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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
On Autonomous Phase Balancing of the Coplanar Stripline as a Feedline for a Quasi-Yagi Antenna Lee, Jung-Seok Lee, JS 1 Kyungpook Natl Univ, Sch Elect & Elect Engn, Daegu 41566, South Korea L-6826-2019 Lee, Jung-Seok j.seok1020@knu.ac.kr;minbc4658@knu.ac.kr;sachinkr@srmist.edu.in;hcchoi@ee.knu.ac.kr;kang_kim@ee.knu.ac.kr;
On Autonomous Phase Balancing of the Coplanar Stripline as a Feedline for a Quasi-Yagi Antenna Min, Byung-Cheol Min, BC 2 Kyungpook Natl Univ, Sch Elect & Elect Engn, Daegu 41566, South Korea 0000-0003-0791-2769 Min, Byungcheol j.seok1020@knu.ac.kr;minbc4658@knu.ac.kr;sachinkr@srmist.edu.in;hcchoi@ee.knu.ac.kr;kang_kim@ee.knu.ac.kr;
On Autonomous Phase Balancing of the Coplanar Stripline as a Feedline for a Quasi-Yagi Antenna Kumar, Sachin Kumar, S 3 SRM Inst Sci & Technol, Dept Elect & Commun Engn, Kattankulathur 603203, India W-2211-2019 Kumar, Sachin 0000-0002-6260-6353 Kumar, Sachin j.seok1020@knu.ac.kr;minbc4658@knu.ac.kr;sachinkr@srmist.edu.in;hcchoi@ee.knu.ac.kr;kang_kim@ee.knu.ac.kr;
On Autonomous Phase Balancing of the Coplanar Stripline as a Feedline for a Quasi-Yagi Antenna Choi, Hyun-Chul Choi, HC 4 Kyungpook Natl Univ, Sch Elect & Elect Engn, Daegu 41566, South Korea j.seok1020@knu.ac.kr;minbc4658@knu.ac.kr;sachinkr@srmist.edu.in;hcchoi@ee.knu.ac.kr;kang_kim@ee.knu.ac.kr;
On Autonomous Phase Balancing of the Coplanar Stripline as a Feedline for a Quasi-Yagi Antenna Kim, Kang-Wook Kim, KW 5 교신저자 Kyungpook Natl Univ, Sch Elect & Elect Engn, Daegu 41566, South Korea j.seok1020@knu.ac.kr;minbc4658@knu.ac.kr;sachinkr@srmist.edu.in;hcchoi@ee.knu.ac.kr;kang_kim@ee.knu.ac.kr;
On Collaborative Multi-UAV Trajectory Planning for Data Collection Rahim, Shahnila Rahim, S 1 Kyungpook Natl Univ, Sch Comp Sci & Engn, Daegu, South Korea LRC-2927-2024 Rahim, Shahnila shahnila.rahim@knu.ac.kr;auroraplm@knu.ac.kr;shihyu.chang@sjsu.edu;p4ho@uwaterloo.ca;
On Collaborative Multi-UAV Trajectory Planning for Data Collection Peng, Limei Peng, LM 2 교신저자 Kyungpook Natl Univ, Sch Comp Sci & Engn, Daegu, South Korea shahnila.rahim@knu.ac.kr;auroraplm@knu.ac.kr;shihyu.chang@sjsu.edu;p4ho@uwaterloo.ca;
On Collaborative Multi-UAV Trajectory Planning for Data Collection Chang, Shihyu Chang, SH 3 San Jose State Univ, Dept Appl Data Sci, San Jose, CA USA shahnila.rahim@knu.ac.kr;auroraplm@knu.ac.kr;shihyu.chang@sjsu.edu;p4ho@uwaterloo.ca;
On Collaborative Multi-UAV Trajectory Planning for Data Collection Ho, Pin-Han Ho, PH 4 Univ Waterloo, Dept Elect & Comp Engn, Waterloo, ON, Canada shahnila.rahim@knu.ac.kr;auroraplm@knu.ac.kr;shihyu.chang@sjsu.edu;p4ho@uwaterloo.ca;
On current technology for light absorber materials used in highly efficient industrial solar cells Chee, A. Kuan-Way Chee, AKW 1 교신저자 Kyungpook Natl Univ, Sch Elect & Elect Engn, Daegu 41566, South Korea kwac2@cantab.net;
On current technology for light absorber materials used in highly efficient industrial solar cells Chee, A. Kuan-Way Chee, AKW 1 교신저자 Kyungpook Natl Univ, Coll IT Engn, Sch Elect Engn, Daegu 41566, South Korea kwac2@cantab.net;
On developing accurate prediction models for residual tensile strength of GFRP bars under alkaline-concrete environment using a combined ensemble machine learning methods Go, Chaeyeon Go, C 1 Hanbat Natl Univ, Dept Civil & Environm Engn, Daejeon 34158, South Korea skwag@hanbat.ac.kr;bju2@khu.ac.kr;
On developing accurate prediction models for residual tensile strength of GFRP bars under alkaline-concrete environment using a combined ensemble machine learning methods Kwak, Yun-Ji Kwak, YJ 2 Hanbat Natl Univ, Dept Civil & Environm Engn, Daejeon 34158, South Korea skwag@hanbat.ac.kr;bju2@khu.ac.kr;
On developing accurate prediction models for residual tensile strength of GFRP bars under alkaline-concrete environment using a combined ensemble machine learning methods Kwag, Shinyoung Kwag, S 3 교신저자 Hanbat Natl Univ, Dept Civil & Environm Engn, Daejeon 34158, South Korea skwag@hanbat.ac.kr;bju2@khu.ac.kr;
On developing accurate prediction models for residual tensile strength of GFRP bars under alkaline-concrete environment using a combined ensemble machine learning methods Eem, Seunghyun Eem, S 4 Kyungpook Natl Univ, Sch Convers & Fus Syst Engn, Sangju, South Korea KVB-1493-2024 Eem, Seunghyun 0000-0001-9776-5429 Eem, Seunghyun skwag@hanbat.ac.kr;bju2@khu.ac.kr;
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