部分线性模型的随机约束岭估计

研究了部分线性回归模型附加有随机约束条件时的估计问题.基于Profile最小二乘方法和混合估计方法提出了参数分量随机约束下的Profile混合估计,并研究了其性质.为了克服共线性问题,构造了参数分量的Profile混合岭估计,并给出了估计量的偏和方差.     

浅析相关系数的显著性检验

本文用最小二乘法作直线拟合,给出相关系数显著性检验的判断方法,指出了用概率理论判断线性相关的必要性。     

Block-Projections Algorithms with Blocks Containing Mutually Orthogonal Rows and Columns

In this paper we present an algorithm which, for a given (sparse) matrix, constructs a partition of its set of row-indices, such that each subset of this partition (except the last one obtained) contains indices which correspond to mutually orthogonal rows. We then use such decompositions in some classes of block-projections methods, previously extended by the author to general inconsistent linear least-squares problems. Numerical experiments on an inconsistent and rank-deficient least-squares model problem are described in the last section of the paper.     

Application of computer vision and laser interferometer to the inspection of line scale

In the paper machine vision, laser interferometer and coordinate measuring machine (CMM) are combined to develop a vision inspection system. The measurement capability of the developed system is investigated by measuring the distances between the lines on a standard line scale. The vision camera is used to replace the probe of the CMM to take the images of the interested lines on a line scale at two different positions. Meanwhile, the displacement of the CCD camera is measured using laser interferometer. Using subpixel edge localization and outlier-excluding least-squares regression, the distance between two interested lines is computed under an image plane coordinate system. By adding the displacement of the CCD camera measured using laser interferometer, the line space can be determined. Experiments have been performed repeatedly to measure the line space on the 1.00 and 300.0 mm line scales. Results indicate that the measured data only have a little fluctuation and are close to those obtained by the NML (National Measurement Laboratory, Taiwan).     

超声全矩阵数据联合稀疏重构的多测量向量模型应用

针对单测量向量模型(Single Measurement Vector,SMV)等传统压缩感知方法处理超声全矩阵数据时,存在重构精度低和重构耗时长等问题,本文研究了多测量向量模型(Multiple Measurement Vectors,MMV)应用的可行性。针对铝合金试块中不同深度的φ2 mm横通孔,分别使用MMV模型中的多测量稀疏贝叶斯(Multiple Sparse Bayesian Learning,MSBL)算法和SMV模型中的稀疏贝叶斯(Sparse Bayesian Learning,SBL)算法进行超声全矩阵数据重构,并实施全聚焦成像。随后,引入归一化均方误差和阵列性能因子评价图像和信号的重构效果。实验结果表明,SBL算法在25%采样率时的归一化均方误差为1.9%,而MSBL算法仅需15%采样率即可达到相似效果且耗时更少。     

On the Validity of Darcy's Law for Stable High-Concentration Displacements in Granular Porous Media

Recently, it has been suggested that Darcy's Law might not be applicable for modelling miscible, density-dependent flow in porous media. To investigate this, three sets of careful laboratory column experiments were performed on coarse and medium sands, consisting of upward displacement of water by sodium chloride solutions with concentrations ranging from 5 to 200g/l. Data on salt concentrations and water pressures were collected in horizontal transects along the flow direction. Salt concentration data were also collected in the influent and exit lines. The experimental data were analysed using a simplified approach based on Darcy's Law alone, applied with the assumption of a sharp interface. Darcy's Law was used to estimate porous medium permeability by fitting predictions to experimental data. Consistent estimates of permeability were obtained for each set of experiments. The results indicate that Darcy's Law adequately describes high concentration displacements through saturated coarse- and medium-grained porous media.     

Structure dependent sampling in compressed sensing: Theoretical guarantees for tight frames

Many of the applications of compressed sensing have been based on variable density sampling, where certain sections of the sampling coefficients are sampled more densely. Furthermore, it has been observed that these sampling schemes are dependent not only on sparsity but also on the sparsity structure of the underlying signal. This paper extends the result of Adcock, Hansen, Poon and Roman (arXiv:1302.0561, 2013) [2] to the case where the sparsifying system forms a tight frame. By dividing the sampling coefficients into levels, our main result will describe how the amount of subsampling in each level is determined by the local coherences between the sampling and sparsifying operators and the localized level sparsities – the sparsity in each level under the sparsifying operator.     

改进的移动最小二乘法

近年来发展的无网格方法大多采用移动员小二乘法来构造试函数，而应用移动最小二乘法形成的方程组有时会是病态的甚至奇异的，从而限制了它的发展和应用。本文采用带权正交函数作为基函数对移动最小二乘法做了改进，避免出现病态方程组，且在计算过程中不需要进行短阵求逆运算，提高了计算速度。之后，借鉴牛顿法、平衡法和摄动法对由移动最小二乘法得到的非线性代数方程组提出了新的求解方法。