正极性操作冲击电压下长短空气间隙放电特性

现场真型试验是考核长空气间隙绝缘性能的关键环节,但由于电板结构各异、布置复杂等实际条件限制,试验对象一般为典型间隙结构,并不能涵盖所有实际工程问题.中外学者均在努力探索长、短间隙试验的内在联系,通过研究不同间隙距离下放电各阶段异同点,实现对放电特性的精细掌握.搭建了由光电倍增管、电场传感器组成的光电联合测量系统,获得了操作冲击电压下5 cm和3 m针-板间隙放电发展过程中的空间场强、瞬时光功率等信号,分析得到了初始流注起始时延、暗期持续时间、初始流注电场跃升幅值和初始流注光功率跃升幅值等关键特征参数.结果 表明:5 cm与3m针-板间隙流注起始时延、暗期持续时间、场强跃升幅值相近,且光功率跃升幅值均与场强跃升幅值呈正相关的关系,长、短间隙在初始流注和暗期阶段呈现一定相似性;另外,5 cm间隙的特征参数方差均小于3 m间隙,短间隙下的放电随机性较小.获得的5 cm和3 m间隙距离下放电发展过程特征参数及各特征参量变化规律,为后期长、短间隙放电特性的电磁学仿真提供了准确的计算参数.     

基于Box-Cox变换分位数回归与负荷关联因素辨识的中长期概率密度预测

中长期电力负荷预测是电力部门制定电力系统发展规划和稳定运行的重要前提.针对影响中长期电力负荷预测精度的多个因素,本文利用逐步回归方法,从众多影响负荷预测精度的关联因子中,对关键的影响因子进行辨识,并提出基于Box-Cox变换分位数回归和核密度估计相结合的概率密度预测方法,得出不同分位点下未来连续几年的概率密度预测结果,实现了对未来年用电量准确波动区间的预测.以安徽省的历史用电量和社会经济数据为例,进行仿真实验.结果表明:该方法不仅实现了中长期电力负荷概率密度预测,而且利用强关联因素提高了中长期电力负荷概率密度预测的精度,有效解决了考虑多因子的中长期电力负荷概率密度预测问题.     

Forecasting the duration of short‐term deflation episodes

The paper proposes a simulation‐based approach to multistep probabilistic forecasting, applied for predicting the probability and duration of negative inflation. The essence of this approach is in counting runs simulated from a multivariate distribution representing the probabilistic forecasts, which enters the negative inflation regime. The marginal distributions of forecasts are estimated using the series of past forecast errors, and the joint distribution is obtained by a multivariate copula approach. This technique is applied for estimating the probability of negative inflation in China and its expected duration, with the marginal distributions computed by fitting weighted skew‐normal and two‐piece normal distributions to autoregressive moving average ex post forecast errors and using the multivariate Student t copula.     

互助担保:连带责任、互助保证金与成员努力程度

构建了针对现行互助担保模式的简单模型，分析了在互助担保中连带责任以及互助保证金对成员企业努力程度的影响.为了进一步激励成员企业的努力程度，提出增加监督惩罚机制和次序担保贷款机制，并通过建立动态激励模型验证了这些机制的有效性.研究结果如下:在现行的互助担保机制下，连带责任越大成员企业努力程度越低，且保证金越多成员企业努力程度越低，而引入监督惩罚机制以及次序贷款机制能够一定程度避免成员企业低努力程度行为的发生.     

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.     

Forecasting in long horizons using smoothed direct forecast

This paper constructs a forecast method that obtains long‐horizon forecasts with improved performance through modification of the direct forecast approach. Direct forecasts are more robust to model misspecification compared to iterated forecasts, which makes them preferable in long horizons. However, direct forecast estimates tend to have jagged shapes across horizons. Our forecast method aims to “smooth out” erratic estimates across horizons while maintaining the robust aspect of direct forecasts through ridge regression, which is a restricted regression on the first differences of regression coefficients. The forecasts are compared to the conventional iterated and direct forecasts in two empirical applications: real oil prices and US macroeconomic series. In both applications, our method shows improvement over direct forecasts.     

Enhancing survey‐based investment forecasts

We investigate the accuracy of capital investment predictors from a national business survey of South African manufacturing. Based on data available to correspondents at the time of survey completion, we propose variables that might inform the confidence that can be attached to their predictions. Having calibrated the survey predictors' directional accuracy, we model the probability of a correct directional prediction using logistic regression with the proposed variables. For point forecasting, we compare the accuracy of rescaled survey forecasts with time series benchmarks and some survey/time series hybrid models. In addition, using the same set of variables, we model the magnitude of survey prediction errors. Directional forecast tests showed that three out of four survey predictors have value but are biased and inefficient. For shorter horizons we found that survey forecasts, enhanced by time series data, significantly improved point forecasting accuracy. For longer horizons the survey predictors were at least as accurate as alternatives. The usefulness of the more accurate of the predictors examined is enhanced by auxiliary information, namely the probability of directional accuracy and the estimated error magnitude.     

基于修正斜压场的RC框架边节点剪切性能分析

为研究钢筋屈服强度和混凝土抗压强度对钢筋混凝土(RC)框架边节点剪切性能的影响规律,基于修正斜压场理论(MCFT),对某框架边节点的剪切应力-应变骨架曲线进行数值模拟,并与试验结果进行对比.在验证MCFT算法的精度后,研究钢筋屈服强度和混凝土抗压强度两个参数变化对RC框架边节点剪切应力-应变骨架曲线和峰值剪切应力的影响规律.结果表明:基于MCFT可以较好地模拟RC框架边节点的剪切应力-应变关系曲线; RC框架边节点的峰值剪切应力对混凝土抗压强度的变化更为敏感,但当试件的钢筋(HRB400)屈服应力下降幅度超过16.45%时,对钢筋屈服应力的变化更为敏感;当试件的钢筋(HRB400)屈服应力下降幅度超过10%时,节点发生钢筋滑移破坏,此时峰值剪切应力会大幅下降.     

2021年经济景气形势监测、分析和预测

我国经济增长在遭遇前所未有的疫情冲击后进入为期一年的超常快速扩张期，并于2021年2月形成扩张高峰，此后转入经济周期收缩阶段。2020年11月至2021年7月经济运行已恢复至“正常”景气区间，但需求端的恢复弱于供给端。2021年3季度的综合警情指数明显下滑，发出“偏冷”预警信号，且4季度可能继续下行,但物价总体保持稳定。预计全年GDP增长8.1%左右（两年平均增长5.2%左右），全年CPI上涨0.9%左右。建议宏观调控应做好跨周期设计，注意处理好稳增长、防风险和节能环保的关系，提高疫情应对的精准性，适度加大稳增长力度，努力保持经济在合理区间的平稳运行。     

基于跟踪信息的多帧联合检测方法

传统多帧联合检测方法针对搜索阶段进行设计, 由于无任何目标信息, 需要采用较高的检测门限以降低虚警数量。然而, 一旦成功建航, 目标存在性已确定, 可利用目标跟踪信息调整多帧联合检测策略, 以提升微弱目标检测能力。基于上述考虑, 本文提出了一种基于跟踪信息的多帧联合检测方法。首先利用当前时刻跟踪信息建立预测波门, 然后设定较低的单帧检测门限保障微弱目标信号的高概率检测, 最后在航迹恒虚警约束下调整多帧联合检测门限, 从而完成航迹维持和撤销。仿真实验表明所提方法可有效提高目标检测概率和跟踪维持概率。