The draw theory is the foundation for decreasing ore loss and dilution indices while extracting deposits from mines. Therefore, research on draw theory is of great significance to optimally guide the draw control and improve the economy efficiency of mines. The laboratory scaled physical draw experiments under inclined wall condition conducted showed that a new way was proposed to investigate the flow zone of granular materials. The flow zone was simply divided into two parts with respect to the demarcation point of the flow axis. Based on the stochastic medium draw theory, theoretical movement formulas were derived to define the gravity flow of fragmented rocks in these two parts. The ore body with 55° dip and 10 m width was taken as an example, the particle flow parameters were fitted, and the corresponding theoretical shape of the draw body was sketched based on the derived equation of draw-body shape. The comparison of experimental and theoretical shapes of the draw body confirmed that they coincided with each other; hence, the reliability of the derived equation of particle motion was validated.
This paper proposes a speech enhancement method using the multi-scales and multi-thresholds of the auditory perception wavelet transform, which is suitable for a low SNR (signal to noise ratio) environment. This method achieves the goal of noise reduction according to the threshold processing of the human ear's auditory masking effect on the auditory perception wavelet transform parameters of a speech signal. At the same time, in order to prevent high frequency loss during the process of noise suppression, we first make a voicing decision based on the speech signals. Afterwards, we process the unvoiced sound segment and the voiced sound segment according to the different thresholds and different judgments. Lastly, we perform objective and subjective tests on the enhanced speech. The results show that, compared to other spectral subtractions, our method keeps the components of unvoiced sound intact, while it suppresses the residual noise and the background noise. Thus, the enhanced speech has better clarity and intelligibility.
This paper presents a geomagnetic detection method for pipeline defects using complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) and wavelet energy product (WEP) – Teager energy operator (TEO), which improves detection accuracy and defect identification ability as encountering strong inference noise. The measured signal is first subtly decomposed via CEEMDAN into a series of intrinsic mode functions (IMFs), which are then distinguished by the Hurst exponent to reconstruct the filtered signal. Subsequently, the scale signals are obtained by using gradient calculation and discrete wavelet transform and are then fused by using WEP. Finally, TEO is implemented to enhance defect signal amplitude, completing geomagnetic detection of pipeline defects. The simulation results created by magnetic dipole in a noisy environment, indoor experiment results and field testing results certify that the proposed method outperforms ensemble empirical mode decomposition (EEMD)-gradient, EEMD-WEP-TEO, CEEMDAN-gradient in terms of detection deviation, peak side-lobe ratio (PSLR) and integrated side-lobe ratio (ISLR).