神龙一号直线感应加速器X光转换靶破坏诊断

 利用能量约450 keV、焦斑直径1~4 mm的低能X光对神龙一号直线感应加速器束靶作用后钽靶的破坏进行诊断,利用增强型电荷耦合器件（ICCD）对诊断过程记录,得到束靶作用后数μs时间内钽靶材料密度的变化。结果表明：在束靶作用后约1 μs内靶材料密度基本没有变化,且该时间段内ICCD相机没有观察到有靶前钽靶材料的微粒喷射。     

基于无衍射光的表面粗糙度三角测量及其灰色评定方法

 激光三角法是表面形貌非接触测量中的一种常用方法,在几何测量领域应用广泛。传统的激光三角法采用高斯光束作为指示光源,在机械扫描机构的配合下,通过对被测表面逐点扫描完成表面形貌的测量。采用新型的无衍射光替代传统激光光源,解决了普通高斯光束存在的“焦深”问题,简化了机械结构。并采用基于灰色系统理论的灰色滤波进行表面形貌的分离与评定,克服了原有方法对测量数据样本量和统计特性的依赖,并通过实验表明该系统能够准确地完成表面形貌的三维测量,所提出的灰色评定方法能够比较有效地进行表面形貌的分离与评定。     

加偏压中心对称双光子光折变晶体中多变量空间灰孤子

 为了得到有偏压的中心对称双光子光折变晶体中存在多变量空间灰孤子的结果,基于中心对称双光子光折变晶体中空间灰孤子的基本理论,采用数值方法推导出了中心对称双光子多变量空间灰孤子归一化包络解的积分形式,并对其特性进行研究。结果表明：这种多变量空间灰孤子是由多束偏振方向和波长都相同的互不相干光束耦合形成的。当多变量空间灰孤子只包含有1个或2个光束分量成分时,它自动退化到中心对称双光子空间灰孤子或中心对称双光子非相干耦合灰 灰空间孤子对的情况。当这一多变量空间灰孤子在有偏压的中心对称双光子光折变晶体中传播时,各分量成分光束都能稳定传播。     

Forecasting the volatility of crude oil futures using intraday data

We use the information in intraday data to forecast the volatility of crude oil at a horizon of 1–66 days using a variety of models relying on the decomposition of realized variance in its positive or negative (semivariances) part and its continuous or discontinuous part (jumps). We show the importance of these decompositions in predictive (in-sample) regressions using a number of specifications. Nevertheless, an important empirical finding comes from an out-of-sample analysis which unambiguously shows the limited interest of considering these components. Overall, our results indicates that a simple autoregressive specification mimicking long memory and using past realized variances as predictors does not perform significantly worse than more sophisticated models which include the various components of realized variance.     

二维区间密度加权算子及其应用

针对不确定多属性决策中的属性信息分布不均匀,且评价信息多数为二维信息的情况,本文提出了二维区间密度加权算子(TDIDW算子)的属性信息集结方法.依据密度算子的集结过程特点,文章首先定义了二维区间密度加权算子及其合成算子,然后介绍了基于灰色区间聚类法的评价信息分组方法以及基于非线性模型的密度加权向量确定方法,最后进行了算例验证.验证结果表明,该方法可以有效地解决由于属性信息分布不均匀而导致评价结果不准确的问题.     

Forecasting retained earnings of privately held companies with PCA and L1 regression

We use proprietary data collected by SVB Analytics, an affiliate of Silicon Valley Bank, to forecast the retained earnings of privately held companies. Combining methods of principal component analysis (PCA) and L¹/quantile regression, we build multivariate linear models that feature excellent in‐sample fit and strong out‐of‐sample predictive accuracy. The combined PCA and L¹ technique effectively deals with multicollinearity and non‐normality of the data, and also performs favorably when compared against a variety of other models. Additionally, we propose a variable ranking procedure that explains which variables from the current quarter are most predictive of the next quarter's retained earnings. We fit models to the top five variables identified by the ranking procedure and thereby, discover interpretable models with excellent out‐of‐sample performance. Copyright © 2013 John Wiley & Sons, Ltd.     

Forecasting stock index price based on M-factors fuzzy time series and particle swarm optimization

In real time, one observation always relies on several observations. To improve the forecasting accuracy, all these observations can be incorporated in forecasting models. Therefore, in this study, we have intended to introduce a new Type-2 fuzzy time series model that can utilize more observations in forecasting. Later, this Type-2 model is enhanced by employing particle swarm optimization (PSO) technique. The main motive behind the utilization of the PSO with the Type-2 model is to adjust the lengths of intervals in the universe of discourse that are employed in forecasting, without increasing the number of intervals. The daily stock index price data set of SBI (State Bank of India) is used to evaluate the performance of the proposed model. The proposed model is also validated by forecasting the daily stock index price of Google. Our experimental results demonstrate the effectiveness and robustness of the proposed model in comparison with existing fuzzy time series models and conventional time series models.     

基于主成分—灰色关联度的黑龙江省城镇化水平综合评价

从城市和农村两方面入手,构建了城镇化水平综合评价指标体系.由于主成分分析法和灰色关联分析法各有优缺点,所以这里将主成分分析法和灰色关联分析法进行融合,在规避了两种方法的缺点后,对黑龙江省各市的城镇化水平进行了排序.最后对排序结果进行分析,给出了未来黑龙江省各市城镇化发展方向的一些建议.     

基于证据推理的混合型多指标群组决策方法

针对决策方案的属性值为语言评价等级和区间灰数的灰色多指标群组决策问题,提出一种基于证据推理的灰色多指标群组决策方法.首先,根据语言评价信息的概率分布和效用值等价原理确定定性指标和定量指标的信用结构,进而得到群体等级信用结构决策矩阵,然后,依据证据推理方法,对群组评价信息进行合成,求出各方案在各等级的信任度,最后,利用期望方差排序方法确定整个方案集的排序.具体算例表明方法合理有效.     

用不确定德尔菲法预测GDP

德尔菲法是一种建立在专家意见基础上的预测评估方法.不确定统计是利用不确定理论收集和整理分析专家数据的一种统计方法,其中关键的一点是如何构造不确定变量的不确定分布.把德尔菲法和不确定统计相结合,就得到了一种估计不确定分布的新方法——不确定德尔菲法.对该方法的估计误差进行了改进,得到了一种预测GDP的新方法,并利用其预测邯郸市的生产总值(GDP).