城市道路交叉口借道右转车道设置方法

为了弥补交叉口空间布局中对借道右转车道研究的不足,分析了借道右转车辆在两相邻交叉口间的运行过程,将两交叉口间距分为四部分。采用波动理论、考虑驾驶员的操作顺适性以及采用概率论方法,建立了借道右转的右转车道适宜开口位置模型、借道右转的绿化带开口长度模型和路段右转车辆的合流段长度模型,在此基础上得到了实施借道右转的两交叉口最小间距模型。实例分析表明,所得模型符合两相邻交叉口间的交通流运行实际,对于判断两相邻交叉口是否适合于设置借道右转,以及设置借道右转时如何对各构成部分进行分析具有指导意义。     

关联效应对CaCuO2的电荷与自旋密度分布的影响

采用杂化密度泛函方法和哈垂一福克方法对CaCuO2的电荷与自旋密度的分布进行了研究．CuO2层的电子结构具有明显的二维特征．电子间的关联效应使Cu离子的自旋极化被削弱的同时，增强了O离子的自旋极化效应．     

装配式梁桥横向协同工作性能度量指标的研究

为了表征运营期间装配式梁桥的横向协同工作性能,提出了一种间接度量指标——相邻预制梁间的应变相关系数;同时建立了装配式梁桥的简化力学模型,通过推导建立了该指标的解析表达式。理论分析和数值仿真研究表明,在保证足够的样本长度条件下,交通流荷载趋向统计稳定,此时结构特性成为该度量指标的唯一影响因素,证明了该指标具备表征装配式梁桥横向协同工作性能的条件。     

平面交叉口左转车道的几何设计方法研究

针对平面交叉口左转车道的设计方法进行研究,参考发达国家平交路口安全设计规范,分析现有的设计技术规范中不合理的因素,从左转车道的布置、车道宽度的确定、车道长度的确定、过渡段的设计等几个方面,进行了详细的分析研究,提出了平面交叉口左转车道的几何设计方法。     

极线约束下的多分辨率相关匹配算法

针对计算机视觉在几何测量应用中图像匹配特点,提出了将多分辨率和极线约束与相关法相结合的图像匹配方法。该方法采用金字塔分层和极线约束来弥补相关匹配带来运算量大的不足,克服了图像匹配中精度低的缺点。通过计算机视觉在几何测量检测系统中图像匹配的实际应用,验证了该算法具有较高的测量精度、速度及其实用性。     

城市道路平面交叉口的改善对策及探讨

城市道路平面交叉口在城市道路网中居于核心地位,也是交通事故最集中的地方。针对城市道路平面交叉口出现的问题,该文从平面交叉口的几何构造、渠化、交通控制、行人过街安全等方面介绍了平面交叉口的改善措施,并结合实例进行探讨,以提高交叉口的通行能力。     

城市主干道调头口设置探讨

随着城市的不断发展,城市主要干道中调头需求已成为交通流中不可忽视的一部分。该文通过对北京市通州区城市主干道现况调头口的运行状况进行调查研究,分析车辆调头对交叉口交通功能的影响程度,就如何合理地设置调头口,得出初步的结论,从设计和管理的角度提出建议以供参考。     

Links between subjective and objective evaluations regarding the steering character of automobiles

This work presents an experiment on the relationships between subjective and objective evaluations of vehicle handling. Ten cars were examined objectively in several open-loop driving dynamics manoeuvres and subjectively by test persons in typical traffic situations. Results are extracted from a stationary test (the Slowly Increasing Steer Test), and a dynamical test (the Frequency Response Test). The subjective measurements are obtained from drivers on a rural road course via a questionnaire, which was developed to separately investigate the quantity level perception, the so-called “Niveau”, and the more qualitative “Liking”. These subjective “measurements” are embedded into a two-channel definition of “Steering Comfort” as a genus for “Steering Discomfort” and “Character”. The article concentrates on developing a statistical method for the consideration of correlations amongst the subjective/objective data. In doing so, the variance in example subjective Niveau ratings can be significantly explained by several objective quantities. Indicators for co-domains of validated discomfort characteristics and hints for endeavouring character Liking ranges are detected.     

Bolster spring fault detection strategy for heavy haul wagons

An on-board health monitoring system is proposed for heavy haul wagons in this paper including a signal-based fault detection and isolation (FDI) method and an on-line fault diagnose strategy. Such a system, to be feasible on freight wagons, must be sufficiently cheap and robust, hence the design assumes the constraint of using only two accelerometers mounted on the front left and right rear part of each carbody in a heavy haul train. This paper focuses on the detection of bolster spring fault conditions. The problem is made more complex by the modes of failure which might be expected in bolster spring nests. Types of spring failure are firstly identified and discussed covering situations of broken (shortening springs) and softening (individual spring loss from a nest or cross-section loss through corrosion). The effects of these faults and their detectability were investigated using simulations on straight and curved track and using a fully detailed model of a typical 40?t axle-load heavy haul wagon. The simulation results were then examined and compared using cross-correlation analysis and an FDI system was proposed. The FDI system introduced five possible fault indicators. Initial results indicated that it was possible to detect changes in bolster stiffness of ±25%. An on-line fault diagnoses strategy is proposed for bolster spring faults which only requires data from wagon monitoring during travel around sharp curves to detect and the occurrence of confirm faults. The functionality envisaged needs only a ‘once per trip’ monitoring site, such as a sharper curve, and is aimed at condition monitoring rather than the provision of alarms or comprehensive monitoring of all events.     

基于遗传算法和极限学习机的智能算法在基坑变形预测中的应用

为解决传统智能算法网络结构参数复杂、运算速度慢等问题,基于遗传算法和极限学习机构建基坑变形的新型优化智能预测模型。先利用皮尔逊相关系数评价不同影响因素与基坑沉降变形之间的相关性,以确定极限学习机的输入层; 再采用试算法确定最优激励函数和隐层节点数,并将遗传算法和极限学习机耦合,利用遗传算法优化极限学习机的初始权值和阈值,以提高预测精度。经实例检验表明： 1)开挖时间、开挖深度、土体抗剪参数及重度均与基坑沉降变形显著相关,为构建极限学习机输入层提供了依据; 2)在预测过程中,激励函数和隐层节点数对极限学习机的预测效果具有一定的影响,以Sigmiod型激励函数和13个隐层节点数的预测效果为最优; 3)通过遗传算法的优化,能进一步提高预测精度,验证了遗传算法的优化能力和有效性。预测模型在不同工况下的预测结果均较优,说明该模型具有较高的稳定性和可靠性。