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김건호

Kim, Gun-Ho
SoftHeat Lab.
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dc.citation.startPage 122952 -
dc.citation.title APPLIED THERMAL ENGINEERING -
dc.citation.volume 246 -
dc.contributor.author Ko, Juhee -
dc.contributor.author Son, Hyunjoon -
dc.contributor.author Lee, Bong Jae -
dc.contributor.author Kim, Gun-Ho -
dc.contributor.author Lee, Jungchul -
dc.date.accessioned 2024-05-24T10:35:09Z -
dc.date.available 2024-05-24T10:35:09Z -
dc.date.created 2024-05-23 -
dc.date.issued 2024-06 -
dc.description.abstract Accurate information of subcutaneous temperature is crucial to the effectiveness and safety of a variety of medical procedures by energy -based devices, such as cooling treatments. Numerous strategies for invasive subcutaneous temperature monitoring have been investigated; however, they are impractical mainly due to their requirements for precise heat transfer modeling. In the case of cooling treatment by cryogenic substances, heat transfer rate largely varies with their phase change and procedure condition, which prevents accurate prediction of subcutaneous temperature. In this work, we propose a non-invasive, accurate, and practical prediction of subcutaneous temperature during CO 2 spray jet cooling without numerical modeling by utilizing recurrent neural networks (RNNs) with surface temperature measurements for the first time. A patchable temperature sensor array on a flexible PCB is used to measure surface temperature. With this measured surface temperature, RNNs are developed with two functionalities: extraction of thermophysical properties (thermal conductivity and thermal diffusivity) of the target substrate and prediction of temperature response at 1 -mm depth from the surface, which is the typical location of pain points. Five polymer thin films are used with the reference thermocouple embedded inside to train RNNs. A porcine skin is used to validate the RNNs, which show a prediction accuracy of 99.2%. -
dc.identifier.bibliographicCitation APPLIED THERMAL ENGINEERING, v.246, pp.122952 -
dc.identifier.doi 10.1016/j.applthermaleng.2024.122952 -
dc.identifier.issn 1359-4311 -
dc.identifier.scopusid 2-s2.0-85188250289 -
dc.identifier.uri https://scholarworks.unist.ac.kr/handle/201301/82697 -
dc.identifier.wosid 001216847400001 -
dc.language 영어 -
dc.publisher PERGAMON-ELSEVIER SCIENCE LTD -
dc.title Subcutaneous temperature prediction during cryogenic jet cooling by surface temperature measurements and RNNs -
dc.type Article -
dc.description.isOpenAccess FALSE -
dc.relation.journalWebOfScienceCategory Thermodynamics; Energy & Fuels; Engineering, Mechanical; Mechanics -
dc.relation.journalResearchArea Thermodynamics; Energy & Fuels; Engineering; Mechanics -
dc.type.docType Article -
dc.description.journalRegisteredClass scie -
dc.description.journalRegisteredClass scopus -
dc.subject.keywordAuthor CO2 jet cooling -
dc.subject.keywordAuthor Recurrent neural network -
dc.subject.keywordAuthor Subcutaneous temperature -
dc.subject.keywordAuthor Temperature measurement -
dc.subject.keywordAuthor Thermophysical properties -
dc.subject.keywordPlus HEAT-TRANSFER -
dc.subject.keywordPlus SKIN -

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