船舶甲板智能抛丸机器人设计

为解决在船舶外场涂装阶段除锈和拉毛施工效率低、质量不稳定、环境污染严重等问题,设计一款用于船舶甲板的智能抛丸机器人。简介该型机器人系统组成,阐述其抛丸分系统、除尘分系统和越障辅助机构的硬件设计及其软件设计,并设计手动遥控和自主导引操作方式。实船测试结果表明,该型机器人可满足船舶甲板除锈清理的工作要求。     

基于特征熵权与样本加权的大跨桥梁多车道交通荷载聚类模型

为满足交通流荷载作用下大跨桥梁结构评估的需要，研究了基于荷载参数特征的交通流状态划分方法。首先，基于实测交通流数据，按照车道属性统计分析得到交通流的单位小时特征参数样本，选择单位小时内车型比例、车头间距及交通流速度作为交通流状态划分的参考特征；其次，改进经典k-means聚类算法以增强其对高维、复杂交通流荷载分类的鲁棒性，即通过引入特征熵值来表征各特征参数对聚类效果的重要性，同时计算样本点与周围样本点的接近程度来赋予样本点权值，以削弱样本离散性对聚类质量的不利影响；最后，通过聚类算法得到11种具有不同参数特征的交通流荷载，分析了其作用下某大跨斜拉桥拉索应力响应及造成的疲劳损伤。结果表明：改进算法的聚类质量指标比原始k-means算法提高了40%以上，对交通流状态划分具有良好的适用性；通过算法得到的不同类别的交通流荷载的特征参数差异性明显，其占有率也大不相同，同一类别的交通流荷载各样本特征参数聚拢效果良好；同车道内不同类别的交通流荷载的拉索等效应力差别较大，其变异系数均在0.2以上，尤其在考虑了不同交通流荷载模型的占有率后，这种差异性进一步增大。上述结果表明该交通流荷载聚类与模拟方法是有效、准确的，对相关大跨桥梁结构安全及耐久性评估有一定的参考价值。     

基于信令数据的出行强度影响因素分析模型

为探究城市交通出行强度影响因素及不同因素的影响程度，本文从土地利用与交通基础设施建设两方面出发，分析包括土地利用混合指数，职住混合率熵指数，公共交通站点 500 m覆盖率，路网可达性等17个指标与出行强度的相关关系；基于相关系数和拟合优度分析，提取7个与出行强度强相关指标，基于所识别指标构建北京市中心城区出行强度多元回归模型.模型结果表明，职住混合率熵指数对出行强度的影响最为显著，公共交通站点覆盖率对出行强度的影响比道路网密度和可达性更为明显.此外，给出单一土地利用/交通基础设施指标对出行强度拟合结果的离群特征分析方法，用于评估不同区域基础设施供给与交通出行需求之间的平衡关系.     

基于深度强化学习的智能船舶航迹跟踪控制

[目的]智能船舶的航迹跟踪控制问题往往面临着控制环境复杂、控制器稳定性不高以及大量的算法计算等问题。为实现对航迹跟踪的精准控制,提出一种引入深度强化学习技术的航向控制器。[方法]首先,结合视线(LOS)算法制导,以船舶的操纵特性和控制要求为基础,将航迹跟踪问题建模成马尔可夫决策过程,设计其状态空间、动作空间、奖励函数;然后,使用深度确定性策略梯度(DDPG)算法作为控制器的实现,采用离线学习方法对控制器进行训练;最后,将训练完成的控制器与BP-PID控制器进行对比研究,分析控制效果。[结果]仿真结果表明,设计的深度强化学习控制器可以从训练学习过程中快速收敛达到控制要求,训练后的网络与BP-PID控制器相比跟踪迅速,具有偏航误差小、舵角变化频率小等优点。[结论]研究成果可为智能船舶航迹跟踪控制提供参考。     

The identification and empirical characterization of vehicular (Lagrangian) fundamental diagrams in multilane traffic flow

Traditional macroscopic traffic flow modeling framework adopts the spatial–temporal coordinate system to analyze traffic flow dynamics. With such modeling and analysis paradigm, complications arise for traffic flow data collected from mobile sensors such as probe vehicles equipped with mobile phones, Bluetooth, and Global Positioning System devices. The vehicle‐based measurement technologies call for new modeling thoughts that address the unique features of moving measurements and explore their full potential. In this paper, we look into the concept of vehicular fundamental diagram (VFD) and discuss its engineering implications. VFD corresponds to a conventional fundamental diagram (FD) in the kinematic wave (KW) theory that adopts space–time coordinates. Similar to the regular FD in the KW theory, VFD encapsulates all traffic flow dynamics. In this paper, to demonstrate the full potential of VFD in interpreting multilane traffic flow dynamics, we generalize the classical Edie's formula and propose a direct approach of reconstructing VFD from traffic measurements in the vehicular coordinates. A smoothing algorithm is proposed to effectively reduce the nonphysical fluctuation of traffic states calculated from multilane vehicle trajectories. As an example, we apply the proposed methodology to explore the next‐generation simulation datasets and identify the existence and forms of shock waves in different coordinate systems. Our findings provide empirical justifications and further insight for the Lagrangian traffic flow theory and models when applied in practice. Copyright © 2015 John Wiley & Sons, Ltd.     

Innovative strategies for urban car-sharing systems and a simulator to assess their performance

In this paper, three innovative car-sharing systems for urban areas are proposed, based on fleets of individual intelligent vehicles with three service characteristics: instant access, open-ended reservations and one-way trips. These features provide high flexibility but create an uneven distribution of vehicles among stations. Therefore, relocation of vehicles must be performed. Three different system procedures are proposed: in the first system, relocations are performed by users; in the other two, vehicles relocate automatically, thanks to their automation. In the first two systems, vehicles are accessible only at stations, whereas in the third they are also accessible along roads. In order to provide transport managers with a tool to test systems in different realities, an object-oriented simulator is developed. The simulation provides outputs of system performance, in terms of user waiting times and system efficiency. The proposed systems are simulated for the city of Genoa, in Italy, and a comparative analysis is presented.     

考虑空车调配协调的货物列车开行方案优化研究

为实现空车调配与货物列车开行方案协调优化，结合基本运行图架构与车流径路，构建货运时空服务拓展网络。考虑配空与装卸取送、集编发等环节的时间接续要求，节点与区段不对流空车要求，以重车流全程运送与空车配送等广义总费用最少为目标，建立整数规划弧路模型。针对既有算法设计局限性，结合重车或空车配空的时间接续要求，提出将不同的 k 短路重车流方案与空车配空方案相关联的改进可行解构造方法，设计混合差分进化求解算法。实例研究表明，考虑空车调配进行重车、空车流组织协调优化，能够减少空车走行费用，及时满足装车需求，有效保证作业车流配合中转车流集结编组及时挂线，提高方案可实施性。     

基于完全耦合算法的绕水翼流固耦合特性研究

基于完全耦合算法对绕二维NACA0009水翼流固耦合特性进行了数值模拟研究。采用Theodorsen模型和Munch模型对刚性和弹性水翼的水弹性响应进行了数值计算,分析了流体与结构的相互作用关系,研究了影响结构水弹性响应和流固耦合特性的因素。研究结果表明:考虑了流体黏性的Munch模型与基于势流理论的Theodorsen模型对气动弹性响应的数值计算结果基本一致,而Theodorsen模型由于没有考虑流体黏性在一定程度上低估了结构的水弹性响应。结构的惯性、阻尼和刚度力矩与流体的相应附加载荷均处于同一数量级,故流体与结构的相互作用不可忽略,尤其对于弹性水翼,流体的惯性、附加阻尼作用增大,流固耦合算法的数值稳定性对流固耦合特性的计算结果影响将更大。外部激励频率为非共振频率时,结构的刚度作用是影响水弹性响应的主要因素,外部激励频率为共振频率时,流体的附加阻尼和附加刚度作用减弱,除结构的刚度作用外,流体与结构的惯性作用对水弹性响应和流固耦合特性的影响也较大。     

逐日路径演化中出行者信息偏好的实验分析

在逐日出行过程中,出行者对各类信息的偏好是一个动态变化的过程,不同出行者对同类信息的偏好也不同.本研究搭建不同信息条件下逐日路径选择实验场景,用信息偏好系数表征不同类别信息对出行者感知时间影响的相对权重,研究出行者信息偏好的演化规律与分布特征.实验结果表明,出行个体信息偏好的演化存在3种类型,出行群体对感知时间的信息偏好系数先震荡增加后逐步稳定,出行个体对感知时间的信息偏好系数在出行群体中成正态分布.与只提供完全历史信息相比,同时提供基于指数平滑法的系统预测信息会降低出行者对感知时间的偏好.     

Strain prediction for critical positions of FPSO under different loading of stored oil using GAIFOA-BP neural network

FPSO (floating, production, storage and offloading) units are widely used in the offshore oil and gas industry. Generally, FPSOs have excellent oil storage capacity owing to their huge oil cargo holds. The volume and distribution of stored oil in the cargo holds influence the strain level of hull girder, especially at critical positions of FPSO. However, strain prediction using structural analysis tools is computationally expensive and time consuming. In this study, a prediction tool based on back-propagation (BP) neural network called GAIFOA-BP is proposed to predict the strain values of concerned positions of an FPSO model under different oil storage conditions. The GAIFOA-BP combines BP model and GAIFOA which is a combination of genetic algorithm (GA) and an improved fruit fly optimization algorithm (IFOA). Results from three benchmark tests show that the GAIFOA-BP model has a remarkable performance. Subsequently, a total of 81 sets of training data and 25 sets of testing data are obtained from experiment using fiber Bragg grating (FBG) sensors installed on the surface of an FPSO model. The numerical results show that the GAIFOA-BP is capable of predicting the strain values with higher accuracy as compared with other BP models. Finally, the reserved GAIFOA-BP model is utilized to predict the strain values under the inputs of a 10-day time series of volume and distribution of stored oil. The predicted strain results are further used to calculate the fatigue consumption of measurement points.