一种基于微波雷达回波信号的车型分类方法

车辆类型识别方法是智能交通系统的关键技术之一。利用深度学习的高维特征泛化学习能力，将改进的LeNet-5卷积神经网络用于基于交通微波雷达的大小车型分类识别。首先，以雷达触发前的N帧信号为基础，对雷达的回波信号进行分析并构建数据集；然后，分析LeNet-5卷积神经网络的特点；最后提出一种改进的LeNet-5卷积神经网络。实验结果表明，与传统的支持向量机方法相比，所提方法能够智能学习大小车的雷达时频信号特征，大小车型识别准确率达到97%以上，可为交通场景下的车型识别研究提供新的技术途径。     

Is the environment‐friendly factor attractive to customers when purchasing electric vehicles? Evidence from South Korea

In a bid to reduce greenhouse gas emissions, several countries worldwide are implementing policies to promote electric vehicles (EVs). However, contrary to expectations, the diffusion speed of EVs has been rather slow in South Korea. This study analyzes consumer preferences for the technological and environmental attributes of EVs and derives policy and environmental implications to promote market diffusion of EVs in South Korea. We conduct a choice‐based conjoint survey of 1,008 consumers in South Korea and estimate the consumer utility function using a mixed logit model considering consumer heterogeneity. Based on the consumer utility function, we analyze consumers' willingness‐to‐pay (WTP) for EV attributes such as driving range, charging method, charging time, autonomous driving function, carbon dioxide (CO₂) reduction rate, and purchase price. The results indicate that the current low acceptance of EVs is due to their relatively high price and lack of a battery charging technology that satisfies consumers' expectations of the charging method and time. One interesting finding is that Korean consumers have a relatively higher WTP for the CO₂ reduction rate of EVs than consumers in other countries; however, they do not consider CO₂ reduction over other technological attributes when choosing EVs. This implies that the rate of CO₂ reduction of EVs is not an important factor for South Korean consumers when buying EVs. We also calculate the effect of CO₂ reduction with the market penetration of EVs and find that CO₂ reduction through the diffusion of EVs depends on the country's electricity generation mix.     

基于OID编码体系的汽车电子标识编码应用研究

本文为了解决物联网所连接对象的多样性带来的标识体系存在不兼容、相互矛盾的问题，基于车联网RFID汽车电子标识的使用特点，创造性地提出将OID编码和汽车电子标识编码相结合的方案。首先简单介绍了基于发证机构及时间和基于汽车VIN码的两种OID编码规则；然后提出了OID编码在汽车电子标识芯片中的规划，主要是内存分配方案；最后介绍了相应区域的读写操作的具体方法。     

高动态临近空间飞行器海上组网测控方案设计

为解决高动态临近空间飞行器海上测控对测量船数量需求较多、现有船队规模无法满足的难题,在分析测控需求特点的基础上,提出了一种基于测量船与临近空间飞艇组网测控的新模式。结合近中程、远程飞行试验分别进行了测控总体方案设计,并讨论了海上组网测控需要研究的关键技术。相关研究对我国后续开展高动态临近空间飞行器海上测控系统建设具有一定的参考价值。     

航材备件需求预测模型与应用研究

分析了航材消耗与需求特点,从计算航材换件次数的角度进行分析,采用简单的函数拟合算法,给出需求规律拟合公式,结合具体实例进行了器材需求预测,并给出预测。计算结果表明,计算过程简单,拟合效果较好,易于工程采用。     

油田特种作业车辆燃油管理系统的研究

为解决油田特种作业车辆和台上设备在生产运行中个别操作者盗卖燃油的问题,我们利用高精度燃油测量装置,对特种作业车辆台上设备的燃油消耗进行准确计量,并通过车载GPS终端通讯信道实时上传到监控管理系统进行展现和统计分析,解决了特种车辆燃油消耗考核不能落实到单车的难题,经济效益和社会效益明显,有很好的推广应用前景。     

我国整车与零部件产业共聚的空间分布研究

本研究利用我国整车与零部件产业2002～2012年的数据,分析了中国整车和零部件产业协同共聚的空间布局及其空间演化趋势,以期为我国汽车、零部件产业和区域经济发展提供借鉴意义。研究发现：我国整车产业布局与零部件产业布局存在显著的空间相关性;2002年我国省域样本中,山东、安徽、北京与天津的整车与零部件产业空间共聚趋势明显;而在2012年的样本中,辽宁、北京、天津、河北、湖北、安徽、江苏的共聚趋势明显,这些省份逐步形成了2个整车与零部件空间上协同共聚的群落,一个是以北京为中心,天津、河北、辽宁环绕的共聚群;另外一个则是以安徽为中心,湖北、江苏环绕的共聚群。     

机动车尾气污染危害的生成机理与治理发展

本文介绍了汽车尾气对人类健康和生态环境的危害,及其生成机理和影响因素并简述汽车尾气治理与发展。     

Pareto efficient allocation of an in-motion wireless charging infrastructure for electric vehicles in a multipath network

Electric vehicles (EV) use an eco-friendly technology that limits the greenhouse gas emissions of the transport sector, but the limited battery capacity and the density of the battery are the major barriers to the widespread adoption of EV. To mitigate this, a good method seems to be the innovative wireless charging technology called ‘On-Line EV (OLEV)’, which is a contactless electric power transfer technology. This EV technology has the potential to charge the vehicle’s battery dynamically while the vehicle is in motion. This system helps to reduce not only the size of the battery but also its cost, and it also contributes to extending the driving range before the EV has to stop. The high cost of this technology requires an optimal location of the infrastructure along the route. For this reason, the objective of this paper is to study the problem of the location of the wireless charging infrastructure in a transport network composed of multiple routes between the origin and the destination. To find a strategic solution to this problem, we first and foremost propose a nonlinear integer programming solution to reach a compromise between the cost of the battery, which is related to its capacity, and the cost of installing the power transmitters, while maintaining the quality of the vehicle’s routing. Second, we adapt the multi-objective particle swarm optimization (MPSO) approach to our problem, as the particles were robust in solving nonlinear optimization problems. Since we have a multi-objective problem with two binary variables, we combine the binary and discrete versions of the particle swarm optimization approach with the multi-objective one. The port of Le Havre is presented as a case study to illustrate the proposed methodology. The results are analyzed and discussed in order to point out the efficiency of our resolution method.     

不确定环境下的地震应急救援路径优化研究

针对地震应急救援的特点,引入不确定理论,在地震灾害的背景下,研究震后动态网络环境下的应急救援路径选择问题。用不确定变量表示网络中各路段的破坏程度,综合考虑路径破坏程度及车辆路径连续性等约束条件,以救援时间最短为目标,基于不确定理论建立了动态优化模型,并用改进的遗传算法求解,最后设计算例验证了模型和算法的有效性。