基于仿真驱动的高速公路主动限速效用评价与推荐

传统的路侧被动限速方式对于特定的惩处区域以外缺少管控,间接导致车辆行为在时空上的不一致性甚至突变,影响了交通的通行效率与安全性。从车侧主动限速方式入手,提出主动限速效用评价与推荐方法,结合道路线形、交通流量、车型比例,开展多情景主动、被动限速交通仿真,利用安全间接分析模型及交通流运行状态,从安全与效率2个层面提取效用评价指标及其权重,采用集成学习方法进行预测分析。结果显示：主动限速方式相较于被动限速方式更有利于提高安全性和调节效率,而在主动限速方面,GBDT(gradient boosting decision tree)回归模型的预测稳定性和准确率更高(R²=0.984)。     

空中联合投送任务规划仿真与效能评估系统

空中投送体系在现代战争中发挥着重要作用。与美俄等军事强国相比,我国空中投送体系还存在明显的短板与不足。针对投送体系论证与使用分析需求,围绕运输机与货物、机场、航线之间的交互关系以及投送体系内多机型的协同关系,开发建立了一套具有模型构建与管理、想定编辑、运输任务规划、仿真推演、效能评估等模块的投送体系仿真与分析系统,可实现装载方案自动生成、任务流程自动规划、多机型组合使用优选、多任务动态调度等功能,能为我国未来投送体系与投送装备的发展论证提供技术手段。     

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.     

On the Modelling and Forecasting of Multivariate Realized Volatility: Generalized Heterogeneous Autoregressive (GHAR) Model

Recent multivariate extensions of the popular heterogeneous autoregressive model (HAR) for realized volatility leave substantial information unmodelled in residuals. We propose to employ a system of seemingly unrelated regressions to model and forecast a realized covariance matrix to capture this information. We find that the newly proposed generalized heterogeneous autoregressive (GHAR) model outperforms competing approaches in terms of economic gains, providing better mean–variance trade‐off, while, in terms of statistical precision, GHAR is not substantially dominated by any other model. Our results provide a comprehensive comparison of the performance when realized covariance, subsampled realized covariance and multivariate realized kernel estimators are used. We study the contribution of the estimators across different sampling frequencies, and show that the multivariate realized kernel and subsampled realized covariance estimators deliver further gains compared to realized covariance estimated on a 5‐minute frequency. In order to show economic and statistical gains, a portfolio of various sizes is used. Copyright © 2016 John Wiley & Sons, Ltd.     

基于能力分析的空间对抗装备体系效能评估方法

首先研究了空间对抗的核心能力要素;然后研究了空间对抗装备体系的构成,即将核心能力要素细化为基本能力要素并映射到空间对抗装备子系统;最后构建由作战能力指标→核心能力指标及权重赋值→基本能力指标及权重赋值→单项效能指标及权重赋值组成的指标体系,并提出基于该指标体系的单项效能评价→系统综合评价→体系综合评价→作战效能评价→结果验证的综合评估途径.     

How Informative are the Subjective Density Forecasts of Macroeconomists?

In this paper, we propose a framework to evaluate the subjective density forecasts of macroeconomists using micro data from the euro area Survey of Professional Forecasters (SPF). A key aspect of our analysis is the use of evaluation measures which take account of the entire predictive densities, and not just the probability assigned to the outcome that occurs. Overall, we find considerable heterogeneity in the performance of the surveyed densities at the individual level. However, it is hard to exploit this heterogeneity and improve aggregate performance by trimming poorly performing forecasters in real time. Relative to a set of simple benchmarks, density performance is somewhat better for GDP growth than for inflation, although in the former case it diminishes substantially with the forecast horizon. In addition, we report evidence of an improvement in the relative performance of expert densities during the recent period of macroeconomic volatility. However, our analysis also reveals clear evidence of overconfidence or neglected risks in expert probability assessments, as reflected in frequent occurrences of events which are assigned a zero probability. Copyright © 2014 John Wiley & Sons, Ltd.     

基于变异系数权重的灰色关联投影法在安全绩效评价中的应用

在提出施工企业安全绩效评价指标体系的基础上,运用灰色关联投影法对企业安全绩效进行评价。为避免指标的相对重要程度因人为赋权的主观性影响和指标权重分配的均衡化以及指标之间因量纲不同而对权重的影响,采取了变异系数法来对各指标确定权重。之后针对实地考察的施工企业进行实证分析,运用比较安全学的思想对安全绩效进行纵向对比,依据评价体系灰色关联度大小来判断施工企业安全绩效的水平高低。进而诊断出各企业安全绩效的优劣,为同类型的企业实际开展安全绩效评价提供指导性的方向和建议。     

基于多特征的域间路由节点安全状态评估方法

域间路由节点安全状态评估能够实现对BGP节点安全状态的直观、实时描述,可为制定合理的安全策略,及时定位、抑制异常路由事件提供数据参考.然而由于完整的异常域间路由集难以获取,使得传统基于数据融合的状态评估方法不再适用.分析BGP节点间交互路由过程中存在的统计特征以及这些特征与域间路由节点安全状态的关系,进而提出一种基于多特征的安全状态评估方法.以平均路径长度和路由事件发生频率等属性为安全特征,并借鉴云模型理论转换定量特征为定性概念的思想,构建域间路由安全特征云,将正常态下的多属性综合安全特征转换为安全正常态,然后通过度量安全特征偏离正常态的程度来计算节点偏离正常态的程度,由此得到域间路由节点面临安全威胁的概率.实验结果表明,该方法能够实现对域间路由节点安全状态的评估,准确性高、实时性强,可为域间路由系统的安全稳定运行提供有力支撑.     

摩托车声品质评价的神经网络方法

针对声品质评价过程中线性回归模型评价结果的不足,采用BP神经网络对人的主观评价结果进行预测.采集摩托车在不同发动机转速下驾驶员耳旁的声信号样本,采用分组成对比较法进行主观评价试验,选取了响度、尖锐度、粗糙度作为神经网络模型输入参数,结合主观评价结果对模型进行训练与检验,并与线性回归模型输出结果进行比较.结果表明,选取驾驶员双耳响度、尖锐度、粗糙度作为模型输入能够较为准确地反映人耳对摩托车噪声的主观感觉.     

针对要点防空模型的作战兵力优化研究

为了在现代化联合防空作战中,根据蓝方火力配置,红方能够做出最优的兵力分配,使防空效能最大化。从要点防空的任务角度出发,对作战兵力分配进行建模优化,并对优化后的兵力分配效能进行评估。首先选定任务背景为近海小规模局部战争,对复杂作战情况进行抽象得到模型。对模型进行优化,求解得到红方最优兵力分配结果,提出一种新方法,简化了对有限制的类二项分布期望的求解,得到蓝方各类进攻力量成功突防的期望以及红方要点平均损耗期望,并用此值定量评估联合防空的兵力分配效能。最后,进行了实例分析,验证了模型的有效性。