Three-dimensional localization of near-field and strictly noncircular sources using steering vector decomposition

The three-dimensional localization problem for noncircular sources in near-field with a centro-symmetric cross array is rarely studied.In this paper,we propose an algorithm with improved estimation performance.We decompose the multiple parameters of the steering vector in a specific order so that it can be converted into the products of several matrices,and each of the matrices includes only one parameter.On this basis,each parameter to be resolved can be estimated by performing a one-dimensional spatial spectral search.Although the computational complexity of the proposed algorithm is several times that of our previous algorithm,the estimation performance,including its error and resolution,with respect to the direction of arrival,is improved,and the range estimation performance can be maintained.The superiority of the proposed algorithm is verified by simulation results.

Vectorization and Parallel Computation of a CFD Code on YH－2 Parallel Supercomputer

MacCormack explicit scheme and Baldwin-Lomax algebraic turbulent model are employed to solve the axisymmetric compressible Navier-Stokes equations for the numerical simulation of the supersonic mustanl floats interacted with transverse injection at the base of a cone. A temperature switch function must be added to the artificial viscous model suggested by jameson etc to enhance the scheme's ability to eliminate oscillation for some injection case.The typical code optimization techniques about vectorization and some useful concepts and terminology about multiprocessing of YH-2 parallel supercmputer is given and explatined with some examples After reconstruction and optimization the code gets a spedup 5 .973 on pipeline computer YH- 1 and gets a speedup 1 886 for 2 processors and 3.545 for 4 processors on YH-2 parallel supeercomputer by using domain decomposition method..

Convex Decomposition Based Cluster Labeling Method for Support Vector Clustering

Support vector clustering （SVC） is an important boundary-based clustering algorithm in multiple applications for its capability of handling arbitrary cluster shapes.However,SVC＇s popularity is degraded by its highly intensive time complexity and poor label performance.To overcome such problems,we present a novel efficient and robust convex decomposition based cluster labeling （CDCL） method based on the topological property of dataset.The CDCL decomposes the implicit cluster into convex hulls and each one is comprised by a subset of support vectors （SVs）.According to a robust algorithm applied in the nearest neighboring convex hulls,the adjacency matrix of convex hulls is built up for finding the connected components;and the remaining data points would be assigned the label of the nearest convex hull appropriately.The approach＇s validation is guaranteed by geometric proofs.Time complexity analysis and comparative experiments suggest that CDCL improves both the efficiency and clustering quality significantly.

Fault Diagnosis Model Based on Feature Compression with Orthogonal Locality Preserving Projection

Based on feature compression with orthogonal locality preserving projection（OLPP）,a novel fault diagnosis model is proposed in this paper to achieve automation and high-precision of fault diagnosis of rotating machinery.With this model,the original vibration signals of training and test samples are first decomposed through the empirical mode decomposition（EMD）,and Shannon entropy is constructed to achieve high-dimensional eigenvectors.In order to replace the traditional feature extraction way which does the selection manually,OLPP is introduced to automatically compress the high-dimensional eigenvectors of training and test samples into the low-dimensional eigenvectors which have better discrimination.After that,the low-dimensional eigenvectors of training samples are input into Morlet wavelet support vector machine（MWSVM） and a trained MWSVM is obtained.Finally,the low-dimensional eigenvectors of test samples are input into the trained MWSVM to carry out fault diagnosis.To evaluate our proposed model,the experiment of fault diagnosis of deep groove ball bearings is made,and the experiment results indicate that the recognition accuracy rate of the proposed diagnosis model for outer race crack、inner race crack and ball crack is more than 90%.Compared to the existing approaches,the proposed diagnosis model combines the strengths of EMD in fault feature extraction,OLPP in feature compression and MWSVM in pattern recognition,and realizes the automation and high-precision of fault diagnosis.

True Amplitude Extraction Technology and Its Application in Pre-Stack Seismic Data

Common-reflection-point （CRP） gather is a bridge that connects seismic data and petro- physical parameters. Pre-stack attributes extraction and pre-stack inversion, both of them are impor- tant means of reservoir prediction. Quality of CRP gather usually has great impact on the accuracy of seismic exploration. Therefore, pre-stack CRP gathers noise suppression technology becomes a major research direction. Based on the vector decomposition principle, here we propose a method to suppress noise. This method estimates optimal unit vectors by searching in various directions and then sup- presses noise through vector angle smoothing and restriction. Model tests indicate that the proposed method can separate effective signal from noise very well and suppress random noise effectively in single wavenumber case. Application of our method to real data shows that the method can recover effective signal with good amplitude preserved from pre-stack noisy seismic data even in the case of low signal to noise ratio （SNR）.