Constructing and evaluating core inflation measures from component‐level inflation data

This paper undertakes a comprehensive examination of 10 measures of core inflation and evaluates which measure produces the best forecast of headline inflation out‐of‐sample. We use the Personal Consumption Expenditure Price Index as our measure of inflation. We use two sets of components (17 and 50) of the Personal Consumption Expenditure Price Index to construct these core inflation measures and evaluate these measures at the three time horizons (6, 12 and 24 months) most relevant for monetary policy decisions. The best measure of core inflation for both sets of components and over all time horizons uses weights based on the first principal component of the disaggregated (component‐level) prices. Interestingly, the results vary by the number of components used; when more components are used the weights based on the persistence of each component is statistically equivalent to the weights generated by the first principal component. However, those forecasts using the persistence of 50 components are statistically worse than those generated using the first principal component of 17 components. The statistical superiority of the principal component method is due to the fact that it extracts (in the first principal component) the common source of variation in the component level prices that accurately describes trend inflation over the next 6–24 months.     

基于三维点云的岩体结构面自动分类与参数计算

结构面间距是岩体稳定性和力学特性分析中的一个重要参数，在岩石力学、采矿工程、边坡监测等领域的数值计算中广泛应用.本文以岩体边坡露头为研究对象，基于非接触测量获得的三维点云数据，提出一种基于密度聚类的结构面细化分类方法；在结构面粗略分组提取的基础上，通过投影变换、散乱点拟合等算法，求得结构面间距和岩体体积节理数.设计开发了结构面细化分类及间距等参数计算与分析原型系统，实际案例分析表明，本文方法可有效实现结构面的自动细化分类，并能够计算出间距等相关参数，可为岩体质量分级和岩体稳定性分析等提供方法支撑.     

中国和美国信贷冲击跨境溢出效应的比较研究

本文构建了基于符号约束识别的GVAR模型,并用以考察中国和美国的信贷市场冲击对全球41个国家的不同溢出效应.研究结果表明,中国紧缩性信贷市场冲击对本国实体经济有显著的负效应,但仅限于短中期;而美国信贷冲击对本国乃至全球经济都具有相当大且持久的负影响.中国信贷冲击的跨国效应较小,且主要通过贸易渠道.中国信贷市场的本国冲击能解释中国产出近10%的波动,但无论是本国效应还是跨境传导,总需求冲击仍是驱动中国经济周期波动的最主要力量.     

基于模糊证据推理的突发事件风险分析模型

突发事件发生、发展和演化过程中的风险属性来源复杂,且相关信息具有不确定、不完备的特点.为准确分析这一特点下的突发事件运行风险问题,引入模糊证据推理构建分析模型.首先根据突发事件的结构化描述框架和故障模式影响危害分析(FMECA)方法构建突发事件风险属性识别框架,获取信息作为风险分析证据并对其进行规范化处理;然后以模糊证据推理构建突发事件风险分析的模型,通过计算,给出事件风险程度的合理判断;最后选取CRH某型动车组行车安全事件进行实例分析.分析结果表明,在分析突发事件风险时,针对信息不确定、不完备特点构建的模糊证据推理模型具有可行性与有效性,同时该模型能够为突发事件风险分析和风险应对决策的制定提供辅助参考.     

复杂结构簇合物的新稠合规则

提出了计算复杂结构族合物价电子数 (NVE)的公式 ,并提出了应用该公式的几条规则 .同时 ,把公式与著名的Mingos稠合规则进行了简单的比较     

有限元法的一般思想

介绍了有限元法的基本思想，探讨了能量法求解的一般思路，并简要说明了杆系结构求解的一般思路。     

An Empirical Study of Effects of Chinese Government＇s Tax Revenue and Expenditure on Its GDP＇s Growth

Public spending is a major policy tool for the government to adjust the macro economic progress. This paper characterizes the effects of shocks in government spending and taxes on Chinese economy growth since 1978. It does so by using a mixed structural VAR （SVAR） approach. Identification is achieved by using institutional information about the tax and spending to identify automatic and discretionary response, and by implication, to infer fiscal shocks. The results show positive government spending shocks as having a positive effect on output, and positive tax shocks as having a negative effects.     

A NEW DESCRIPA NEW DESCRIPTOR OF AMINO ACIDS BASED ON THE THREEDIMENSIONAL VECTOR OF ATOMIC INTERACTION FIELD

QUANTITATIVE STRUCTURE ACTIVITY RELATIONSHIP (QSAR) IS A VERY IMPORTANT TOPIC FOR MANY SCIENTIFIC FIELDS AND CAN BE TERMED AS QUANTITATIVE SEQUENCE ACTIVITY MODELS(QSAM)[1] WHEN REFERRED TO THE RESEARCH ON RELATION- SHIPS BETWEEN STRUCTURES AND ACTIVITIES OF BIOLOGICAL MOLECULES, SUCH AS PROTEINS, AND NUCLEIC ACIDS. HOW- EVER, IT IS VERY DIFFICULT TO ESTABLISH A QSAM MODEL,…     

识别混凝土板内部缺陷的人工神经网络算法

以一维有缺陷混凝土板为研究对象，分别采用Leverberg-Marcluardt和径向基神经网络算法，对缺陷的深度与厚度进行识别，从而实现对混凝土板内部缺陷的三维重构，称为红外CT模拟．两类神经网络算法的识别结果表明：Leverberg-Marcluardt神经网络较径向基神经网络具有更好的收敛精度与计算效率．     

The detection of structural deformation errors in attitude determination

In the determination of the attitude parameters from a multi-antenna GPS array, one of the major assumptions is that the body frame is rigid at all times. If this assumption is not true then the derived attitude parameters will be in error. It is well known that in airborne platforms the wings often experience some displacement during flight, especially during periods of initializing maneouvres, such as taking off, landing,and banking. Often it is at these points in time that it is most critical to have the most precise attitude parameters.There are a number of techniques available for the detection of modeling errors.The CUSUM algorithm has successfully been implemented in the past to detect small persistent changes. In this paper the authors investigate different methods of generating the residuals, to be tested by the CUSUM algorithm, in an effort to determine which technique is best suited for the detection of structural deformation of an airborne platform. The methods investigated include monitoring the mean of the residuals generated from the difference between the known body frame coordinates, and those calculated from the derived attitude parameters. The generated residuals are then passed to a CUSUM algorithm to detect any small persistent changes. An alternative method involves transforming the generated residuals into the frequency domain through the use of the Fast Fourier Transform. The CUSUM algorithm is then used to detect any frequency changes. The final technique investigated involves transforming the generated residuals using the Haar wavelet. The wavelet coefficients are then monitored by the CUSUM algorithm in order to detect any significant change to the rigidity of the body frame.Detecting structural deformation, and quantifying the degree of deformation, during flight will ensure that these effects can be removed from the system, thus ensuring the most precise and reliable attitude parameter solutions. This paper, through a series ofsimulations, will assess the effectiveness of the above mentioned techniques for detecting structural deformation effects on a GPS multi-antenna array. These principles are then tested with experimental data.