皮尔曲线模型的推广及其应用

在分析皮尔曲线模型增长特性的基础上 ,提出了 1 4种推广模型 ,并依据这些模型建立了几种主要耐用消费品拥有量的预测模型 .预测模型的精度很高     

SS*:一种嵌入视觉特性的多维图像索引算法

图像检索因信息量巨大，查询速度至关重要。一种有效的方法是对图像特征进行多维索引，然后按多维索引算法检索。但是，现有的多维索引算法并不是专门针对图像数据库设计的，没有考虑图像的视觉特性，检索性能不理想。文章针对图像信息的特点，结合SS多维索引算法，设计了一种新的基于内容的图像检索的多维索引算法——SS^*算法。实验表明，该算法对图像检索是有效的。     

Influences of tissue composition and structural features of biological media on the ultrasonic nonlinearity parameter

The Acoustic nonlinearity parameter is an important parameter innonlinear acoustics.In this article,the nonlinearity parameter B/A of normaland eight kinds of pathological porcine liver tissues were measured by finiteamplitude insert-substitution method.The mixture law for nonlinearityparameter is used to analyze and predict the volume fractions of the compo-nents in a given tissue.It was found that the nonlinearity parameter is sensitiveto the pathological forms of biological tissues and the values of B/A dependon the tissue composition and structural features.     

丝阵Z箍缩等离子体内爆过程单丝特性研究

研究了丝阵Z箍缩等离子体内爆过程中单丝行为、物理参数匹配、X射线发射情况。根据丝阵Z箍缩等离子体动力学一维模型研制了相关的计算程序，对单丝的内爆过程进行了数值模拟，对丝阵箍缩匹配关系进行了计算，给出了丝阵初始半径、电流脉冲宽度和峰值电流的匹配关系，同时对丝阵内的最小丝间距、材料的选择等问题进行优化研究。     

Null Space Algorithm and Spanning Trees in Solving Darcy's Equation

A Null Space algorithm is considered to solve the augmented system produced by the mixed finite element approximation of Darcy's Law. The method is based on the combination of a LU factorization technique for sparse matrices with an iterative Krylov solver. The computational efficiency of the method relies on the use of spanning trees to compute the LU factorization without fill-in and on a suitable stopping criterion for the iterative solver. We experimentally investigate its performance on a realistic set of selected application problems.This revised version was published online in October 2005 with corrections to the Cover Date.     

Periodic interpolation and wavelets on sparse grids

Nested spaces of multivariate periodic functions forming a non-stationary multiresolution analysis are investigated. The scaling functions of these spaces are fundamental polynomials of Lagrange interpolation on a sparse grid. The approach based on Boolean sums leads to sample and wavelet spaces of significantly lower dimension and good approximation order. The algorithms for complete decomposition and reconstruction are of simple structure and low complexity. This revised version was published online in June 2006 with corrections to the Cover Date.     

A class of methods for solving large,convex quadratic programs subject to box constraints

In this paper we analyze conjugate gradient-type algorithms for solving convex quadratic programs subject only to box constraints (i.e., lower and upper bounds on the variables). Programs of this type, which we denote by BQP, play an important role in many optimization models and algorithms. We propose a new class of finite algorithms based on a nonfinite heuristic for solving a large, sparse BQP. The numerical results suggest that these algorithms are competitive with Dembo and Tulowitzski's (1983) CRGP algorithm in general, and perform better than CRGP for problems that have a low percentage of free variables at optimality, and for problems with only nonnegativity constraints.     

Matrix-free preconditioning using partial matrix estimation

We consider matrix-free solver environments where information about the underlying matrix is available only through matrix vector computations which do not have access to a fully assembled matrix. We introduce the notion of partial matrix estimation for constructing good algebraic preconditioners used in Krylov iterative methods in such matrix-free environments, and formulate three new graph coloring problems for partial matrix estimation. Numerical experiments utilizing one of these formulations demonstrate the viability of this approach. AMS subject classification (2000) 65F10, 65F50, 49M37, 90C06     

利用半群代数中良序基构造解稀疏多项式方程组特征值方法

本文利用半群代数k[A]中良序基,构造了求稀疏多项式方程组解的特征值矩阵,并给出了可以构造方阵的条件.     

Fast Encoding Method for Image Vector Quantization Based on Multiple Appropriate Features to Estimate Euclidean Distance

The encoding process of finding the best-matched codeword (winner) for a certain input vector in image vector quantization (VQ) is computationally very expensive due to a lot of k-dimensional Euclidean distance computations. In order to speed up the VQ encoding process, it is beneficial to firstly estimate how large the Euclidean distance is between the input vector and a candidate codeword by using appropriate low dimensional features of a vector instead of an immediate Euclidean distance computation. If the estimated Euclidean distance is large enough, it implies that the current candidate codeword could not be a winner so that it can be rejected safely and thus avoid actual Euclidean distance computation. Sum (1-D), L₂ norm (1-D) and partial sums (2-D) of a vector are used together as the appropriate features in this paper because they are the first three simplest features. Then, four estimations of Euclidean distance between the input vector and a codeword are connected to each other by the Cauchy–Schwarz inequality to realize codeword rejection. For typical standard images with very different details (Lena, F-16, Pepper and Baboon), the final remaining must-do actual Euclidean distance computations can be eliminated obviously and the total computational cost including all overhead can also be reduced obviously compared to the state-of-the-art EEENNS method meanwhile keeping a full search (FS) equivalent PSNR.