• Title/Summary/Keyword: CHAI

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Study on Poly(3,4-ethylenedioxythiophene) Thin Film Vapour Phase-Polymerized with Iron(III)Tosylate on High Quality 3-Aminopropyltriethoxysilane Self-Assembled Monolayer

  • Choi, Sangil;Kim, Wondae;Cho, Sung Jun;Kim, Sungsoo
    • Journal of Integrative Natural Science
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    • v.5 no.4
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    • pp.237-240
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    • 2012

  • In this study, PEDOT thin films polymerized with Iron(III)tosylate ($Fe(PTS)_3$) and grown on atomically smooth and highly dense 3-aminopropyltriethoxysilane self-assembled monolayer (APS-SAM) surfaces by VPP method have been investigated. PEDOT thin films were synthesized on APS self-assembled $SiO_2$ wafer surface at two different concentrations (20 wt% and 40 wt%) and growth time (3 and 30 minutes), and then their sheet resistance were measured and compared. PEDOT thin films grown with 20 wt% $Fe(PTS)_3$ oxidant are highly conductive when compared with the film grown with 40 wt% $Fe(PTS)_3$, as ascertained by the measured sheet resistance values down to 0.06 ${\Omega}/cm$. It clearly suggests that 20 wt% is more effective oxidant concentration for VPP than 40 wt% even though the film grown with 40 wt% oxidant has better quality than the film with 20 wt% $Fe(PTS)_3$ does.

  • https://doi.org/10.13160/ricns.2012.5.4.237 인용 PDF

Customized recommendation system through product review analysis (상품 리뷰 분석을 통한 사용자 맞춤형 추천 시스템)

  • Hwang, Doyeun;Bae, Sangjung;Kim, Changsoo;Jung, Heokyung
    • Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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    • 2018.05a
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    • pp.460-461
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    • 2018

  • The traditional recommendation system is developed on the assumption that users behave independently, and have problem of readability and efficiency are inferior due to simply sort products or lack of function for associate product attributes with user's taste. To solve this problem in this study we propose a system that provides user customized information that the analysis of the unstructured review data with the purchase histories of users processed with meaningful information after crawling product review data using text mining with R. This allows to help user make decisions can be provided only necessary information without analyze massive amounts of products review data.

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