The robustness of cargo ship transportation networks is essential to the stability of the world trade system. The current research mainly focuses on the coarse-grained, holistic cargo ship transportation network while...
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The robustness of cargo ship transportation networks is essential to the stability of the world trade system. The current research mainly focuses on the coarse-grained, holistic cargo ship transportation network while ignoring the structural diversity of different sub-networks. In this paper, we evaluate the robustness of the global cargo ship transportation network based on the most recent Automatic Identification System（AIS） data available. First, we subdivide three typical cargo ship transportation networks（i.e., oil tanker, container ship and bulk carrier） from the original cargo ship transportation network. Then, we design statistical indices based on complex network theory and employ four attack strategies, including random attack and three intentional attacks（i.e., degree-based attack, betweenness-based attack and flux-based attack） to evaluate the robustness of the three typical cargo ship transportation networks. Finally, we compare the integrity of the remaining ports of the network when a small proportion of ports lose their function. The results show that 1） compared with the holistic cargo ship transportation network, the fine-grain-based cargo ship transportation networks can fully reflect the pattern and process of global cargo transportation; 2） different cargo ship networks behave heterogeneously in terms of their robustness, with the container network being the weakest and the bulk carrier network being the strongest; and 3） small-scale intentional attacks may have significant influence on the integrity of the container network but a minor impact on the bulk carrier and oil tanker transportation networks. These conclusions can help improve the decision support capabilities in maritime transportation planning and emergency response and facilitate the establishment of a more reliable maritime transportation ***： The robustness of cargo ship transportation networks is essential to the stability of the world trade system. The current resear
Application of cloud computing technologies in power system has made a great contribution to the establishment of smart grid. Among applications of smart grid, electrical load prediction plays an important role in eff...
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ISBN:
(纸本)9781509054442
Application of cloud computing technologies in power system has made a great contribution to the establishment of smart grid. Among applications of smart grid, electrical load prediction plays an important role in efficient use of power resource. However, the exponential growth of data has posed a great challenge to the existing algorithms. In this paper, we firstly propose a novel parallel hybrid algorithm, combining the Improved Particle Swarm Optimization (PSO) with ELM, named PIPSO-ELM. Here a modified particle swarm optimization is presented to find the optimal number of hidden neurons as well as the corresponding input weights and hidden biases. Furthermore, in the iterative search process of PSO, an update strategy employs the mutation operator of evolutionary algorithms is introduced for further improving the global search capability and convergence speed of PSO. After that, to handle the large-scale dataset efficiently, the parallel implementation of PIPSO-ELM is achieved using Spark. Finally, extensive experiments on real-life electrical load data and comprehensive evaluation are conducted to verify the performance of PIPSO-ELM in electrical load prediction. Extensive experimental results demonstrate that PIPSO-ELM outperforms the compared algorithms in terms of stability, efficiency and scalability simultaneously.
Automata theory is an important branch of theoretical computer science. ω-automaton is an important part of automata theory. The judgment standard for the equivalence of two automata is the equivalence of their accep...
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This paper describes the need for and the proposed designing of the high performance quantitative retrieval model to be used on Computational Grid for study of aerosol properties, with particular emphasis on Aerosol O...
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This paper describes the need for and the proposed designing of the high performance quantitative retrieval model to be used on Computational Grid for study of aerosol properties, with particular emphasis on Aerosol Optical Thickness (AOT) determination. A methodology using multi-resource remotely sensed data and adapting available aerosol retrieval model in a Grid environment is demonstrated. The algorithm comprises two complementary parts, collectively used in a distributed application. This paper focused on parallelization method based on a resource management and task partition strategy. A module, called DPPA (Dynamic Partition Points Algorithm for workload estimation), is designed as a portable technology for developing and deploying Grid execution in a generic data parallel paradigm. Experimental results are presented in a realistic application, using data collected by MODIS over China land. Derived result and computing performance of the proposed algorithm is given using the Grid test-bed at the Institute of Remote Sensing applications of Chinese Academy of Sciences (IRSA, CAS). Combined, the experimental results show that Grid-enabled model allowed on-demand large volume of ground-based data assimilation with parameters, and achieved substantial reductions in computational times. The research gives a thoughtful perspective on the potential of applying high performance computing practices to remote sensing quantitative retrieving problems.
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