Numerical simulation study of gas explosion in confined space based on deep learning algorithm
Journal of Intelligent and Fuzzy Systems, ISSN: 1875-8967, Vol: 37, Issue: 3, Page: 3239-3246
2019
- 6Captures
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Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
Citation Benchmarking is provided by Scopus and SciVal and is different from the metrics context provided by PlumX Metrics.
Metrics Details
- Captures6
- Readers6
Article Description
In order to determine the explosion value in the confined space, this time the simulation model of the deep learning algorithm is used to study it. The research status of deep learning algorithm is first expounded, and the numerical record of gas explosion in confined space is constructed according to computer technology. In order to ensure the optimization of numerical processing, the deep learning algorithm is used to process the simulation data to ensure the accuracy of the explosion value. In order to further test the numerical accuracy of the numerical model of gas limited space explosion, the comparison of different values in the constrained space is carried out, and the efficiency and accuracy of the deep learning algorithm are tested. The test results show the application of deep learning algorithm. The accuracy of the explosion value is further guaranteed and needs further application.
Bibliographic Details
http://www.scopus.com/inward/record.url?partnerID=HzOxMe3b&scp=85073724319&origin=inward; http://dx.doi.org/10.3233/jifs-179125; https://www.medra.org/servlet/aliasResolver?alias=iospress&doi=10.3233/JIFS-179125; https://dx.doi.org/10.3233/jifs-179125; https://content.iospress.com:443/articles/journal-of-intelligent-and-fuzzy-systems/ifs179125
SAGE Publications
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