基于物元贝叶斯网络的舰船战损评估模型研究

分析了舰船战场损伤评估过程,从而得到舰船战场损伤评估的基本流程。分析了过去关于舰船战场损伤评估研究的缺陷和不足。在物元理论和贝叶斯网络理论分析的基础上,建立了节点物元模型和物元贝叶斯网络模型,进而提出了物元贝叶斯网络理论。根据舰船战场损伤评估的特点,分析了建立舰船战场损伤评估模型的步骤及相关工作,并以某舰艇操船系统为例,建立了操船系统损伤评估的物元贝叶斯网络模型。应用示例表明了所提理论的可行性、适用性和可操性。     

基于出行行为的铁路出行信息传递指数模型

为了准确评价铁路出行信息的传递水平, 基于出行链, 分析了铁路旅客出行行为特性, 建立了铁路旅客出行信息搜寻模型。基于出行决策特征, 分析了旅客的出行信息需求, 描述了出行全过程的信息需求强度分布, 利用物元分析方法建立了出行信息传递指数模型, 以评价出行信息传递水平。评价结果表明: 中国铁路客运出行信息传递处于一般水平, 旅客进行出行信息搜寻的便捷性较差, 已对旅客出行满意度造成显著影响, 因此, 必须改进目前的出行信息传递模式, 积极推进公众出行信息服务系统的建设, 以提高出行信息的传递水平。     

民航发动机车间维修能力近似平均值分析方法

为了评估民航发动机维修车间在当前维修能力下可保持的机队规模, 提出了车间维修能力近似平均值分析方法。采用扩展随机Petri网建立了发动机在维修车间的转移模型, 利用单维修等级下系统的性能指标对多维修等级性能指标进行了修正, 采用平方变差系数方法实现服务时间从指数分布向正态分布的逼近, 并评价了多等级与服务时间为正态分布的车间维修能力, 分析了近似平均分析法与仿真方法的计算结果。比较结果表明: 各维修中心等待队长最大相对误差为9.14%, 此时零件检查、手工修理及机械加工为限制维修车间能力的瓶颈, 因此, 近似平均值分析方法是有效的。     

交通事故贝叶斯最小风险控制模型

为了预测交通事故与控制事故风险, 引进贝叶斯最小风险理论, 构建了交通事故贝叶斯最小风险控制模型。当车辆在不同半径曲线上运行时, 采集其位移坐标数据, 并进行换算处理, 如果速率梯度模的变化率出现不正常的振荡, 则交通事故前兆出现。模拟结果表明: 在车辆正常运行情况下, 速率梯度模对时间的绝对变化率服从三参数的威布尔分布, 利用柯尔莫哥洛夫检验可以判定交通事故前兆出现与否, 从而实现对高速公路交通事故的动态监控。     

苏州河河口水闸水上拦阻网动力分析

对于水下闸门,为了保障水闸和船舶的通行安全,需要设计水上安全设施.拦阻网主索的设计计算是此类工程的难点和重点.以苏州河河口水闸水上拦阻网为例,利用动量原理,建立船舶的运动微分方程,运用数值计算方法,得到船舶冲程和拦阻时间,并分析船舶行程和速度随时间的变化规律,研究冲程和拦阻时间与主索拉力的关系,得到一些有益的结论.     

基于无线传感器网络的危险品灾难应急救援系统

把无线传感器应用于危险品灾难应急救灾是一个有益的尝试。介绍了无线传感器网络和根据危险品灾难应急救援系统的概况。详细介绍了应急救援的无线传感器网络系统的结构,应急救援指挥中心软件的功能。     

Long distance truck tracking from advanced point detectors using a selective weighted Bayesian model

Truck flow patterns are known to vary by season and time-of-day, and to have important implications for freight modeling, highway infrastructure design and operation, and energy and environmental impacts. However, such variations cannot be captured by current truck data sources such as surveys or point detectors. To facilitate development of detailed truck flow pattern data, this paper describes a new truck tracking algorithm that was developed to estimate path flows of trucks by adopting a linear data fusion method utilizing weigh-in-motion (WIM) and inductive loop point detectors. A Selective Weighted Bayesian Model (SWBM) was developed to match individual vehicles between two detector locations using truck physical attributes and inductive waveform signatures. Key feature variables were identified and weighted via Bayesian modeling to improve vehicle matching performance. Data for model development were collected from two WIM sites spanning 26 miles in California where only 11 percent of trucks observed at the downstream site traversed the whole corridor. The tracking model showed 81 percent of correct matching rate to the trucks declared as through trucks from the algorithm. This high accuracy showed that the tracking model is capable of not only correctly matching through vehicles but also successfully filtering out non-through vehicles on this relatively long distance corridor. In addition, the results showed that a Bayesian approach with full integration of two complementary detector data types could successfully track trucks over long distances by minimizing the impacts of measurement variations or errors from the detection systems employed in the tracking process. In a separate case study, the algorithm was implemented over an even longer 65-mile freeway section and demonstrated that the proposed algorithm is capable of providing valuable insights into truck travel patterns and industrial affiliation to yield a comprehensive truck activity data source.     

Semi-autonomous vehicle motor insurance: A Bayesian Network risk transfer approach

All developed economies mandate at least third party auto insurance resulting inW a vast global liability industry. The evolution towards semi-autonomous and eventually driverless vehicles will progressively remove the leading cause of vehicle accidents, human error, and significantly lower vehicle accident rates. However, this transition will force a departure from existing actuarial methods requires careful management to ensure risks are correctly assigned. Personal motor insurance lines are anticipated to diminish as liability shifts towards OEMs, tier 1 and 2 suppliers and software developers. Vehicle accident risks will hinge on vehicular characteristics in addition to driver related risks as drivers alternate between autonomous and manual driving modes. This paper proposes a Bayesian Network statistical risk estimation approach that can accommodate changing risk levels and the emergence of new risk structures. We demonstrate the use of this method for a Level 3 semi-autonomous vehicle for two scenarios, one where the driver is in control and one where the vehicle is in control. This approach is especially suited to use telematics data generated from the vehicle inherent technologies. We validate the efficacy of this approach from the perspective of the insurer and discuss how vehicle technology development will require a greater degree of collaboration between the insurance company and the manufacturers in order to develop a greater understanding of the risks semi-autonomous and fully autonomous vehicles.     

A Bayesian network approach for population synthesis

Agent-based micro-simulation models require a complete list of agents with detailed demographic/socioeconomic information for the purpose of behavior modeling and simulation. This paper introduces a new alternative for population synthesis based on Bayesian networks. A Bayesian network is a graphical representation of a joint probability distribution, encoding probabilistic relationships among a set of variables in an efficient way. Similar to the previously developed probabilistic approach, in this paper, we consider the population synthesis problem to be the inference of a joint probability distribution. In this sense, the Bayesian network model becomes an efficient tool that allows us to compactly represent/reproduce the structure of the population system and preserve privacy and confidentiality in the meanwhile. We demonstrate and assess the performance of this approach in generating synthetic population for Singapore, by using the Household Interview Travel Survey (HITS) data as the known test population. Our results show that the introduced Bayesian network approach is powerful in characterizing the underlying joint distribution, and meanwhile the overfitting of data can be avoided as much as possible.     

Inference on mode preferences,vehicle purchases,and the energy paradox using a Bayesian structural choice model

Discrete choice modeling is experiencing a reemergence of research interest in the inclusion of latent variables as explanatory variables of consumer behavior. There are several reasons that motivate the integration of latent attributes, including better-informed modeling of random consumer heterogeneity and treatment of endogeneity. However, current work still is at an early stage and multiple simplifying assumptions are usually imposed. For instance, most previous applications assume all of the following: independence of taste shocks and of latent attributes, exclusion restrictions, linearity of the effect of the latent attributes on the utility function, continuous manifest variables, and an a priori bound for the number of latent constructs. We derive and apply a structural choice model with a multinomial probit kernel and discrete effect indicators to analyze continuous latent segments of travel behavior, including inference on the energy paradox. Our estimator allows for interaction and simultaneity among the latent attributes, residual correlation, nonlinear effects on the utility function, flexible substitution patterns, and temporal correlation within responses of the same individual. Statistical properties of the Bayes estimator that we propose are exact and are not affected by the number of latent attributes.