热-力-电耦合下天线罩电性能仿真及分析

在实际飞行工况下,热力载荷会改变高速飞行器天线罩原有的电磁特性,从而影响制导性能。提出了一种基于六面体网格划分的热-力-电耦合模型及仿真方法,可准确表征高速飞行工况下天线罩介电温漂和结构变形对电性能的影响。基于天线罩热-力-电共享网格模型,首先通过瞬态热仿真得到天线罩响应温度场,通过静力分析得到天线罩结构变形场。然后,将天线罩介电温漂和结构变形准确传递到其电磁仿真模型中,并采用三维射线跟踪法计算其电性能。最后通过一个典型算例对高速飞行工况下天线罩电性能的变化进行仿真和分析,结果表明电性能变化非常明显,也进一步说明了所提方法的可行性和研究的必要性。     

基于威胁博弈理论的决策级融合模型

针对多传感器信息融合系统中普遍存在的冲突信息环境下的融合系统决策问题,提出了一种新的基于威胁博弈的决策级信息融合模型·该模型将冲突环境下的融合过程视为局部决策空间、策略空间和全局决策空间上的变换过程,并以此构建融合中心的一种结构模型·在融合算法的研究中,以威胁博弈的算法实现融合中心的多传感器交互决策·在应用中将该模型用于丰满水电仿真系统调速器PID模型的参数在线调整·经现场应用表明,该模型可以有效地解决原PID模型参数整定困难而导致的系统转速和频率振荡等问题·     

普通公路车速分布特性的回归分析

采用多元线性回归的方法,分别建立了平均车速、15％位车速、85％位车速与车道数、限速值、车道位置、车型、道路饱和度、大型车比例等6个因素之间的回归模型.然后,采用Logistic回归的方法,建立了车速选择的回归模型.分析结果表明:对于双向六车道普通公路,依据15％位车速,对超车道进行最低车速限制;依据85％位车速,对最高车速实施可变限速、分车道限速和分车型限速.外侧车道上的大型车在饱和度较高的情况下,容易选择低速行驶;双向两车道或四车道公路上的小型车在饱和度较低的情况下,容易选择高速行驶.根据车辆选择高速行驶的概率,设置不同的限速设施.利用这2个回归模型可以计算出特定道路条件下的15％位车速、85％位车速以及车辆选择高速行驶和低速行驶的概率,为普通公路在限速及限速设施选择方面提供依据.     

C4ISR仿真系统和实际装备系统互联设计及其互操作

为实现 C^4ISR仿真系统和实际装备（实装）系统之间的联网和互操作,提出了一种实现 C^4ISR仿真系统和实装系统互联的设计方案.该方案使用基于 HLA的代理联邦将分布在广域网上的各个实装系统连接到C^4ISR仿真系统中,并通过HLA/RTI完成仿真系统与实装系统间的交互.系统设计实现了网络层、数据层、模型层和导调层 4个层次的互操作.     

An ensemble of LSTM neural networks for high‐frequency stock market classification

We propose an ensemble of long–short‐term memory (LSTM) neural networks for intraday stock predictions, using a large variety of technical analysis indicators as network inputs. The proposed ensemble operates in an online way, weighting the individual models proportionally to their recent performance, which allows us to deal with possible nonstationarities in an innovative way. The performance of the models is measured by area under the curve of the receiver operating characteristic. We evaluate the predictive power of our model on several US large‐cap stocks and benchmark it against lasso and ridge logistic classifiers. The proposed model is found to perform better than the benchmark models or equally weighted ensembles.     

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)。     

信号交叉口人机混驾交通流速度控制策略建模

为分析人机混驾交通流下网联自动驾驶车辆（connected and autonomous vehicles,CAV）速度控制策略对交通流运行特征的影响,构建了考虑驾驶员对行车信息获取不确定性的人工驾驶车辆交叉口通行决策模型。提出考虑前车速度影响的自动驾驶速度控制策略,构建信号交叉口连续型元胞自动机更新规则,通过引入不同CAV渗透率、道路饱和度、控制区长度参数,研究CAV速度控制策略对信号交叉口交通流运行特征的影响。结果表明：CAV能显著提高交叉口通行能力,且车流通过交叉口区域的延误显著降低;同时速度控制策略的实施效果还受控制区长度的影响,呈现出随着控制区长度的增加,车均延误逐渐降低并趋于稳定。     

建材取样与管理不规范对工程质量的影响

建筑材料检验是保证工程质量的重要措施.本文针对目前施工现场建筑材料在取样方面存在的问题,就如何规范建材取样,确保工程质量提出了建议.     

Testing Interval Forecasts: A GMM‐Based Approach

This paper proposes a new evaluation framework for interval forecasts. Our model‐free test can be used to evaluate interval forecasts and high‐density regions, potentially discontinuous and/or asymmetric. Using a simple J‐statistic, based on the moments defined by the orthonormal polynomials associated with the binomial distribution, this new approach presents many advantages. First, its implementation is extremely easy. Second, it allows for a separate test for unconditional coverage, independence and conditional coverage hypotheses. Third, Monte Carlo simulations show that for realistic sample sizes our GMM test has good small‐sample properties. These results are corroborated by an empirical application on SP500 and Nikkei stock market indexes. It confirms that using this GMM test leads to major consequences for the ex post evaluation of interval forecasts produced by linear versus nonlinear models. Copyright © 2011 John Wiley & Sons, Ltd.     

A Neuro‐wavelet Model for the Short‐Term Forecasting of High‐Frequency Time Series of Stock Returns

We propose a wavelet neural network (neuro‐wavelet) model for the short‐term forecast of stock returns from high‐frequency financial data. The proposed hybrid model combines the capability of wavelets and neural networks to capture non‐stationary nonlinear attributes embedded in financial time series. A comparison study was performed on the predictive power of two econometric models and four recurrent neural network topologies. Several statistical measures were applied to the predictions and standard errors to evaluate the performance of all models. A Jordan net that used as input the coefficients resulting from a non‐decimated wavelet‐based multi‐resolution decomposition of an exogenous signal showed a consistent superior forecasting performance. Reasonable forecasting accuracy for the one‐, three‐ and five step‐ahead horizons was achieved by the proposed model. The procedure used to build the neuro‐wavelet model is reusable and can be applied to any high‐frequency financial series to specify the model characteristics associated with that particular series. Copyright © 2013 John Wiley & Sons, Ltd.