基于多代理技术的传感器管理系统

针对分布式对空监视网中混合式传感器管理结构存在的问题，提出了基于多代理技术的混合式传感器管理结构。该结构较好地克服了混合式管理结构中在融合中心存在的瓶颈问题，通过多个传感器代理、融合中心代理之间的相互协商来实现传感器任务的分配。传感器代理与传感器相结合使传感器的工作模式实现高度的自动化和模块化，便于传感器的灵活组网。     

Fisher最优分割法在星星哨水库汛期分期划分中的应用

Fisher最优分割法是对有序样本进行最优化分段的一种数学方法。本文利用该方法对星星哨水库汛期进行相应的划分,结果表明该方法是可行的。因此,可利用该结果对分期实施不同的汛限水位,使水库的防洪和兴利效益达到最大。     

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).     

信息融合的飞机识别方法

以信息融合的理论为基础，利用从可见光图像序列和8-12μm长波红外图像序列中提取的信息对不同种类飞机进行识别。采用矩特征并结合BP神经网络的方法，分别在特征级和决策级两个不同层次上实现了信息融合。实验结果表明，通过信息融合进行飞机识别的准确率可达到90％以上。     

Bayesian and Dempster-Shafer fusion

The Kalman Filter is traditionally viewed as a prediction-correction filtering algorithm. In this work we show that it can be viewed as a Bayesian fusion algorithm and derive it using Bayesian arguments. We begin with an outline of Bayes theory, using it to discuss well-known quantities such as priors, likelihood and posteriors, and we provide the basic Bayesian fusion equation. We derive the Kalman Filter from this equation using a novel method to evaluate the Chapman-Kolmogorov prediction integral. We then use the theory to fuse data from multiple sensors. Vying with this approach is the Dempster-Shafer theory, which deals with measures of “belief”, and is based on the nonclassical idea of “mass” as opposed to probability. Although these two measures look very similar, there are some differences. We point them out through outlining the ideas of the Dempster-Shafer theory and presenting the basic Dempster-Shafer fusion equation. Finally we compare the two methods, and discuss the relative merits and demerits using an illustrative example.     

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.     

A Bayesian model for measurements on a set of instruments

A Bayesian model is proposed based on randomizing the systematic errors of the instruments. Conditions are identified under which the randomization reduces the expected bias in estimating a measured quantity. __________ Translated from Izmeritel’naya Tekhnika, No. 3, pp. 22–25, March, 2007.