航班快速恢复建模与仿真研究

随着民用航空的发展与竞争,航班延误不仅影响航空飞行的安全与正常,更与航空公司的运营效率、运营成本及乘客利益息息相关。针对某一恶劣天气影响,对某公司受影响航班进行重新调配,考虑到航班的备降、盘旋等待、延误、取消等多种状态,以总成本最小为目标函数,建立航班快速恢复模型,通过MATLAB运用遗传算法设计航班恢复算法进行求解,得出最经济的航班恢复方案。     

基于模糊隶属度曲面的遗传算法图像分割

介绍了遗传算法的发展历史和图像分割的应用现状，分析了基于基本的遗传算法的图像分割方法，并将遗传算法与模糊集理论相结合用于医学图像分割，提出了基于模糊隶属度的遗传算法的医学图像分割方法。并对不同方法，如分割效果、计算代价进行了分析比较。     

基于自适应遗传算法的路堤边坡稳定性分析方法

基于圆弧滑动面假定,提出了一种用自适应遗传算法搜索最危险滑动面及其对应的最小安全系数的新方法。该方法是一种改进的遗传算法,采用自适应求取适值、动态调整交叉率和变异率、自适应区间收缩。自适应遗传算法有效克服了传统方法易陷入局部极小的缺陷,提高了算法的搜索效率、精度和稳定性。     

基于混合算法的大跨度斜拉桥可靠度评估

为研究大跨度顸应力混凝土(PC)斜拉桥的可靠度评估问题,提出了适用于大跨度PC斜拉桥这类复杂结构可靠性分析的混合算法.该方法综合运用了有限元分析(FEA)、径向基函数(RBF)神经网络、遗传算法(GA)和Monte Carlo重要抽样(MCIS)方法,并对算法中的关键步骤(RBF神经网络的初始样本点设计方法、MCIS的抽样中心点位置等)进行了改进,使结构分析模块与可靠度计算模块智能结合.利用数值算例的可靠度分析对该算法的有效性进行了验证.最后,以一座主跨为420 m的双塔PC斜拉桥为工程背景,进行了正常使用极限状态下的可靠度分析.参数分析表明:在汽车荷载作用下,该斜拉桥的主梁跨中位移超限失效概率比最长斜拉索强度失效概率高;汽车荷载的均值和标准差是影响斜拉桥可靠度的重要因素;随着汽车荷载均值系数的增大,主梁跨中位移超限失效的可靠指标下降的趋势较为显著.     

高速公路场景下的电子稳像技术研究

为了去除高速公路监控视频中自身位置不稳定的事物给全局运动矢量估计带来误差,先通过Canny算子检测图像中的直线,并根据车道边界的特征筛选出道路边界,确定道路区域。再检测道路区域内的Harris角点信息,并与背景图中道路区域内的角点位置进行匹配,从而得到全局运动矢量,并计算出稳定的图像。实验表明,该算法能够满足高速公路场景下的稳像需求。     

基于LabVIEW管道泄漏检测系统的研究

介绍了管道泄漏检测系统的设计原理.利用LabVIEW中的小波分析函数对采集到的信号进行处理,并利用小波变换模极大值捕捉压力信号的突变点,从而得出负压波传播到上下游监测点的时间差,进而利用负压波定位原理进行泄漏点的定位.另外,对于优化程序内存和提高CPU的利用率提出了一些有效的解决方法.     

Approximate network loading and dual-time-scale dynamic user equilibrium

In this paper we present a dual-time-scale formulation of dynamic user equilibrium (DUE) with demand evolution. Our formulation belongs to the problem class that Pang and Stewart (2008) refer to as differential variational inequalities. It combines the within-day time scale for which route and departure time choices fluctuate in continuous time with the day-to-day time scale for which demand evolves in discrete time steps. Our formulation is consistent with the often told story that drivers adjust their travel demands at the end of every day based on their congestion experience during one or more previous days. We show that analysis of the within-day assignment model is tremendously simplified by expressing dynamic user equilibrium as a differential variational inequality. We also show there is a class of day-to-day demand growth models that allow the dual-time-scale formulation to be decomposed by time-stepping to yield a sequence of continuous time, single-day, dynamic user equilibrium problems. To solve the single-day DUE problems arising during time-stepping, it is necessary to repeatedly solve a dynamic network loading problem. We observe that the network loading phase of DUE computation generally constitutes a differential algebraic equation (DAE) system, and we show that the DAE system for network loading based on the link delay model (LDM) of Friesz et al. (1993) may be approximated by a system of ordinary differential equations (ODEs). That system of ODEs, as we demonstrate, may be efficiently solved using traditional numerical methods for such problems. To compute an actual dynamic user equilibrium, we introduce a continuous time fixed-point algorithm and prove its convergence for effective path delay operators that allow a limited type of nonmonotone path delay. We show that our DUE algorithm is compatible with network loading based on the LDM and the cell transmission model (CTM) due to Daganzo (1995). We provide a numerical example based on the much studied Sioux Falls network.     

Multi-objective optimization of a road diet network design

The present study focuses on the development of a model for the optimal design of a road diet plan within a transportation network, and is based on rigorous mathematical models. In most metropolitan areas, there is insufficient road space to dedicate a portion exclusively for cyclists without negatively affecting existing motorists. Thus, it is crucial to find an efficient way to implement a road diet plan that both maximizes the utility for cyclists and minimizes the negative effect on motorists. A network design problem (NDP), which is usually used to find the best option for providing extra road capacity, is adapted here to derive the best solution for limiting road capacity. The resultant NDP for a road diet (NDPRD) takes a bi-level form. The upper-level problem of the NDPRD is established as one of multi-objective optimization. The lower-level problem accommodates user equilibrium (UE) trip assignment with fixed and variable mode-shares. For the fixed mode-share model, the upper-level problem minimizes the total travel time of both cyclists and motorists. For the variable mode-share model, the upper-level problem includes minimization of both the automobile travel share and the average travel time per unit distance for motorists who keep using automobiles after the implementation of a road diet. A multi-objective genetic algorithm (MOGA) is mobilized to solve the proposed problem. The results of a case study, based on a test network, guarantee a robust approximate Pareto optimal front. The possibility that the proposed methodology could be adopted in the design of a road diet plan in a real transportation network is confirmed.     

基于蚁群算法和群决策理论的动态路径优化研究

文章针对城市交通信号控制中的动态路径优化问题,综合考虑相邻交叉口间距和路段交通饱和度两个参数的影响,提出了一种基于蚁群算法和群决策理论的动态路径优化算法模型,并通过仿真实验,对比分析了该算法模型的有效性。     

基于FPGA的里程轮信号优选算法设计

文中通过对里程信号产生机理进行分析,提出了一种基于FPGA架构的里程优选算法,给出了实现算法的硬件架构图,并设计相应硬件电路和模拟实验。结果表明:该算法可以对3个里程轮脉冲信号进行优选采集,当里程轮处于超速或者停转状态时也能输出正确的脉冲信号。