考虑横向漂移的船舶路径跟踪自抗扰控制

针对船舶运动系统中内部动态不确定和外部干扰等问题,进行了欠驱动船舶路径跟踪的自抗扰方法研究。利用Backstepping设计参考航向角,并通过线性扩张状态观测器对流干扰和横向运动引起的横向漂移进行估计。其次,根据自抗扰算法对航向进行控制,采用线性扩张状态观测器对外界干扰及内部不确定项进行估计。最后仿真结果表明,在风流干扰下所设计的控制器仍能使船准确地跟踪上参考路径,验证了所提控制方案的有效性。     

集装箱专用平车加装承载板实现滚装运输技术研究

为弥补应急情况下滚装运输所需平车数量缺口,同时为完善车辆自身应急保障能力,分析集装箱专用平车用于滚装运输需求,针对集装箱专用平车用于滚装运输存在的主要问题,提出在不改变车体结构前提下,通过加装承载板实现滚装运输的解决思路。结合集装箱专用平车结构特点,分析承载板的数量、尺寸、材质,设计承载板由承载面、横支撑架、渡板、斜支腿、受力面、绳拴构成,给出加装改造方法,并通过校核承载能力和研究适运范围,研究加装改造后的集装箱专用平车滚装运输能力,得出改造后的集装箱专用平车可以满足大部分滚装运输货物运输需要。     

Modeling time-of-day car use behavior: A Bayesian network approach

In this research, a Bayesian network (BN) approach is proposed to model the car use behavior of drivers by time of day and to analyze its relationship with driver and car characteristics. The proposed BN model can be categorized as a tree-augmented naive (TAN) Bayesian network. A latent class variable is included in this model to describe the unobserved heterogeneity of drivers. Both the structure and the parameters are learned from the dataset, which is extracted from GPS data collected in Toyota City, Japan. Based on inferences and evidence sensitivity analysis using the estimated TAN model, the effects of each single observed characteristic on car use measures are tested and found to be significant. The features of each category of the latent class are also analyzed. By testing the effect of each car use measure on every other measure, it is found that the correlations between car use measures are significant and should be considered in modeling car use behavior.     

车挡报警监控记录系统在地铁中的应用

分析了地铁车挡报警监控记录系统的基本功能、组成及原理;论述了该系统在地铁应用中的必要性和重要性;提出了设计优化方案,并对系统后期的发展趋势进行展望.     

Did Chinese cities that implemented driving restrictions see reductions in PM10?

Imposing driving restrictions is becoming increasingly popular as a policy intended to control urban air pollution. Existing studies on this topic offer highly mixed observations, and each study tends to focus on only one city. In this paper, we used 11 Chinese cities with driving restrictions as the treatment group, and compared them to other cities that did not implement the policy. Based on a propensity score matching and difference-in-difference analysis, we found no evidence of a decrease in PM10 concentrations in cities after they implemented driving restrictions. This finding may be attributed to an increase in the number of cars in these cities after implementing driving restrictions, but we also found no evidence of an improvement in air quality for a given number of cars after implementation of the policies.     

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.     

Dynamic demand estimation and prediction for traffic urban networks adopting new data sources

Nowadays, new mobility information can be derived from advanced traffic surveillance systems that collect updated traffic measurements, both in fixed locations and over specific corridors or paths. Such recent technological developments point to challenging and promising opportunities that academics and practitioners have only partially explored so far.The paper looks at some of these opportunities within the Dynamic Demand Estimation problem (DDEP). At first, data heterogeneity, accounting for different sets of data providing a wide spatial coverage, has been investigated for the benefit of off-line demand estimation. In an attempt to mimic the current urban networks monitoring, examples of complex real case applications are being reported where route travel times and route choice probabilities from probe vehicles are exploited together with common link traffic measurements.Subsequently, on-line detection of non-recurrent conditions is being recorded, adopting a sequential approach based on an extension of the Kalman Filter theory called Local Ensemble Transformed Kalman Filter (LETKF).Both the off-line and the on-line investigations adopt a simulation approach capable of capturing the highly nonlinear dependence between the travel demand and the traffic measurements through the use of dynamic traffic assignment models. Consequently, the possibility of using collected traffic information is enhanced, thus overcoming most of the limitations of current DDEP approaches found in the literature.     

浅析西门子BDPC系统在CTV上的 可行性

本文主要目的是分析西门子Blue Drive Plus C（随后简称BDPC）系统及其配置是否能够应用在深水动力定位原油输送船（随后称CTV）上。通过供需关系对比法,首先实现CTV实现海上自由航行、跟随定位、连接FPSO与～30万吨油轮之间浮管、加油跟随定位、紧急脱离、绿色环保运行等工况下的正常与应急储备动力需求,随后分析西门子BDPC直流系统及其运用技术,针对本项目提供的特殊配置,与CTV提出需求进行对比,结论是BDPC依靠它的直流发电、配电、逆变、储能等技术,可以满足CTV的配置需求,论证了BDPC系统运用在性能优越的深水原油输送装置CTV是可行的。     

串联混合动力汽车功率跟随控制模式的实现

以电动汽车仿真软件ADVISOR为基础,根据混合动力汽车的不同工况,分析和建立了串联混合动力电动汽车的一种控制模式——功率跟随控制模式。并且详细分析了功率跟随控制模式在ADVISOR软件中的实现方法。     

车门内水切升降异响分析及设计优化

主要针对汽车车门玻璃在下降过程中由于内水切"咕噜咕噜"异响问题的研究,结合实车案例分析影响内水切升降过程中异响的原因,并通过不断的设计优化断面和调整CLD值效果验证,最终解决异响问题;弥补了设计阶段需注意控制的相关设计参数,为以后新项目的设计提供了参考。