光谱仪焦平面阵列非均匀性校正的研究

针对成像光谱仪相关技术要求,对辐射源定标焦平面阵列非均匀性校正的数学方法进行了深入研究,导出了适于成像光谱仪应用的相关算法并分析了其特点,实验结果表明焦平面阵列非均匀性校正算法能有效提高成像光谱仪干涉图的成像质量,显著提高光谱复原的精确性.     

非结构网格下两步压力校正算法的潮流模拟

针对非结构网格下的潮流数值计算,采用有限体积法离散基本方程,对流-扩散项离散采用幂率格式,交叉扩散项引入超松弛校正方法,水位校正方程应用Rhie-Chow动量插值思想和SIMPLEC类算法导出.采用两步压力校正法将交叉扩散项单独考虑,以提高模型对高度畸变网格的适应能力.计算结果表明,该处理方法可提高压力欠松弛系数,并能有效改善复杂区域潮流模拟的健壮性.通过对黄浦江感潮河段潮流的模拟,表明该模型的预测结果与实测资料吻合良好,模型对潮流的模拟结果是可靠的.     

我国货币政策时效性的协整分析

运用1978年至2003年的年度统计数据,对现金、狭义货币和广义货币与各主要经济变量之间因果关系进行Granger因果检验.在此基础上,具体分析相应货币供给量与主要经济变量之间的协整关系,建立误差修正模型.分析结果表明,货币供应量的变化对经济增长GDP的作用力不大,对消费和投资的影响力较小,货币供应量的变化对信贷的变化影响较大;从协整关系来看,信贷与货币供应量几乎同比例变化,两者的均衡误差也较大,货币供给对利率的影响也有较明显的滞后.     

Short‐term salmon price forecasting

This study establishes a benchmark for short‐term salmon price forecasting. The weekly spot price of Norwegian farmed Atlantic salmon is predicted 1–5 weeks ahead using data from 2007 to 2014. Sixteen alternative forecasting methods are considered, ranging from classical time series models to customized machine learning techniques to salmon futures prices. The best predictions are delivered by k‐nearest neighbors method for 1 week ahead; vector error correction model estimated using elastic net regularization for 2 and 3 weeks ahead; and futures prices for 4 and 5 weeks ahead. While the nominal gains in forecast accuracy over a naïve benchmark are small, the economic value of the forecasts is considerable. Using a simple trading strategy for timing the sales based on price forecasts could increase the net profit of a salmon farmer by around 7%.     

A Method on Non-Linear Correction of Broadband LFMCW Signal Utilizing Its Relative Sweep Non-Linear Error

1. INTaODUCTIONLinear frequency modulation continuous wave (LFMCW) radar with broadband has a high range Precisinlland high resollltion because of its large time bandwidth product and has no blind range at the'sarne take.LFMCW radax has more extensive applications in many fields such as microwave ranging, crashworthy car, andmissile's terminal guidance because broadband LFMCW devices are being miniaturized and integrated with therapid developmellt of microwave and millimeter wave ill…     

三维空间中圆锥、圆柱和平面交线的绘制

在三维空间中,要绘出任意圆锥、圆柱和任意平面的交线,一般先选取适当坐标系,得到交线的标准型方程,绘出后,再经过一组坐标变换,显示在当前屏幕上。本文通过分析圆柱、圆锥和平面的几何关系,推导出它们的交线的显式表示,从而给出在三维空间中直接绘制这类交线的方法。     

Forecast robustness in macroeconometric models

This paper investigates potential invariance of mean forecast errors to structural breaks in the data generating process. From the general forecasting literature, such robustness is expected to be a rare occurrence. With the aid of a stylized macro model we are able to identify some economically relevant cases of robustness and to interpret them economically. We give an interpretation in terms of co‐breaking. The analytical results resound well with the forecasting record of a medium‐scale econometric model of the Norwegian economy.     

Bias‐corrected bootstrap prediction intervals for autoregressive model: new alternatives with applications to tourism forecasting

This paper proposes the use of the bias‐corrected bootstrap for interval forecasting of an autoregressive time series with an arbitrary number of deterministic components. We use the bias‐corrected bootstrap based on two alternative bias‐correction methods: the bootstrap and an analytic formula based on asymptotic expansion. We also propose a new stationarity‐correction method, based on stable spectral factorization, as an alternative to Kilian's method exclusively used in past studies. A Monte Carlo experiment is conducted to compare small‐sample properties of prediction intervals. The results show that the bias‐corrected bootstrap prediction intervals proposed in this paper exhibit desirable small‐sample properties. It is also found that the bootstrap bias‐corrected prediction intervals based on stable spectral factorization are tighter and more stable than those based on Kilian's stationarity‐correction. The proposed methods are applied to interval forecasting for the number of tourist arrivals in Hong Kong. Copyright © 2010 John Wiley & Sons, Ltd.     

Beating the VAR: Improving Swedish GDP Forecasts Using Error and Intercept Corrections

This paper examines the forecast accuracy of an unrestricted vector autoregressive (VAR) model for GDP, relative to a comparable vector error correction model (VECM) that recognizes that the data are characterized by co‐integration. In addition, an alternative forecast method, intercept correction, is considered for further comparison. Recursive out‐of‐sample forecasts are generated for both models and forecast techniques. The generated forecasts for each model are objectively evaluated by a selection of evaluation measures and equal accuracy tests. The result shows that the VECM consistently outperforms the VAR models. Further, intercept correction enhances the forecast accuracy when applied to the VECM, whereas there is no such indication when applied to the VAR model. For certain forecast horizons there is a significant difference in forecast ability between the intercept corrected VECM compared to the VAR model. Copyright © 2015 John Wiley & Sons, Ltd.     

准同期参数测量算法研究

在发电机并网过程中,对信号的频率、相位、幅值等参数要进行快速、准确的测量,传统的测量方法参数误差比较大.本文提出利用窗函数及频谱校正的DFT算法来对信号进行参数测量,仿真结果表明,该算法不需要对电压信号进行整周期采样,可以对频率在较大范围内变化的信号进行准确测量,从而较好的解决了由于并列双方电压的频率不断变化而引起的测量误差大的问题.