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Quantitative analysis of metal scraps using laser-induced breakdown spectroscopy measurements via unsupervised adversarial domain adaptation.

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Title: Quantitative analysis of metal scraps using laser-induced breakdown spectroscopy measurements via unsupervised adversarial domain adaptation.
Authors: Srivastava, Ekta1, Kim, Hyebin1,2, Lee, Jaepil3, Shin, Sungho4, Jeong, Sungho3, Hwang, Euiseok1,5, euiseokh@gist.ac.kr
Source: Chemometrics & Intelligent Laboratory Systems; Nov2022, Vol. 230, pN.PAG-N.PAG, 1p
Database: Applied Science & Technology Source
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DbLabel: Applied Science & Technology Source
An: 159907595
AccessLevel: 2
PubType: Academic Journal
PubTypeId: academicJournal
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  Data: <searchLink fieldCode="JN" term="%22Chemometrics+%26+Intelligent+Laboratory+Systems%22">Chemometrics & Intelligent Laboratory Systems</searchLink>; Nov2022, Vol. 230, pN.PAG-N.PAG, 1p
PLink https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=aci&AN=159907595
RecordInfo BibRecord:
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      – Type: doi
        Value: 10.1016/j.chemolab.2022.104667
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      – Code: eng
        Text: English
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      – TitleFull: Quantitative analysis of metal scraps using laser-induced breakdown spectroscopy measurements via unsupervised adversarial domain adaptation.
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              Text: Nov2022
              Type: published
              Y: 2022
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