基于贝叶斯网络的声呐系统战场损伤评估及修复模型

为了解决战场环境下的声呐系统损伤评估及修复（SSBDAR）问题,本文研究了战场损伤评估及修复模型。针对舰艇声呐系统战场损伤评估的因果推理特性,建立了基于贝叶斯网络的战场损伤评估模型,提出了基于贝叶斯网络的评估流程,给出了基于贝叶斯网络的评估决断算法。该模型利用贝叶斯网络对各评估修复节点进行分析,能够解决损伤信息不确定性,而且充分利用舰艇声呐系统各节点的损伤信息,提高了模型评估及修复效率,是解决舰艇声呐系统战场损伤评估及修复问题的一个有效途径。     

Analysis of factors that may be essential in the decision to fly on fully autonomous passenger airliners

This research investigates factors that influence opinion in the decision to fly on fully autonomous passenger airliners primarily from the perspective of aviation and technology professionals. Bayesian statistical inference and a two‐level fractional factorial survey are used to sample passengers' views on fully autonomous airliners. Eight trust, safety, and cost factors are incorporated into a vignette set in the future. Factors include automation levels, safety records, liability guarantees, airline integrity, and service disruptions. Dependent variables exist in five post‐vignette questions and essentially ask “Would you” or “Would you not” be willing to fly on a fully autonomous airliner? Sixteen versions of the vignette, each with unique trust, safety, and cost levels, present varying (unknown) degrees of influence to the survey respondents. For every demographic, the research shows a 99% statistically significant difference between the “prior” and “posterior” sampled population proportions willing to fly. The most significant positive influence involves integrity characteristics of the airline, while the most negative influence relates to life insurance liability guarantees. Research from 2003 suggested that this mode of travel would be acceptable to only 10.5% of respondents. When the 2003 research is used as a Bayesian prior probability, the resulting posterior probability for the demographics sampled can be modeled as a beta distribution, indicating 95% probability that the sampled proportion of the population willing to fly is between 33.2% and 36.4%. After adjusting for age and profession demographics to match the US population, the 95% probability bounds on the proportion willing to fly are 31.35% and 34.15%. Copyright © 2015 John Wiley & Sons, Ltd.     

Recursive estimation of average vehicle time headway using single inductive loop detector data

Vehicle time headway is an important traffic parameter. It affects roadway safety, capacity, and level of service. Single inductive loop detectors are widely deployed in road networks, supplying a wealth of information on the current status of traffic flow. In this paper, we perform Bayesian analysis to online estimate average vehicle time headway using the data collected from a single inductive loop detector. We consider three different scenarios, i.e. light, congested, and disturbed traffic conditions, and have developed a set of unified recursive estimation equations that can be applied to all three scenarios. The computational overhead of updating the estimate is kept to a minimum. The developed recursive method provides an efficient way for the online monitoring of roadway safety and level of service. The method is illustrated using a simulation study and real traffic data.     

人的可靠性分析方法及其在军事活动中的应用

回顾了人的可靠性分析发展的历史和现状,重点对现在已存在的人的可靠性分析方法进行了介绍分析,提出并简要介绍了两种在军事活动中适用的人的可靠性评估方法的基本原理：基于粗糙集的人的可靠性评估法和基于贝叶斯的人的可靠性评估法,最后分析了人的可靠性研究中存在的问题。     

A Bayesian Methodology for Estimating the Impacts of Improved Coastal Ocean Information on the Marine Recreational Fishing Industry

The study develops a model of recreational fish catch probabilities, based on angler fishing strategies, that is conditional on uncertain information about the coastal ocean environment. We calculate expected catch based on a hypothetical Baseline Data Set and hypothetical data from an Integrated Ocean Observation System (IOOS) to demonstrate potential benefits from IOOS. The role of Bayesian probabilities in Random Utility Models of recreational fishing is identified. The study discusses the types of information that will be required by recreational anglers in the Gulf of Mexico. Results have implications for the construction of ocean observation systems for recreational fishermen.     

交通事件持续时间预测贝叶斯网方法研究

交通事件持续时间的预测是事件管理系统的重要组成部分,根据I-880实测数据集,利用逐步回归分析的方法确定事件持续时间的主要影响因素,分别建立了应用于事件持续时间预测的朴素贝叶斯(NB)模型、加树朴素贝叶斯(TAN)模型以及一般贝叶斯网(BN)模型,在分析数据特点的基础上确定了贝叶斯网的推理算法、参数学习以及结构学习方法.在不同数据缺失的程度和不同训练样本规模下,分别对三种模型的预测准确率进行了评价,结果表明贝叶斯网预测模型在数据缺失30%的情况下30min准确率高于80%.     

山区公路穿村镇路段过境车辆事故严重程度推理分析

为获取道路线形、驾驶员属性、车辆类型、事故形态等因素对山区公路穿村镇路段过境车辆事故严重程度的影响机制。本文基于元双公路(元谋—牟定)2012—2017年事故数据,利用社会网络分析法从人、车、路、环境等方面筛选出15个影响因素;基于机器学习方法构建贝叶斯网络模型;以事故严重程度为决策变量,分析不同证据变量与驾驶员行为共同作用的推理结果。结果表明：不安全驾驶行为与危险事故因素的共同作用,将会增加事故严重程度;当涉及货车时,由于未保持安全距离,伤人事故率增加8.2%;在弯坡组合路段,由于驾驶员判断失误,伤人事故率增加 19.6%;阴雨天行驶时,由于驾驶员判断失误,伤人事故率增加5.4%;由于操作不当,发生侧翻事故时,伤人事故概率增加3.1%。     

A competing Markov model for cracking prediction on civil structures

Cracks on the surface of civil structures (e.g. pavement sections, concrete structures) progress in several formations and under different deterioration mechanisms. In monitoring practice, it is often that cracking type with its worst damage level is selected as a representative condition state, while other cracking types and their damage levels are neglected in records, remaining as hidden information. Therefore, the practice in monitoring has a potential to conceal with a bias selection process, which possibly result in not optimal intervention strategies. In overcoming these problems, our paper presents a non-homogeneous Markov hazard model, with competing hazard rates. Cracking condition states are classified in three types (longitudinal crack, horizontal crack, and alligator crack), with three respective damage levels. The dynamic selection of cracking condition states are undergone a competing process of cracking types and damage levels. We apply a numerical solution using Bayesian estimation and Markov Chain Monte Carlo method to solve the problem of high-order integration of complete likelihood function. An empirical study on a data-set of Japanese pavement system is presented to demonstrate the applicability and contribution of the model.