多源多目标环境下配送中心服务半径的确定（模型研究）

面对日益高效、快速及个性化的客户配送服务需求,配送中心服务半径的科学决策和确定十分必要。本文在分析配送中心服务半径基本含义及其影响因素的基础上,针对多源多目标环境下配送中心服务半径提出了相应的数学模型,并通过数学模型对实体环境进行模拟研究,确定了一个区域内多个配送中心配送多种商品给若干客户时的服务半径。通过本研究,旨在为配送中心服务半径的科学决策提供借鉴。     

面向电子商务企业的物流配送服务半径界定

为界定面向电子商务企业的物流配送服务半径,运用模糊综合评判决策模型,将层次分析法与德尔菲法相结合确定配送企业的综合物流配送能力。通过对“断裂点”公式的修正,对在一定区域内为电子商务企业客户提供配送服务的多个配送企业及其在竞争条件下的服务半径进行了量化界定。分析认为,物流配送服务半径与其综合物流配送能力呈正相关,与竞争对手综合物流配送能力成负相关。     

数控铣削加工沟槽方法与控制

本文主要介绍在数控铣削加工沟槽时,如何利用刀具半径补偿功能,使刀具偏离工件沟槽轮廓一定距离,实现利用一个程序完成沟槽内、外轮廓粗、精加工,以及选用合适的下刀方式进行沟槽加工质量控制,并用宏指令进行编程。     

A monte carlo analysis of two spectral tests of the martingale hypothesis

Summary The size, power, and robustness properties of the Kolmogorov-Smirnov and Cramér-von Mises spectral tests of the martingale (difference) hypothesis are investigated by Monte Carlo methods. The results highlight a marked superiority of the Cramér-von Mises with respect to the Kolmogorov-Smirnov test. The paper also shows that the Cramér-von Mises test is simple to compute, more general and more powerful than other converntionally used tests.     

改进运动规律凸轮基圆半径的确定

本文针对凸轮机构推杆作改进梯形加速度、改进正弦加速度和３－４－５次多项式运动规律的情况，研究对心式盘形凸轮机构凸轮基圆半径的确定方法，并利用数值计算方法绘制了基圆半径系数β（ｒ＿０／ｈ）的线图．本文是对已有的用于四种基本运动规律确定基圆半径的诺模图￣［３］和补充诺模图［６］的进一步的发展．     

关于Deutsch定理的注记

本文就任意n阶非负分块矩阵,给出了谱半径的估计公式,推广了Deutsch定理.     

旅游吸引半径数理模型及实证研究——以水利旅游为例

旅游吸引半径是旅游地与旅游客源市场供求关系的有效边界,而旅游地识别目标客源市场的基础性研究,则是长线型、中线型和短线型旅游战略布局的参考。遵从理论到实证的思路,立足于距离衰减规律和效用论,从旅游吸引力、旅游效用和旅游成本的角度构建旅游吸引半径数理模型,讨论旅游吸引半径有意义的情况;将水利旅游作为实证对象,并引入2004年和2008年的数据,计算中国31个省市自治区的水利旅游吸引半径。研究表明:从时间看,水利旅游吸引半径已经随着时间推移而向外扩展;从空间看,中国江苏、河南等地区的水利旅游吸引半径较大,适宜开发长线型水利旅游客源,而内蒙古、吉林等地区的水利旅游吸引半径较小,仅适宜开发短线型水利旅游客源等。     

Time Series Classification Based on Spectral Analysis

For time series data with obvious periodicity (e.g., electric motor systems and cardiac monitor) or vague periodicity (e.g., earthquake and explosion, speech, and stock data), frequency-based techniques using the spectral analysis can usually capture the features of the series. By this approach, we are able not only to reduce the data dimensions into frequency domain but also utilize these frequencies by general classification methods such as linear discriminant analysis (LDA) and k-nearest-neighbor (KNN) to classify the time series. This is a combination of two classical approaches. However, there is a difficulty in using LDA and KNN in frequency domain due to excessive dimensions of data. We overcome the obstacle by using Singular Value Decomposition to select essential frequencies. Two data sets are used to illustrate our approach. The classification error rates of our simple approach are comparable to those of several more complicated methods.     

On the Spectral Decomposition in Normal Discriminant Analysis

This article enlarges the covariance configurations, on which the classical linear discriminant analysis is based, by considering the four models arising from the spectral decomposition when eigenvalues and/or eigenvectors matrices are allowed to vary or not between groups. As in the classical approach, the assessment of these configurations is accomplished via a test on the training set. The discrimination rule is then built upon the configuration provided by the test, considering or not the unlabeled data. Numerical experiments, on simulated and real data, have been performed to evaluate the gain of our proposal with respect to the linear discriminant analysis.     

On the Distribution of Matrix Quadratic Forms

A characterization of the distribution of the multivariate quadratic form given by X A X′, where X is a p × n normally distributed matrix and A is an n × n symmetric real matrix, is presented. We show that the distribution of the quadratic form is the same as the distribution of a weighted sum of non central Wishart distributed matrices. This is applied to derive the distribution of the sample covariance between the rows of X when the expectation is the same for every column and is estimated with the regular mean.