基于自适应独立成分分析的化工过程监测

针对独立成分分析（independent component analysis,ICA）方法应用于过程监测时如何选择独立成分（independent component,IC）的问题,提出了自适应独立成分分析（adaptation independent component analysis,AICA）方法。AICA方法首先利用分离矩阵建立关联矩阵,该关联矩阵表示IC的相似性,同时通过核密度估计选择概率密度最小的IC作为特殊独立成分（particular independent component,PIC）,再通过关联矩阵选择与PIC具有相似变异特征的几个普通独立成分（common independent components,CICs）,最后将PIC与CICs用于构造监测统计量。将AICA方法应用于田纳西-伊士曼（Tennessee Eastman,TE）仿真过程,测试结果表明所提方法对于过程监测是有效的。     

卷烟主要化学成分与感官质量相关性分析

为研究卷烟化学成分及其比例对卷烟感官质量(评吸结果)的影响,对四川中烟主要品牌的卷烟进行样本抽取并测定水溶性总糖、总氮、烟碱、钾、氯等主要化学成分含量,并计算糖碱比、糖氮比、钾氯比等相关化学指标,同时进行感官质量评价,对卷烟的化学指标和感官评吸指标进行相关分析和回归分析,研究化学指标与感官质量之间的相互关系。结果表明:卷烟感官评吸指标中的香气、杂气、刺激性、余味均与糖氮比、糖碱比、总糖呈极显著正相关,但与氯、钾、总氮呈极显著负相关;对卷烟感官质量(香气、杂气、刺激性、余味)影响较大的化学指标依次为:糖氮比总糖糖碱比钾氯总氮;逐步回归分析结果表明:卷烟的糖氮比对卷烟香气、刺激性、余味的变化有显著影响,总糖对杂气的变化有显著影响。     

Flow of conjunctural information and forecast of euro area economic activity

Combining forecasts, we analyse the role of information flow in computing short‐term forecasts up to one quarter ahead for the euro area GDP and its main components. A dataset of 114 monthly indicators is set up and simple bridge equations are estimated. The individual forecasts are then pooled, using different weighting schemes. To take into consideration the release calendar of each indicator, six forecasts are compiled successively during the quarter. We found that the sequencing of information determines the weight allocated to each block of indicators, especially when the first month of hard data becomes available. This conclusion extends the findings of the recent literature. Moreover, when combining forecasts, two weighting schemes are found to outperform the equal weighting scheme in almost all cases. Compared to an AR forecast, these improve by more than 40% the forecast performance for GDP in the current and next quarter. Copyright © 2010 John Wiley & Sons, Ltd.     

改性粉煤灰处理高铁高锰酸性矿井水资源化研究

治理酸性矿井水是矿山环境保护的重要内容,粉煤灰以其独有的特性对酸性矿井水有一定的处理效果.粉煤灰处理酸性高铁高锰矿井水是实现资源化利用的关键途径,同时可以达到以度治废的效果.该文利用GENESIS XM260SX-射线能谱仪(EDS)分析粉煤灰化学组分,通过NaOH、CaO、Na2CO3、NaCl、HCl、H2SO4等改性剂改性粉煤灰,以此来提高粉煤灰对酸性矿井水的处理性能,实验确定Na2CO3为最佳改性剂,出水水质达到并优于GB50335-2002《污水再生利用工程设计规范》水质指标.     

一类2个相同部件并联可修系统非零初始状态解的研究

从生产实践出发修改了系统的初始条件,推证出系统非负解的存在唯一性。     

小麦产量性状与品质性状的对应分析及偏相关分析

运用对应分析和偏相关分析对河南省2006-2007年小麦品种区试高肥冬水组连续参试两年共19个品种的12个产量和品质性状进行了分析。对应分析结果将供试品种分为三大类,第一类包括9个品种,属高产中质类品种;第二类含2个品种,属中产中质类品种;第三类包括8个品种,属中低产、中上质品种。并对各类品种的改良目标进行了讨论。从产量、产量性状及品质性状的偏相关分析结果得知,增加穗粒数、千粒质量与密度是获致小麦高产优质的有效途径。图1,表5,参8。     

电子元器件学习论述

文章阐述了在教学和实践中掌握学习电子元器件的方法可从电子元器件的识别、元器件的工作特性以及元器件的检测方面入手。     

Forecasting US employment growth using forecast combining methods

We examine different approaches to forecasting monthly US employment growth in the presence of many potentially relevant predictors. We first generate simulated out‐of‐sample forecasts of US employment growth at multiple horizons using individual autoregressive distributed lag (ARDL) models based on 30 potential predictors. We then consider different methods from the extant literature for combining the forecasts generated by the individual ARDL models. Using the mean square forecast error (MSFE) metric, we investigate the performance of the forecast combining methods over the last decade, as well as five periods centered on the last five US recessions. Overall, our results show that a number of combining methods outperform a benchmark autoregressive model. Combining methods based on principal components exhibit the best overall performance, while methods based on simple averaging, clusters, and discount MSFE also perform well. On a cautionary note, some combining methods, such as those based on ordinary least squares, often perform quite poorly. Copyright © 2008 John Wiley & Sons, Ltd.     

基于NF-QFD的构件选择方法

在利用构件进行软件开发时,用户的需求通常是模糊和不确定的.提出了基于非线性模糊质量功能展开(NF-QFD)的构件选择模型.该模型在考虑构件之间高内聚、低耦合的同时还兼顾用户需求最大化,通过该模型可以解决软件系统开发时构件选择的优化问题,为设计人员提供更多的决策支持.     

Prediction in an Unbalanced Nested Error Components Panel Data Model

This paper derives the best linear unbiased predictor for an unbalanced nested error components panel data model. This predictor is useful in many econometric applications that are usually based on unbalanced panel data and have a nested (hierarchical) structure. Examples include predicting student performance in a class in a school, or house prices in a neighborhood in a county or a state. Using Monte Carlo simulations, we show that this predictor is better in root mean square error performance than the usual fixed‐ or random‐effects predictors ignoring the nested structure of the data. This is applied to forecasting the productivity of public capital in the private sector using nested panel data of 48 contiguous American states. Copyright © 2013 John Wiley & Sons, Ltd.