Data dependent input control for origin–destination demand estimation using observability analysis

In this paper, we address the observability issue of static O–D estimation based on link counts. Unlike most classic observability analyses that relied only on network topological relationships, our analysis incorporates the actual values of input parameters, thus including network operational relations as well. We first analyze possible mathematical properties of an O–D estimation problem with different data input. We then propose a modeling approach based on mixed-integer program for selecting model input that ensures observability and estimation quality. Through establishing a stronger connection between observability analysis and the corresponding estimation problem, the proposed method aims to improve estimation quality while reducing reliance on erroneous data.     

Validating and improving public transport origin–destination estimation algorithm using smart card fare data

Smart card data are increasingly used for transit network planning, passengers’ behaviour analysis and network demand forecasting. Public transport origin–destination (O–D) estimation is a significant product of processing smart card data. In recent years, various O–D estimation methods using the trip-chaining approach have attracted much attention from both researchers and practitioners. However, the validity of these estimation methods has not been extensively investigated. This is mainly because these datasets usually lack data about passengers’ alighting, as passengers are often required to tap their smart cards only when boarding a public transport service. Thus, this paper has two main objectives. First, the paper reports on the implementation and validation of the existing O–D estimation method using the unique smart card dataset of the South-East Queensland public transport network which includes data on both boarding stops and alighting stops. Second, the paper improves the O–D estimation algorithm and empirically examines these improvements, relying on this unique dataset. The evaluation of the last destination assumption of the trip-chaining method shows a significant negative impact on the matching results of the differences between actual boarding/alighting times and the public transport schedules. The proposed changes to the algorithm improve the average distance between the actual and estimated alighting stops, as this distance is reduced from 806 m using the original algorithm to 530 m after applying the suggested improvements.     

采油工艺管柱自动导向器

设计了一种新型采油工艺管柱导向器,通过应用该导向器,在作业中实现管柱铰接,可有效解决由套管套变引起的封隔器损坏、失效现象,起到保护封隔器胶筒的作用.并可解决大斜度井、定向井工艺管柱偏磨与导向问题,减少对管柱接箍的损坏及对套管的刮磨破坏,提高其使用寿命.可广泛应用于油水井增产增注工艺管柱上.     

Java语言的字符编码问题探讨

由于Java语言的字符集有很多套编码方法,这就使得有相当多的人对Java语言的字符编码方法不易完全掌握.通过查看Java帮助文件及编程实践,在摸清该问题规律的基础上,分析讨论了3种情况下的Java语言字符编码问题,并提出了有实用价值的4点结论.     

三岸邕江特大桥悬臂拼装钢桁拱施工监控方法

文章结合三岸邕江特大桥工程实例,对比分析了悬臂拼装钢桁拱的施工监控特点,阐述了悬臂拼装钢桁拱施工监控的主要内容及组织实施流程。     

高墩大箱室梁墩顶0号段托架的设计与施工技术

介绍了高墩大箱室箱梁墩顶0号段悬托架的设计与施工方法,通过直接在墩身预埋牛腿,在牛腿上安放工字钢大梁和贝雷架分配梁以作为托架支撑,经计算后满足施工需要,从而优化施工方案。     

船舶柴油发电机转速神经网络容错控制

结合人工神经网络与智能容错控制,形成船舶柴油发电机转速神经网络容错控制。对由故障诊断后获取的特征值进行归一化处理,把经过处理的特征值作为神经网络的输入样本集,设计输出样本集,建立BP神经网络和ELMAN神经网络,用整理后的数据训练神经网络,使神经网络具有容错控制功能,并对神经网络模型进行仿真测试。仿真试验显示可以实现对船舶电力系统容错控制,保证船舶的安全运行。     

基于牛顿迭代法的概位修正误差分析

介绍了双信号台测向定位中利用牛顿迭代法进行概位修正的方法,分析了牛顿迭代法对概位修正误差的消除过程。仿真结果表明：在满足舰船导航精度要求下,牛顿迭代法的迭代次数少,且误差很小,达到了概位修正的要求。     

350t自航起重船吊臂结构强度评估及加强方案的模糊评判

为对起重臂内部结构进行改进,提高其结构强度.利用MSC.Nastran软件对350t自航起重臂结构在不同工况下进行有限元强度分析.基于模糊综合评判理论,结合船体强度、工艺和重量等因素作为评判参数,按成对比较法和构建满意度函数,对350t自航起重臂结构的三种加强方案进行评判,确定出最优方案.     

基于贝叶斯网的反潜直升机对潜艇的识别模型

快速准确地对潜艇进行的识别在航空反潜作战中是进行战场态势评估和战场决策的基础。通过搜索潜艇中提取搜索的特征信息,结合贝叶斯网络,使用了“两步”对潜艇目标进行识别,在此基础上进行了实例验证,其结果与事实相一致,显示了该方法的正确性和有效性。