光接收机调谐前端的分析

为了设计高性能的谐振式光接收机,应用场效应管模型和多种调谐网络的拓扑形式,提出了一个统一的光接收机调谐前端分析方法。推导出了对多种调谐网络通用的计算公式,用以计算谐振频率、传输函数和等效输入噪声电流密度,便于应用计算机优化光接收机的电路拓扑形式。使用该方法,编制计算机辅助程序模拟了各种不同型式的电感谐振式光接收机的特性,对于光接收机前端设计具有指导作用。     

基于统计分析的绩效评价方法研究

对绩效进行评价,指标的层次结构及其权重的确定直接影响评价指标体系的实用性和评价结果的客观性与科学性．为确保分析结果客观、科学,在分析了层次分析法分层和权重确定时存在不足的基础上,提出一种新方法．首先,运用统计分析中的因子分析方法,为指标进行分层归类;其次,根据因子分析中子因子在主因子上的载荷,确定各个子因子在其主因子上的权重值;再次,根据子因子在其主因子上的权重值,并结合各主因子在总目标（一级指标）上的贡献率,确定出各个子因子在总目标上的权重值;最后,通过实例证明了方法的实用性．     

结构损伤识别的周期小波方法

通过对结构损伤前后模态特征的变化进行分析,利用损伤结构的位移振型函数构造出曲率振型函数γi(x),然后再在曲率振型函数的基础上提出一个用于探测和评定损伤程度的新指标———损伤因子r(x)=∑i∈Idγi2(x);基于小波分析的方法,对损伤因子进行周期小波变换,通过小波系数的取值范围提出了一种结构损伤定位和损伤程度的评估方法.数值仿真表明,这是一种行之有效的损伤识别方法.     

空域色噪声背景下双基地MIMO雷达角度估计

空域有色噪声会导致现有多输入多输出(multiple input multiple output, MIMO)雷达算法性能下降, 甚至完全失效。针对空域色噪声背景下双基地MIMO雷达联合波离角(direction of departure, DOD)和波达角(direction of arrival, DOA)估计问题, 分析了现有算法失效的原因。考虑到匹配滤波后无噪协方差矩阵的低秩特性、色噪声协方差矩阵的稀疏特性以及MIMO雷达数据的多维结构特性, 提出一种基于张量分析的角度估计算法。首先, 构造角度估计的协方差张量, 通过去除协方差张量中受噪声协方差影响的元素对色噪声进行抑制。其次,利用张量填充技术对无噪协方差矩阵进行恢复。然后,利用平行因子分解获得目标角度的方向矩阵。最后, 采用最小二乘算法对目标的DOA和DOD进行拟合。仿真结果表明, 所提算法对色噪声不敏感, 且无孔径损失。相比现有矩阵及张量分析算法, 所提算法具有更高的估计精度。     

周期性需求下多级库存缺货控制模型研究

从一个以区域分销中心(DC)为主导地位且由多个供应商与零售商共同组成的多级库存系统出发,系统在假设DC和零售商都实行连续性盘点的(R,Q)库存控制策略,提前期为常量,零售商需求为正态分布的前提下,采用缺货策略的思想来确定订货临界点及订货批量.并且以确定DC的库存策略为目标,建立了缺货条件下的多级库存控制模型,从而达到有效控制库存的目的.最后,给出了一个算例,说明了该库存控制模型的可行度.     

Forecasting and nowcasting emerging market GDP growth rates: The role of latent global economic policy uncertainty and macroeconomic data surprise factors

In this paper, we assess the predictive content of latent economic policy uncertainty and data surprise factors for forecasting and nowcasting gross domestic product (GDP) using factor-type econometric models. Our analysis focuses on five emerging market economies: Brazil, Indonesia, Mexico, South Africa, and Turkey; and we carry out a forecasting horse race in which predictions from various different models are compared. These models may (or may not) contain latent uncertainty and surprise factors constructed using both local and global economic datasets. The set of models that we examine in our experiments includes both simple benchmark linear econometric models as well as dynamic factor models that are estimated using a variety of frequentist and Bayesian data shrinkage methods based on the least absolute shrinkage operator (LASSO). We find that the inclusion of our new uncertainty and surprise factors leads to superior predictions of GDP growth, particularly when these latent factors are constructed using Bayesian variants of the LASSO. Overall, our findings point to the importance of spillover effects from global uncertainty and data surprises, when predicting GDP growth in emerging market economies.     

考虑关键性的多约束下辐射干扰对消装备随舰备件配置优化方法

可用度是评估备件对装备完好性影响的重要指标,针对备件关键性对装备可用性造成影响而又难以将其考虑在内建立可用度模型的问题,以舰载通信电台辐射干扰对消装备为对象,采用对备件关键性实行专家打分的方法,将备件关键性作为备件配置优化的约束之一,建立以备件费用、备件体积、备件关键性为约束的备件配置优化模型。在备件费用、体积及关键性归一化的基础上,采用权重因子方法将多约束转化为单一约束,并给出了权重因子更新方法。列举案例,分别计算了费用约束、体积约束、关键性约束和多约束下的备件方案,并对比了装备改进前后的备件配置方案,将装备使用过程中的备件保障延伸至装备可靠性设计阶段。案例分析结果表明:备件配置方法在考虑了体积、费用等限制约束下,能同时兼顾备件关键性对装备完好性的影响;装备可靠性设计时,提高装备可靠性低的现场可更换单元(line replaceable unit, LRU)等薄弱点,能有效减少装备维修保障资源。     

中国经济增长与杠杆率的非线性关系研究--基于制造业面板数据门限回归分析

传统理论基于有效市场理论(EMH)主要从公司财务视角研究杠杆率问题,忽略了生产环节的决定机制.本文从决定生产效率的全要素生产率视角对杠杆率的决定机制重新认识.并采用制造业29个行业1995-2016年的面板数据,以杠杆率为门限变量构建动态面板门限模型,主要研究结论为:1)经济增长-杠杆率的门限值位于名义杠杆率114,在门限值左侧,杠杆率增加促进经济增长,在门限值右侧,杠杆率增加"拖累"经济增长,杠杆率对经济增长整体表现"倒U"型关系;2)经济增长-杠杆率的泡沫杠杆率门限值位于泡沫杠杆率65,当名义杠杆率包含的泡沫杠杆率低于65时,杠杆率增加对经济具有显著的促进效应,当包含的杠杆率大于65时,杠杆率对经济增长的作用并不明显,说明经济可能进入了滞胀阶段;3)经济增长-足值杠杆率的关于对泡沫杠杆率存在双门门限效应,分别是43、49,当泡沫杠杆率小于43时,增加泡沫杠杆率对经济的增长的促进效应小于泡沫杠杆率介于[43,49]区间的效应,当泡沫杠杆率大于49时,泡沫杠杆率对经济增长的促进作用不再明显.     

Fabrication of solenoid-type microinductor with different magnetic core schemes by MEMS technique

0 IntroductionThere has been a great demand on micro DC/DCconverter for the applications of the portable electronicproducts such as mobile communication product CDMA(Code Division Multiple Access) ,portable notebookcom-puter ,microprocessor ,digital camcorder andso on.Mag-netic components such as inductors and transformers arethe essential components in constructingthe power supplytransformer ,filter , oscillator , amplifier and micro DC/DCconverter ,andthe miniaturization of inductor an…     

Methods for backcasting,nowcasting and forecasting using factor‐MIDAS: With an application to Korean GDP

We utilize mixed‐frequency factor‐MIDAS models for the purpose of carrying out backcasting, nowcasting, and forecasting experiments using real‐time data. We also introduce a new real‐time Korean GDP dataset, which is the focus of our experiments. The methodology that we utilize involves first estimating common latent factors (i.e., diffusion indices) from 190 monthly macroeconomic and financial series using various estimation strategies. These factors are then included, along with standard variables measured at multiple different frequencies, in various factor‐MIDAS prediction models. Our key empirical findings as follows. (i) When using real‐time data, factor‐MIDAS prediction models outperform various linear benchmark models. Interestingly, the “MSFE‐best” MIDAS models contain no autoregressive (AR) lag terms when backcasting and nowcasting. AR terms only begin to play a role in “true” forecasting contexts. (ii) Models that utilize only one or two factors are “MSFE‐best” at all forecasting horizons, but not at any backcasting and nowcasting horizons. In these latter contexts, much more heavily parametrized models with many factors are preferred. (iii) Real‐time data are crucial for forecasting Korean gross domestic product, and the use of “first available” versus “most recent” data “strongly” affects model selection and performance. (iv) Recursively estimated models are almost always “MSFE‐best,” and models estimated using autoregressive interpolation dominate those estimated using other interpolation methods. (v) Factors estimated using recursive principal component estimation methods have more predictive content than those estimated using a variety of other (more sophisticated) approaches. This result is particularly prevalent for our “MSFE‐best” factor‐MIDAS models, across virtually all forecast horizons, estimation schemes, and data vintages that are analyzed.