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)。     

一种实用的混合交通流BPR模型

针对城市道路混合交通问题,分析了小、中、大3类车型的车道、区位、断面分布特征,依据各车型车辆的道路路段固有阻抗和车型比例仿真构建了三幅路、四幅路、三幅路单行且公交可逆行等典型道路路段机动车流的车速流量实用模型(BPR模型),并以小、大型车比例差为变量对模型进行了修正.预测结果表明,修正模型能够适应各种交通组成情况,且具有相当高的计算精度,可以较准确地预测缺乏实地调查资料的道路路段交通流参数.     

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。     

叶顶泄漏流对半开式离心泵叶轮流动结构的影响

为研究不同叶顶间隙下叶顶泄漏流对半开式叶轮离心泵流动结构的影响,设计5种不同尺寸叶顶间隙方案,采用SST k-ω湍流模型,对半开式叶轮离心泵进行全流道三维数值计算,分析半开式叶轮叶顶间隙内部流动结构并进行可视化实验验证.结果表明:随着叶顶间隙的减小,离心泵的扬程和效率明显上升,间隙值减小到0.7 mm时,进一步减小叶顶...     

凝结水精处理系统工艺流程研究——以安庆电厂凝结水精处理系统为例

对安庆电厂由武汉凯迪股份公司提供的中压凝结水精处理的高速混床和再生系统的设备、设计、工艺流程的特点进行分析研究.     

仿生六足机器人步态规划策略实验研究

采用日本弓背蚁进行常规步态实验学研究和仿生学探索,通过对高速数字摄像机采样结果的判断和分析,发现了蚂蚁后足的滑行过程并定义为“滑动相“.此外,还针对蚂蚁直线前进步态给出了合理的步态关系表达式,通过与周期规则步态的对比证明了该式的现实可行性,为仿生六足机器人进行周期不规则步态的规划奠定了理论基础.所采用的实验学研究和仿生学探索相结合的方法能有效提高仿生机器人的技术水平.     

Predicting Stock Return Volatility: Can We Benefit from Regression Models for Return Intervals?

We study the performance of recently developed linear regression models for interval data when it comes to forecasting the uncertainty surrounding future stock returns. These interval data models use easy‐to‐compute daily return intervals during the modeling, estimation and forecasting stage. They have to stand up to comparable point‐data models of the well‐known capital asset pricing model type—which employ single daily returns based on successive closing prices and might allow for GARCH effects—in a comprehensive out‐of‐sample forecasting competition. The latter comprises roughly 1000 daily observations on all 30 stocks that constitute the DAX, Germany's main stock index, for a period covering both the calm market phase before and the more turbulent times during the recent financial crisis. The interval data models clearly outperform simple random walk benchmarks as well as the point‐data competitors in the great majority of cases. This result does not only hold when one‐day‐ahead forecasts of the conditional variance are considered, but is even more evident when the focus is on forecasting the width or the exact location of the next day's return interval. Regression models based on interval arithmetic thus prove to be a promising alternative to established point‐data volatility forecasting tools. Copyright ©2015 John Wiley & Sons, Ltd.     

基于自适应退火遗传算法的船舶管路布局优化方法

采用自适应遗传算法来确定标准遗传算法的杂交率和变异率,尤其对变异率的调整,使其不但能根据个体适应值的大小进行自适应修正,而且能随进化状态的改变而改变,从而增强了算法摆脱局部最优解的能力.同时引入模拟退火思想,通过对标准遗传算法接受算子的退火处理,使其在搜索过程中除了接受优化解以外还以Metropolis准则接受恶化解,提高了种群的多样性,有效地增强了全局寻优能力.通过对适应值函数的退火拉伸,调整了进化前后期的适应值差异,从而加速了寻优过程.最终以形成的自适应模拟退火遗传算法进行船舶管路的三维布局优化,仿真实验表明,该算法不但加快了寻优速度,而且与标准遗传算法相比全局收敛率提高了近30%.     

Forecasting the Daily Time‐Varying Beta of European Banks During the Crisis Period: Comparison Between GARCH Models and the Kalman Filter

This intention of this paper is to empirically forecast the daily betas of a few European banks by means of four generalized autoregressive conditional heteroscedasticity (GARCH) models and the Kalman filter method during the pre‐global financial crisis period and the crisis period. The four GARCH models employed are BEKK GARCH, DCC GARCH, DCC‐MIDAS GARCH and Gaussian‐copula GARCH. The data consist of daily stock prices from 2001 to 2013 from two large banks each from Austria, Belgium, Greece, Holland, Ireland, Italy, Portugal and Spain. We apply the rolling forecasting method and the model confidence sets (MCS) to compare the daily forecasting ability of the five models during one month of the pre‐crisis (January 2007) and the crisis (January 2013) periods. Based on the MCS results, the BEKK proves the best model in the January 2007 period, and the Kalman filter overly outperforms the other models during the January 2013 period. Results have implications regarding the choice of model during different periods by practitioners and academics. Copyright © 2016 John Wiley & Sons, Ltd.     

Long Memory of Financial Time Series and Hidden Markov Models with Time‐Varying Parameters

Hidden Markov models are often used to model daily returns and to infer the hidden state of financial markets. Previous studies have found that the estimated models change over time, but the implications of the time‐varying behavior have not been thoroughly examined. This paper presents an adaptive estimation approach that allows for the parameters of the estimated models to be time varying. It is shown that a two‐state Gaussian hidden Markov model with time‐varying parameters is able to reproduce the long memory of squared daily returns that was previously believed to be the most difficult fact to reproduce with a hidden Markov model. Capturing the time‐varying behavior of the parameters also leads to improved one‐step density forecasts. Finally, it is shown that the forecasting performance of the estimated models can be further improved using local smoothing to forecast the parameter variations. Copyright © 2016 John Wiley & Sons, Ltd.