高排放标准下道路燃油车处境分析

针对国六排放标准实施后道路燃油车竞争和生存压力加大的问题,调查统计近5 a汽车及新能源汽车的保有量,汽车保有量以年均12%的比例增长,新能源汽车保有量以每年约50%的比例增长;研究和对比近年来机动车排放标准,分析道路燃油车节能减排存在的问题,从政策引导制约、供油企业升级改造、燃油车本身的改进优化等几方面提出时效性方案与建议。     

Shape optimization of bi-directional flow passage components based on a genetic algorithm and computational fluid dynamics

The inlet/outlet is an important part of a water conveyance system in a pumped storage power station (PSPS). Its hydraulic characteristics are directly related to the operation and economic benefit of the PSPS. Frequent changes between inflow and outflow operations pose significant challenges in the design of the inlet/outlet diffusion segment shape. In this study, an effective optimization method, including three-dimensional parametric modelling, computational fluid dynamics and a genetic algorithm, is introduced and coupled to the design of the diffusion segment shape. The hydraulic characteristics of bi-directional flow, including the head loss, velocity uneven distribution and uneven discharge distribution, are selected as the objective function in the optimization method. Using this method, the recommended shape of the inlet/outlet is studied and its hydraulic characteristics are discussed. The results indicate that the optimized inlet/outlet has better performance.     

温拌LDPE改性沥青混合料性能研究

选择Sasobit和Evotherm 3G两种温拌剂对温拌LDPE沥青混合料制备工艺和性能进行研究。首先,比较变温等体积法和等黏温度法两种方法,并确定两种温拌混合料的拌和及击实温度。在此基础上,分别采用车辙试验、低温弯曲试验、中点加载弯曲试验以及冻融劈裂试验评价70#、LDPE、Sasobit-LDPE以及3G-LDPE四种沥青混合料的高温性能、低温性能、疲劳性能和水稳定性。结果表明,两种温拌技术均是可行的,Sasobit和3G温拌技术可使施工温度降低20℃和15℃。与热拌LDPE沥青混合料相比,Sasobit温拌沥青混合料具有更好的高温性能和水稳定性,在高应力比条件下抗疲劳性能更好,低温性能虽变差,但是优于70#沥青混合料;3G温拌沥青混合料的高温性能,低温性能以及水稳定性则与热拌LDPE沥青混合料各项性能较为接近,抗疲劳性能变差,但是优于70#沥青混合料。     

基于深度残差学习的自动驾驶道路场景理解

随着道路场景理解技术的快速发展，自主驾驶领域取得了长足的进步。在相关任务中，包括道路分割、分类和车辆检测的实时性和准确性是安全性的一个关键问题。为此，提出了一个具有编/解码器网络结构的基于深度残差学习的方法。一方面，编码器网络结构使用不同层次的残差网络来提取高维中的抽象特征，这些特征在接下来的三个任务中共享使用；另一方面，解码器网络结构采用一种子任务的并行计算机制，即道路分割、车辆检测和道路分类任务同时执行。此外，全卷积神经网络用于对提取的图像特征进行上采样以解决道路分割问题。最终，实验结果表明在保证高精度的前提下处理帧率可达到15 fps以上。     

基于高斯混合隐马尔科夫模型与人工神经网络的紧急换道行为预测方法

为了有效降低因驾驶员紧急换道行为而诱发的交通事故，提高道路交通事故链阻断效率，提出一种基于高斯混合隐马尔科夫模型（GMM-HMM）和人工神经网络（ANN）的紧急换道行为预测方法。首先利用GMM-HMM对车辆行驶状态以及驾驶行为连续观察序列进行换道意图辨识，采用ANN预测下一时段的驾驶行为，再预测换道过程中的横向加速度变化率，从而判断紧急换道的危险程度。驾驶员在环仿真实验及实车实验结果表明，该方法预测避险成功率达92.83%，实验避险成功率达90.32%。该方法能有效地对紧急换道行为进行提前警告与干预。     

抗凝冰乳化沥青涂层的性能研究

为了研究自制融雪剂对抗凝冰乳化沥青涂层性能的影响,将自制固态型融雪剂按5%、10%、15%的比例加入乳化沥青制备涂层乳液,采用水煮法、摆式摩擦仪、电导率试验分别对涂层材料的粘附性、抗滑性能和抗凝冰性能进行了测试。结果表明:自制型融雪剂对乳化沥青与集料的粘附性影响不大;随着融雪剂掺配比例的增大,抗凝冰乳化沥青涂层的摩擦系数有所降低,但均满足规范要求;相比于普通沥青涂层,抗凝冰乳化沥青涂层的抗凝冰性能得到改善,且随着融雪剂掺配比例的增大,凝冰性能逐渐提高。     

Locally one-dimensional-alternating segment explicit–implicit and locally one-dimensional-alternating segment Crank–Nicolson methods for two-dimension parabolic equations

In the paper two new locally one-dimensional alternating segment schemes for solving the 2-D parabolic problems are developed. The parabolic problems can be solved efficiently over decomposed sub-domains. Two schemes are proved to be unconditionally stable and numerical experiments show the validity of the schemes.     

煤矿工业场地道路照明接地系统问题探讨

文章简要介绍了煤矿工业场地的特点和低压配电系统接地方式分类,从安全性、经济性的角度分析了TN接地系统和TT接地系统在道路照明回路中的优缺点。通过对比TN-S系统与TT系统的接地故障电流差异、TN-S接地系统用于道路照明的等电位联结困难程度,总结出煤矿工业场地的道路照明配电系统适宜采用TT接地方式。并且详细阐述了TT接地系统剩余电流动作保护器保护整定方法和灵敏度校验方法,列举了煤矿工业场地道路照明选用TT系统时需要注意的中性线不可重复接地的问题。     

旋挖钻机设备油液智能分析与健康监测

为了对直升机盒段件入水载荷进行分析，开展了模型垂直入水冲击试验，同时采用有限元-光滑粒子流体动力学（finite element method-smooth particle hydrodynamics，简称FEM-SPH）耦合数值方法进行仿真分析，计算了不同材料构型和速度下的过载、压力及应变结果。与试验结果进行对比表明，仿真结果与试验值吻合较好，预测的结构失效破坏模式与试验结果基本一致，可为真实直升机入水冲击的结构设计提供参考。     

How to predict the global instantaneous feeling induced by a facial picture?

Picture selection is a time-consuming task for humans and a real challenge for machines, which have to retrieve complex and subjective information from image pixels. An automated system that infers human feelings from digital portraits would be of great help for profile picture selection, photo album creation or photo editing. In this work, two models of facial pictures evaluation are defined. The first one predicts the overall aesthetic quality of a facial image, and the second one answers the question “Among a set of facial pictures of a given person, on which picture does the person look like the most friendly?”. Aesthetic quality is evaluated by the computation of 15 features that encode low-level statistics in different image regions (face, eyes, and mouth). Relevant features are automatically selected by a feature ranking technique, and the outputs of 4 learning algorithms are fused in order to make a robust and accurate prediction of the image quality. Results are compared with recent works and the proposed algorithm obtains the best performance. The same pipeline is considered to evaluate the likability of a facial picture, with the difference that the estimation is based on high-level attributes such as gender, age, and smile. Performance of these attributes is compared with previous techniques that mostly rely on facial keypoint positions, and it is shown that it is possible to obtain likability predictions that are close to human perception. Finally, a combination of both models that selects a likable facial image of good quality for a given person is described.