一种提高薄互层储集层分辨率的新方法

目前，薄储集层横向追综、对比的困难，在于薄互层结构的高频信息经地层吸收，采集和接收后变得很弱，甚至被强干扰掩没，从记录上很难对薄互层储集层作追踪对比。本文提出了一种在复数域提高地震记录分辨率的处理方法，实际资料处理表明；它既提高了地震记录的分辨率又不降低其信噪比，是一种既经济又理想的处理方法。     

电子产品研制过程的可靠性管理

为建立闭环式可靠性管理,本文从如何确定可靠性参数和指标开始,对如何进行可靠性预计、如何确定定时截尾可靠性鉴定试验方案{T,C}和开展软件可靠性定量评估等方面进行了讨论。     

Polytomous logistic regression

In this paper a review will be given of some methods available for modelling relationships between categorical response variables and explanatory variables. These methods are all classed under the name polytomous logistic regression (PLR). Models for PLR will be presented and compared; model parameters will be tested and estimated by weighted least squares and by likelihood. Usually, software is needed for computation, and available statistical software is reported.
An industrial problem is solved to some extent as an example to illustrate the use of PLR. The paper is concluded by a discussion on the various PLR-methods and some topics that need a further study are mentioned.     

基于GLM的未决赔款准备金评估的随机性链梯法

为度量未决赔款准备金评估结果的波动性,需要研究随机性评估方法。基于GLM的随机性方法,得到准备金估计及预测均方误差。特别地,在过度分散泊松模型中,分别应用参数Bootstrap方法和非参数Bootstrap方法,得到两种方法下未决赔款准备金的预测分布,进而由该分布得到各个分位数以及其它分布度量,并通过精算实务中的数值实例应用R软件加以实证分析。实证结果表明,两种Bootstrap方法得到的参数误差、过程标准差、预测均方误差都与解析表示估计的结果很接近。     

基于支持向量回归的现代气象雷达寿命预测

为了准确地预测气象雷达使用寿命, 提高气象雷达运行可靠性、安全性和可维护性, 提出了一种新的基于多元回归的气象雷达使用寿命预测框架。在该框架内,采用了支持向量 回归(SVR)方法来求解气象雷达使用寿命的多元回归问题;提出了基于SVR的气象雷达特征参 数选择FSSSVR（Feature Subset Selection SVR）算法去掉冗余和无效的特征参数。实验结 果表明,基于SVR方法的预测算法能够准确地预测气象雷达的使用寿命,能够为雷达全寿命 周期管理提供参考依据。     

瓦斯地质图在下霍矿井的应用研究

煤矿矿区、矿井、采掘工作面瓦斯地质图,是瓦斯预测成果最为直观的表达。瓦斯地质图在预测瓦斯涌出量、瓦斯突出危险性等方面有着非常重要的价值。文章介绍了瓦斯地质图编制的意义,分析了瓦斯地质图在下霍煤矿生产过程中的实际应用,为治理矿井瓦斯提供了依据。     

煤炭库存上升 动力煤价格回落

分析了近期港口、电厂电煤库存,国际煤价,国内煤炭产量,煤炭进口以及2012年第一季度动力煤炭价格运行情况,认为2012年第2季度在雨季来水良好的情况下,煤炭库存继续上升、动力煤价格上涨无望。     

河南省耕地与粮食变化分析及预测

河南省是我国重要的粮食生产基地,其稳定的耕地资源对粮食生产具有重要意义.本文利用河南省29年耕地资源和粮食产量数据进行分析,研究了粮食生产和耕地资源的变化特征.结果表明,随着河南省人口的不断增长,耕地资源在不断减少,其人均耕地面积也在不断下降;河南省粮食总产量和单产极不稳定,给社会经济发展和粮食安全带来了隐患.最后,建立GM(1,1)预测模型,对2010年、2015年和2020年粮食产量、人均耕地等分别进行了预测.针对预测结果,提出了稳定、提高耕地资源和粮食生产的具体建议与对策,为政府正确决策提供了科学依据.     

Random Effects Misspecification Can Have Severe Consequences for Random Effects Inference in Linear Mixed Models

There has been considerable and controversial research over the past two decades into how successfully random effects misspecification in mixed models (i.e. assuming normality for the random effects when the true distribution is non‐normal) can be diagnosed and what its impacts are on estimation and inference. However, much of this research has focused on fixed effects inference in generalised linear mixed models. In this article, motivated by the increasing number of applications of mixed models where interest is on the variance components, we study the effects of random effects misspecification on random effects inference in linear mixed models, for which there is considerably less literature. Our findings are surprising and contrary to general belief: for point estimation, maximum likelihood estimation of the variance components under misspecification is consistent, although in finite samples, both the bias and mean squared error can be substantial. For inference, we show through theory and simulation that under misspecification, standard likelihood ratio tests of truly non‐zero variance components can suffer from severely inflated type I errors, and confidence intervals for the variance components can exhibit considerable under coverage. Furthermore, neither of these problems vanish asymptotically with increasing the number of clusters or cluster size. These results have major implications for random effects inference, especially if the true random effects distribution is heavier tailed than the normal. Fortunately, simple graphical and goodness‐of‐fit measures of the random effects predictions appear to have reasonable power at detecting misspecification. We apply linear mixed models to a survey of more than 4 000 high school students within 100 schools and analyse how mathematics achievement scores vary with student attributes and across different schools. The application demonstrates the sensitivity of mixed model inference to the true but unknown random effects distribution.     

“北斗”救生终端海上自适应功率退避算法

受海况、浸水、结冰、对星等因素影响，飞行员海上遇险后报位可靠性较低，工作时长较短。为此，提出一种自适应功率退避方法，在对报位影响因素分析基础上，利用深度神经网络，通过对经纬度、高度、俯仰角、方向、加速度、速度、浸水、信号、电量等多维度数据进行学习，拟合出覆冰、浪涌、液面等信号特征与环境特征联想模型，自适应控制信号发射时机，实现功耗的有效控制。试验表明，该方法将通信成功率及工作时长分别由36.3%、6.0 h提升至73.3%、8.6 h。