Fuel economy comparison of conventional drive trains series and parallel hybrid electric step vans

A hybrid electric vehicle (HEV) is a vehicle that combines a conventional propulsion system with an on-board rechargeable energy storage system to achieve better fuel economy than a conventional vehicle HEVs do not have limited ranges like battery electric vehicles, which use batteries charged by an external source. The different propulsion power systems may have common subsystems or components. The objective of this study is to compare the fuel economies of a conventional step van, a series hybrid electric step van (HESV), and a parallel HESV by calculating the fuel consumption using the ADVISOR software by NREL. We also showed the results of the vehicles in different driving cycles including the Central Business District bus cycles, the New York City Cycle, and the US EPA City and Highway cycles.     

轻轨车辆系列型谱研究

在详细调查我国各大城市公交客流的基础上,对客流进行了科学规划,并以此为基础,对轻轨车辆的几何、动力、舒适性参数进行了详细的计算分析,制定了适合我国国情的轻轨车辆系列型谱。综合以上分析结果,结合我国目前经济技术条件,并参考国外车辆发展状态,制定了包括2.6m宽和2.8m宽的二大系列的轻轨车辆系列型谱标准。     

自组织人工神经网络在民航旅客盈利能力细分中的应用

目前采用的基于里程的旅客划分方法不能清晰区分旅客价值, 应用Kohonen T自组织特征映射的人工神经网络模型将反映旅客盈利能力的多维行为特征属性数据以有序的方式映射到对旅客盈利性判别等级的低维空间, 形成对旅客正确识别的一种拓扑意义的有序图。试验结果表明, 此模型对客户细分识别成功率较高, 准确率可达90.8%。     

理想薄平板气动导数的人工神经网络识别

介绍一种用BP人工神经网络方法识别理想薄平板的气动导数的方法．通过构造BP网络，比较各种因素对识别结果的影响．数据处理方式对神经网络的训练结果影响很大．隐层单元数量、训练次数、随机赋值次数、样本数量等对训练结果也有影响，但影响较小．采用这种方法识别理想薄平板的气动导数是可行的，并且具有较高的精度．     

A Novel Evolutionary Feedforward Neural Network with Artificial Immunology

A hybrid algorithm to design the multi-layer feedforward neural network was proposed. Evolutionary programming is used to design the network that makes the training process tending to global optima. Artificial im-munology combined with simulated annealing algorithm is used to specify the initial weight vectors, therefore im-proves the probabiligy of training algorithm to converge to global optima. The applications of the neural network in the modulation-style recognition of analog modulated rader signals demonstrate the good performance of the network.     

基于知识的智能CAD系统设计

通过对工程智能ＣＡＤ系统及其设计模型的研究,提出了工程智能ＣＡＤ系统设计理论;规划控制下的二阶段设计理论,论述了基于这一理论的工程智能ＣＡＤ系统设计方法、设计对象的模型构造方法和智能ＣＡＤ系统中各类工程设计知识的应用。并已将这一方法成功地应用于工程智能ＣＡＤ系统的开发实践。     

大型地下埋设管道智能施工机械手的路径规划研究

针对机械手的动态不均匀性和运动学冗余性特点,基于人工势场理论与模糊理论,利用模糊系统逼近调和函数的梯度的方法进行路径规划,通过仿真来检验该方法的可行性。结果表明:该方法具有明显的柔性和自适应性。表现出某种智能特征,实现了路径的寻优规化。     

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轨道几何形状检测数据是一个随时间变化具有随机特征的时间序列,反映轨道几何状态的变化.在本文中,灰色关联度理论用于研究轨道水平不平顺时间序列数据,挖掘时间序列数据之间隐含的关系;经过普遍适应性改进和残差修正改进的灰色GM（1,1）模型预测固定测点轨道不平顺长期状态变化趋势,随机线性AR和卡尔曼滤波模型分析单元区段轨道不平顺短期变化趋势,探索轨道状态变化随机数据序列中隐藏的规律并进行预测.短期和长期预测模型验证结果表明,三种模型是有效的,能够达到预期的精度.     

利用GNSS时序分析辨识大跨桥梁结构荷载响应研究

使用小波分析、功率谱密度等方法,对武汉市阳逻大桥GNSS监测数据进行分析,识别并分离大桥不同位置结构车辆移动荷载响应、车辆振动荷载响应和噪声,首先去除噪声,然后利用信号不同频率特性,分离静态信号和动态信号,从而得到更为清晰的大跨度桥动力响应特性。     

Modelling heavy vehicle car‐following behaviour in congested traffic conditions

This study develops a car‐following model in which heavy vehicle behaviour is predicted separately from passenger car. Heavy vehicles have different characteristics and manoeuvrability compared with passenger cars. These differences could create problems in freeway operations and safety under congested traffic conditions (level of service E and F) particularly when there is high proportion of heavy vehicles. With increasing numbers of heavy vehicles in the traffic stream, model estimates of the traffic flow could be degrades because existing car‐following models do not differentiate between these vehicles and passenger cars. This study highlighted some of the differences in car‐following behaviour of heavy vehicle and passenger drivers and developed a model considering heavy vehicles. In this model, the local linear model tree approach was used to incorporate human perceptual imperfections into a car‐following model. Three different real world data sets from a stretch of freeway in USA were used in this study. Two of them were used for the training and testing of the model, and one of them was used for evaluation purpose. The performance of the model was compared with a number of existing car‐following models. The results showed that the model, which considers the heavy vehicle type, could predict car‐following behaviour of drivers better than the existing models. Copyright © 2013 John Wiley & Sons, Ltd.