Automatic calibration of fundamental diagram for first‐order macroscopic freeway traffic models

Despite its importance in macroscopic traffic flow modeling, comprehensive method for the calibration of fundamental diagram is very limited. Conventional empirical methods adopt a steady state analysis of the aggregate traffic data collected from measurement devices installed on a particular site without considering the traffic dynamics, which renders the simulation may not be adaptive to the variability of data. Nonetheless, determining the fundamental diagram for each detection site is often infeasible. To remedy these, this study presents an automatic calibration method to estimate the parameters of a fundamental diagram through a dynamic approach. Simulated flow from the cell transmission model is compared against the measured flow wherein an optimization merit is conducted to minimize the discrepancy between model‐generated data and real data. The empirical results prove that the proposed automatic calibration algorithm can significantly improve the accuracy of traffic state estimation by adapting to the variability of traffic data when compared with several existing methods under both recurrent and abnormal traffic conditions. Results also highlight the robustness of the proposed algorithm. The automatic calibration algorithm provides a powerful tool for model calibration when freeways are equipped with sparse detectors, new traffic surveillance systems lack of comprehensive traffic data, or the case that lots of detectors lose their effectiveness for aging systems. Furthermore, the proposed method is useful for off‐line model calibration under abnormal traffic conditions, for example, incident scenarios. Copyright © 2015 John Wiley & Sons, Ltd.     

An efficient recursive least square-based condition monitoring approach for a rail vehicle suspension system

A model-based condition monitoring strategy for the railway vehicle suspension is proposed in this paper. This approach is based on recursive least square (RLS) algorithm focusing on the deterministic ‘input–output’ model. RLS has Kalman filtering feature and is able to identify the unknown parameters from a noisy dynamic system by memorising the correlation properties of variables. The identification of suspension parameter is achieved by machine learning of the relationship between excitation and response in a vehicle dynamic system. A fault detection method for the vertical primary suspension is illustrated as an instance of this condition monitoring scheme. Simulation results from the rail vehicle dynamics software ‘ADTreS’ are utilised as ‘virtual measurements’ considering a trailer car of Italian ETR500 high-speed train. The field test data from an E464 locomotive are also employed to validate the feasibility of this strategy for the real application. Results of the parameter identification performed indicate that estimated suspension parameters are consistent or approximate with the reference values. These results provide the supporting evidence that this fault diagnosis technique is capable of paving the way for the future vehicle condition monitoring system.     

中等雷诺数方柱绕流的直接数值模拟及涡系分析

作为一种典型的钝体绕流,方柱绕流具有物体几何外形简单而流场结构非常复杂的特性,常规的基于RANS的数值计算方法难以准确模拟。DNS不使用任何湍流模型,直接求解完整的非定常流动控制方程组,模拟包括脉动在内的湍流所有非定常流动量的时空演变过程,是湍流数值模拟中最精确的方法,在复杂流动数值模拟方面的应用潜力巨大。本文自主设计和编制并行数值模拟程序,使用基于"神威·太湖之光"国产CPU架构的大规模并行计算,开展了中等雷诺数(Re=10 000)方柱绕流的直接数值模拟。其中:不可压N-S方程组采用基于交错网格的有限体积法离散;压力-速度耦合采用SIMPLE算法处理;离散得到的代数方程组采用Gauss-Seidel迭代求解;时间步进采用Euler隐式格式,对流项采用QUICK格式,耗散项采用中心差分格式;数值模拟程序的并行化使用MPI方法处理。文中重点分析了方柱绕流的复杂涡系结构,同时给出了部分湍流统计结果,并通过与RANS和LES模拟结果的对比分析,展现了DNS在复杂精细流场模拟方面的优势。     

联邦滤波器及其在船舶组合导航中的应用

介绍了联邦滤波器原理,设计了一个基于联邦滤波理论的船用组合导航系统.与传统的集中式滤波器相比,联邦滤波器结构简单,容易实现,并且容错性好,计算量少.仿真结果表明该滤波器能满足系统精度和容错性能的要求.     

基于水动力系数摄动的船舶横向运动姿态与受扰Kalman滤波的统计建模

该文考察船舶水动力参数在标称值附近按均匀分布发生随机变化时,船舶横向运动姿态及受扰Kalman估计的统计性能.针对某船,在5级海情,18kns航速及0°、45°、90°、135°、180°航向工况下,当船舶水动力系数按均匀分布发生±10%随机变化时,给出船舶姿态及受扰Kalman估计性能的概率建模方法及其应用.仿真结果表明,当水动力系数出现摄动时,估计精度服从对数正态分布,平均估计精度与未出现摄动时相比无显著变化.     

A New Multi-sensor Data Fusion Algorithm Based on EMD-MMSE

A new multi-sensor data fusion algorithm based on EMD-MMSE was proposed.Empirical mode decomposition(EMD)is used to extract the noise of every time series for estimating the variance of the noise.Then minimum mean square error(MMSE)estimator is used to calculate the weights of the corresponding series.Finally,the fused signal is the weighted addition of all these series.The experiments in lab testified the efficiency of this method.In addition,the comparison in fusion time and fusion results with existing fusion method based on wavelet and average technique shows the advantage of this method greatly.     

Matlab的图像处理工具箱中图像复原函数的比较

Matlab是当今流行的科学计算软件,它具有很强的数据处理能力。在其图像处理工具箱中有四个图像复原函数,本文将就这些函数的算法原理、运用条件、和恢复处理效果作一简要的比较性讨论。     

线性模型下摄像机标定

主要讨论线性模型下控制点的选取方法。基本原理建立在对超定方程组系数矩阵的讨论上。理论推导和图像实验表明本讨论结果是有效的,具有一定的实用价值。     

弯曲路径识别中感兴趣区设定方法的研究

在路径识别中设置搜索感兴趣区以提高弯曲道路边界的识别实时性。用抛物线模型拟合弯曲道路边界,根据道路边界位置不会发生突变的特性,在识别出的道路抛物线模型上选取有代表性的3个点,针对每个点建立预测模型,应用Kalman滤波理论准确预测道路边界的位置,并据此设置搜索感兴趣区。依照合理的目标函数,在感兴趣区范围内搜索并确定抛物线参数,从而将搜索范围较为准确地限定在较小区域,能够有效提高识别的实时性。试验验证该方法在大大提高识别实时性的同时,增强了识别的精确性和鲁棒性。     

考虑电堆寿命的氢燃料电池汽车能量管理策略研究

提出了一种在满足动力性需求并且以氢燃料电池堆作为主要能源的前提下，能有效延长电堆使用寿命的能量管理策略。提出将需求功率 SG滤波后再进行规则控制的能量管理策略，将多种循环工况的结果进行手动优化后作为训练数据集，设计三输入一输出的自适应神经模糊推理系统控制器，根据其输出结果再进行一次滤波最终形成基于自适应神经模糊推理系统优化的能量管理策略。使用CLTC-P循环工况对能量管理策略进行仿真验证，结果表明，基于自适应神经模糊推理系统优化的能量管理策略能有效延长氢燃料电池剩余使用寿命，相比滤波加规则策略剩余使用寿命增加了33%，并能保持动力电池SOC处于适宜水平。