基于线路实测数据统计推断的应力谱分布类型比较

在分析研究轨道车辆结构实测动应力谱分布的基础上,提出用威布尔分布和对数正态分布的组合分布来描述动应力分布.利用实测数据分别拟合出威布尔、对数正态、组合分布三个分布函数,利用拟合结果及实测应力谱进行应力谱统计推断,得到各自的推断应力谱并与实测谱比较,证明了组合分布对实测应力谱分布描述的优越性.同时利用各分布函数估计得到实测中未出现的可能最大值,然后扩展出包含可能最大值的扩展应力谱,在损伤累计理论的基础上,证明了威布尔分布对分布估计的保守性,以及组合分布对应力谱分布估计的有效性.     

建筑招投标价格博弈与市场均衡

建筑市场价格扭曲带来了一系列质量和安全问题,严重制约建筑市场的健康发展。为解决这一问题,应首先对建筑招投标价格的博弈模型进行分析。运用博弈论原理,构建建筑招投标过程中业主与承包商不完全信息动态非合作的双价二手车交易博弈模型。探讨业主与承包商就建筑产品价格所展开的博弈过程以及最终的市场均衡成交价格。将建筑招投标的价格均衡划分为市场完全成功、市场完全失败和市场部分成功,分别讨论3种市场类型存在的条件。对建筑招投标价格博弈模型及市场均衡的分析,有助于市场管理者运用经济激励手段调控市场,保障市场的健康发展。     

故障诊断的时间因果贝叶斯网模型

在一类诊断所依据的可观信号(警报)具有时序特性的故障诊断问题中,诊断所依据的警报、这些警报出现的先后顺序以及它们之间的时间间隔,都与诊断结果有关联。针对这一类故障诊断问题,提出了时间因果贝叶斯网模型,采用模糊方式对故障与警报之间的时间因果关系进行离散化处理,用模糊运算来合成多个时间因果关系,通过概率计算获得最大可能的故障假说。理论与算例表明该方法有效可行。     

高铁引线的客运需求分析

在出行成本和出行时间上对民航和高铁进行对比分析,引申出高铁引线的基本概念.从社会经济发展水平、居民收入水平、人口数量、产业结构调整及城市化进程几个方面,对高铁引线客运需求的影响因素进行分析,提出了基于贝叶斯网络原理的旅客期望出行方式的网络模型.讨论了机场旅客在选择换乘方式和期望换乘方式中,起直接作用的影响因素以及这些因素之间的相互关系.由于具有节约时间、价格低廉、乘坐方便的优点,高铁引线是大部分旅客期望换乘使用的交通方式,从而为高铁引线的建设提供了理论支持.     

三大统计学派推断思想的比较研究——以贝伦斯—费歇尔问题为例

本文介绍了贝伦斯-费歇尔问题的历史及三大统计学派在此问题上的主要解决方法,从一个侧面反映了奈曼置信推断、贝叶斯辩证推断与费歇尔信念推断思想的差异。     

铁路调度指挥系统故障诊断方法

根据铁路调度指挥系统中的故障传播路径特点,建立故障传播的有向图及其模型,设计基于贝叶斯后验概率准则的故障诊断算法.该算法的基本原理是,每1个新增的故障现象都对应着1个故障原因域,依据贝叶斯后验概率准则,计算故障原因域中每1个故障原因的疑似概率,然后按其大小顺序插入既有的故障原因疑似概率集合中.随着故障现象的增加,当故障原因疑似概率集合不再改变时,运算终止.故障原因疑似概率集合中前n个元素能覆盖故障现象时,这n个元素所对应的故障原因,即是诊断结果.实验表明,在单故障模式和双故障模式下,运用这种故障诊断算法得出的结论完全正确,并且在计算机上的运算时间不超过0.8s.     

Understanding parking decisions with a Bayesian network

In this paper, a Bayesian network is developed to investigate three intertwining parking decisions, namely parking period, parking location, and parking duration, and the impacts of a number of parking-related factors on these decisions. With parking information from Beijing, China in 2005, the structure and parameter of a Bayesian network were learnt by employing the K2 algorithm and Bayesian parameter estimation method respectively. The results show that the decision on how long to park follows that on where to park, and both of them are affected by the decision of when to park. This suggests that parking policies aimed at intervening in one specific parking decision may have an indirect influence on other parking decisions, which embraces an integrated view in the development of parking policies. The findings facilitate the development of measures for regulating parking behavior by identifying important contributing factors.     

Bayesian network-based formulation and analysis for toll road utilization supported by traffic information provision

Congestion pricing has been proposed and investigated as an effective means of optimizing traffic assignment, alleviating congestion, and enhancing traffic operation efficiencies. Meanwhile, advanced traffic information dissemination systems, such as Advanced Traveler Information System (ATIS), have been developed and deployed to provide real-time, accurate, and complete network-wide traffic information to facilitate travelers’ trip plans and routing selections. Recent advances in ATIS technologies, especially telecommunication technology, allow dynamic, personalized, and multimodal traffic information to be disseminated and impact travelers’ choices of departure times, alternative routes, and travel modes in the context of congestion pricing. However, few studies were conducted to determine the impact of traffic information dissemination on toll road utilizations. In this study, the effects of the provisions of traffic information on toll road usage are investigated and analyzed based on a stated preference survey conducted in Texas. A Bayesian Network (BN)-based approach is developed to discover travelers’ opinions and preferences for toll road utilization supported by network-wide traffic information provisions. The probabilistic interdependencies among various attributes, including routing choice, departure time, traffic information dissemination mode, content, coverage, commuter demographic information, and travel patterns, are identified and their impacts on toll road usage are quantified. The results indicate that the BN model performs reasonably well in travelers’ preference classifications for toll road utilization and knowledge extraction. The BN Most Probable Explanation (MPE) measurement, probability inference and variable influence analysis results illustrate travelers using highway advisory radio and internet as their primary mode of receiving traffic information are more likely to comply with routing recommendations and use toll roads. Traffic information regarding congested roads, road hazard warnings, and accident locations is of great interest to travelers, who tend to acquire such information and use toll roads more frequently. Travel time formation for home-based trips can considerably enhance travelers’ preferences for toll road usage. Female travelers tend to seek traffic information and utilize toll roads more frequently. As expected, the information provided at both pre-trip and en-route stages can positively influence travelers’ preferences for toll road usage. The proposed methodology and research findings advance our previous study and provide insight into travelers’ behavioral tendencies concerning toll road utilization in support of traffic information dissemination.     

A context aware system for driving style evaluation by an ensemble learning on smartphone sensors data

There are many systems to evaluate driving style based on smartphone sensors without enough awareness from the context. To cover this gap, we propose a new system namely CADSE system to consider the effects of traffic levels and car types on driving evaluation. CADSE system includes three subsystems to calibrate smartphone, to classify the maneuvers, and to evaluate driving styles. For each maneuver, the smartphone sensors data are gathered in three successive time intervals referred as pre-maneuver, in-maneuver, and post-maneuver times. Then, we extract some important mathematical and experimental features from these data. Afterwards, we propose an ensemble learning method on these features to classify the maneuvers. This ensemble method includes decision tree, support vector machine, multi-layer perceptron, and k-nearest neighbors. Finally, we develop a rule-based fuzzy inference system to integrate the outputs of these algorithms and to recognize dangerous and safe maneuvers. CADSE saves this result in driver’s profile to consider more for dangerous driving recognition. The experimental results show that accuracy, precision, recall, and F-measure of CADSE system are greater than 94%, 92%, 92%, and 93%, respectively that prove the system efficiency.     

基于快速贝叶斯网络的S700K转辙机故障诊断研究

转辙机故障对铁路运输安全和效益影响重大,针对转辙机故障原因与现象之间的复杂不确定性关系,提出一种基于粗糙集约简的高效贝叶斯网络故障诊断方法。首先,建立故障诊断决策表,利用改进的差别矩阵算法剔除对结果影响较小的属性,得到最简故障诊断决策表。其次,根据表中故障现象与故障类型连接关系建立贝叶斯网络模型,利用推理算法求解各类故障发生的概率。算法通过约简属性简化贝叶斯网络结构,降低算法复杂度,加快计算速度。最后,用某车站转辙机故障实例验证该智能故障诊断方法的正确性。