希尔伯特-黄变换应用于宽带信号参数估计

多普勒效应对主动声纳接收信号的根本影响是接收信号相对发射信号的时间延迟和尺度伸缩。在宽带信号体制下对高速运动目标进行参数估计时,利用传统的窄带信号模型会产生模糊,必须采用时间尺度域模型,即利用接收信号和发射信号的相关性实现运动目标距离和速度的估计。宽带信号模型计算量大,计算时间长。希尔伯特-黄变换具有较高的频率分辨率,利用希尔伯特-黄变换对信号进行频率预估计,减小时间尺度域二维扫描的区域和运算量。在DSP硬件平台上实现希尔伯特-黄变换有关算法。     

基于出租车GPS数据的路段平均速度估计模型

为了解决现有基于GPS数据的路段平均速度估计模型应用条件苛刻、难以满足低成本和高精度信息需求问题,考虑不同类型GPS车辆运行特征,设计了两个基于出租车GPS数据估计路段平均速度的改进模型.基于两个改进模型,设计了路段平均速度的融合估计方法.用某城市局部路网的出租车GPS数据验证两个改进模型,并与传统模型进行了对比分析....     

稀土元素对精密铸钢裂纹形成的影响

设计了精密铸件裂纹敏感试样,通过在铸件成分中添加不同比例的稀土元素,探讨稀土元素对铸件产生裂纹倾向的影响.试验结果表明:合适的稀土添加量的增加,铸件裂纹率显著降低:金相组织分析表明,稀土元素添加能较好地细化晶粒,并有效改善夹杂物的数量、尺寸和分布,从而降低了铸钢的裂纹倾向.     

非线性滤波器在动力定位船位估计中的应用

针对动力定位船舶在作业过程中因丢失GPS位置测量信号,从而导致动力定位系统不能正常工作,而使船舶偏离作业点的问题,设计了一种非线性滤波器,它可以利用以前保存的历史数据进行船舶位置的实时估计,从而使船舶在一定的时间内可以维持在作业点附近,当重新获得GPS信号后,又可以快速地进入正常工作状态。为了使历史数据能够更准确地反映船舶的实时位置,利用历史数据相对于作业点的平均偏差对历史数据进行了补偿。最后给出了此非线性滤波器在船舶仿真系统中的仿真曲线,分析表明在有滤波器的情况下,船舶真实位置估计的准确度比没有滤波器的情况下有明显的改善。     

融合卡尔曼滤波的高速公路状态估计误差界限分析

为分析高速公路交通流检测数据质量,本文构建平方流量误差界(Squared Flow Error Bound, SFEB)和扩展卡尔曼滤波(Extended Kalman Filter, EKF)的决策级融合模型SFEB-EKF,在检测器空间覆盖不足情况下,计算检测路段和无检测器路段的交通状态估计误差界限。与SFEB 算法相比,融合模型利用EKF交通状态估计模型估计全路段交通状态,基于得到的估计样本计算全路段交通状态估计误差下界。同时,采用最近邻法(Nearest Neighbor Method, NNM)计算全路段交通状态估计误差上界。应用开源高速公路数据集测试模型,结果表明,与需要输入真实样本的SFEB算法相比,融合模型SFEB-EKF在缺少真实样本情况下,能取得相似的结果且误差保持 在5%以内,不同检测器覆盖率实验下模型表现出良好的稳定性。本文模型通过给出无检测器路段交通状态估计界限,为高速公路交通检测器布设方案提供参考。     

构建和完善港口基础设施系统投融资体制

在分析当前我国港口基础设施系统投融资体制现状的基础上,根据公共经济学理论对港口设施系统要素按投资来源、投资方式进行了合理划分,并充分借鉴国外港口建设的投融资体制,提出应根据不同类型项目分类融资的思想,最后给出了建立与完善市场经济条件下港口投融资体制的具体建议。     

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.     

基于分类投资建设的铁路分类标准探讨

国发〔2013〕33号文提出对新建铁路实行分类投资建设,明确了具体放开的铁路类型,但缺乏具体判别标准。本文结合铁路项目公益性或经营性属性,在对既有铁路根据运输性质进行分类统计研究的基础上,从利于分类投资建设出发,对铁路划分类别及其标准进行初步探讨,重点量化了干、支线分类和经营性公益性分类,提出经营性公益性分类以经济评价作为主要指标,干、支线铁路以在路网中作用、运量、速度目标值、线路连接、线路长度等5个主要指标进行界定。     

Suspension system state estimation using adaptive Kalman filtering based on road classification

This paper provides a new method to solve the problem of suspension system state estimation using a Kalman Filter (KF) under various road conditions. Due to the fact that practical road conditions are complex and uncertain, the influence of the system process noise variance and measurement noise covariance on the estimation accuracy of the KF is first analysed. To accurately estimate the road condition, a new road classification method through the vertical acceleration of sprung mass is proposed, and different road process variances are obtained to tune the system’s variance for the application of the KF. Then, road classification and KF are combined to form an Adaptive Kalman Filter (AKF) that takes into account the relationship of different road process noise variances and measurement noise covariances under various road conditions. Simulation results show that the proposed AKF algorithm can obtain a high accuracy of state estimation for a suspension system under varying International Standards Organisation road excitation levels.     

Adhesion estimation at the wheel–rail interface using advanced model-based filtering

The railway industry in the UK is currently expanding the use of condition monitoring of railway vehicles. These systems can be used to improve maintenance procedures or could potentially be used to monitor current vehicle running conditions without the use of cost prohibitive sensors. This paper looks at a novel method for the online detection of areas of low adhesion in the wheel/rail contact that cause significant disruption to the running of a network, particularly in the autumn season. The proposed method uses a Kalman–Bucy filter to estimate the creep forces in the wheel–rail contact area; post-processing is then applied to provide information indicative of the actual adhesion level. The algorithm uses data that, in practice, would be available from a set of modest cost inertial sensors mounted on the vehicle bogie and wheel-sets. The efficacy of the approach is demonstrated using simulation data from a nonlinear dynamic model of the vehicle and its track interface.