城市轨道交通网络乘客换乘路径选择行为模型

为定量分析成网运行条件下乘客在轨道交通系统内的换乘路径选择行为,在梳理乘客换乘路径选择直接与间接影响因素的基础上,引入开销系数用于描述由于列车过度拥挤导致乘客心理感受运行时间的额外增加值.提出含有惩罚系数的乘客换乘时间函数,基于乘客心理感知构建以乘车时间和换乘时间为广义费用的路径选择行为模型,并设计模型的求解算法.最后,将模型运用于重庆轨道交通网络实例分析中,结果表明:乘客倾向于选择换乘次数更少的路径3,较传统模型选择概率提高1.44%;同时,换乘惩罚系数对路径选择结果的影响灵敏度高于运行时间开销系数.

COPART:一种面向约束条件的自适应软硬件划分算法

本文介绍一种将自适应算法、遗传算法和列表调度算法结合起来应用于软硬件划分问题的算法COPART,并提出了一种基于约束条件的开销系数自适应调整方法,该方法所获得的划分结果能够良好体现设计目标。

Predictive Spectrum Sensing Strategy Based on Reinforcement Learning

In this paper,we consider a cognitive radio(CR) system with a single secondary user(SU) and multiple licensed channels.The SU requests a fixed number of licensed channels and must sense the licensed channels one by one before transmission.By leveraging prediction based on correlation between the licensed channels,we propose a novel spectrum sensing strategy,to decide which channel is the best choice to sense in order to reduce the sensing time overhead and further improve the SU's achievable throughput.Since the correlation coefficients between the licensed channels cannot be exactly known in advance,the spectrum sensing strategy is designed based on the model-free reinforcement learning(RL).The experimental results show that the proposed spectrum sensing strategy based on reinforcement learning converges and outperforms random sensing strategy in terms of long-term statistics.