空间波法测地物介电常数的反演方法

空间波法适合于测量大面积目标的介电常数,所以是测量地物微波介电特性的一种较好的方法。这种方法从测量数据反演复介电常数要用菲涅尔公式,如果测量数据包含相位信息,则反演是简单的;但是如果测量数据只是幅度,则反演过程需要求解一个二次非线性方程组,以往都采用近似的数字迭代求解,迭代过程对初值和测量数据本身都太敏感,解的存在性、唯一性和稳定性难以判定。本文给出了严格的解析反演法,并证明了解的唯一性和反演结果对测量误差的敏感性。

空间波法测介质板介电常数的实验研究

从光学原理出发，分析了空间波法测介质板介电常数时的表面反射问题，指出它对测量结果的影响与介质材料的特点及不同的测量方法有关，并结合几种低耗介质材料的测量结果，对上述分析进行了实验验证．

流固耦合的周期加强板的振动及声辐射研究

研究了流体负载下的无穷大双周期加强板,在周期谐振力作用下的振动响应和声辐射,并提出了一种基于有限元和空间波数法的半解析半数值方法.首先利用有限元的方法对周期结构进行单元离散,并将结构对薄板的作用力等效为节点力的作用.然后通过周期结构的振动方程,结合薄板与结构的位移边界条件,建立了节点力与薄板节点位移的函数方程.最后应用空间波数法和傅里叶变换,并采用数值计算的方法求解出薄板的节点位移,得到了周期加强板关于离散节点位移的振动和辐射声压方程.在数值算例中,对该方法的正确性进行了验证,并且分析了周期结构对薄板的振动和声辐射的影响.

谐振面力作用下的无穷大双周期加筋板的振动响应分析

文章主要研究了流体负载下的无穷大双周期加筋薄板,在周期谐振力作用下的振动响应。假设薄板和加强筋只存在法向力的作用,通过薄板和加强筋的位移连续条件,以及格林函数及傅立叶变换公式的应用,建立了频域内双周期加筋薄板的振动位移方程。利用空间波数法,将加筋薄板的振动位移表达为波数分量的迭加形式。通过数值计算的方法对波数分量进行求解,最后通过方程的傅立叶逆变换,得到了加筋薄板的振动方程。数值计算的结果表明文中的计算方法收敛速度快。

Application of Spatial Filtering Techniques in the Measurement of the Velocity

As one of the interesting optical techniques for measurements of the velocity,the spatial filtering method is treated briefly in this paper.We shown theoretical analysis and calculation of spatial filtering velocimetry,and discussed two-dimensional measurements of the velocity.About the data processing,we used A/D conversion and interfaced with a microcomputer,so that the data can be processed automatically by the microcomputer.The preliminary experiment was performed and the experimental results show the usefulness of the present method for measurements of the velocity.

The study of perfectly matched layer absorbing boundaries for SH wave fields

When modeling wave propagation in infinite space, it is necessary to have stable absorbing boundaries to effectively eliminate spurious reflections from the truncation boundaries. The SH wave equations for Perfectly Matched Layers （PML） are deduced and their Crank-Nicolson scheme are presented in this paper. We use the second-, sixth-, and tenth-order finite difference and pseudo-spectral algorithms to compute the spatial derivatives. Two numerical models, a homogeneous isotropic medium and a multi-layer model with a cave, are designed to investigate how the absorbing boundary width and the algorithms determine PML effects. Numerical results show that, for PML, the low-order finite difference algorithms have fairly good absorbing effects when the absorbing boundary is thin, whereas, high-order algorithms always have good absorption when the boundary is thick. Finally, we discuss the reflection coefficient and point out its shortcomings, which is why we use the SNR to quantitatively scale the PML effects,

Forecasting available parking space with largest Lyapunov exponents method

The techniques to forecast available parking space(APS) are indispensable components for parking guidance systems(PGS). According to the data collected in Newcastle upon Tyne, England, the changing characteristics of APS were studied. Thereafter, aiming to build up a multi-step APS forecasting model that provides richer information than a conventional one-step model, the largest Lyapunov exponents(largest LEs) method was introduced into PGS. By experimental tests conducted using the same dataset, its prediction performance was compared with traditional wavelet neural network(WNN) method in both one-step and multi-step processes. Based on the results, a new multi-step forecasting model called WNN-LE method was proposed, where WNN, which enjoys a more accurate performance along with a better learning ability in short-term forecasting, was applied in the early forecast steps while the Lyapunov exponent prediction method in the latter steps precisely reflect the chaotic feature in latter forecast period. The MSE of APS forecasting for one hour time period can be reduced from 83.1 to 27.1(in a parking building with 492 berths) by using largest LEs method instead of WNN and further reduced to 19.0 by conducted the new method.

AN AUTOMATIC SEGMENTATION METHOD FOR MOVING OBJECTS BASED ON THE SPATIAL-TEMPORAL INFORMATION OF VIDEO

The new MPEG-4 video coding standard enables content-based functions. In order to support the new standard, frames should be decomposed into Video Object Planes （VOP）, each VOP representing a moving object. This paper proposes an image segmentation method to separate moving objects from image sequences. The proposed method utilizes the spatial-temporal information. Spatial segmentation is applied to divide each image into connected areas and to find pre~：ise object boundaries of moving objects. To locate moving objects in image sequences, two consecutive image frames in the temporal direction are examined and a hypothesis testing is performed with Neyman-Pearson criterion. Spatial segmentation produces a spatial segmentation mask, and temporal segmentation yields a change detection mask that indicates moving objects and the background. Then spatial-temporal merging can be used to get the final results. This method has been tested on several images. Experimental results show that this segmentation method is efficient.

New Extended Jacobi Elliptic Function Rational Expansion Method and Its Application

In this paper, an extended Jacobi elliptic function rational expansion method is proposed for constructing new forms of exact Jacobi elliptic function solutions to nonlinear partial differential equations by means of making a more general transformation. For illustration, we apply the method to the （2＋1）-dimensional dispersive long wave equation and successfully obtain many new doubly periodic solutions, which degenerate as soliton solutions when the modulus m approximates 1. The method can also be applied to other nonlinear partial differential equations.

Forecasting the Coke Price Based on the Kalman Filtering Algorithm

Research on coke price forecasting is of theoretical and practical signiifcance. Here, the Kalman ifltering algorithm was used to analyze the price of coke. As the only state variable, the historical coke price is sorted out to build the state space model. The algorithm makes use of innovation composed of the difference between observed and predicted values, and alows us to obtain the optimal estimated value of the coke price via continuous updating and iteration of innovation. Our results show that this algorithm is effective in the ifeld of coke price tracking and forecasting.

Forecasting the Ethiopian Coffee Price Using Kalman Filtering Algorithm

Ethiopian coffee price is highly fluctuated and has significant effect on the economy of the country. Conducting a research on forecasting coffee price has theoretical and practical importance.This study aims at forecasting the coffee price in Ethiopia. We used daily closed price data of Ethiopian coffee recorded in the period 25 June 2008 to 5 January 2017 obtained from Ethiopia commodity exchange（ECX） market to analyse coffee prices fluctuation. Here, the nature of coffee price is non-stationary and we apply the Kalman filtering algorithm on a single linear state space model to estimate and forecast an optimal value of coffee price. The performance of the algorithm for estimating and forecasting the coffee price is evaluated by using root mean square error（RMSE）. Based on the linear state space model and the Kalman filtering algorithm, the root mean square error（RMSE） is 0.000016375, which is small enough, and it indicates that the algorithm performs well.