电动汽车双电机耦合驱动系统发展与趋势

阐述了国内外电动汽车双电机耦合驱动系统的发展现状,对不同双电机耦合驱动系统的结构特点和应用等进行了总结。从动力性、经济性、布置灵活性、控制复杂性和成本五个方面对不同耦合驱动系统进行了综合对比分析,并对其发展潜力和趋势做出了归纳和展望。     

长江上游航运中心发展的SWOT分析及对策

重庆地处长江上游,位于"长江经济带"的西部中心,航运优势明显。加快长江上游航运中心的建设和发展是加强长江经济带建设这一国家战略的重要举措。总结航运中心应具备的5个基本要素,把航运中心分为3种发展模式和2大类,给出长江上游航运中心的发展定位;运用SWOT分析法分析重庆的优势、劣势、机遇和挑战,并提出相应的发展对策;指出重庆应充分发挥自身优势,抓住难得的政策机遇,通过逐步完善航运服务体系、稳定货源、加强合作、发展综合运输等措施,推动长江上游航运中心发展。     

Mesoscale subduction at the Almeria–Oran front: Part 1: Ageostrophic flow

This paper presents a detailed diagnostic analysis of hydrographic and current meter data from three, rapidly repeated, fine-scale surveys of the Almeria–Oran front. Instability of the frontal boundary, between surface waters of Atlantic and Mediterranean origin, is shown to provide a mechanism for significant heat transfer from the surface layers to the deep ocean in winter. The data were collected during the second observational phase of the EU funded OMEGA project on RRS Discovery cruise 224 during December 1996. High resolution hydrographic measurements using the towed undulating CTD vehicle, SeaSoar, traced the subduction of Mediterranean Surface Water across the Almeria–Oran front. This subduction is shown to result from a significant baroclinic component to the instability of the frontal jet. The Q-vector formulation of the omega equation is combined with a scale analysis to quantitatively diagnose vertical transport resulting from mesoscale ageostrophic circulation. The analyses are presented and discussed in the presence of satellite and airborne remotely sensed data; which provide the basis for a thorough and novel approach to the determination of observational error.     

交通流特征深度认知的车队运行参数优化方法

为提升车队对周围交通流环境的认知能力，获取车队周围多车的运行模式，同时通过改变车队运行参数实现车队群体对周围多车群体运行模式的诱导变化，为提升车队及交通流整体运行效率提供优化策略，提出了基于狄利克雷混和高斯过程的车队周围多车运行模式获取算法，将车队周围多车车辆的复杂运行模式视为混和高斯过程，利用狄利克雷分布作为高斯混和权重的先验分布，建立车队周围多车运行模式速度场，从而获取车队周围多车运行模式并分类；通过比较不同多车运行模式下车队的运行效率，提升车队对所处运行环境的认知能力。研究结果表明：利用非参数贝叶斯算法将复杂的多车运行状态进行分类，获取的车队周围多车运行模式可表现为对车队运行效率产生不同影响的速度场；通过将车队周围的多车运行仿真数据分成多个运行模式，可获取不同多车运行模式下车队及交通流整体运行特性；通过更改车队运行参数，观察不同多车运行模式的占比变化，可获取车队运行参数对所处运行环境改变的影响趋势；车队周围多车运行模式的获取，不仅可以提升车队对周围运行环境的认知能力，使得车队能够选取有利路径行驶，同时能够为车队运行策略的优化提供有效的信息。     

船舶机舱电动往复机械的维护特点

通过对船舶机舱最常见的3种电动往复机械进行分析,得出电动往复机械维护管理的主要特点,并进一步弄清了3种电动往复机械的结构特点与工作原理。比较3种电动往复机械的维护特点,不仅对船舶机舱设备的日常维护管理起到一定的促进作用,还可以在一定程度上提高轮机管理人员的专业知识。     

A theoretical foundation for the relationship between generalized origin–destination matrix and flow matrix based on ordinal graph trajectories

This paper shows the relationship between flow, generalized origin–destination (OD), and alternative route flow from a set of ordinal graph trajectories. In contrast to traffic assignment methods that employ OD matrix to produce flow matrix, we use ordinal trajectory on a network graph as input and produce both the generalized OD matrix and the flow matrix, with the alternative and substitute route flow matrices as additional outputs. By using linear algebra‐like operations on matrix sets, the relationship between network utilization (in terms of flow, generalized OD, alternative route flow, and desire line) and network structure (in terms of distance matrix and adjacency matrix) are derived. Copyright © 2012 John Wiley & Sons, Ltd.     

基于多维信息特征分析的驾驶人认知负荷研究

准确评估驾驶人认知负荷水平，对于深入研究驾驶人行为特性，改善驾驶安全性具有重要意义。现有的驾驶人认知负荷分类方法，大多基于心电、脑电等生理信息和车辆信息，由于特征选择上的单一性，导致驾驶人认知负荷分类模型的分类精度不高。设计基于跟驰场景的不同认知负荷N-back次任务试验，通过采集受试者的生理信号和车辆信号，结合NASA_TLX主观评分和机器学习算法，提出了基于多维信息特征融合的驾驶人认知负荷分类方法。研究表明：基于生理信息和车辆信息的多维信息特征认知负荷分类方法，其精度显著高于传统的基于生理信息的认知负荷分类方法，以多维信息特征为输入，随机森林法以其稳定性好、抗过拟合能力强的特点，表现出优异的分类效果，相比神经网络和支持向量机，具有最高的平均分类精度。     

Daily activity pattern recognition by using support vector machines with multiple classes

The focus of this paper is to learn the daily activity engagement patterns of travelers using Support Vector Machines (SVMs), a modeling approach that is widely used in Artificial intelligence and Machine Learning. It is postulated that an individual’s choice of activities depends not only on socio-demographic characteristics but also on previous activities of individual on the same day. In the paper, Markov Chain models are used to study the sequential choice of activities. The dependencies among activity type, activity sequence and socio-demographic data are captured by employing hidden Markov models. In order to learn model parameters, we use sequential multinomial logit models (MNL) and multiclass Support Vector Machines (K-SVM) with two different dependency structures. In the first dependency structure, it is assumed that type of activity at time ‘t’ depends on the last previous activity and socio-demographic data, whereas in the second structure we assume that activity selection at time ‘t’ depends on all of the individual’s previous activity types on the same day and socio-demographic characteristics. The models are applied to data drawn from a set of California households and a comparison of the accuracy of estimation of activity types and their sequence in the agenda, indicates the superiority of K-SVM models over MNL. Additionally, we show that accuracy in estimating activity patterns increases using different sets of explanatory variables or tuning parameters of the kernel function in K-SVM.     

高疲劳抗力钢桥面板的疲劳问题Ⅱ：结构体系抗力

为了深刻认识高疲劳抗力钢桥面板的疲劳特性，准确评估其结构体系的疲劳抗力，基于等效结构应力建立了考虑焊接微裂纹对钢桥面板疲劳性能劣化效应的结构体系疲劳抗力评估方法，并通过疲劳试验对所建立的评估方法进行了验证。在此基础上采用所建立的结构体系疲劳抗力评估方法对高疲劳抗力钢桥面板的疲劳开裂模式、疲劳抗力及其影响因素等相关关键问题进行系统研究。研究结果表明：焊接微裂纹的存在会显著降低钢桥面板的疲劳性能，导致主导疲劳开裂模式发生迁移；结构体系设计参数对纵肋与顶板双面焊构造细节和纵肋与横隔板新型交叉构造细节疲劳性能的影响有显著区别，其中纵肋与顶板双面焊构造细节的疲劳性能主要对顶板厚度的变化较为敏感，其疲劳性能随着顶板厚度的增加而显著提升，而纵肋与横隔板新型交叉构造细节的疲劳性能同时受多个参数的影响，其疲劳性能随着顶板厚度、横隔板厚度和纵肋高度的增大而提升，随着横隔板间距和纵肋底板与横隔板之间焊缝长度的增大而降低；传统钢桥面板的主导疲劳开裂模式为纵肋腹板与横隔板交叉构造细节围焊焊趾开裂，高疲劳抗力钢桥面板的主导疲劳开裂模式为纵肋底板与横隔板交叉构造细节纵肋焊趾开裂；相对于传统正交异性钢桥面板，高疲劳抗力钢桥面板结构实现了主导疲劳开裂模式的迁移，疲劳性能显著提高。