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To manage communications to large numbers of endpoints in a secure, scalable and highly-available environment, in this paper we provide a cloud computing framework for a group of smart energy hubs.
As the primary objective and the contribution of the paper are to propose a parallel computing framework for CA and focus on its computational performance, we simply try to see if the power flow can still converge and the system is still secure after a line trips out.
The implementation of the ConvNets classifier was performed using Torch 7, a scientific computing framework for machine learning54.
We present a scalable computing framework for the solution stochastic multiobjective optimization problems.
Networks of sensors with wireless communication and computational capabilities can be used to increase the spatial resolution of data collection while providing a distributed computing framework for implementing structural health monitoring algorithms.
MapReduce is a scalable parallel computing framework for big data processing.
We present a high performance computing framework for large scale simulation of compressible multicomponent flows, applied to cloud cavitation collapse.
To overcome these limitations, a novel self-validated computing framework for indirect loadability analysis of overhead line is proposed in this article.
To address the challenge, this paper presents an integrated modeling and computing framework for systemic analysis of regional- and project-level transportation environmental impacts for land use mix patterns and associated transportation activities.
This paper proposes a simple parallel and distributed computing framework for the conventional Newton Raphson load flow (NRLF) solution of large interconnected power systems.
SMART is a generic and loosely-coupled software computing framework for creating AR visual applications with accurate registration algorithms and visually credible occlusion effects.
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