城市公交线网优化的线性模型

为了对现有公交线网进行优化,有效利用现有交通资源,解决城市交通问题,运用系统科学的思想,通过对城市公交线网优化的主要内容、优化原则、优化目标以及约束条件的分析,兼顾考虑乘客出行时间、公交线网密度和公交企业的利益以及公交线网的布局对整个城市的交通系统的影响,提出了优化目标的函数表达式及相应约束条件的数学表达式,建立了公交线网优化的线性模型,并给出了运用逐步筛选法对所提出的数学模型进行求解的方法。     

5000 PCC“SHANGHAI HIGHWY”船主机启动分析与处理

分析5 000 PCC"SHANGHAI HIGHWAY"船主机伺服油压低引起主机启动故障的原因、介绍调查与处理过程,在实施了恰当的处理方法后,解决了问题。以期为类似问题的处理与解决提供借鉴和参考。     

复杂城市环境泥水盾构泥浆绿色处理技术分析

泥水平衡盾构法修建隧道,在施工阶段会产生大量的废浆,如果废浆没有得到科学、妥善的处理,对环境的污染是无法估计的。本文以新建京张高铁清华园隧道工程为依托,结合在城市复杂繁华地区施工场地狭小、泥浆处理难度大等工程难题,对盾构掘进产生的废弃泥浆绿色处理技术进行了适应性研究,分析了综合处理之后泥浆循环利用的应用效果。泥水处理设备、压滤设备及离心设备相结合的泥浆综合处理技术,有效地实现了盾构施工"零排放",为今后类似施工条件的工程提供了技术参考。     

平交路口远引掉头设置方法分析

平交路口远引是解决路口流向禁限后车流转向问题的有效方法。通过对掉头地点的选址规划以及交叉口之间的协调控制进行研究,提出最佳掉头位置应满足的条件以及交叉口协调控制的相位差计算方法。通过对掉头通道通行能力的分析,提出实施掉头信号控制条件和具体实施方法。     

基于惯导解算与数字滤波的舰船振荡运动的分离研究

舰船在风浪激励或（和）其它因素的作用下产生六自由度的随机摇荡运动,而摇荡运动将降低舰载武器的打击精度。针对该问题,论文在捷联惯导（SINS）导航解算的基础上,提出了数字滤波的舰船振荡运动分离方法,以实时分离舰船振荡运动,并实时修正舰载武器的运动轨迹,提高其打击精度。由于惯性导航系统为避免加速度对平台跟踪地垂线的干扰而自然形成舒勒振荡,此振荡必然会引入舰船运动的导航解算结果中,因此为提取出瞬时运动,引入数字高通滤波器消除舒勒振荡。论文给出了瞬时运动定义及滤波器的实现方法,仿真结果证明了该方法的有效性。     

宽带低剖面微带滤波器天线设计

论文设计了一种具有全向辐射特性的宽带低剖面微带滤波器天线。利用作为导纳变换器的耦合线,连接微带天线和滤波器的第二个谐振器。在该设计中,微带天线不仅是辐射器也是带通滤波器的最后一阶谐振器。因此,减少了滤波器与天线之间的传输损耗。该天线具有25%的阻抗带宽,3.5mm厚的低剖面,全向辐射方向图和高的带外抑制度。     

基于双轴位置转台的捷联惯导系统级标定技术

本文研究一种基于双轴位置转台的激光陀螺捷联惯导系统的系统级标定方法。建立了附加约束条件的陀螺和加速度计标定模型,从捷联惯导系统的误差方程出发,建立30维Kalman滤波标定模型,以速度和位置作为观测量,并设计双轴位置旋转路径,对惯性仪表标定参数进行激励与辨识。仿真实验结果表明,该方法能够准确估计出陀螺和加速度计的15个标定误差参数,具有一定的参考价值。     

基于入侵野草优化算法的粒子滤波算法

入侵野草优化(IWO)算法是近年来提出的一种简单、有效的基于种群的新颖数值优化算法,在许多领域得到成功运用。将IWO算法与粒子滤波(PF)算法相结合,提出一种基于IWO算法的粒子滤波算法(IWOPF)。该算法将IWO算法运用于粒子滤波的重采样中,以保证粒子的有效性和多样性。最后通过计算仿真验证该算法的有效性和优越性。     

Quasi-dynamic estimation of o–d flows from traffic counts: Formulation,statistical validation and performance analysis on real data

The paper proposes a “quasi-dynamic” framework for estimation of origin–destination (o–d) flow from traffic counts, under the assumption that o–d shares are constant across a reference period, whilst total flows leaving each origin vary for each sub-period within the reference period. The advantage of this approach over conventional within-day dynamic estimators is that of reducing drastically the number of unknowns given the same set of observed time-varying traffic counts. Obviously, the gain in accuracy depends on how realistic is the underlying assumption that total demand levels vary more rapidly over time than o–d shares. Firstly, the paper proposes a theoretical specification of the quasi-dynamic estimator. Subsequently, it proposes empirical and statistical tests to check the quasi-dynamic assumption and then compares the performances of the quasi-dynamic estimator of o–d flows with both classical off-line simultaneous dynamic estimators and on-line recursive Kalman filter-based estimators. Experiments are carried out on the real test site of A4–A23 motorways in North-Eastern Italy. Results confirm the acceptability of the assumption of quasi-dynamic o–d flows, even under the hypothesis of constant distribution shares for the whole day and show that the quasi-dynamic estimator outperforms significantly the simultaneous estimator. Data also suggest that using the quasi-dynamic estimates instead of the simultaneous estimates as historical o–d flows improves significantly the performances of the Kalman filter, which strongly depends of the quality of the seed o–d flows. In addition, it is shown that the aggregation of quasi-dynamic o–d estimates across subsequent time slices represents also the most effective way to obtain o–d estimates for larger time horizons (e.g. hourly estimates). Finally, a validation based on an hold-out sample of link flows (i.e. counts not used as inputs in the o–d estimation/updating process) revealed the quasi-dynamic estimator to be overall more robust and effective with respect to the other tested estimators.     

Dual extended Kalman filter for vehicle state and parameter estimation

The article demonstrates the implementation of a model-based vehicle estimator, which can be used for combined estimation of vehicle states and parameters. The estimator is realised using the dual extended Kalman filter (DEKF) technique, which makes use of two Kalman filters running in parallel, thus 'splitting' the state and parameter estimation problems. Note that the two problems cannot be entirely separated due to their inherent interdependencies. This technique provides several advantages, such as the possibility to switch off the parameter estimator, once a sufficiently good set of estimates has been obtained. The estimator is based on a four-wheel vehicle model with four degrees of freedom, which accommodates the dominant modes only, and is designed to make use of several interchangeable tyre models. The paper demonstrates the appropriateness of the DEKF. Results to date indicate that this is an effective approach, which is considered to be of potential benefit to the automotive industry.