DC Field | Value | Language |
---|---|---|
dc.contributor.author | Kim, Jihwan | - |
dc.contributor.author | Go, Taesik | - |
dc.contributor.author | Lee, Sang Joon | - |
dc.date.accessioned | 2022-06-23T02:41:39Z | - |
dc.date.available | 2022-06-23T02:41:39Z | - |
dc.date.created | 2021-10-10 | - |
dc.date.issued | 2021-09 | - |
dc.identifier.issn | 0304-3894 | - |
dc.identifier.uri | https://oasis.postech.ac.kr/handle/2014.oak/113074 | - |
dc.description.abstract | Airborne particulate matter (PM) has become a global environmental issue. This PM has harmful effects on public health and precision industries. Conventional air-quality monitoring methods usually utilize expensive equipment, and they are cumbersome to handle for accurate and high throughput measurements. In addition, commercial particle counters have technical limitations in high-concentration measurement, and data fluctuations are induced during air sampling. In this study, a novel smartphone-based technique for monitoring airborne PM concentrations was developed using smartphone-based digital holographic microscopy (S-DHM) and deep learning network called Holo-SpeckleNet. Holographic speckle images of various PM concentrations were recorded by the S-DHM system. The recorded speckle images and the corresponding ground truth PM concentrations were used to train deep learning algorithms consisting of a deep autoencoder and regression layers. The performance of the proposed smartphone-based PM monitoring technique was validated through hyperparameter optimization. The developed S-DHM integrated with Holo-SpeckleNet can be smartly and effectively utilized for portable PM monitoring and safety alarm provision under perilous environmental conditions. | - |
dc.language | English | - |
dc.publisher | ELSEVIER | - |
dc.relation.isPartOf | JOURNAL OF HAZARDOUS MATERIALS | - |
dc.title | Volumetric monitoring of airborne particulate matter concentration using smartphone-based digital holographic microscopy and deep learning | - |
dc.type | Article | - |
dc.identifier.doi | 10.1016/j.jhazmat.2021.126351 | - |
dc.type.rims | ART | - |
dc.identifier.bibliographicCitation | JOURNAL OF HAZARDOUS MATERIALS, v.418 | - |
dc.identifier.wosid | 000689727500002 | - |
dc.citation.title | JOURNAL OF HAZARDOUS MATERIALS | - |
dc.citation.volume | 418 | - |
dc.contributor.affiliatedAuthor | Kim, Jihwan | - |
dc.contributor.affiliatedAuthor | Lee, Sang Joon | - |
dc.identifier.scopusid | 2-s2.0-85107914966 | - |
dc.description.journalClass | 1 | - |
dc.description.journalClass | 1 | - |
dc.description.isOpenAccess | N | - |
dc.type.docType | Article | - |
dc.subject.keywordPlus | AIR-POLLUTION | - |
dc.subject.keywordPlus | SCATTERING | - |
dc.subject.keywordPlus | FIELD | - |
dc.subject.keywordPlus | SIZE | - |
dc.subject.keywordPlus | MASS | - |
dc.subject.keywordAuthor | Particulate matter (PM) | - |
dc.subject.keywordAuthor | Smartphone | - |
dc.subject.keywordAuthor | Digital holographic microscopy (DHM) | - |
dc.subject.keywordAuthor | Deep learning | - |
dc.relation.journalWebOfScienceCategory | Engineering, Environmental | - |
dc.relation.journalWebOfScienceCategory | Environmental Sciences | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
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