新型负阶跃动态扭矩校准装置

针对扭矩传感器静态校准无法完全满足实际需要的难题,设计一种新型100 N·m负阶跃动态扭矩校准装置,并阐述该装置的工作原理和关键技术。校准装置由扭矩产生装置、连接系统、制动系统、信号处理控制系统和空气轴承系统组成,利用火工拔销器产生负阶跃动态扭矩,较大程度上降低负阶跃动态扭矩的下降时间,并采用空气轴承的支承方式提高动态扭矩的传递精度。通过对试验数据进行分析和处理,结果表明该装置负阶跃扭矩产生时间低于0.1 ms。不确定度分析结果显示,该装置的扩展不确定度U为4.22%,扩展因子k为2。     

基于Q-learning 的定制公交跨区域路径规划研究

考虑城市大客流通勤者跨区域出行需求，结合城市公交线网中乘客出行密集、客流走向规律等特点，提出一种跨区域定制公交的搭乘方案. 通过改进的Q-learning 模型对公交线路进行优化，为城市通勤者提供更加便捷和高效的出行服务. 通过综合路段拥堵状态、乘客需求及居民小区位置，设定了Q-learning 强化学习的奖惩函数，提升定制公交区域路径的直线系数、满载率、通行时间. 结果表明，所提出的改进方法能够降低通勤者跨区域通行的旅行时间，有效提高髙峰时段定制公交线网的通行效率.     

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。     

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.     

铺缆船用电缆埋设犁土壤挖掘阻力计算

传统的计算埋设犁土壤挖掘阻力的方法一般采用经验公式计算。经验公式依赖丰富的试验数据，且只能适应特定形状和尺寸的埋设犁。由于埋设犁的形状和尺寸各不相同，基于经验公式计算土壤挖掘阻力的方法准确性不高。根据土力学基本原理，在力学分析的基础上，运用数学方法进行公式推导，得到用于计算不同形状尺寸埋设犁挖掘阻力的理论方法，解决依赖经验公式计算土壤挖掘阻力的局限性。     

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。     

基于一个二维水弹塑性方法和极限强度评估的集装箱船结构优化研究

海上极端波过去常常导致船舶结构的极限破坏,而船舶的极限崩溃涉及到船体结构的动态极限强度和结构非线性.该文通过二维的水弹塑性方法研究了集装箱船在极端波中的非线性动态强度,该方法考虑了船体的极限强度以及船体结构的非线性和波浪之间的耦合.并通过该二维水弹塑性方法和极限评估方法研究了船体结构的结构优化.文中还通过二次规划法(SQP)来优化基于非线性的动态强度的集装箱船体结构.最少的结构成本是本优化的目标函数,约束条件保证船体的强度要小于结构的极限强度,并且结构设计尺寸要满足规范的要求.随着设计波高的变化,这些优化的设计变量的变化趋势得以发现,一些研究的结论可用于船舶规范的参考.     

基于AIGA-WLSSVM的埋地管道腐蚀速率预测方法

为了降低埋地管道腐蚀影响因素之间的复杂相关性,提高腐蚀预测精度,文中提出一种基于自适应免疫遗传算法-加权最小二乘支持向量机(AIGA-WLSSVM)的埋地管道腐蚀速率预测建模方法,并采用AIGA优化模型参数,进一步提高模型的学习能力和稳定性。最后通过实例分析验证了AIGA-WLSSVM建模方法在埋地管道腐蚀速率预测中的可行性和有效性,为埋地管道的检修与更换提供参考。     

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.     

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.