Browse "College of Engineering(공과대학)" by Title
Showing results 90761 to 90780 of 201450
| Machine learning algorithms for sparse supervision = 저지도 상황에서의 기계학습 알고리즘 연구link Seo, Jun; Moon, Jaekyun; et al, 한국과학기술원, 2022 |
| Machine Learning and Autonomous Systems in the Military Realm: An Engineer’s Point of View Lee, Soo-Young, 3rd SIPRI Workshop on Mapping the Impact of Machine Learning and Autonomy on Strategic Stability and Nuclear Risk, STOCKHOLM INTERNATIONAL PEACE RESEARCH INSTITUTE, 2019-02-25 |
| Machine learning applications in genome-scale metabolic modeling Kim, Yeji; Kim, Gi Bae; Lee, Sang Yup, Current Opinion in Systems Biology, v.25, pp.42 - 49, 2021-03 |
| Machine learning applications in systems metabolic engineering Kim, Gi Bae; Kim, Won Jun; Kim, Hyun Uk; Lee, Sang Yup, CURRENT OPINION IN BIOTECHNOLOGY, v.64, pp.1 - 9, 2020-08 |
| Machine learning approach for anonymizing electronic medical records = 전자의무기록의 기계학습 기반 익명화 기법link Shin, Moon-Shik; 신문식; et al, 한국과학기술원, 2012 |
| Machine Learning Approach for Inverse Scattering Problem = 기계학습을 이용한 역산란 문제 연구link Yoo, Jaejun; Ye, Jong Chul; et al, 한국과학기술원, 2018 |
| Machine learning approaches for materials discovery: Predictive and generative models Jung, Yousung, Telluride Workshop Machine Learning and Informatics for Chemistry and Materials, Telluride Science Research Center, 2018-10-01 |
| Machine learning approaches to the configuration energies and chemisorption models in solids Jung, Yousung, Machine Learning for Energy Materials Discovery, MIT, 2017-05-30 |
| Machine learning approaches to the configuration energies and chemisorption models in solids Jung, Yousung, IPAM(Institute for Pure & Applied mathematics)Workshop I: Machine Learning Meets Many-Particle Problems, ipam, 2016-09-26 |
| Machine learning approaches to the configuration energies and chemisorption models in solids Jung, Yousung, 대한화학회 제 118회 총회 및 학술발표회, 대한화학회, 2016-10-13 |
| Machine Learning Approaches to the Configuration Energies and Chemisorption Models in Solids Jung, Yousung, 2017 Symposium for the Promotion of Applied Research Collaboration in Asia, Asia Pacific Society for Materials Science (APSMR), 2017-02-26 |
| Machine learning assisted characterization of atherosclerotic plaque components via multispectral fluorescence lifetime imaging microscopy Han, Jeongmoo; Kim, Sunwon; Kim, Hyun Jung; Nam, Hyeong Soo; Kim, Jin Won; Yoo, Hongki, SPIE Photonics West 2023, SPIE, 2023-01-29 |
| Machine learning assisted synthesis of lithium-ion batteries cathode materials Liow, Chi Hao; Kang, Hyeonmuk; Kim, Seunggu; Na, Moony; Lee, Yongju; Baucour, Arthur; Bang, Kihoon; et al, NANO ENERGY, v.98, 2022-07 |
| Machine learning based approach for large-scale drug-target binding prediction = 기계 학습 기법을 통한 대규모 약물-표적 결합 예측link Lee, KyoungYeul; Kim, Dongsup; et al, 한국과학기술원, 2020 |
| Machine learning based catalytic property prediction of metal nanoparticles = 머신러닝 기반 금속나노입자 촉매 성능 예측link Bang, Kihoon; Lee, Hyuck Mo; et al, 한국과학기술원, 2020 |
| Machine learning based english-to-Korean transliteration using grapheme and phoneme information Oh, JH; Choi, Key-Sun, IEICE TRANSACTIONS ON INFORMATION AND SYSTEMS, v.E88D, no.7, pp.1737 - 1748, 2005-07 |
| Machine learning based epoxy resins properties prediction = 머신러닝을 활용한 에폭시수지 물성 예측link Jang, Huiwon; Kim, Jihan; et al, 한국과학기술원, 2022 |
| Machine learning based evolutionary algorithms and optimization for transportation and logistics Preface Cheng, John; Yang, Bin; Gen, Mitsuo; Jang, Young Jae; Liang, Cheng-Ji, COMPUTERS & INDUSTRIAL ENGINEERING, v.143, 2020-05 |
| Machine Learning based Flexible Piezoelectric sensor Lee, Keon Jae, Acoustics 2023, Acoustical Society, 2023-12-06 |
| Machine learning based photovoltaic energy prediction scheme by augmentation of on-site IoT data Park, Jaeeun; Kim, Jangkyum; Lee, Sanghyun; Choi, Jun Kyun, FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE, v.134, pp.1 - 12, 2022-09 |
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