A novel current vector decomposition controller design for six-phase permanent magnet synchronous motor

The vector control algorithm based on vector space decomposition （VSD） transformation method has a more flexible control freedom, which can control the fundamental and harmonic subspace separately. To this end, a current vector decoupling control algorithm for six-phase permanent magnet synchronous motor （PMSM） is designed. Using the proposed synchronous rotating coordinate transformation matrix, the fundamental and harmonic components in d-q subspace are changed into direct current （DC） component, only using the traditional proportional integral （PI） controller can meet the non-static difference adjustment, and the controller parameter design method is given by employing intemal model principle. In addition, in order to remove the 5th and 7th harmonic components of stator current, the current PI controller parallel with resonant controller is employed in x-y subspace to realize the specific harmonic component compensation. Simulation results verify the effectiveness of current decoupling vector controller.

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.

Automatic target recognition of moving target based on empirical mode decomposition and genetic algorithm support vector machine

In order to improve measurement accuracy of moving target signals, an automatic target recognition model of moving target signals was established based on empirical mode decomposition(EMD) and support vector machine(SVM). Automatic target recognition process on the nonlinear and non-stationary of Doppler signals of military target by using automatic target recognition model can be expressed as follows. Firstly, the nonlinearity and non-stationary of Doppler signals were decomposed into a set of intrinsic mode functions(IMFs) using EMD. After the Hilbert transform of IMF, the energy ratio of each IMF to the total IMFs can be extracted as the features of military target. Then, the SVM was trained through using the energy ratio to classify the military targets, and genetic algorithm(GA) was used to optimize SVM parameters in the solution space. The experimental results show that this algorithm can achieve the recognition accuracies of 86.15%, 87.93%, and 82.28% for tank, vehicle and soldier, respectively.

Improving signal to noise ratio of seismic profiles using an image processing technique

This paper introduces an image processing technique into seismic data processing as a noise attenuation technology. The image separation of the seismic profile is obtained by using grating operators based on different time dips and a set of relative single dip profiles is obtained. A high signal to noise ratio profile can be obtained during reconstruction by statistical weighting. With further processing analysis and geological study, a high signal to noise profile that can meet geological requirements can be produced. The real data examples show that the signal to noise ratio of the profile is greatly improved, the resolution of the profile is maintained, and the fault terminations are much clearer after using the image processing method.

A SVD-based ensemble projection algorithm for calculating the conditional nonlinear optimal perturbation

Conditional nonlinear optimal perturbation(CNOP) is an extension of the linear singular vector technique in the nonlinear regime.It represents the initial perturbation that is subjected to a given physical constraint,and results in the largest nonlinear evolution at the prediction time.CNOP-type errors play an important role in the predictability of weather and climate.Generally,when calculating CNOP in a complicated numerical model,we need the gradient of the objective function with respect to the initial perturbations to provide the descent direction for searching the phase space.The adjoint technique is widely used to calculate the gradient of the objective function.However,it is difficult and cumbersome to construct the adjoint model of a complicated numerical model,which imposes a limitation on the application of CNOP.Based on previous research,this study proposes a new ensemble projection algorithm based on singular vector decomposition(SVD).The new algorithm avoids the localization procedure of previous ensemble projection algorithms,and overcomes the uncertainty caused by choosing the localization radius empirically.The new algorithm is applied to calculate the CNOP in an intermediate forecasting model.The results show that the CNOP obtained by the new ensemble-based algorithm can effectively approximate that calculated by the adjoint algorithm,and retains the general spatial characteristics of the latter.Hence,the new SVD-based ensemble projection algorithm proposed in this study is an effective method of approximating the CNOP.

Structure of the Indian Ocean Meridional Overturning Circulation and its relationship with the zonal wind stress

We studied the structure of the Indian Ocean(IO)Meridional Overturning Circulation(MOC)by applying a nonlinear inertia theory and analyzed the coupled relationship between zonal wind stress and MOC anomalies.Our results show that the inertia theory can represent the main characteristics of the IO MOC:the subtropical cell(STC)and cross-equator cell(CEC).The stream function in equatorial and northern IO changes a sign from winter to summer.The anomalies of the zonal wind stress and stream function can be decomposed into summer monsoon mode,winter monsoon mode,and abnormal mode by using the singular vector decomposition(SVD)analysis.The first two modes correlate with the transport through 20°S and equator simultaneously whereas the relationship obscures between the third mode and transports across 20°S and equator,showing the complex air-sea interaction process.The transport experiences multi-time scale variability according to the continuous power spectrum analysis,with major periods in inter-annual and decadal scale.

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）.