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2024 연구성과별 연구자 정보 (406 / 2344)

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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
Clustered ultra-small iron oxide nanoparticles as potential T1/T2 dual-modal magnetic resonance imaging contrast agents and application to tumor model Lee, Sangyeol Lee, S 8 Kyungpook Natl Univ, Dept Med & Biol Engn, Taegu 41944, South Korea ychang@knu.ac.kr;ghlee@mail.knu.ac.kr;
Clustered ultra-small iron oxide nanoparticles as potential T1/T2 dual-modal magnetic resonance imaging contrast agents and application to tumor model Lee, Hansol Lee, H 9 Kyungpook Natl Univ, Dept Med & Biol Engn, Taegu 41944, South Korea ychang@knu.ac.kr;ghlee@mail.knu.ac.kr;
Clustered ultra-small iron oxide nanoparticles as potential T1/T2 dual-modal magnetic resonance imaging contrast agents and application to tumor model Kim, Seungho Kim, S 10 Kyungpook Natl Univ, Inst Biomed Engn Res, Taegu 41566, South Korea ychang@knu.ac.kr;ghlee@mail.knu.ac.kr;
Clustered ultra-small iron oxide nanoparticles as potential T1/T2 dual-modal magnetic resonance imaging contrast agents and application to tumor model Han, Mun Han, M 11 Daegu Gyeongbuk Med Innovat Fdn K MEDIhub, Med Device Dev Ctr, Taegu 41061, South Korea ychang@knu.ac.kr;ghlee@mail.knu.ac.kr;
Clustered ultra-small iron oxide nanoparticles as potential T1/T2 dual-modal magnetic resonance imaging contrast agents and application to tumor model Chae, Kwon Seok Chae, KS 12 Kyungpook Natl Univ, Teachers Coll, Dept Biol Educ, Taegu 41566, South Korea ychang@knu.ac.kr;ghlee@mail.knu.ac.kr;
Clustered ultra-small iron oxide nanoparticles as potential T1/T2 dual-modal magnetic resonance imaging contrast agents and application to tumor model Chang, Yongmin Chang, YM 13 교신저자 Kyungpook Natl Univ, Sch Med, Dept Mol Med, Taegu 41944, South Korea ychang@knu.ac.kr;ghlee@mail.knu.ac.kr;
Clustered ultra-small iron oxide nanoparticles as potential T1/T2 dual-modal magnetic resonance imaging contrast agents and application to tumor model Lee, Gang Ho Lee, GH 14 교신저자 Kyungpook Natl Univ, Coll Nat Sci, Dept Chem, Taegu 41566, South Korea ychang@knu.ac.kr;ghlee@mail.knu.ac.kr;
Clustering-Aided Grid-Based One-to-One Selection-Driven Evolutionary Algorithm for Multi/Many-Objective Optimization Palakonda, Vikas Palakonda, V 1 Kyungpook Natl Univ, Dept Artificial Intelligence, Daegu 41566, South Korea heechul@knu.ac.kr;
Clustering-Aided Grid-Based One-to-One Selection-Driven Evolutionary Algorithm for Multi/Many-Objective Optimization Kang, Jae-Mo Kang, JM 2 Kyungpook Natl Univ, Dept Artificial Intelligence, Daegu 41566, South Korea 0000-0002-8181-5994 Kang, Jae-Mo heechul@knu.ac.kr;
Clustering-Aided Grid-Based One-to-One Selection-Driven Evolutionary Algorithm for Multi/Many-Objective Optimization Jung, Heechul Jung, H 3 교신저자 Kyungpook Natl Univ, Dept Artificial Intelligence, Daegu 41566, South Korea heechul@knu.ac.kr;
CNN-Based Real-Time Walking Direction Estimation for Pedestrian Navigation Scenarios Lee, Eunji Lee, E 1 Seoul Natl Univ, Dept Elect Engn, Seoul 08826, South Korea ej9309@maxwell.snu.ac.kr;km92.park@samsung.com;byeongho.lee@samsung.com;sckim@maxwell.snu.ac.kr;jeongsik.choi@knu.ac.kr;
CNN-Based Real-Time Walking Direction Estimation for Pedestrian Navigation Scenarios Lee, Eunji Lee, E 1 Seoul Natl Univ, Inst New Media & Commun, Seoul 08826, South Korea ej9309@maxwell.snu.ac.kr;km92.park@samsung.com;byeongho.lee@samsung.com;sckim@maxwell.snu.ac.kr;jeongsik.choi@knu.ac.kr;
CNN-Based Real-Time Walking Direction Estimation for Pedestrian Navigation Scenarios Park, Kyoung-Min Park, KM 2 Samsung Elect, Samsung Res, Seoul 16677, South Korea 0000-0003-4927-8882 , Min ej9309@maxwell.snu.ac.kr;km92.park@samsung.com;byeongho.lee@samsung.com;sckim@maxwell.snu.ac.kr;jeongsik.choi@knu.ac.kr;
CNN-Based Real-Time Walking Direction Estimation for Pedestrian Navigation Scenarios Lee, Byeong-ho Lee, BH 3 Samsung Elect, Samsung Res, Seoul 16677, South Korea ej9309@maxwell.snu.ac.kr;km92.park@samsung.com;byeongho.lee@samsung.com;sckim@maxwell.snu.ac.kr;jeongsik.choi@knu.ac.kr;
CNN-Based Real-Time Walking Direction Estimation for Pedestrian Navigation Scenarios Kim, Seong-Cheol Kim, SC 4 Seoul Natl Univ, Dept Elect Engn, Seoul 08826, South Korea ej9309@maxwell.snu.ac.kr;km92.park@samsung.com;byeongho.lee@samsung.com;sckim@maxwell.snu.ac.kr;jeongsik.choi@knu.ac.kr;
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