查询词典 covariance function

Though comparing Canny operator and center B spline dyadic wavelet, the following conclusion is proven in this dissertation: a Center B spline function has tight support and Canny operator hasn't. b Center B spline function asymptotic convergence to Gaussian function and the derivative of Center B spline function asymptotic convergence to Canny operator. c The derivative of fourth order center spline B function is more suitable as a optimal edge detector than Canny operator. d Center B spline function can balance the smoothing and approximation of original data, and the fourth center B spline function is the only optimal solution of two order smoothing problem. e The error between the valve of time-frequency uncertainty of the fourth center B spline function and the lower bound of time-frequency uncertainty does not exceed 0.143% of the lower bound. f The derivative of center spline B function can construct a stability dyadic wavelet and can give a fast algorithm for multiscale edge detection, but Canny operator can do neither.

作者给出了Canny算子与中心B样条二进小波严格的比较证明，得出如下结论：a中心B样条函数具有紧支集，Canny算子不具有紧支集。b中心B样条函数的极限收敛于高斯函数，中心B样条函数的导数收敛于Canny算子。c四阶中心B样条函数的导数比Canny算子更接近最佳边缘检测滤波器。d中心B样条函数比高斯函数更能兼顾对原函数平滑和逼近的折中要求，并且四阶中心B样条函数是二阶逼近问题的唯一最优解。e四阶中心B样条函数的时频测不准关系值与时频测不准关系下界的逼近误差不超过0.143％。f中心B样条函数的导数可以构成稳定的二进小波，存在快速的多尺度算法；而Canny算子不构成稳定的二进小波，无法给出快速的多尺度算法。

The problem of multi-objective usually meet many conflict each other and can not use the objective of the same standard unit, then to employ membership function of fuzzy theory,at first each objective function to change fuzzy set and express with membership function, membership function include the maximum constraint, minimum constraint, the decrease function of monotonic property, to become optimal of the multi-objective function, but still to scanty of weighting value with regard to adjustment of objective function, thus this thesis proposes the method of combine orthogonal arrays and particle swarm optimization to solve the problem of multi-objective optimal power flow, each objective function separately add weighting value, to setting each weighting value of objective function in order to the result of anticipating.

多目标最佳化的问题通常会面临到许多互相冲突且不能用同ㄧ标准单位的目标，於是利用模糊理论中的归属函数，首先将各目标函数转换成模糊集合并以归属函数表示，归属函数包含了最大限制值、最小限制值、单调的递减函数所组成，将各目标函数利用归属函数表示，形成单一目标函数的最佳化，但是仍缺乏权重值对於目标函数的调整，於是本篇论文提出ㄧ种权重值设定与粒子群优演算法的方法去解决多目标最佳电力潮流的问题，将各目标函上分别加上权重值设定各目标函数的权重值，得到预期的效果。

Along with the time development, the computer technology more and more thorough various trades and occupations, have provided thorougher and a convenient service for the general users In order to realize raises department's working efficiency, uses the resources fully, reduces the nonessential manpower, physical resource and financial resource goals and so on disbursement, presented various information management system The paper will design university student's dormitory information management system's Web and realizes using the solution The university student's dormitory information management system is one safe and the highly effective proprietory system, designs specially for the information engineering institute This system has used many technologies, for instance JSP, JDBC, Web service and so on The proprietory system use's database is SQL the Server 2000 databases The university student's dormitory information management system's major function includes: May realize to the student's dormitory information inquiry function, including fast inquiry function and classified inquiry function; To the student's dormitory information's input function, the revision function, deletes the function and the statistical function Realizes the dormitory Control section through the system above function regarding the student's dormitory information current management, namely facilitates the dormitory administrative personnels to keep abreast of the student lodging situation and the billeting situation comprehensively promptly; Has provided some simple data inquiry for the user and outputs each kind of information and so on

随着时代的发展，计算机技术越来越深入各行各业，为广大的用户提供了更为周到和便捷的服务。为了实现提高部门的工作效率，充分利用资源，减少不必要的人力，物力和财力的支出等目的，出现了各种各样的信息管理系统。论文将设计一个高校学生公寓信息管理系统的Web应用解决方案并予以实现。高校学生公寓信息管理系统是一个安全和高效的专用系统，专为信息工程学院设计。这个系统使用了很多技术，比如JSP，JDBC ，Web服务等。专用系统使用的数据库是SQL Server 2000 数据库。高校学生公寓信息管理系统的主要功能包括：可以实现对学生公寓信息的查询功能，包括快速查询功能和分类查询功能；对学生公寓信息的录入功能，修改功能，删除功能和统计功能。通过系统上述功能实现公寓管理部门对于学生公寓信息的日常管理，即方便公寓管理人员及时全面地掌握学生住宿情况及公寓分配情况；为用户提供了一些简单的数据查询并输出各种信息等。

The proposed method first employs covariance matrix tapers to obtain the interference covariance function and its corresponding spatial spectrum in non-ideal scenarios.

该方法首先利用协方差矩阵加权得到非理想情况下的干扰协方差函数和对应的空间谱，然后根据周期延拓后的干扰空间谱估计干扰自由度。

Collocation needs to know the covariance matrix of the signal vector,which is usually evaluated by a chosen covariance function and corresponding coefficient fitting.

拟合推估解算必须首先求得信号向量的方差协方差矩阵，该协方差矩阵一般通过选定的协方差函数，并通过已测点数据进行拟合得到。

The paper focuses on the multivariate Laplacian distribution. Based on MLD, we have obtained the variance of the quadric form, the covariance of two quadric forms and the covariance of the linear function and the quadric form.

对于多元拉普拉斯分布，我们推导出了多元拉普拉斯分布的二次型的方差，两个二次型的协方差，线性函数与二次型的协方差。

As for the characters of the Doppler Radar radial velocity data, the single variable background error covariance is used. And the anisotropic covariance function is adopted in the horizontal plane.

其根据多普勒雷达径向速度资料的特点，水平方向采用非各向同性的单变量背景场误差协方差函数。

The program is divided into five blocks: the main function, contracting keys function, numbers and decimal memory function, processing function, the value function, display function,rest function and positive and negative function.

将程序分为五个模块：主函数、判键函数、数字和小数点存储函数、运算处理函数、求值函数、显示函数、正负和清零处理函数。

Special attributes: func doc or doc is the function's documentation string, or None if unavailable; func name or name is the function's name; module is the name of the module the function was dened in, or None if unavailable; func defaults is a tuple containing default argument values for those arguments that have defaults, or None if no arguments have a default value; func code is the code object representing the compiled function body; func globals is the dictionary that holds the function's global variables — it denes the global namespace of the module in which the function was dened; func dict or dict contains the namespace supporting arbitrary function attributes; func closure is None or a tuple of cells that contain bindings for the function's free variables.

func defaults是一个包括有参数中设有默认值的元组；func name 或 name 是函数名。func code是一个编译后的代码对象；func globals是一个引用，指向保存了函数的全局变量的字典—-如果你在函数所在模块中定义了全局变量的话。func dict或 dict 包括了支持任意函数属性的名字空间；func closure 要么是None，要么是包括有函数自由变量捆绑的单元的元组。

Events,Operation and Relation of Sets, Classical Probability, Geometrical Probability , Statistical Stability of a Frequency, Axioms of Probability, Conditional Probability, Total Probability Theorem, Bayes' Rule,Independent Events,Independent Repeated Trials, One Dimensional Random Variables, Discrete Random Variables, Distribution Function of a Random Variables , Continuous Random Variables, Normal Distribution, Distribution of a Function of a Random Variable, Multidimensional Random Variables, Joint Distribution Function, Marginal Distribution Function,Discrete Two—Dimensional Random Variables,Continuous Two—Dimensional Random Variables, Independent Random Variables, Distribution of Functions of Random Variables,Expectation,Variance, Covariance, Coefficient of Correlation, Bivariate Normal Distribution, Law of Large Numbers, The Central Limit Theorems, Sample and Population ,Chi—Squared, T and F Distributions , Sampling Distributions , Point Estimation , Interval Estimation , Testing Hypotheses , A Test of Significance for Parameters in a Single Sample From a Normally Distributed Population , A Test of Significance for Parameters in Two Sample From Normally Distributed Populations .

本课程的主要内容：概率的概念与运算、随机变量及其分布、随机变量的数字特征与极限定理、数理统计的基本概念、估计和检验的基本方法，随机事件与概率随机事件、事件的关系与运算、几何概率、统计概率等，条件概率、全概率公式、贝叶斯公式、事件的独立性、二项概率公式，随机变量的概念、离散型随机变量、随机变量的分布函数、连续型随机变量、随机变量函数的分布，多维随机变量及其分布函数、边缘分布函数、随机变量的独立性、二维随机变量函数的分布，数学期望、方差、协方差和相关系数、大数定律、中心极限定理，总体与样本， X 2-分布、 t-分布和 F-分布，统计量及抽样分布，假设检验的基本概念、单个正态总体参数的显著性检验、两个正态总体参数的显著性检验。

Hanna: That's over now, isn't it?

都结束了，对吗

You must be ill. You look so pale.

你一定是病了，你的脸色苍白。