Economic modeling of board test strategies

A long and deep recession, coupled with continuous competitive pressure to reduce costs, is forcing many companies to review their test strategies. Testing costs have become a more significant proportion of the overall manufacturing cost even though manufacturing yields have increased dramatically over the past ten or twelve years. This causes attention to be focused on testing costs as a key source of cost reduction. The increased use of DFT and the integration of design and test are very positive moves towards controlling testing costs but other methods employed can often backfire. The increased use of low priced testers is one such method. The pressure to reduce costs, higher process yields and exhortations that testing adds no value can lead the test engineering manager to take the cheap route. In reality this can often turn out to be an expensive decision. The only way to avoid expensive mistakes is to perform an economic analysis of the alternative courses of action. In most cases this is done, but not always in the right manner or with the necessary amount of detail to make the comparisons meaningful. This article discusses the need for effective cost analysis of test strategies and highlights some of the pitfalls.     

考虑电网阻抗耦合的模块化多电平换流器交直流侧阻抗通用计算方法

以模块化多电平换流器(modular multilevel converter,MMC)为技术路线的柔性直流输电技术在风电接入、电网异步互联等领域迅速发展。MMC的阻抗建模是分析柔性直流输电系统振荡起源的重要依据。文中分析MMC在交、直流侧激励下的谐波传递机制,建立一种适用于交、直流侧阻抗计算的通用方法。对一次系统频域建模关联了MMC所连接的交、直流侧网络阻抗,并考虑包含解耦双同步参考坐标系锁相环及负序内环控制在内的全部控制模块。通过求解线性方程组对MMC阻抗进行计算,并在PSCAD/EMTDC环境中对双端背靠背MMC系统在多种工况下1～5kHz频段内进行扫频,验证该方法的准确性,并详细分析网络阻抗耦合、控制链路延时、功率等级、负序内环和外环控制等因素对MMC阻抗的影响。     

高能超声分散纳米晶须的数值和物理模拟

为研究高能超声处理制备纳米复合材料过程中纳米增强相在熔体中的分散过程,采用甘油为介质分别进行了数值模拟以及物理模拟。数值模拟结果表明,当超声作用于甘油中时,甘油中会形成中心-底面-壁面-中心的环形流动,变幅杆探头端面边缘附近甘油流体存在最大的流动速度,且随着超声功率的增大,流体运动速度增大。物理模拟实验结果显示,高能超声作用下甘油的实际运动行为与数值模拟结果相符合,存在环形流动;此外,高能超声作用下甘油中存在明显的空化效应;纳米晶须在超声作用下于甘油中分散良好,且随着超声功率的增大,达到充分分散所需时间变短。     

Erosion coupled dynamic mesh analysis for location of maximum erosion in 90-degree bends for gas-solid flows

The progressive change in the surface geometry of the component due to erosive wear affects the correct estimation of erosive wear performance and service life of the components handling particulate flows. The current study focuses on determining the change in the location of higher erosion on the bend surface during the pneumatic conveying of solids with continuous geometric modification due to erosive wear. Computational fluid dynamics (CFD) based erosion-coupled dynamic mesh methodology is adopted to simulate the time-dependent surface modification of the 90° bend geometry due to erosive wear. Available experimental data are used to validate the numerical results. Further, the erosion distribution and the location of the maximum erosion for different flow velocities, particle sizes, and bend radius ratios with the increase in solid throughput are investigated. It has been found that the modification in the bend geometry due to erosion influences the location of the maximum erosion. The increase in thickness loss due to erosion increases the variation in the location of the maximum erosion. Furthermore, an equation for predicting the location of maximum erosion of bend geometry is obtained based on the bend radius ratio and the thickness loss.     

Theory and techniques of data mining in CGF behavior modeling

For the large quantity of data,rules and models generated in the course of computer generated forces (CGFs) behavior modeling,the common analytical methods are statistical methods based on the tactical rules,tactical doctrine and empirical knowledge.However,from the viewpoint of data mining,we can find many of these analytical methods are also each-and-every different data mining methods.In this paper,we survey the data mining theory and techniques that have appeared in the course of CGF behavior modeling f...     

Stochastic approximation learning for mixtures of multivariate elliptical distributions

Most of the current approaches to mixture modeling consider mixture components from a few families of probability distributions, in particular from the Gaussian family. The reasons of these preferences can be traced to their training algorithms, typically versions of the Expectation-Maximization (EM) method. The re-estimation equations needed by this method become very complex as the mixture components depart from the simplest cases. Here we propose to use a stochastic approximation method for probabilistic mixture learning. Under this method it is straightforward to train mixtures composed by a wide range of mixture components from different families. Hence, it is a flexible alternative for mixture learning. Experimental results are presented to show the probability density and missing value estimation capabilities of our proposal.     

A compensation cost analysis of service-aggregate transaction for DTNs clients

Clients from Delay Tolerant Networks (DTNs) might encounter overtime exceptions when they aggregate autonomic commercial web service references to form an all-or-nothing transaction. Such exceptions may cause extra overhead—“compensation cost” when aborting transactions. Regarding the delay-related characteristics of DTNs, this paper models the expected compensation cost (ECC) for the schedulings of commit-requests, presents a calculability analysis for quantitative ECC prediction, and explores the limitation of ECC’s probabilistic prediction accordingly. The analysis is based on the timed-2PC plus compensation (t2PC+) protocol and the multi-step price tag pattern, which is verified conditionally by simulation tests through randomly-chosen severely-fluctuant samplings of compensation cost and delays. The ultimate target of this work is to demonstrate the high complexity, and in particular, the uncertainty of ECC predictions due to the randomicity of DTNs’ delays.