高职学生自我效能感与学习兴趣激发的研究

通过对107名高职院校学生的学习动机、自我效能感以及两者关系的测量统计，发现高职学生的学习动机、内生动机与学业自我效能感和一般自我效能感之间均存在显著正相关，并以此为依据，探索提出了以增强自我效能感来提高学习兴趣的思路和方法。     

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.     

Key technologies of ship remote control system in inland waterways under ship-shore cooperation conditions北大核心CSCD

［Objective ］ To meet the requirements of remotely controlling ship in curved, narrow and crowded inland waterways, this paper proposes an approach that consists of CNN-based algorithms and knowledge based models under ship-shore cooperation conditions. ［Method］On the basis of analyzing the characteristics of ship-shore cooperation, the proposed approach realizes autonomous perception of the environment with visual simulation at the core and navigation decision-making control based on deep reinforcement learning, and finally constructs an artificial intelligence system composed of image deep learning processing, navigation situation cognition, route steady-state control and other functions. Remote control and short-time autonomous navigation of operating ships are realized under inland waterway conditions, and remote control of container ships and ferries is carried out. ［Results］The proposed approach is capable of replacing manual work by remote orders or independent decision-making, as well as realizing independent obstacle avoidance, with a consistent deviation of less than 20 meters. ［Conclusions］The developed prototype system carries out the remote control operation demonstration of the above ship types in such waterways as the Changhu Canal Shenzhou line and the Yangtze River, proving that a complete set of algorithms with a CNN and reinforcement learning at the core can independently extract key navigation information, construct obstacle avoidance and control awareness, and lay the foundation for inland river intelligent navigation systems. © 2022 Journal of Clinical Hepatology. All rights reserved.     

VTS中雷达信息与AIS信息融合的探讨

文章对VTS中雷达信息与AIS信息的来源、种类与特点进行了比较,论证了两者信息融合的可行性与必要性,同时给出了信息的融合模型与方法,也提出信息融合需要进一步解决的问题.     

Influence of AC system design on the realisation of tractive efforts by high adhesion locomotives

The main task for heavy haul railway operators is to reduce the cost of exported minerals and enhance the long-term viability of rail transport operations through increasing productivity by running longer and heavier trains. The common opinion is that this is achievable by means of implementation of high adhesion locomotives with advanced AC traction technologies. Modern AC high adhesion locomotives are very complex mechatronic systems and can be designed with two alternative traction topologies of either bogie or individual axle controls. This paper describes a modelling approach for these two types of AC traction systems with the application of an advanced co-simulation methodology, where an electrical system and a traction algorithm are modelled in Matlab/Simulink, and a mechanical system is modelled in a multibody software package. Although the paper concentrates on the analysis of the functioning for these two types of traction control systems, the choice of reference slip values also has an influence on the performance of both systems. All these design variations and issues have been simulated for various adhesion conditions at the wheel–rail interface and their influence on the high traction performance of a locomotive equipped with two three-axle bogies has been discussed.     

基于响应面和遗传算法的柴油机瞬态过程喷油参数优化

基于台架试验数据,利用响应面法建立了某工程机械用柴油机瞬态过程喷油参数与性能的近似高精度模型,基于此模型采用遗传算法对瞬态过程喷油参数分别进行离线优化研究。结果表明:采用单目标优化确定的燃油消耗率(BSFC)、NO_x比排放量和颗粒质量(PM)比排放量的优化极限分别可达180.23g/(kW·h),8.92g/(kW·h)和0.011 8g/(kW·h),相对原机可降低多达4.5%,34.0%和37.3%。双目标优化的Pareto解集表明,相比于同时优化BSFC和NO_x比排放量,BSFC和PM比排放量更容易同时得到优化。采用权重因子适应度函数的三目标优化结果对应的BSFC,NO_x比排放量及PM比排放量分别为184.70g/(kW·h),12.62g/(kW·h)和0.012 2g/(kW·h),较原机分别降低2.1%,6.6%和35.3%。改进优化模型后,性能优化Pareto解集对应的BSFC和PM比排放量水平都非常接近其优化极限,但NO_x比排放量相对其优化极限仍然较高。     

浅议诊断性测试在高职英语教学中的应用

诊断测试能有效地诊断出英语教学中存在的弱点,它是评价英语教学的一个有效手段。文章在介绍诊断测试概念的基础上,分析一次实际进行的诊断性测试。根据测试结果发现存在的问题并以此来改进高职荚语教学的模式。     

高速公路中收费和清分的动态解决方案

在高速公路收费和清分过程中,由于新增的高速公路和收费站点特别是环型路段的增加使得原有的静态算法已经不适宜于现实情况。本文提出了高速公路中基于最短路径和环型路段的动态收费和清分解决方案,并给出了详细的基于环型路段的动态算法。环型路段问题解决后,可以判知车辆实际行车路线,从而大幅提高收费及清分的准确性和效率。     

公交通行能力约束的智能调度优化模型

公交通行能力是解决城市交通拥堵问题、促进城市公共交通系统高效运行的要素之一。介绍了公交通行能力的计算要素,并针对目前我国城市由于盲目调度造成的公交系统运营效率、服务水平下降的问题,提出将公交通行能力作为约束条件对公交调度进行优化。鉴于公交企业制订调度方案时需兼顾公交服务水平和企业效益,将智能调度的目标确定为候车时间满意度、候车空间满意度、车内舒适满意度和企业满意度加权平均值最大,并给出了相应的计算方法。最后讨论了利用遗传算法对模型进行求解的过程。