Interpolation of near offset using surface-related multiples

In this research, we present a seismic trace interpolation method which uses seismic data with surface-related multiples. It is different from conventional seismic data interpolation using information transformation or extrapolation of adjacent channels for reconstruction of missing seismic data. In this method there are two steps, first, we construct pseudo-primaries by cross-correlation of surface multiple data to extract the missing near- offset information in multiples, which are not displayed in the acquired seismic record. Second, we correct the pseudo-primaries by applying a Least-squares Matching Filter （LMF） and RMS amplitude correction method in time and space sliding windows. Then the corrected pseudo-primaries can be used to fill the data gaps. The method is easy to implement, without the need to separate multiples and primaries. It extracts the seismic information contained by multiples for filling missing traces. The method is suitable for seismic data with surfacerelated multiples.

Buck converter stability techniques for secondary filters and adaptive voltage positioning

To reduce output voltage noise and improve dynamic response performance,this study designed a buck converter on the basis of secondary filters and adaptive voltage positioning(AVP).A hybrid control method was proposed for the compensation of the secondary filter.The introduction of a high-frequency feedback path,in addition to the traditional feedback path,effectively improved the influence of the secondary filter on the loop stability and direct current regulation performance.A small-signal model of the buck converter based on the proposed control method was derived,and the stability and selection of control parameters were analyzed.AVP is realized using an easy-to-implement and low-cost control method that was proposed to improve dynamic response performance by changing the low-frequency gain of the control loop and load regulation of the output voltage.The experimental results of the buck converter showed that the proposed method effectively reduced the output voltage noise by 50%and improved the dynamic response capability to meet the target requirements of mainstream electronic systems.

Multiple subtraction method based on a non-causal matching filter

The filter operator used in normal multichannel matching filter is physically realizable. This filter operator only delays seismic data in the filtering process. A non- causal multichannel matching filter based on a least squares criterion is proposed to resolve the problem in which predicted multiple model data is later than real data. The differences between causal and non-causal multichannel matching filters are compared using a synthetic shot gather, which demonstrates the validity of the non-causal matching filter. In addition, a variable length sliding window which changes with offset and layer velocity is proposed to solve the count of events increasing with increasing offset in a fixed length sliding window. This variable length sliding window is also introduced into the modified and expanded multichannel matching filter. This method is applied to the Pluto1.5 synthetic data set. The benefits of the non-causal filter operator and variable length sliding window are demonstrated by the good multiple attenuation result.

Nonlinear static filtering and prediction considering quadratic terms

The parameters are considered as normal random quantities in filtering and prediction, and the observation equations usually are nonlinear ones. The nonlinear equations should be deployed in Taylor抯 formula, adopting to first power of term, by linear static filtering and prediction, and transformed into linear equations, and then the tested estimating values and their variances according to some statistical methods such as maximum tested estimation. The formulas of nonlinear static filtering and prediction, adapting to quadratic and cross terms by Taylor抯 progression formula, and the compu-tation formulas were also deduced that filtering the corresponding function nonlinear sig-nals and predicting the signals with nonlinear function. Meanwhile, it is been testified that the formula of static filtering and prediction is a special case of nonlinear filtering formulas.

Satellite Simulator Control System Design Using SDRE Method

This paper presents the application of the State-Dependent Riccati Equation （SDRE） method in conjunction with Kalman filter technique to design a satellite simulator control system. The performance and robustness of the SDRE controller is compared with the Linear Quadratic Regulator （LQR） controller. The Kalman filter technique is incorporated to the SDRE method to address the presence of noise in the process, measurements and incomplete state estimation. The effects of the plant non-linearities and noises （uncertainties） are considered to investigated the controller performance and robustness designed by the SDRE plus Kalman filter. A general 3-D simulator Simulink model is developed to design the SDRE controller using the states estimated by the Kalman filter. Simulations have demonstrated the validity of the proposed approach to deal with nonlinear system. The SDRE controller has presented good stability, great performance and robustness at the same time that it keeps the simplicity of having constant gain which is very important as for satellite onboard computer implementation.