基于条件熵约简和粗糙集规则匹配的反辐射无人机作战目标威胁识别

文章采用粗糙集的方法来处理威胁识别中的不确定、未知信息,利用条件熵度量威胁识别属性的重要程度,并提出了一种新的规则匹配方法识别威胁等级。通过实例分析与对比,证明此方法能够为反辐射无人机作战目标威胁等级的判断提供了一种可行的途径。     

Simulation-based severe weather-induced container terminal economic loss estimation

Container terminals play a critical role in maritime supply chains. However, they show vulnerabilities to severe weather events due to the sea–land interface locations. Previous severe weather risk analysis focused more on larger assessment units, such as regions and cities. Limited studies assessed severe weather risks on a smaller scale of seaports. This paper aims to propose a severe weather-induced container terminal loss estimation framework. Based on a container terminal operation simulation model, monthly average loss and single event-induced loss are obtained by using historical hazard records and terminal operation records as model inputs. By studying the Port of Shenzhen as the case study, we find that the fog events in March lead to the longest monthly port downtime and the highest monthly severe weather-induced economic losses in the studied port. The monthly average loss is estimated to be 30 million USD, accounting for 20% of the intact income. The worst-case scenario is found to be a red-signal typhoon attack which results in nearly 20% decrease in the month’s income. The results provide useful references for various container terminal stakeholders in severe weather risk management.     

Severity of pedestrian injuries due to traffic crashes at signalized intersections in Hong Kong: a Bayesian spatial logit model

The present study intended to (1) investigate the injury risk of pedestrian casualties involved in traffic crashes at signalized intersections in Hong Kong; (2) determine the effect of pedestrian volumes on the severity levels of pedestrian injuries; and (3) explore the role of spatial correlation in econometric crash‐severity models. The data from 1889 pedestrian‐related crashes at 318 signalized intersections between 2008 and 2012 were elaborately collected from the Traffic Accident Database System maintained by the Hong Kong Transport Department. To account for the cross‐intersection heterogeneity, a Bayesian hierarchical logit model with uncorrelated and spatially correlated random effects was developed. An intrinsic conditional autoregressive prior was specified for the spatial correlation term. Results revealed that (1) signalized intersections with greater pedestrian volumes generally exhibited a lower injury risk; (2) ignoring the spatial correlation potentially results in reduced model goodness‐of‐fit, an underestimation of variability and standard error of parameter estimates, as well as inconsistent, biased, and erroneous inference; (3) special attention should be paid to the following factors, which led to a significantly higher probability of pedestrians being killed or sustaining severe injury: pedestrian age greater than 65 years, casualties with head injuries, crashes that occurred on footpaths that were not obstructed/overcrowded, heedless or inattentive crossing, crashes on the two‐way carriageway, and those that occurred near tram or light‐rail transit stops. Copyright © 2017 John Wiley & Sons, Ltd.     

Short‐term traffic flow prediction with linear conditional Gaussian Bayesian network

Traffic flow prediction is an essential part of intelligent transportation systems (ITS). Most of the previous traffic flow prediction work treated traffic flow as a time series process only, ignoring the spatial relationship from the upstream flows or the correlation with other traffic attributes like speed and density. In this paper, we utilize a linear conditional Gaussian (LCG) Bayesian network (BN) model to consider both spatial and temporal dimensions of traffic as well as speed information for short‐term traffic flow prediction. The LCG BN allows both continuous and discrete variables, which enables the consideration of categorical variables in traffic flow prediction. A microscopic traffic simulation dataset is used to test the performance of the proposed model compared to other popular approaches under different predicting time intervals. In addition, the authors investigate the importance of spatial data and speed data in flow prediction by comparing models with different levels of information. The results indicate that the prediction accuracy will increase significantly when both spatial data and speed data are included. Copyright © 2016 John Wiley & Sons, Ltd.     

任意随机序列关于随机选择系统的一类强极限定理

主要研究任意随机序列在随机选择系统中的随机条件概率其调和平均的强极限定理。在证明中采用了一种把网微分法与条件矩母函数相结合应用于随机选择系统强极限定理研究的一种途径。作为推论,得到m重非齐次马氏链的一个强极限定理,并将已有的结果加以推广。     

城市轨道交通断面客流不确定性分析的广义自回归条件异方差改进模型

对比分析指出,城市轨道交通线路断面客流量变化与道路交通断面客流量变化具有相似性,但城市轨道交通线路断面客流时间序列具备特有的尖峰厚尾特性,其变化的敏感程度依赖时空条件,常用于道路领域的广义自回归条件异方差(GARCH)模型无法直接用于城市轨道交通领域。基于此,引入广义误差分布(GED)虚变量,构建改进的GARCH模型,并基于成都地铁1号线下行断面客流时间序列数据,借助EViews和Matlab软件对改进前后的模型效果进行实证对比分析。结果表明,改进后的虚变量GARCH模型比原始的GARCH模型具有更好的适用性。     

尺寸可变的装箱问题的近似算法的研究

给定物品系列,要求将所有物品装入到不同类型的箱子中,以实现从第1个箱子到最后1个箱子被使用的箱子的总尺寸最小化.用最坏情况绝对性能研究在线算法,给出了一种最坏情况绝对性能比是3的近似算法.作为这种算法的应用,给出了一种脱线算法,其最坏情况绝对性能比是2.     

基于CVaR的海运集装箱超订模型

将条件风险价值(CVaR)度量准则应用于集装箱舱位超订的风险管理研究.建立了在CVaR准则下的海运集装箱舱位超订模型,解此模型得到舱位最优超订水平需满足的方程组,并讨论了风险厌恶程度和空箱调运对舱位最优超订水平的影响.分析结果表明,在风险厌恶环境中,集装箱舱位的最优超订水平依赖于需求的分布和风险厌恶程度,不一定小于风险中性时的最优超订水平;与不考虑空箱调运时比较,考虑空箱调运时,最优超订水平会降低.     

基于多目标的城市公交站点布设模型的研究

基于车辆配置限制的站距模型，重点研究城市公交站距和站点布局的方法，同时结合模型模拟路网对其进行检验，结果证明．该方法原理简单易行。与实际结合比较紧密，对我国城市公交规划具有一定的指导意义。     

拟概率与条件拟概率

讨论拟概率的基本性质，引入拟概率的Ｆｕｚｚｙ积分和条件拟概率等定义，并在此基础上建立拟概率的Ｒａｄｏｎ－Ｎｉｋｏｄｙｍ定理。