基于集成方法的气体扩散模拟

K. Gwak, Young J. Rho
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引用次数: 0

摘要

第四次工业革命正在推动制造业重新焕发活力。制造业需要许多工业材料。其中,不同的气体也用于许多领域。虽然工业气体是有用的,但同时也可能是有害的。为了控制这些气体的不良特性,需要了解它们的动态特性。在本文中,我们试图通过应用MLP、DLP和LSTM等几种机器学习方法来理解这些特征。采用两种集成方法来补偿原始数据的不足。仿真输出相互比较,以了解哪种方法适合于这种情况。
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Gas Diffusion Simulation Based on Ensemble Approach
The 4th 1 industrial revolution is promoting manufacturing industry to be vitalized again. The manufacturing industry requires many industrial materials. Among them, different gases are also used in many fields. While they are useful, industrial gases can be also hazardous at the same time. In order to control those bad features of gases, their dynamic characteristics are required to be understood. In this paper we tried to understand the characteristics by applying several machine learning methods such as MLP, DLP and LSTM. Two ensemble methods are applied to compensate the lack of raw data. Simulation outputs are compared each other to know which method is proper for this case.
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