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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. 相似文献
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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。 相似文献
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《International Journal of Hydrogen Energy》2021,46(80):40022-40040
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. 相似文献
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《International Journal of Hydrogen Energy》2020,45(4):3193-3208
Efforts from all sectors of society including the shipping industry are needed to limit the overall global temperature rise to within 2°C of pre-industrial levels by 2050. The hybridisation of Proton Exchange Membrane Fuel Cells (PEMFC) and Lithium-ion batteries for coastal ship propulsion systems may potentially offer beneficial emission performance. However, such hybrid systems are constrained by power and energy density limitations, lifetime; and costs as well as life-cycle emissions of alternative fuel/energy. There is a lack of holistic design methodology dealing with these uncertainties in the literature. This paper proposes a holistic design methodology for coastal hybrid ships based upon a developed model. The power source sizing problem is solved using constrained mixed-integer multi-objective optimisation in the external layer. The global optimum energy management strategies for an averaged operating profile are obtained from deterministic dynamic programming in the inner layer, while considering power source degradations in the sizing algorithm. The proposed methodology was applied to a coastal ferry to investigate the feasibility and benefit potential of adopting the hybrid PEMFC and battery propulsion system in Matlab. The case studies indicate that the proposed propulsion system can achieve at least a 65% life-cycle greenhouse gas reduction for the considered two cases. 相似文献
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《International Journal of Hydrogen Energy》2020,45(2):1359-1372
A concept risk assessment of a hydrogen and fuel cell driven high speed passenger ferry has been performed. The study focused on fatality risk related to the hydrogen systems on the vessel, both during operation and while moored in harbour overnight. The main objective with the study was to evaluate whether the risk related to the hydrogen systems is equivalent to that of conventionally fuelled vessels and can be considered acceptable according to the requirements of the IGF-code (International Code of Safety for Ships Using Gases or Other Low-flashpoint Fuels). Since hydrogen behaves differently than other flammable gases, some adjustments to existing models and vulnerability criteria have been proposed. The conclusion of the study is that the estimated risk related to hydrogen systems is relatively low, and much lower than the expected acceptable risk tolerance level of 0.5–1.0 fatalities per 109 passenger km. Furthermore, for the overnight mooring in harbour the estimated risks are well within acceptable limits. The work presented is part of a maritime case study performed within MoZEES, a Norwegian research centre for environmentally friendly technology and zero emission transport. 相似文献
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船只开进的自动导引可以提高船只开进和结合的速度,可以提高船只结合的准确性.该文研究船只的开进过程,建立模拟其开进控制的数学模型.通过编制相应计算机程序,能实现船只开进和结合过程的自动导引. 相似文献
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