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Prediction of Ultraviolet Corrosion Levels of High Density Polyethylene Using Artificial Intelligence
| DC Field | Value | Language |
|---|---|---|
| dc.contributor.author | Seo, J. | - |
| dc.contributor.author | Park, G. | - |
| dc.contributor.author | Kim, M. | - |
| dc.contributor.author | Joo, J. | - |
| dc.contributor.author | Koh, J. | - |
| dc.date.accessioned | 2023-05-15T09:40:09Z | - |
| dc.date.available | 2023-05-15T09:40:09Z | - |
| dc.date.issued | 2023-03 | - |
| dc.identifier.issn | 0000-0000 | - |
| dc.identifier.uri | https://scholarworks.gnu.ac.kr/handle/sw.gnu/59427 | - |
| dc.description.abstract | Currently, as the intensity of ultraviolet rays increases due to the severe global warming problem, it is difficult to make materials that exclude ultraviolet rays. As a result, high-density polyethylene (HDPE) is a non-toxic ecofriendly plastic with no environmental hormones detected and has been found to have excellent strength and less damage to ultraviolet rays, and interest in eco-friendly ship technology is increasing. This research predicted damage to high-density polyethylene by adding a carbon black sample, which is a UV stabilizer, to HDPE using a long-lasting UV-A lamp because it requires a lot of time due to the presence or absence of ultraviolet limitation. Since it takes a lot of time and money to check the corrosion of the material using ultraviolet rays, research was conducted using artificial intelligence to solve this problem, and as a result, it was confirmed that the degree of corrosion of HDPE according to the irradiation time can be predicted with 90% accuracy. © 2023 IEEE. | - |
| dc.format.extent | 5 | - |
| dc.language | 영어 | - |
| dc.language.iso | ENG | - |
| dc.publisher | Institute of Electrical and Electronics Engineers Inc. | - |
| dc.title | Prediction of Ultraviolet Corrosion Levels of High Density Polyethylene Using Artificial Intelligence | - |
| dc.type | Article | - |
| dc.identifier.doi | 10.1109/CSPA57446.2023.10087881 | - |
| dc.identifier.scopusid | 2-s2.0-85153729074 | - |
| dc.identifier.bibliographicCitation | 2023 19th IEEE International Colloquium on Signal Processing and Its Applications, CSPA 2023 - Conference Proceedings, pp 278 - 282 | - |
| dc.citation.title | 2023 19th IEEE International Colloquium on Signal Processing and Its Applications, CSPA 2023 - Conference Proceedings | - |
| dc.citation.startPage | 278 | - |
| dc.citation.endPage | 282 | - |
| dc.type.docType | Conference Paper | - |
| dc.description.isOpenAccess | N | - |
| dc.description.journalRegisteredClass | scopus | - |
| dc.subject.keywordAuthor | artificial intelligence | - |
| dc.subject.keywordAuthor | carbon black | - |
| dc.subject.keywordAuthor | high-density polyethylene | - |
| dc.subject.keywordAuthor | ultra violet Absorber | - |
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