基于模拟退火-自适应布谷鸟算法的城市公交调度优化研究

为改善城市常规公交运营效率,提出基于模拟退火-自适应布谷鸟算法的公交调度优化模型.通过结合线路实际客流数据反映的客流特征,建立考虑公交公司和乘客双方利益的公交调度优化模型;改进布谷鸟算法固定步长并加入模拟退火算法退火操作,设计模拟退火-自适应布谷鸟算法,改善寻优过程中跳出局部最优解而全局寻优的能力;以福州125路公交线...     

基于分离矩阵的步长自适应EASI算法

首次提出将分离矩阵用于对在线盲分离算法中步长自适应控制,并在认真分析相关固定步长和变步长EASI算法的基础上,将其加以运用,提出了一种新的步长自适应EASI算法。该算法步长是基于分离矩阵的,其学习速率由信号的分离程度自适应地选取,因而能很好地解决收敛速度和稳态误差之间的矛盾。计算机仿真结果与理论分析相一致,证实了该算法明显优于其他固定步长或变步长的EASI算法。     

基于层次分析法和D-S证据理论的码头风险评估

在码头风险评估过程中,目前常用的方法对风险事件发生的概率和影响上有一些主观的定性分析,带有结论上的模糊性.运用一种层次分析法和证据理论结合的方法可以提高评估的准确性和可靠度,该方法通过层次分析法确定各评估因素的权重,在此过程中用D-S证据理论对专家评分数据进行融合修正,运用D-S证据理论的合成法则确定码头的风险值.将该...     

AIS与VTS的雷达／ARPA信息融合的研究与实现

分析了信息融合的必要性,并在信息融合基本原理的基础上,提出了信息融合的步骤,采用模糊数学中正态型隶属度函数算法实现AIS与VTS的雷达/ARPA信息融合,解决了不同设备处理同一信息的差异问题。     

The Alternative Motor Fuels Act, alternative-fuel vehicles, and greenhouse gas emissions

The corporate average fuel economy (CAFE) standard is the major policy tool to improve the fleet average miles per gallon of automobile manufacturers in the US. The Alternative Motor Fuels Act (AMFA) provides special treatment in calculating the fuel economy of alternative-fuel vehicles to give manufacturers CAFE incentives to produce more alternative-fuel vehicles. AMFA has as its goals an increase in the production of alternative-fuel vehicles and a decrease in gasoline consumption and greenhouse gas emissions. This paper examines theoretically the effects of the program set up under AMFA. It finds that, under some conditions, this program may actually increase the production of fuel-inefficient gasoline vehicles, gasoline consumption and greenhouse gas emissions.     

An adaptive information fusion model to predict the short-term link travel time distribution in dynamic traffic networks

As intelligent transportation systems (ITS) approach the realm of widespread deployment, there is an increasing need to robustly capture the variability of link travel time in real-time to generate reliable predictions of real-time traffic conditions. This study proposes an adaptive information fusion model to predict the short-term link travel time distribution by iteratively combining past information on link travel time on the current day with the real-time link travel time information available at discrete time points. The past link travel time information is represented as a discrete distribution. The real-time link travel time is represented as a range, and is characterized using information quality in terms of information accuracy and time delay. A nonlinear programming formulation is used to specify the adaptive information fusion model to update the short-term link travel time distribution by focusing on information quality. The model adapts good information by weighing it higher while shielding the effects of bad information by reducing its weight. Numerical experiments suggest that the proposed model adequately represents the short-term link travel time distribution in terms of accuracy and robustness, while ensuring consistency with ambient traffic flow conditions. Further, they illustrate that the mean of a representative short-term travel time distribution is not necessarily a good tracking indicator of the actual (ground truth) time-dependent travel time on that link. Parametric sensitivity analysis illustrates that information accuracy significantly influences the model, and dominates the effects of time delay and the consistency constraint parameter. The proposed information fusion model bridges key methodological gaps in the ITS deployment context related to information fusion and the need for short-term travel time distributions.     

面向智能船舶的容错卡尔曼融合估计方法

针对智能船舶多传感器系统因未知海洋环境干扰和设备间干扰等因素导致的一个或数个传感器产生随机间歇性故障从而导致融合估计结果出现偏差甚至失真的问题,设计1种基于四分位滤波的容错方法,并针对该方法导致的观测时滞问题设计1种预报方法,提前预报观测值,进而抵消容错方法导致的时滞问题。此外,针对多传感器之间的互协方差难以准确估计的问题,采用CI融合估计方法进行融合估计。为验证算法的有效性和融合估计的精度,对带有间歇性故障的两传感器系统进行仿真试验,并与按矩阵、按对角阵和按标量3种分布式融合估计方法得到的结果进行对比。4种方法的均方误差系数大小对比结果显示,对于带间歇性故障的多传感器系统,设计的融合滤波不仅具有鲁棒性,而且具有较高的融合精度。     

Empirical analysis of short period traffic counts and their efficiency: the case of Indian traffic

Short period traffic counts (SPTCs) are conducted routinely to estimate the annual average daily traffic (AADT) at a particular site. This paper uses Indian traffic volume data to methodically and extensively study the effect of four aspects related to the design of SPTCs. These four aspects are: (i) for how long, (ii) on which days should SPTCs be carried out, (iii) how many times, and (iv) on which months should SPTCs be carried out? The analyses indicate that the best durations for conducting SPTCs are 3 days (starting with a Thursday) and 7 days, for total traffic and truck traffic, respectively. Further, these counts should be repeated twice a year keeping a separation of two months between the counts to obtain good estimates of AADT at minimal cost. An additional outcome of this study has been the determination of seasonal factor values for roads in developing economies, like India.     

基于AIGA-WLSSVM的埋地管道腐蚀速率预测方法

为了降低埋地管道腐蚀影响因素之间的复杂相关性,提高腐蚀预测精度,文中提出一种基于自适应免疫遗传算法-加权最小二乘支持向量机(AIGA-WLSSVM)的埋地管道腐蚀速率预测建模方法,并采用AIGA优化模型参数,进一步提高模型的学习能力和稳定性。最后通过实例分析验证了AIGA-WLSSVM建模方法在埋地管道腐蚀速率预测中的可行性和有效性,为埋地管道的检修与更换提供参考。     

客货滚装码头车辆定位系统需求分析和技术实现

针对某客货滚装码头中车辆运输的智能化调度需求,进行车辆位置智能感知的研究,采用物联网感知和计算机视觉技术,得出一种基于视频监控的车辆定位系统方案。该系统能够实时监控码头内进出港车辆,跟踪和记录其路径,支持越界报警,为智能化调度和安全监控提供基础数据。