{"title":"Gas Diffusion Simulation Based on Ensemble Approach","authors":"K. Gwak, Young J. Rho","doi":"10.7763/ijmo.2020.v10.765","DOIUrl":null,"url":null,"abstract":"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.","PeriodicalId":134487,"journal":{"name":"International Journal of Modeling and Optimization","volume":"10 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2020-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"International Journal of Modeling and Optimization","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.7763/ijmo.2020.v10.765","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
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.