Local joint flexibility of tubular X-joints stiffened with external ring or external plates

In the present paper, the Local Joint Flexibility (LJF) of the ring-stiffened X-joints and plate-stiffened X-joints under compressive load is investigated. In the first phase, a finite element (FE) model was generated and verified with the results of available experimental tests and equations. In the next phase, a set number of 234 FE models were created to evaluate the role of the external ring size (β_r and τ_r), the external plate size (β_p and τ_p), and the connection geometry (γ, τ, and β) on the LJF factor (f_LJF). In these FE models, the weld connecting the chord and brace members was generated. The results indicated that the f_LJF of a plate stiffened joint can be down to 76% of the f_LJF of the corresponding un-stiffened joint. Also, the effect of the ring size on the f_LJF was more than the effect of the plate size on the f_LJF, because of the stiffener position. Despite the notable effect of the ring and the plate on the f_LJF, there is not any study or formula on tubular connections stiffened with ring or plate. Therefore, the FE results were used to propose two parametric formulas for determining the f_LJF in X-joints with external ring or external plate under brace compressive load. Moreover, the derived formulas were checked based on the UK DoE acceptance criteria.     

面向智能船舶的容错卡尔曼融合估计方法

针对智能船舶多传感器系统因未知海洋环境干扰和设备间干扰等因素导致的一个或数个传感器产生随机间歇性故障从而导致融合估计结果出现偏差甚至失真的问题,设计1种基于四分位滤波的容错方法,并针对该方法导致的观测时滞问题设计1种预报方法,提前预报观测值,进而抵消容错方法导致的时滞问题。此外,针对多传感器之间的互协方差难以准确估计的问题,采用CI融合估计方法进行融合估计。为验证算法的有效性和融合估计的精度,对带有间歇性故障的两传感器系统进行仿真试验,并与按矩阵、按对角阵和按标量3种分布式融合估计方法得到的结果进行对比。4种方法的均方误差系数大小对比结果显示,对于带间歇性故障的多传感器系统,设计的融合滤波不仅具有鲁棒性,而且具有较高的融合精度。     

“s”型覆岩空间结构的冲击地压危险区预测

基于微地震监测和覆岩空间结构理论，以山东华丰矿1410工作面为例，分析了“s”型覆岩空间结构的形成，利用理论方法和微地震监测预测并圈定了华丰矿1410工作面发生冲击地压的危险区域，研究了“s”型覆岩空间结构的冲击地压危险区分布规律，为现场预测和控制冲击地压灾害提供了理论基础。     

Human-like autonomous car-following model with deep reinforcement learning

This study proposes a framework for human-like autonomous car-following planning based on deep reinforcement learning (deep RL). Historical driving data are fed into a simulation environment where an RL agent learns from trial and error interactions based on a reward function that signals how much the agent deviates from the empirical data. Through these interactions, an optimal policy, or car-following model that maps in a human-like way from speed, relative speed between a lead and following vehicle, and inter-vehicle spacing to acceleration of a following vehicle is finally obtained. The model can be continuously updated when more data are fed in. Two thousand car-following periods extracted from the 2015 Shanghai Naturalistic Driving Study were used to train the model and compare its performance with that of traditional and recent data-driven car-following models. As shown by this study’s results, a deep deterministic policy gradient car-following model that uses disparity between simulated and observed speed as the reward function and considers a reaction delay of 1 s, denoted as DDPGvRT, can reproduce human-like car-following behavior with higher accuracy than traditional and recent data-driven car-following models. Specifically, the DDPGvRT model has a spacing validation error of 18% and speed validation error of 5%, which are less than those of other models, including the intelligent driver model, models based on locally weighted regression, and conventional neural network-based models. Moreover, the DDPGvRT demonstrates good capability of generalization to various driving situations and can adapt to different drivers by continuously learning. This study demonstrates that reinforcement learning methodology can offer insight into driver behavior and can contribute to the development of human-like autonomous driving algorithms and traffic-flow models.     

A Delphi analysis on green performance evaluation indices for ports in China

This study identified a set of applicable and practical green performance evaluation indices for Chinese ports using the Delphi technique. The research was conducted by targeting three representative Chinese ports in Eastern China: the Shanghai Port, Ningbo Port, and Qingdao Port. An effective expert panel was structured with direct stakeholders related to green performance, which was based on a sufficient consideration of the unique and the special decision-making system for Chinese ports. Twenty-one green performance indices in six dimensions (i.e. liquid pollution management, air pollution management, noise control, low carbon regulations and energy savings, marine biology preservation, and organization and management) were eventually identified through three rounds of iterations. The identified green performance indices for Chinese ports are highly consistent with the Chinese Government’s environmental policies. Compared with European ports, the green port performance indicators in China are not as comprehensive. The indicators that are strictly consistent with national macro policies and the factors that are more easily seen, heard, and felt were perceived to be more important. From an academic perspective, this study offers a new approach for system establishment and green practice criteria exploration and thus provides a significant basis for further green port studies.     

Empirical analysis of short period traffic counts and their efficiency: the case of Indian traffic

Short period traffic counts (SPTCs) are conducted routinely to estimate the annual average daily traffic (AADT) at a particular site. This paper uses Indian traffic volume data to methodically and extensively study the effect of four aspects related to the design of SPTCs. These four aspects are: (i) for how long, (ii) on which days should SPTCs be carried out, (iii) how many times, and (iv) on which months should SPTCs be carried out? The analyses indicate that the best durations for conducting SPTCs are 3 days (starting with a Thursday) and 7 days, for total traffic and truck traffic, respectively. Further, these counts should be repeated twice a year keeping a separation of two months between the counts to obtain good estimates of AADT at minimal cost. An additional outcome of this study has been the determination of seasonal factor values for roads in developing economies, like India.     

Interactive travel choices and traffic forecast in a doubly dynamical system with user inertia and information provision

This study models the joint evolution (over calendar time) of travelers’ departure time and mode choices, and the resulting traffic dynamics in a bi-modal transportation system. Specifically, we consider that, when adjusting their departure time and mode choices, travelers can learn from their past travel experiences as well as the traffic forecasts offered by the smart transport information provider/agency. At the same time, the transport agency can learn from historical data in updating traffic forecast from day to day. In other words, this study explicitly models and analyzes the dynamic interactions between transport users and traffic information provider. Besides, the impact of user inertia is taken into account in modeling the traffic dynamics. When exploring the convergence of the proposed model to the dynamic bi-modal commuting equilibrium, we find that appropriate traffic forecast can help the system converge to the user equilibrium. It is also found that user inertia might slow down the convergence speed of the day-to-day evolution model. Extensive sensitivity analysis is conducted to account for the impacts of inaccurate parameters adopted by the transport agency.     

船舶通信英语口语与听力课程标准

结合船舶驾驶员的岗位职责,依照现代职业教育理论和成果,设计了船舶通信英语口语与听力课程标准.对该课程的课程概述、培养目标、与前后课程的联系、教学内容与学时分配、学习资源、教师要求、学习场地与设施要求、考核方式与标准、学习情境设计进行了设计和描述,将船舶通信中典型工作任务转化为学习型任务.     

加强教风学风建设 不断提高人才培养质量

在改革不断深入、经济社会深度转型的当下,大学生群体呈现出新的特点和个性。在新的形势下,要加强高校教风学风建设,更好的发挥高校培养人才的作用,促使人才培养质量提升。本文以辽宁省交通高等专科学校为例,着重从文化建设、教师育人能力提升、学生职业梦想等角度入手,提出了加强教风学风建设的具体举措和途径。     

公路客运市场退出影响因素实证分析

通过对广东省公路客运企业和政府主管部门的问卷调查,采用因子分析方法,从影响客运市场退出的相关主体及相关行为中筛选出关键变量．运用非参数检验过程,就各关键变量对退出的影响程度进行比较,最终得出驾驶员和承包经营人的相关行为对公路客运市场退出最具影响的结论,为政府部门进行行业管理和政策制定提供了实证依据．