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Document Title Author Full Name Author Short Name Index Corresponding Address ResearcherID ResearcherID Author Name ORCID ORCID Author Name Related Email
Establishment of Database for Automated Building Codes Compliance Checking in the Pre-Design Phase Hong, Soon-min Hong, SM 1 교신저자 Kyungpook Natl Univ, Sch Architecture, Daegu, South Korea soonmin_hong@knu.ac.kr;te01066@knu.ac.kr;ghm3186@knu.ac.kr;choo@knu.ac.kr;
Establishment of Database for Automated Building Codes Compliance Checking in the Pre-Design Phase Kim, Dong-wuk Kim, DW 2 Kyungpook Natl Univ, Sch Architecture, Daegu, South Korea soonmin_hong@knu.ac.kr;te01066@knu.ac.kr;ghm3186@knu.ac.kr;choo@knu.ac.kr;
Establishment of Database for Automated Building Codes Compliance Checking in the Pre-Design Phase Gu, Hyeong-mo Gu, HM 3 Kyungpook Natl Univ, Sch Architecture, Daegu, South Korea soonmin_hong@knu.ac.kr;te01066@knu.ac.kr;ghm3186@knu.ac.kr;choo@knu.ac.kr;
Establishment of Database for Automated Building Codes Compliance Checking in the Pre-Design Phase Choo, Seung-yeon Choo, SY 4 Kyungpook Natl Univ, Sch Architecture, Daegu, South Korea JFB-0390-2023 Choo, Seungyeon soonmin_hong@knu.ac.kr;te01066@knu.ac.kr;ghm3186@knu.ac.kr;choo@knu.ac.kr;
ESTIMATES FOR THE NORM OF A MULTILINEAR FORM ON Rn WITH THE lp-NORM Kim, Sung Guen Kim, SG 1 교신저자 Kyungpook Natl Univ, Dept Math, Daegu 41566, South Korea sgk317@knu.ac.kr;
Estimating dose-response curves using splines: a nonparametric Bayesian knot selection method Lee, Jiwon Lee, J 1 Kyungpook Natl Univ, Dept Stat, Daegu 41566, South Korea kymmyself@knu.ac.kr;
Estimating dose-response curves using splines: a nonparametric Bayesian knot selection method Kim, Yongku Kim, Y 2 교신저자 Kyungpook Natl Univ, Dept Stat, Daegu 41566, South Korea kymmyself@knu.ac.kr;
Estimating dose-response curves using splines: a nonparametric Bayesian knot selection method Kim, Young Min Kim, YM 3 교신저자 Kyungpook Natl Univ, Dept Stat, Daegu 41566, South Korea kymmyself@knu.ac.kr;
Estimating the incubated river water quality indicator based on machine learning and deep learning paradigms: BOD5 Prediction Kim, Sungwon Kim, S 1 교신저자 Dongyang Univ, Dept Railrd Construct & Safety Engn, Yeongju 36040, South Korea swkim1968@dyu.ac.kr;
Estimating the incubated river water quality indicator based on machine learning and deep learning paradigms: BOD5 Prediction Alizamir, Meysam Alizamir, M 2 Islamic Azad Univ, Dept Civil Engn, Hamedan Branch, Hamadan, Hamadan, Iran AAK-6312-2021 Alizamir, Meysam swkim1968@dyu.ac.kr;
Estimating the incubated river water quality indicator based on machine learning and deep learning paradigms: BOD5 Prediction Seo, Youngmin Seo, Y 3 Kyungpook Natl Univ, Dept Construct & Environm Engn, Sangju 37224, South Korea swkim1968@dyu.ac.kr;
Estimating the incubated river water quality indicator based on machine learning and deep learning paradigms: BOD5 Prediction Heddam, Salim Heddam, S 4 Univ 20 Aout 1955, Fac Sci, Agron Dept, Hydraul Div,Lab Res Biodivers Interact Ecosyst &, BP 26, Skikda, Algeria B-8647-2015 HEDDAM, SALIM 0000-0002-8055-8463 HEDDAM, SALIM swkim1968@dyu.ac.kr;
Estimating the incubated river water quality indicator based on machine learning and deep learning paradigms: BOD5 Prediction Chung, Il-Moon Chung, IM 5 Korea Inst Civil Engn & Bldg Technol, Dept Hydro Sci & Engn Res, Goyang Si 10223, South Korea 0000-0003-0163-7305 Chung, Il-Moon swkim1968@dyu.ac.kr;
Estimating the incubated river water quality indicator based on machine learning and deep learning paradigms: BOD5 Prediction Kim, Young-Oh Kim, YO 6 Seoul Natl Univ, Dept Civil Engn, Seoul, South Korea swkim1968@dyu.ac.kr;
Estimating the incubated river water quality indicator based on machine learning and deep learning paradigms: BOD5 Prediction Kisi, Ozgur Kisi, O 7 Univ Appl Sci, Dept Civil Engn, D-23562 Lubeck, Germany AAD-8932-2019 Kisi, Ozgur swkim1968@dyu.ac.kr;
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