局部散射源参数估计的非线性算子方法

本文利用单次快摄数据的两个非线性算子估计局部散射源中心波达方向和角扩展,与多维参数搜索和其他低复杂性的局部散射源参数估计算法不同,非线性算子方法给出了单次快摄的局部散射源参数估计的闭式解,无需进行多维搜索和子空间分解。     

Cost estimates to guide pre-selection of processes

A process is a method of shaping, joining or surface-treating a material. Process selection has three steps. The first is to identify from the ‘menu’ of all available processes, the subset, which can give a chosen material the desired shape with the desired detail, precision and finish. The second is to choose, from among these, the ones that will do so at the lowest cost. The final step is to investigate the most promising processes in depth, exploring considerations such as availability, in-house experience, safety and environmental issues. The first two steps can be thought of as process pre-selection. Components have to be assembled and finished to create products. Here too, the ability to rank by cost, however crudely, helps guide pre-selection. Cost models are reviewed from the perspective of material and process selection. An approximate model is useful provided it has generality—that is, it must allow comparison of very different processes. Many approaches fail in this. One that works, based on resource consumption, is developed here and its use for selection is illustrated. It has been implemented as part of a tool that allows rapid pre-selection from a database of 112 processes.     

一种空间自适应小波门限去噪算法

提出了一种空间自适应小波门限去噪算法,该算法在小波域对含噪小波系数做两次自适应去噪,两次自适应门限分别基于最大似然(ML)方差估计和最大后验概率(MAP)方差估计.仿真结果表明,该算法与其它自适应门限去噪算法相比,去噪后的图象具有更高的峰值信噪比(PSNR).     

定容气田开发规划概算法

基于物质平衡方程、产能方程和递减方程的结合,提出了定容气田开发规划的概算法.该法可用于预测地层压力、井底流动压力、井口流动压力和稳产年限.实际应用表明,本文提供的方法是实用有效的.     

A semiparametric model for compositional data analysis in presence of covariates on the simplex

Compositional data occur as natural realizations of multivariate observations comprising element proportions of some whole quantity. Such observations predominate in disciplines like geology, biology, ecology, economics and chemistry. Due to unit sum constraint on compositional data, specialized statistical methods are required for analyzing these data. Dirichlet distributions were originally used to study compositional data even though this family of distribution is not appropriate (see Aitchison, 1986) because of their extreme independence properties. Aitchison (1982) endeavored to provide a viable alternative to existing methods by employing Logistic Normal distribution to analyze such constrained data. However this family does not include the Dirichlet class and is therefore unable to address the issue of extreme independence. In this paper generalized Liouville family is investigated to model compositional data which includes covariates. This class permits distributions that admit negative or mixed correlation and also contains non-Dirichlet distributions with non-positive correlation and overcomes deficits in the Dirichlet class. Semiparametric Bayesian methods are proposed to estimate the probability density. Predictive distributions are used to assess performance of the model. The methods are illustrated on a real data set.     

Temperature Oscillation Calorimetry: Robustness analysis of different algorithms for the evaluation of the heat transfer coefficient

Applicability of different temperature oscillation calorimetry algorithms, previously proposed in the literature, for the evaluation of the heat transfer coefficient during the course of the reaction, is analysed by referring to the experimental data from a methyl methacrylate polymerisation reactor.Results are coincident in the nominal case (no errors on data), while differences appear in the elaboration of real data. The effect of uncertainty in the experimental data has been simulated in terms of error on the amplitude and on the phase of reactor and jacket temperature profiles.This approach allows to indicate algorithms having the best robustness properties and to give an explanation of phase errors in terms of a lumped parameter which accounts for different phenomena, not included in the basic hypotheses of the method. Based on this observation, a procedure to eliminate this source of errors from experimental data is proposed for the more general algorithm presented to handle oscillating temperature profiles generated by different techniques.     

一种放射CT重建的鲁棒估计算法

放射CT的重建问题可以归结为一种基于泊松分布的最大似然估计（MuxnimumLikelihood,ML），其中最著名的如EM算法[1]，这种算法存在的问题是鲁棒性不强，当观测数据中存在一些出格点（outlier）（如2％）时，EM算法的性能就变得很差。本文分析了算法性能变差的原因，将泊松分布近似为高斯分布后，仿照鲁律估计中的M-估计，构造了一种鲁棒性很强的EM改进算法。实验结果表明，即使在投影数据中混有10％的出格点，仍能重建出满意的结果来。     

CROSS-SPECTRAL MOMENT AND SVD METHODS ESTIMATING THE PARAMETERS OF CLOSE SINUSOIDS IN COLORED NOISE

The cross-spectral estimation methods are efficient in estimating the parameters of sinusoidal signals embedded in colored noise. But up to now, only FPT and cross-periodogram methods are used in this field, the modern auto-spectral estimation method is introduced into cross-spectral estimation in this paper, meanwhile the cross-correlation based Yule-Walker equation is proposed theoretically and the moment and singular-value decomposition (SVD)) algorithms for cross-spectral estimation have been developed. Finally, a numerical example is given for comparing the presented methods with the well-known Cadzow's SVD method.     

Robot Pose Estimation in Unknown Environments by Matching 2D Range Scans

A mobile robot exploring an unknown environment has no absolute frame of reference for its position, other than features it detects through its sensors. Using distinguishable landmarks is one possible approach, but it requires solving the object recognition problem. In particular, when the robot uses two-dimensional laser range scans for localization, it is difficult to accurately detect and localize landmarks in the environment (such as corners and occlusions) from the range scans.In this paper, we develop two new iterative algorithms to register a range scan to a previous scan so as to compute relative robot positions in an unknown environment, that avoid the above problems. The first algorithm is based on matching data points with tangent directions in two scans and minimizing a distance function in order to solve the displacement between the scans. The second algorithm establishes correspondences between points in the two scans and then solves the point-to-point least-squares problem to compute the relative pose of the two scans. Our methods work in curved environments and can handle partial occlusions by rejecting outliers.     

优化双圆弧拟合

本文提出一种优化双圆弧拟合的新算法，它采用的方法是用改变双圆弧联结点的切线使双圆弧与样条曲线间的法向误差达到最小（或小于允许误差），与其他双圆弧拟合方法相比较，该算法计算简单，线性，并可消除拟合曲线的不稳定性，拟合效果快速，良好。可直接应用于雕塑曲面的ＣＮＣ加工中。