创建互联网业务价值链

为促进互联网的良性发展,要使设备制造商、电信运营商、虚拟运营商/集团用户、内容提供商和最终用户构成一条互联网业务价值链,价值链的创建对互联网接入设备提出了更高的要求,华为公司的Quid-wayTMA8010Expert多业务综合接入平台可构建宽带城域网分组语音解决方案、可平滑演进为下一代网络中的媒体网关、提供窄带价值链、提供唯一号码业务以及实现多业务综合接入。     

A STEP-BASED CAD/CAPP INTEGRATION

ＡＳＴＥＰ－ＢＡＳＥＤＣＡＤＩＣＡＰＰＩＮＴＥＧＲＡＴＩＯＮＺｈｅｎｇＬｉａｎｙｕ；ＤｕＰｅｉＡＳＴＥＰ－ＢＡＳＥＤＣＡＤＩＣＡＰＰＩＮＴＥＧＲＡＴＩＯＮ￥ＺｈｅｎｇＬｉａｎｙｕ；ＤｕＰｅｉＡｂｓｔｒａｃｔＴｈｅｉｎｆｏｒｍａｔｉｏｎｉｎｔｅｇｒａｔ...     

氧化锌压敏电阻的老化机理

研究了ZnO压敏电阻的老化现象,提出了一种新的老化机理-线性链理论。该理论认为,在外电场作用下,压敏电阻势垒高度降低;当降低到一定值时,晶界可变电阻转化为线性晶界电阻,从而压敏链转化为线性链。线性链是稳定的,因而压敏电阻老化到一定程度后其电性能将不能完全恢复。     

一种构思新颖的抽油机设计方案

介绍了一种与现有抽油机结构完全不同的机 电一体化抽油机 ,它主要由往复滚筒提升总成、减速器总成、蓄能助启系统及电磁自动控制和动力系统组成。其特点是采用机 电一体化设计 ,充分利用飞轮的蓄能及与电磁自动控制器的相互配合来控制电磁离合器的激磁电流和电动机的电力输入 ,实现上冲程时通电、下冲程时断电的工作方式。该机体积小 ,结构紧凑 ,适应性强 ,能满足抽油井超长冲程和节能的要求     

DXDK800自动充填包装机料袋长度的调整

本文主要介绍了在DXDK800自动充填包装机中 ,采用一种新型的齿轮—蜗轮、蜗杆差速器来对膜片的进给速度进行微量的调整。及采用一种新型的可调式偏心链轮来调整横封辊的瞬时线速度 ,使其与料袋膜的进给速度相一致     

关于广义函数δ(x)的讨论

本文应用《电气子教学学报》之邀约而写，旨在针对一些读者对该韩过去刊出的有关广义函数δ(x）的文章中的质疑，从数学角度提供一些有助于正确认识、处理和运用广义函数的辅助材料。     

Semiparametric Estimation in Time‐Series Regression with Long‐Range Dependence

Abstract. We consider semiparametric estimation in time‐series regression in the presence of long‐range dependence in both the errors and the stochastic regressors. A central limit theorem is established for a class of semiparametric frequency domain‐weighted least squares estimates, which includes both narrow‐band ordinary least squares and narrow‐band generalized least squares as special cases. The estimates are semiparametric in the sense that focus is on the neighbourhood of the origin, and only periodogram ordinates in a degenerating band around the origin are used. This setting differs from earlier studies on time‐series regression with long‐range dependence, where a fully parametric approach has been employed. The generalized least squares estimate is infeasible when the degree of long‐range dependence is unknown and must be estimated in an initial step. In that case, we show that a feasible estimate which has the same asymptotic properties as the infeasible estimate, exists. By Monte Carlo simulation, we evaluate the finite‐sample performance of the generalized least squares estimate and the feasible estimate.     

Learning to Perceive and Act by Trial and Error

This article considers adaptive control architectures that integrate active sensory-motor systems with decision systems based on reinforcement learning. One unavoidable consequence of active perception is that the agent's internal representation often confounds external world states. We call this phoenomenon perceptual aliasingand show that it destabilizes existing reinforcement learning algorithms with respect to the optimal decision policy. We then describe a new decision system that overcomes these difficulties for a restricted class of decision problems. The system incorporates a perceptual subcycle within the overall decision cycle and uses a modified learning algorithm to suppress the effects of perceptual aliasing. The result is a control architecture that learns not only how to solve a task but also where to focus its visual attention in order to collect necessary sensory information.     

Integration framework for intelligent manufacturing processes

In this article, we describe a new approach to applying distributed artificial intelligence techniques to manufacturing processes. The construction of intelligent systems is one of the most important techniques among artificial intelligence research. Our goal is to develop an integrated intelligent system for real time manufacturing processes. An integrated intelligent system is a large knowledge integration environment that consists of several symbolic reasoning systems (expert systems) and numerical computation packages. These software programs are controlled by a meta-system which manages the selection, operation and communication of these programs. A meta-system can be implemented in different language environments and applied to many disciplines. This new architecture can serve as a universal configuration to develop high performance intelligent systems for many complicated industrial applications in real world domains.To whom all correspondence should be addressed.     

A Bayesian propensity score adjustment for latent variable modeling and MCMC algorithm

The estimation of the differences among groups in observational studies is frequently inaccurate owing to a bias caused by differences in the distributions of covariates. In order to estimate the average treatment effects when the treatment variable is binary, Rosenbaum and Rubin [1983. The central role of the propensity score in observational studies for causal effects. Biometrika 70, 41-55] proposed an adjustment method for pre-treatment variables using propensity scores. Imbens [2000. The role of the propensity score in estimating dose-response functions. Biometrika 87, 706-710] extended the propensity score methodology for estimation of average treatment effects with multivalued treatments.However, these studies focused only on estimating the marginal mean structure. In many substantive sciences such as the biological and social sciences, a general estimation method is required to deal with more complex analyses other than regression, such as testing group differences on latent variables. For latent variable models, the EM algorithm or the traditional Monte Carlo methods are necessary. However, in propensity score adjustment, these methods cannot be used because the full distribution is not specified.In this paper, we propose a quasi-Bayesian estimation method for general parametric models that integrate out the distributions of covariates using propensity scores. Although the proposed Bayes estimates are shown to be consistent, they can be calculated by existing Markov chain Monte Carlo methods such as Gibbs sampler. The proposed method is useful to estimate parameters in latent variable models, while the previous methods were unable to provide valid estimates for complex models such as latent variable models.We also illustrated the procedure using the data obtained from the US National Longitudinal Survey of Children and Youth (NLSY1979-2002) for estimating the effect of maternal smoking during pregnancy on the development of the child's cognitive functioning.