Uncertainty Relations in Terms of Fisher Information

By virtue of the well-known concept of Fisher information in the theory of statistical inference, we obtain an inequality chain which generalizes and refines the conventional Heisenberg uncertainty relations.     

非线性随机效应模型的置信域

本文对非线性随机效应模型,建立了微分几何框架,推广了Bates＆Wates关于非线性模型几何结构．在吡基础上,我们导出了关于固定效应参数和子集参数的置信域的曲率表示,这些结果是BatesandWates(1980),Hamilton(1986)与Wei(1994)等的推广．     

An exact solution of Fisher equation and its stability

In this paper, the Fisher equation is analysed. One of its travelling wave solution is obtained by comparing it with KdV--Burgers (KdVB) equation. Its amplitude, width and speed are investigated. The instability for the higher order disturbances to the solution of the Fisher equation is also studied.     

Logarithm Versus Square Root： Comparing Quantum Fisher Information

In classical statistics, the Fisher information is unique in the sense that it is essentially the only monotone Riemannian metric on the space of probability densities. In quantum theory, this uniqueness breaks down, and there are many natural quantum analogues of the Fisher information, among which two particular versions distinguish themselves by their intuitive and informational significance： The first has its origin in the skew information introduced by Wigner and Yanase in 1963 in the context of quantum measurement, and is defined via the square root of the density operator. The second arises from Helstrom＇s study of quantum detection in 1967, and is defined via the symmetric logarithmic derivative. The aim of this paper Js to compare these two versions of quantum Fisher information, and to establish two informational inequalities relating them.     

基于Fisher判别分析的有监督特征提取和星系光谱分类

随着天文观测技术的进步、数据获取能力的提高和大型光谱巡天计划的相继实施,光谱数据的自动处理研究越来越受到重视和关注。文章在分析了文献中光谱自动分类研究的特点和无监督特征提取方法所固有的一些不足的基础上指出了光谱有监督特征提取研究的必要性。并重点研究了Fisher判别分析(FDA)有监督特征提取方法在星系光谱自动分类中的应用。该方法： (1) 具有突出的维数约减能力; (2) 能有效地融合训练数据的类别信息,并按照分类能力提取特征。实验表明,将FDA方法用于某些星系细分类不仅明显地提高了分类器的速度,而且具有良好的分类性能。因此,对于较大的光谱识别系统更能体现出该方法的优越性。     

中高分辨力遥感图像中飞机目标自动识别算法研究

提出了一种中高分辨力的航空航天遥感图像中飞机目标快速自动识别的新算法。在分割和分类过程中充分利用飞机目标的先验知识,提出了一种改进区域分割方法,并应用树分类器对飞机目标进行自动识别。所提出的改进区域分割方法较好地实现了区域分割中阈值的准确自动选取,克服了复杂背景图像中小目标的全局阈值自动分割的失效问题。采用二叉树分类器,通过提取简单的目标几何特征,分层进行种类识别,提高了识别速度,降低了漏检率和虚警率。运用该方法进行了实验。结果表明,识别率达到了100%。     

Estimation of decoherence in electromechanical circuits

This letter presents a schematic circuit with capacitive coupling between the CPW Cavity Center Trace (resonated with electromagnetic mode) and the mechanical Nanoresonator mediated by Transmon, resulting in the Cross-Kerr electromechanical effect. Then we show the dynamics of quantum states, showing the generation of superposition states and bipartite entanglement of continuous variables through nonlinear quantum electrodynamics in the circuit. Finally, we calculated and estimated the parameter for mechanical outcome through phase measurements of the CPW Cavity Center Trace electromagnetic mode.     

利用Fisher函数解非线性不等式约束优化问题的梯度投影算法

本文将利用梯度投影与Fisher函数提出一个新的二阶段搜索方向,给出相应的解非线性不等式约束优化问题的梯度投影算法,并证明了该算法具有全局收敛性.     

Optical Entropy Network Mapped from Binary Feature Tree

The concept of a binary feature tree (BFT) and the principle of its formation are described. A pattern is divided into sub-parts by comparing its similarity with other patterns. The BFT is established by sub-parts of a group of patterns and mapped into a three layered neural network which Sethi called an entropy network. The interconnection pattern between the first and hidden layers is formed according to the “AND“ relationship of node feature patterns determined by BFT. The interconnection pattern between the hidden and last layers is obtained by training. The advantage of the proposed network is that the scale is small because a feature neuron is adopted and the interconnection is local instead of full; therefore, it is easily implemented by either hardware or software. Two simulation examples show the success of the entropy network for pattern recognition. A feature extraction by an optical inner product method is also described.Presented at 1996 International Topical Meeting on Optical Computing (OC ‘96), April 21-25, Sendai, Japan.     

Asymptotic expansion of the null distribution of test statistic for linear hypothesis in nonnormal linear model

This paper is concerned with the null distribution of test statistic T for testing a linear hypothesis in a linear model without assuming normal errors. The test statistic includes typical ANOVA test statistics. It is known that the null distribution of T converges to χ² when the sample size n is large under an adequate condition of the design matrix. We extend this result by obtaining an asymptotic expansion under general condition. Next, asymptotic expansions of one- and two-way test statistics are obtained by using this general one. Numerical accuracies are studied for some approximations of percent points and actual test sizes of T for two-way ANOVA test case based on the limiting distribution and an asymptotic expansion.