引入微元法思想的混合测控资源联合调度方法

针对混合测控资源联合调度中存在的资源属性繁多、偏好差异大以及可能出现的调度弧段冲突等问题,借鉴单体制测控网调度研究中的任务需求描述方法,对混合测控任务需求的特点进行分析,给出其任务需求的规范化描述。以测控调度收益值最大为目标,建立混合资源联合调度模型;引入微元法思想,提出了可用弧段扩展复用方法,以此为基础提出了基于改进遗传算法的求解策略。仿真结果表明：所建模型和相应的解冲突算法能有效提高测控需求满足率和测控收益。     

Robust model rankings of forecasting performance

This paper investigates robust model rankings in out‐of‐sample, short‐horizon forecasting. We provide strong evidence that rolling window averaging consistently produces robust model rankings while improving the forecasting performance of both individual models and model averaging. The rolling window averaging outperforms the (ex post) “optimal” window forecasts in more than 50% of the times across all rolling windows.     

Exchange rate forecasting and the performance of currency portfolios

We examine the potential gains of using exchange rate forecast models and forecast combination methods in the management of currency portfolios for three exchange rates: the euro versus the US dollar, the British pound, and the Japanese yen. We use a battery of econometric specifications to evaluate whether optimal currency portfolios implied by trading strategies based on exchange rate forecasts outperform single currencies and the equally weighted portfolio. We assess the differences in profitability of optimal currency portfolios for different types of investor preferences, two trading strategies, mean squared error‐based composite forecasts, and different forecast horizons. Our results indicate that there are clear benefits of integrating exchange rate forecasts from state‐of‐the‐art econometric models in currency portfolios. These benefits vary across investor preferences and prediction horizons but are rather similar across trading strategies.     

Google Trends and the forecasting performance of exchange rate models

In this paper, we use Google Trends data for exchange rate forecasting in the context of a broad literature review that ties the exchange rate movements with macroeconomic fundamentals. The sample covers 11 OECD countries’ exchange rates for the period from January 2004 to June 2014. In out‐of‐sample forecasting of monthly returns on exchange rates, our findings indicate that the Google Trends search query data do a better job than the structural models in predicting the true direction of changes in nominal exchange rates. We also observed that Google Trends‐based forecasts are better at picking up the direction of the changes in the monthly nominal exchange rates after the Great Recession era (2008–2009). Based on the Clark and West inference procedure of equal predictive accuracy testing, we found that the relative performance of Google Trends‐based exchange rate predictions against the null of a random walk model is no worse than the purchasing power parity model. On the other hand, although the monetary model fundamentals could beat the random walk null only in one out of 11 currency pairs, with Google Trends predictors we found evidence of better performance for five currency pairs. We believe that these findings necessitate further research in this area to investigate the extravalue one can get from Google search query data.     

The US Dollar/Euro Exchange Rate: Structural Modeling and Forecasting During the Recent Financial Crises

The paper investigates the determinants of the US dollar/euro within the framework of the asset pricing theory of exchange rate determination, which posits that current exchange rate fluctuations are determined by the entire path of current and future revisions in expectations about fundamentals. In this perspective, we innovate by conditioning on Fama–French and Carhart risk factors, which directly measures changing market expectations about the economic outlook, on new financial condition indexes and macroeconomic variables. The macro‐finance augmented econometric model has a remarkable in‐sample and out‐of‐sample predictive ability, largely outperforming a standard autoregressive specification. We also document a stable relationship between the US dollar/euro Carhart momentum conditional correlation (CCW) and the euro area business cycle. CCW signals a progressive weakening in economic conditions since June 2014, consistent with the scattered recovery from the sovereign debt crisis and the new Greek solvency crisis exploded in late spring/early summer 2015. Copyright © 2016 John Wiley & Sons, Ltd.     

二次母线接有谐波源时谐波电流危害预测估算的分析

具有谐波源的变电所母线,谐波电流的危害程度.可用文中(1).(8).(9)式和表2简捷地进行分析、判断谐波的谐振次数、母线电压畸变率和谐波电流允许值,借以说明谐波电流危害。并用该三式对谐波调研资料进行了分析与对比计算,结果完全符合实际情况,故三式具有普遍意义和应用价值。     

火电厂机组最优组合的一种实用算法

本文在文[1] 、[2] 的基础上,根据等微增率准则,对不考虑启动耗量的火电厂机组投入、切除的经济性问题进行了研究。通过引入切换微增率,切换负荷的概念,提出了一种确定火电厂经济运行最优机组组合的实用算法。根据计算结果,调度人员可以了解在什么负荷范围内应选用哪种机组组合方式才是经济的。     

对一般滤子情形下SLQ问题最优反馈的刻画

控制论中的一个基本问题是为系统设计反馈最优控制.这已在LQ问题中得到了很好的实现.但是,在已有的文献中,对这一问题的随机情形的讨论多集中在自然滤子情形.本文应用转置解这一概念在一般滤子情形下给出了带随机系数的SLQ问题最优反馈控制存在的充分条件,证明了对一维控制问题而言这还是必要条件.     

一种适用于建筑灾难救援系统的目标跟踪方法

在目标跟踪研究中,大量的实验证明粒子滤波算法对于运动轨迹变化复杂的运动目标跟踪效果不理想.基于变率粒子滤波算法(VRPF),以大型建筑灾难救援系统为研究背景,应用无线传感器网络技术,提出了一种自适应变率粒子滤波算法(AVRPF).该算法通过比较某时刻粒子值与测量值的关系自适应地调节采样周期,以提高对复杂运动目标的跟踪精度,尤其当运动轨迹出现大幅度变化时.仿真实验证明了该算法的有效性和实用性.     

氧化石墨烯/微胶囊混凝土抗硫酸盐侵蚀性能研究

为解决西北地区输变电工程对于混凝土材料耐久性和导电性的特殊需求,以硅酸钠和膨润土作为囊芯、乙基纤维素作为囊壁,物理法合成微胶囊。外掺氧化后石墨烯充当导电介质,制备混凝土复合材料标准试件。随后对试样进行硫酸盐浸泡和干湿循环试验,分析了硫酸盐侵蚀后混凝土的表观特征、质量损失率和力学性能。结果表明:微胶囊的加入显著提高了抗硫酸盐性和自愈性,含有乙基纤维素包封的胶囊混凝土表现出良好的抗硫酸盐性和自愈性。