基于故障树的矿山斜井运输跑车事故分析

斜井运输系统是矿山整个生产过程中非常重要的子系统之一.运输跑车的安全状况,关系到人员生命和物质财产的安全.利用故障树技术对斜井运输跑车事故进行了分析,绘制了故障树,计算得出其最小割集与最小径集,判明了影响斜井运输跑车事故的关键因素,并根据分析结果,提出了预防斜井运输跑车事故发生的对策措施,从而为制定解决和预防立井事故的有效措施指明了方向,有利于相关管理部门做出科学的决策.     

基于故障树方法的铁路隧道紧急救援站间距分析

为研究铁路隧道紧急救援站合理间距,给铁路隧道防灾疏散救援土建结构设计提供参考,在分析铁路隧道紧急救援站设置影响因素的基础上,采用故障树分析方法,综合考虑隧道设计参数、列车制动性能、人员操作失误、火灾燃烧特性等关键因素,演绎分析建立相应的故障树模型,得到火灾列车不能到达紧急救援站事故的风险概率.研究结果表明:(1)采用故...     

Co-location decision tree for enhancing decision-making of pavement maintenance and rehabilitation

This paper presents a new decision tree induction method, called co-location-based decision tree (CL-DT), to enhance the decision-making of pavement maintenance and rehabilitation strategies. The proposed algorithm utilizes the co-location characteristics of spatial attribute data in the pavement database. The paper first presented the co-location mining algorithm, including spatial attribute data selection, determination of rough candidate co-locations, determination of candidate co-locations, pruning the non-prevalent co-locations, and induction of co-location rules, and then focused on the development of the co-location decision tree (CL-DT) algorithm, which includes the non-spatial attribute data selection, co-location algorithm modeling, node merging criteria, and co-location decision tree induction. A pavement database covering four counties, which are provided by North Carolina Department of Transportation (NCDOT), is used to verify the proposed method. The experimental results demonstrated that (1) the proposed CL-DT algorithm can make a better decision, and has higher accuracy than the existing decision tree methods do; (2) the training data can be fully played roles in contribution to decision tree induction and the computational time taken for the tree growing, tree drawing and rule generation is largely decreased; (3) quantity and locations of six treatment strategies proposed by the ITRC and by CL-DT is much close for each treatment strategy.     

Accommodating user preferences in ad hoc shared ride trip planning using GIS

Ad hoc shared ride trip planning (SRTP) utilizes mobile devices, geo-sensors and wireless networks to match on-the-fly individual travel demand with transport supply. It represents one of many alternatives to single occupancy vehicle use. This paper outlines a SRTP approach via a two-phase algorithm based on user preferences in a time-dependent routing. Whereas current algorithms use minimization of travel time as the only optimization criterion in trip planning, in the framework presented here, the user can specify multiple trip preferences including travel time, walking time, number of transfers between cars and trip length. Various scenarios are simulated in the city of Tehran (Iran) to demonstrate how preference settings affect the routes of ad hoc shared journeys.     

基于单总线温度测控系统的前端拓扑网络设计

温度测控系统的前端模块的设计基于单总线（1-Wire Bus）技术,利用数字温度传感器DSl8820构建成1-Wire树型拓扑网络,最大限度地减少单片机的硬件资源占用、软件设计中采用二叉树搜索算法自动完成整个网络的器件搜索．系统设计了完善的上下位机间的通讯协议来保证温度采集的数据的传输的可靠性和准确性,为上位机控制系统提供实时、准确的数据．     

大型油罐火灾爆炸危害性研究

论述了大型油罐火灾爆炸危害性故障树分析法以及火灾爆炸指数法等评价方法及其研究进展,经过系统危险性等级划分、评价指标特征值量化处理和危险性评价指标体系权重的分析,提出了建立大型油罐危险性评价指标体系的具体步骤,并指出了大型油罐火灾爆炸研究的重点和难点,对进一步完善现有危险性评价指标体系具有重要的指导意义.     

舰船总体可靠性通用软件包研究

本文着重探讨舰船的总体可靠性的计算问题，作者将故障树分析法和多级数值仿真相结合，编制了计算舰船总体可靠性各项参数（例如任务可靠度，战备完好率等）的软件包。在系统分析方面，作者巧妙应用多级数值仿真法，提供了计算可维修系统可靠性的新途径。并在本文最后给出了一个关于潜艇可靠性参数的计算实例。     

CFTA的应用研究

本文提出一种新的可靠性评价方法──计算机故障树分析法（CFTA），并介绍了它的工作原理和具体应用实例。这一方法与传统的故障树分析法相比较具有准确性高、便于故障定位、实时 监控和生产管理等优点。     

Investigating the predictive performance of computational process activity-based transportation models

The aim of this paper is to achieve a better understanding of computational process activity-based models, by identifying factors that influence the predictive performance of A Learning-based Transportation Oriented Simulation System model. Therefore, the work activity process model, which includes six decision steps, is investigated. The manner of execution in the process model contains two features, activation dependency and attribute interdependence. Activation dependency branches the execution of the simulation while attribute interdependence involves the inclusion of the decision outcome of a decision step as an attribute to subsequent decision steps. The model is experimented with by running the simulation in four settings. The performance of the models is assessed at three validation levels: the classifier or decision step level, the activity pattern sequence level and the origin–destination matrix level. The results of the validation analysis reveal more understanding of the model. Benchmarks and factors affecting the predictive performance of computational activity-based models are identified.