沿海客箱船主尺度的确定

本文首先定义了一个这和船等级评定标准，建立了应用神经网络理论估算客（货）船造价和客舱（位）平均面积的模型，据此编制了确定客箱船主尺度的程序系统，并给出了优选客箱船主尺度的计算例子。     

基于PCA的交通工程防护目标选取方法研究

反空袭战中,对战区内各种交通工程进行重点防护目标选取是一个重要的综合评价问题。通过分析PCA原理,用PCA方法和Matlab工具求解反空袭战中交通工程防护目标重要性的排序问题,并可根据交通工程目标得分的排列顺序,选择出若干需要重点防护和抢修的国防交通工程目标。     

A sensitivity-based approach for adaptive decomposition of anticipatory network traffic control

Anticipatory optimal network control is defined as the problem of determining the set of control actions that minimizes a network-wide objective function. This not only takes into account local consequences on the propagation of flows, but also the global network-wide routing behavior of the users. Such an objective function is, in general, defined in a centralized setting, as knowledge regarding the whole network is needed to correctly compute it. Reaching a level of centralization sufficient to attain network-wide control objectives is however rarely realistic in practice. Multiple authorities are influencing different portions the network, separated either hierarchically or geographically. The distributed nature of networks and traffic directly influences the complexity of the anticipatory control problem.This is our motivation for this work, in which we introduce a decomposition mechanism for the global anticipatory network traffic control problem, based on dynamic clustering of traffic controllers. Rather than solving the full centralized problem, or blindly performing a full controller-wise decomposition, this technique allows recognizing when and which controllers should be grouped in clusters, and when, instead, these can be optimized separately.The practical relevance with respect to our motivation is that our approach allows identification of those network traffic conditions in which multiple actors need to actively coordinate their actions, or when unilateral action suffices for still approximating global optimality.This clustering procedure is based on well-known algebraic and statistical tools that exploit the network’s sensitivity to control and its structure to deduce coupling behavior. We devise several case studies in order to assess our newly introduced procedure’s performances, in comparison with fully decomposed and fully centralized anticipatory optimal network control, and show that our approach is able to outperform both centralized and decomposed procedures.     

基于主成分法的旅客信息需求分析

通过对虹桥机场旅客的问卷调查初步得出了不同旅客的信息需求及对发布设备的选择，在此基础上利用主成分法对不同的旅客对不同信息的需求强度和信息需求与发布设备之间的关系进行深入的分析，以期作为虹桥综合交通枢纽旅客信息诱导的重要依据。     

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以道路网络的路段流量为基础进行OD分布矩阵估计.与以往文献不同的是本文应用了多层前馈神经网络的方法.由于路段流量与相关的OD矩阵分布之间存在连续性关系,这为神经网络模型的逼近特性提供了可行性.本文的方法适用于OD分布矩阵的实时校正.在已知OD分布矩阵的前提下,对两种情境———试验网络和实际Naples农村道路网进行仿真分析.主成分分析法的应用减少了变量个数并有利于改进输入数据.估计误差相对较低,与分析方法相反的是处理的时间几乎是实时的,因此这种方法可用于动态交通管理.本文的神经网络方法在误差和计算时间方面优于传统商业软件得到的OD估计结果.     

确定旋转吊内河航标船主要要素的探讨

本文通过对具有旋转吊内河航标工作船主要要素进行分析，给出了在初步设计阶段确定主要要素的方法，为设计人员提供了有益的参考。     

道路景观设计对驾驶行为的影响研究

道路景观对交通安全有重要影响.为解决当前道路景观设计工作过程中存在的设计方案表现形式落后、动态体验失真、无法定量评价等问题,将驾驶模拟实验与口头问答相结合,设计驾驶行为与道路景观因素水平相关性模拟驾驶实验.驾驶行为数据的方差分析结果表明,道路景观的色彩、株距、高度、冠幅、路边距因素的不同水平会影响驾驶员对道路景观的感受及其驾驶行为.通过主成分分析将速度、前向加速度、横向位置、横向加速度及转向盘操作量等参数的标准差综合成驾驶稳定性评价指标,定量比较了各因素的不同水平对驾驶人驾驶稳定性的影响程度.结果表明,当道路中央隔离带及路侧植株过高或色彩过多过艳时,驾驶人的驾驶稳定性最差,据此提取出了景观设计方案确定过程中应多加关注的问题.     

统计分析在搅拌桩桩体质量评判中的研究

文章在简述现有搅拌桩桩身质量检测评判方法的基础上，运用统计分析原理中主成分分析，对某高速公路桩身质量评分准则的检测部位、SPT-N、Ucc-qu、龄期、稠度状态等检测指标进行分析研究，得出原评分标准中，以5m为界划分不同评分标准、SPT-N与无侧限抗压强度、稠度状态间计分比例不舍理以及今后可考虑检测部位土层的土性对成桩质量的影响，检测标准应有所差异等结论。对今后搅拌桩检测方法及评判标准的改进与完善，具有参考意义。     

Principal components analysis and track quality index: A machine learning approach

Track geometry data exhibits classical big data attributes: value, volume, velocity, veracity and variety. Track Quality Indices-TQI are used to obtain average-based assessment of track segments and schedule track maintenance. TQI is expressed in terms of track parameters like gage, cross-level, etc. Though each of these parameters is objectively important but understanding what they collectively convey for a given track segment often becomes challenging. Several railways including passenger and freight have developed single indices that combines different track parameters to assess overall track quality. Some of these railways have selected certain parameters whilst dropping others. Using track geometry data from a sample mile track, we demonstrate how to combine track geometry parameters into a low dimensional form (TQI) that simplifies the track properties without losing much variability in the data. This led us to principal components. To validate the use of principal components as TQI, we employed a two-phase approach. First phase was to identify a classic machine learning technique that works well with track geometry data. The second step was to train the identified machine learning technique on the sample mile-track data using combined TQIs and principal components as defect predictors. The performance of the predictors were compared using true and false positive rates. The results show that three principal components were better at predicting defects and revealing salient characteristics in track geometry data than combined TQIs even though there were some correlations that are potentially useful for track maintenance.     

港口吞吐量的内在影响因素提取

港口吞吐量的预测对于港口的建设和规划有着极其重要的意义。而影响港口吞吐量的因素很多,文章从可能影响港口吞吐量的若干因素出发,采用主成分分析法提取了最关键的因素,剖析了港口吞吐量的内在影响机理,并提出了基于影响机理的预测模型。最后,以南京港为例,对模型进行验证,结果证明该模型能较好地反映实际系统模型,对系统的拟合是有效的。