CAD中自动调图程序实现

本文对CAD中实现多坐标系、多年代和多比例尺的DWG文件实现自动查找和拼图的方法进行实践,并在此基础上利用VLISP编程技术在AutoCAD中实现图形加载和查找的自动化。     

计算机辅助机构运动分析图解法

借助于 Auoto CAD强有力的绘图功能及其内嵌式Autolisp语言,准确、迅速、直观、简便地解决机构运动分析中的问题。     

面向工程图学的计算机绘图教学与思考

传统的工程图学教学，其内容、方法、手段已远远落后于现实。作为计算机辅助设计与制造(CAD／CAM)技术的重要组成部分，计算机绘图(CAG)在现代工业制造过程中占据着举足轻重的位置。为了适应时代的发展，培养出具有时代气息的人才，在高校工科专业中应加强计算机绘图方面的教学。     

网络游戏外挂司法犯罪化的刑法规范解读

制售网络游戏外挂行为的司法犯罪化现象越来越普遍,而破坏计算机信息系统安全罪、非法经营罪和侵犯著作权罪是制售网络游戏外挂行为比较固定化的司法定性,但无论从技术的运行特征来看,还是从行为的违法性来分析,制售网络游戏外挂行为很难与现行刑法中某一具体犯罪的构成要件完全相吻合。事实上,网络游戏外挂是一种在未经软件著作权人许可的情况下,对原游戏程序的修改,从而增进游戏功能和效率的行为,而慎重适用刑法规范调整网络游戏外挂现象,既能最大限度实现非刑法制度的规范价值,又能更好地体现刑法保障性规范的谦抑性原则。     

Root n consistent and optimal density estimators for moving average processes

Abstract.  The marginal density of a first order moving average process can be written as a convolution of two innovation densities. Saavedra & Cao [Can. J. Statist. (2000), 28, 799] propose to estimate the marginal density by plugging in kernel density estimators for the innovation densities, based on estimated innovations. They obtain that for an appropriate choice of bandwidth the variance of their estimator decreases at the rate 1/ n . Their estimator can be interpreted as a specific U -statistic. We suggest a slightly simplified U -statistic as estimator of the marginal density, prove that it is asymptotically normal at the same rate, and describe the asymptotic variance explicitly. We show that the estimator is asymptotically efficient if no structural assumptions are made on the innovation density. For innovation densities known to have mean zero or to be symmetric, we describe improvements of our estimator which are again asymptotically efficient.     

Evaluation of vectorization/parallelization techniques: application to nonparametric curve estimation

The simulation of statistical models in a computer is a fundamental aspect of research in the field of nonparametric curve estimation. Methods such as the FFT (Fast Fourier Transform) or WARP (Weighted Average of Rounded Points) have been developed and analysed for computer implementation of the different techniques in this realm, with the aim of reducing the computation time as much as possible. In this work we analyse two techniques with this objective. These are the vectorization of the source code in which the different algorithms are implemented, and their distributed execution. It can be observed that the vectorization of the programs can improve the results obtained with techniques such as the FFT or WARP, or, in some cases, can prevent the use of these.     

A plug-in technique in nonparametric regression with dependence

The problem addressed is that of smoothing parameter selection in kernel nonparametric regression in the fixed design regression model with dependent noise. An asymptotic expression of the optimum bandwidth parameter has been obtained in recent studies, where this takes the form h = C ₀ n ^?1/5. This paper proposes to use a plug-in methodology, in order to obtain an optimum estimation of the bandwidth parameter, through preliminary estimation of the unknown value of C ₀.     

高职院校AutoCAD课程改革的思考

AutoCAD课程是高职院校机械类各专业的必修课程,为提高其教学质量,必须深化课程改革。本文在分析AutoCAD课程重要性的基础上,着重探讨了＂任务驱动＂教学模式在AutoCAD课程教学中的应用,并对AutoCAD课程的课外学习以及与职业资格考试的衔接进行了思考。     

Consistent procedures for multiclass classification of discrete diffusion paths

The recent advent of modern technology has generated a large number of datasets which can be frequently modeled as functional data. This paper focuses on the problem of multiclass classification for stochastic diffusion paths. In this context we establish a closed formula for the optimal Bayes rule. We provide new statistical procedures which are built either on the plug-in principle or on the empirical risk minimization principle. We show the consistency of these procedures under mild conditions. We apply our methodologies to the parametric case and illustrate their accuracy with a simulation study through examples.     

Exact mean integrated squared error and bandwidth selection for kernel distribution function estimators

Abstract

An exact, closed form, and easy to compute expression for the mean integrated squared error (MISE) of a kernel estimator of a normal mixture cumulative distribution function is derived for the class of arbitrary order Gaussian-based kernels. Comparisons are made with MISE of the empirical distribution function, the infeasible minimum MISE, and the uniform kernel. A simple plug-in method of simultaneously selecting the optimal bandwidth and kernel order is proposed based on a non asymptotic approximation of the unknown distribution by a normal mixture. A simulation study shows that the method provides a viable alternative to existing bandwidth selection procedures.