Energy-constrained ferry route design for sparse wireless sensor networks

In recent years, using message ferries as mechanical carriers of data has been shown to be an effective way to collect information in sparse wireless sensor networks. As the sensors are far away from each other in such highly partitioned scenario, a message ferry needs to travel a long route to access all the sensors and carry the data collected from the sensors to the sink. Typically, practical constraints （e.g., the energy） preclude a ferry from visiting all sensors in a single tour. In such case, the ferry can only access part of the sensors in each tour and move back to the sink to get the energy refilled. So, the energy-constrained ferry route design （ECFRD） problem is discussed, which leads to the optimization problem of minimizing the total route length of the ferry, while keeping the route length of each tour below a given constraint. The ECFRD problem is proved to be NP-hard problem, and the integer linear programming （ILP） formulation is given. After that, efficient heuristic algorithms are proposed to solve this problem. The experimental results show that the performances of the proposed algorithms are effective in practice compared to the optimal solution.     

在分裂的机会网络中多个消息摆渡的路由设计

针对机会网络中节点分布不规则造成分割的网络区域相隔较远的情况, 提出了一种基于全局消息摆渡和节点中继的多摆渡路由算法（GMF-NRA）。该算法使用一个全局消息摆渡节点和各个区域内的中继节点为运行于区域内部的局部消息摆渡节点中继消息, 共同完成区域间的信息传输。摆渡节点在中继节点处交互区域间的信息, 以至于不再需要摆渡节点之间实现在线协作转发消息。最后, 仿真结果表明GMF-NRA较现有的节点中继算法在网络的平均传输时延和数据交付率方面能获得更好的网络性能。     

电子政务网间信息交换系统的研究

电子政务的建设快速发展，迫切需要信息的交换与整合，尤其是物理隔离的网间信息交换。通过对电子政务信息交换需求的分析，提出了信息交换码头的模型，给出了相应的形式化描述，并通过给出的模型建立出电子政务网间信息交换系统的软件体系结构，最后对此系统的运行特点及其实际应用效果加以说明。     

烟大铁路轮渡羊头洼港区规划设计评析

通过对烟台至大连铁路轮渡羊头洼港港区的规划设计及研究,提出了建设二十一世纪远景港、枢纽港、景观港、人文港的港区规划新理念,引入了发展工业旅游的新思路,探索了作为综合性交通枢纽及工业区的铁路轮渡港区的规划设计新趋势。     

浅议桥渡抗洪灾可靠度的分析及应用

根据可靠笥理论及计算方法,提出了桥渡可靠度的基本概念,可靠度的主要内容和计算方法。对桥渡可靠度指标和灾害风险预报进行了探讨。     

Near-optimal energy management for plug-in hybrid fuel cell and battery propulsion using deep reinforcement learning

Plug-in hybrid fuel cell and battery propulsion systems appear promising for decarbonising transportation applications such as road vehicles and coastal ships. However, it is challenging to develop optimal or near-optimal energy management for these systems without exact knowledge of future load profiles. Although efforts have been made to develop strategies in a stochastic environment with discrete state space using Q-learning and Double Q-learning, such tabular reinforcement learning agents’ effectiveness is limited due to the state space resolution. This article aims to develop an improved energy management system using deep reinforcement learning to achieve enhanced cost-saving by extending discrete state parameters to be continuous. The improved energy management system is based upon the Double Deep Q-Network. Real-world collected stochastic load profiles are applied to train the Double Deep Q-Network for a coastal ferry. The results suggest that the Double Deep Q-Network acquired energy management strategy has achieved a further 5.5% cost reduction with a 93.8% decrease in training time, compared to that produced by the Double Q-learning agent in discrete state space without function approximations. In addition, this article also proposes an adaptive deep reinforcement learning energy management scheme for practical hybrid-electric propulsion systems operating in changing environments.