Zhongli Zhou’s research while affiliated with Chengdu University of Technology and other places

Inspired by the principle of wavelet analysis and blind signal separation in denoising, this paper presents a one-dimensional blind-wavelet algorithm. Some corresponding parameters of the blind-wavelet algorithm are discussed. In this paper, the blind-wavelet algorithm contains the following three main steps. Firstly, the multi-channel seismic signals are decomposed into multi-level wavelet, the scale coefficients and the multi-level wavelet coefficients can be obtained, then, the multi-level wavelet coefficients are processed by soft threshold method. Secondly, all the scale coefficients and the same depth wavelet coefficients of the signals are decomposed by the blind source separation, and the sequences of the decomposed signals can be correctly reflected through an appropriate method. Finally, the source signals are estimated via signal reconstruction. The results show that the organic combination of blind source separation and wavelet analysis (the blind-wavelet algorithm) can effectively eliminate the noise of the deep metal ores seismic data, it meets the requirements of the high resolution and fidelity after the denoising in the deep metal ores seismic exploration. The results of this research demonstrate that the blind-wavelet algorithm is quite fit for two adjacent channel signals processing of metal ore deposits seismic data denoising. It is shown that application of the blind-wavelet algorithm to seismic data processing is effective.

The general coupled matrix equations (including the generalized coupled Sylvester matrix equations as special cases) have numerous applications in control and system theory. In this paper, an iterative algorithm is constructed to solve the general coupled matrix equations over reflexive matrix solution. When the general coupled matrix equations are consistent over reflexive matrices, the reflexive solution can be determined automatically by the iterative algorithm within finite iterative steps in the absence of round-off errors. The least Frobenius norm reflexive solution of the general coupled matrix equations can be derived when an appropriate initial matrix is chosen. Furthermore, the unique optimal approximation reflexive solution to a given matrix group in Frobenius norm can be derived by finding the least-norm reflexive solution of the corresponding general coupled matrix equations. A numerical example is given to illustrate the effectiveness of the proposed iterative algorithm.

Support vector data description (SVDD) is a data description method that can give the target data set a spherically shaped description and be used to outlier detection or classification. In real life the target data set often contains more than one class of objects and each class of objects need to be described and distinguished simultaneously. In this case, traditional SVDD can only give a description for the target data set, regardless of the differences between different target classes in the target data set, or give a description for each class of objects in the target data set. In this paper, an improved support vector data description method named two-class support vector data description (TC-SVDD) is presented. The proposed method can give each class of objects in the target data set a hypersphere-shaped description simultaneously if the target data set contains two classes of objects. The characteristics of the improved support vector data descriptions are discussed. The results of the proposed approach on artificial and actual data show that the proposed method works quite well on the 3-class classification problem with one object class being undersampled severely.