超精密加工机床的关键部件技术

论述了亚微米级超精密加工机床的主轴、直线导轨、,进给与微量进给,隔振与恒温等关键元部件的设计原理、实施方案,及其在ＨＣＭ－１超精密加工机床中的应用。     

应用Active Object模式建立HL7网关

本文介绍了HL7网关在建设数字化医疗系统中的重要作用,分析了建立HL7网关必须解决的并发处理问题。然后介绍了动态对象模式的并发处理机制,详细说明了动态对象的各个组件的作用以及它们之间的相互协作关系,并给出了各个组件的主要实现代码。最后指出了动态对象的优缺点。     

基于构件技术的农业信息系统处理平台的研究

在对构件技术进行深入探讨的基础上,给出构件化农业信息系统处理平台的设计思想,提出了适合它的结构模型和构件描述语言、构件制作的原则、构件制作的算法,并且结合具体的实例加以阐述。     

J2EE和WebGIS系统协同机制研究和实现

在对J2EE技术和webGIs技术进行了简单归纳之后,从理论上分析了两者的原理和协同工作的方法,提出了J2EE和WebGIs系统协同机制。最后以具体技术平台为例,给出了J2EE和WebGIs系统协同工作的应用解决方案。     

一种新的信息隐藏检测方法的探讨

首先建立了隐蔽通信系统模型,再对该模型进行了安全分析,最后利用信号盲分离理论中的独立组件分析等方法对隐藏对象与掩蔽载体进行了分离,并给出了隐藏信息的估计算法。     

基于小波变换和隐马尔可夫模型的人脸识别方法

提出了基于小波变换和隐马尔可夫模型的人脸识别方法。对原始图像采用小波分解后,原始图像被分解到不同的频带上。利用小波理论分析可知,在每一级分解中,低频子图像包含了原始图像的主要描述信息,而其他3个高频子图像包含的信息较少,对模式分类的作用也较小,所以可忽略不计。该算法首先对图像进行3级小波分解,然后把3个不同分辨率的低频子图像由小到大排列成树状结构,形成低频小波树。接着利用主元分析对每个小波树枝进行去相关、降维,形成特征小波树枝,并把它作为观测向量对隐马尔可夫模型进行训练,把优化的模型参数用于人脸识别,实验结果表明,该方法识别率较高,具有很好的发展前景。     

Variable window adaptive Kernel Principal Component Analysis for nonlinear nonstationary process monitoring

On-line control of nonlinear nonstationary processes using multivariate statistical methods has recently prompt a lot of interest due to its industrial practical importance. Indeed basic process control methods do not allow monitoring of such processes. For this purpose this study proposes a variable window real-time monitoring system based on a fast block adaptive Kernel Principal Component Analysis scheme. While previous adaptive KPCA models allow only handling of one observation at a time, in this study we propose a way to fast update or downdate the KPCA model when a block of data is provided and not only one observation. Using a variable window size procedure to determine the model size and adaptive chart parameters, this model is applied to monitor two simulated benchmark processes. A comparison of performances of the adopted control strategy with various Principal Component Analysis (PCA) control models shows that the derived strategy is robust and yields better detection abilities of disturbances.     

ITER components cooling: Satisfying the distinct needs of systems and components

The ITER Tokamak requires multiple auxiliary systems to initiate, support, and monitor the fusion reaction. Heat produced by these systems, as well as the heat produced by the fusion reaction itself is collected by the ITER Cooling Water System (CWS) and rejected to the atmosphere. The CWS is composed of several systems designed for specific cooling roles. One of these systems is the Component Cooling Water System 2 (CCWS-2) whose function is to collect the heat from auxiliary client systems and components and transfer it to the Heat Rejection System. Clients are located throughout the site and have different requirements in terms of pressure, temperature, temperature variation, flow, metallurgy of wetted surfaces, and water quality. To satisfy these different requirements the CCWS-2 is divided into four separate loops, each of which has different operating parameters. For example, the CCWS-2A loop is designed to cool components with wetted surfaces of copper and primarily serves the radio-frequency heating systems, magnet power supplies, and neutral beam injector system components. This paper describes the evolution of the CCWS-2 system to match the needs of groups of compatible clients, and describes the development of the preliminary design of one of its loops, CCWS-2A, to meet individual client needs.     

Estimating a Parametric Component Lifetime Distribution from a Collection of Superimposed Renewal Processes

Maintenance data can be used to make inferences about the lifetime distribution of system components. Typically, a fleet contains multiple systems. Within each system, there is a set of nominally identical replaceable components of particular interest (e.g., 2 automobile headlights, 8 dual in-line memory module (DIMM) modules in a computing server, 16 cylinders in a locomotive engine). For each component replacement event, there is system-level information that a component was replaced, but no information on which particular component was replaced. Thus, the observed data are a collection of superpositions of renewal processes (SRP), one for each system in the fleet. This article proposes a procedure for estimating the component lifetime distribution using the aggregated event data from a fleet of systems. We show how to compute the likelihood function for the collection of SRPs and provide suggestions for efficient computations. We compare performance of this incomplete-data maximum likelihood (ML) estimator with the complete-data ML estimator and study the performance of confidence interval methods for estimating quantiles of the lifetime distribution of the component. Supplementary materials for this article are available online.