2023 연구성과별 연구자 정보 (25 / 2675)

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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
A Facile Surface-Imprinting Strategy for Trypsin-Imprinted Polymeric Chemosensors Using Two-Step Spin-Coating Yang, Jin Chul Yang, JC 2 Kyungpook Natl Univ, Dept Polymer Sci & Engn, 80 Daehak ro, Daegu 41566, South Korea 0000-0003-4419-2158 YANG, Jin Chul jppark@cau.ac.kr;jinpark@knu.ac.kr;
A Facile Surface-Imprinting Strategy for Trypsin-Imprinted Polymeric Chemosensors Using Two-Step Spin-Coating Cho, Chae Hwan Cho, CH 3 Chung Ang Univ, Dept Food Sci & Technol, Basic Res Lab, Anseong 17546, South Korea 0000-0002-9500-3655 CHO, CHAEHWAN jppark@cau.ac.kr;jinpark@knu.ac.kr;
A Facile Surface-Imprinting Strategy for Trypsin-Imprinted Polymeric Chemosensors Using Two-Step Spin-Coating Lim, Seok Jin Lim, SJ 4 Kyungpook Natl Univ, Dept Polymer Sci & Engn, 80 Daehak ro, Daegu 41566, South Korea 0000-0002-2458-6259 Lim, Seok Jin jppark@cau.ac.kr;jinpark@knu.ac.kr;
A Facile Surface-Imprinting Strategy for Trypsin-Imprinted Polymeric Chemosensors Using Two-Step Spin-Coating Park, Jong Pil Park, JP 5 교신저자 Chung Ang Univ, Dept Food Sci & Technol, Basic Res Lab, Anseong 17546, South Korea 0000-0002-4119-1574 Park, Jong Pil jppark@cau.ac.kr;jinpark@knu.ac.kr;
A Facile Surface-Imprinting Strategy for Trypsin-Imprinted Polymeric Chemosensors Using Two-Step Spin-Coating Park, Jinyoung Park, J 6 교신저자 Kyungpook Natl Univ, Dept Polymer Sci & Engn, 80 Daehak ro, Daegu 41566, South Korea P-5981-2015 PARK, JUN-YOUNG 0000-0003-0261-7605 Park, Jinyoung jppark@cau.ac.kr;jinpark@knu.ac.kr;
A framework of data-driven wind pressure predictions on bluff bodies using a hybrid deep learning approach Chen, Zengshun Chen, ZS 1 교신저자 Chongqing Univ, Sch Civil Engn, Chongqing 400044, Peoples R China 0000-0001-5916-1165 zengshun, chen zchenba@connect.ust.hk;xuexuanyi@126.com;
A framework of data-driven wind pressure predictions on bluff bodies using a hybrid deep learning approach Zhang, Likai Zhang, LK 2 Chongqing Univ, Sch Civil Engn, Chongqing 400044, Peoples R China zchenba@connect.ust.hk;xuexuanyi@126.com;
A framework of data-driven wind pressure predictions on bluff bodies using a hybrid deep learning approach Hua Jianmin Hua, JM 3 Chongqing Univ, Sch Civil Engn, Chongqing 400044, Peoples R China zchenba@connect.ust.hk;xuexuanyi@126.com;
A framework of data-driven wind pressure predictions on bluff bodies using a hybrid deep learning approach Kim, Bubryur Kim, B 4 Kyungpook Natl Univ, Dept Robot & Smart Syst Engn, Daegu, South Korea zchenba@connect.ust.hk;xuexuanyi@126.com;
A framework of data-driven wind pressure predictions on bluff bodies using a hybrid deep learning approach Li, Ke Li, K 5 Chongqing Univ, Sch Civil Engn, Chongqing 400044, Peoples R China AAK-9561-2021 LI, KE zchenba@connect.ust.hk;xuexuanyi@126.com;
A framework of data-driven wind pressure predictions on bluff bodies using a hybrid deep learning approach Xue, Xuanyi Xue, XY 6 교신저자 Chongqing Univ, Sch Civil Engn, Chongqing 400044, Peoples R China ABB-5624-2022 Xue, Xuanyi zchenba@connect.ust.hk;xuexuanyi@126.com;
A genetic programming-based optimal sensor placement for greenhouse monitoring and control Ajani, Oladayo S. Ajani, OS 1 Kyungpook Natl Univ, Sch Convergence, Dept Artificial Intelligence, Daegu, South Korea HIR-9607-2022 AJANI, Oladayo 0000-0001-5796-3375 Ajani, Oladayo mallipeddi.ram@gmail.com;
A genetic programming-based optimal sensor placement for greenhouse monitoring and control Aboyeji, Esther Aboyeji, E 2 Kyungpook Natl Univ, Sch Convergence, Dept Artificial Intelligence, Daegu, South Korea IZP-8228-2023 Aboyeji, Esther mallipeddi.ram@gmail.com;
A genetic programming-based optimal sensor placement for greenhouse monitoring and control Mallipeddi, Rammohan Mallipeddi, R 3 교신저자 Kyungpook Natl Univ, Sch Convergence, Dept Artificial Intelligence, Daegu, South Korea AAL-5306-2020 Mallipeddi, Rammohan mallipeddi.ram@gmail.com;
A genetic programming-based optimal sensor placement for greenhouse monitoring and control Uyeh, Daniel Dooyum Uyeh, DD 4 Michigan State Univ, Dept Biosyst & Agr Engn, E Lansing, MI USA mallipeddi.ram@gmail.com;
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