improved in this paper. Mirror extending manner of data is kept unchanged, but the approach for determining envelopes is changed. When the end of data is obviously not extremum, the envelope is determined by the first inner extremum and the image value in the mirror, ignoring the value on the end. This improvement eliminates the frequency compression near the end and decreases the error. Meanwhile, tridiagonal equations are used and the calculation speed is much increased. The temporal process curve is more important in reflecting the real physical process and comparable with other phenomena. Frequency mixing in IMFs makes it impossible. A high frequency reconstruction (HFR) approach is proposed to eliminate common frequency mixing and reconstruct an IMF with all high frequency portions. By this approach, the IMFs without frequency mixing are obtained to express significative processes. The high frequency information restored in high frequency IMF can be extracted by general spectrum method. After obtaining IMFs by EMD method, some of the theoretical and technological issues still exist when using the IMFs. The consistency of IMFs with real physical process is discussed in detail. By virtue of the approach proposed in this paper, the EMD method can be widely used in various fields.
文摘The electromagnetic radiation (EMR) signal collected by monitoring system during coal or rock dynamic disaster may be interferred easily by electromagnetic noises in mines. The noises have a direct influence on the recognition and analysis of the EMR signal features during the disaster. With the aim of removing these noises, an ensemble empirical mode decomposition (EEMD) adaptive morphological filter was proposed. From the result of the simulation and the experiment, it is shown that the method can restrain the random noise and white Gaussian noise mixed with EMR signal effectively. The filter is highly useful for improving the robustness of the coal or rock dynamic disaster monitoring system.