Browsing by Titles

Showing results 1 to 13 of 13

  • Yun, Jong Pil
  • Shin, Woosang Crino
  • Koo, Gyogwon
  • Kim, Min Su
  • Lee, Chungki
  • Lee, Sang Jun
  • 2020-04
  • Yun, Jong Pil. (2020-04). Automated defect inspection system for metal surfaces based on deep learning and data augmentation. doi: 10.1016/j.jmsy.2020.03.009
  • Elsevier BV
  • View : 1119
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  • Seonhyeong Kim
  • 2023
  • Seonhyeong Kim. (2023). Communication-Efficient and Drift-Robust Federated Learning via Elastic Net. doi: 10.22677/THESIS.200000653511
  • DGIST
  • View : 245
  • Download : 0
  • Li, Qing
  • Chen, Yang
  • Kim, Yongjune
  • 2020-12
  • Li, Qing. (2020-12). Compression By and For Deep Boltzmann Machines. IEEE Transactions on Communications, 68(12), 7498–7510. doi: 10.1109/tcomm.2020.3020796
  • Institute of Electrical and Electronics Engineers Inc.
  • View : 610
  • Download : 0
  • Kim, Jinmo
  • Lee, Jungmin
  • Kim, Eunho
  • Choi, Joon Ho
  • Rah, Jong-Cheol
  • Choi, Ji-Woong
  • 2022-06
  • Kim, Jinmo. (2022-06). Dopamine depletion can be predicted by the aperiodic component of subthalamic local field potentials. Neurobiology of Disease, 168. doi: 10.1016/j.nbd.2022.105692
  • Academic Press
  • View : 443
  • Download : 0
  • 2016-03
  • Kim, In-Jung. (2016-03). Improving discrimination ability of convolutional neural networks by hybrid learning. International Journal on Document Analysis and Recognition, 19(1), 1–9. doi: 10.1007/s10032-015-0256-9
  • Springer Verlag
  • View : 933
  • Download : 0
  • Park, Ingyu
  • Lee, Kyeongho
  • Bishayee, Kausik
  • Jeon, Hong Jin
  • Lee, Hyosang
  • Lee, Unjoo
  • 2019-02
  • Park, Ingyu. (2019-02). Machine-Learning Based Automatic and Real-time Detection of Mouse Scratching Behaviors. doi: 10.5607/en.2019.28.1.54
  • 한국뇌신경과학회
  • View : 959
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  • Islem Rekik
  • Gozde Unal
  • Ehsan Adeli
  • Sang Hyun Park
  • 2018
  • Islem Rekik. (2018). PRedictive Intelligence in MEdicine: First International Workshop, PRIME 2018, Held in Conjunction with MICCAI 2018, Granada, Spain, September 16, 2018, Proceedings.
  • Springer
  • View : 669
  • Download : 0
  • Caballero-Vidal, Gabriela
  • Bouysset, Cedric
  • Gevar, Jeremy
  • Mbouzid, Hayat
  • Nara, Celine
  • Delaroche, Julie
  • Golebiowski, Jerome
  • Montagne, Nicolas
  • Fiorucci, Sebastien
  • Jacquin-Joly, Emmanuelle
  • 2021-10
  • Caballero-Vidal, Gabriela. (2021-10). Reverse chemical ecology in a moth: machine learning on odorant receptors identifies new behaviorally active agonists. Cellular and Molecular Life Sciences, 78(19–20), 6593–6603. doi: 10.1007/s00018-021-03919-2
  • Springer Science and Business Media Deutschland GmbH
  • View : 379
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SensDeploy: efficient sensor deployment strategy for real-time localization

  • Lee, Jin Hee
  • Shin, Byeong Seok
  • 2017-12
  • Lee, Jin Hee. (2017-12). SensDeploy: efficient sensor deployment strategy for real-time localization. Human-centric Computing and Information Sciences, 7(1). doi: 10.1186/s13673-017-0117-2
  • Springer Science + Business Media
  • View : 644
  • Download : 104

Subject-specific real time motor imaginary detection scheme for robot-aided hand rehabilitation

  • Chun, Rae Chang
  • 2016
  • Chun, Rae Chang. (2016). Subject-specific real time motor imaginary detection scheme for robot-aided hand rehabilitation. doi: 10.22677/thesis.2228855
  • DGIST
  • View : 770
  • Download : 291

The Impact of Odor Category Similarity on Multimedia Experience

  • 2024-10
  • Kim, Kwangsu. (2024-10). The Impact of Odor Category Similarity on Multimedia Experience. Experimental Neurobiology, 33(5), 238–250. doi: 10.5607/en24020
  • The Korean Society for Brain and Neural Sciences
  • View : 625
  • Download : 47
  • Lee, Seunghoon
  • Kang, Woosung
  • Bertogna, Marko
  • Chwa, Hoon Sung
  • Lee, Jinkyu
  • 2025-06
  • Real-Time Systems, v.61, no.2, pp.259 - 267
  • Springer Nature
  • View : 236
  • Download : 0
  • 김보민
  • 유진우
  • 김은호
  • 임성호
  • 최지웅
  • 2021-07
  • 김보민. (2021-07). 휴대용 fNIRS를 이용한 머신러닝 기반의 알츠하이머병 진단 기술 연구. 한국통신학회논문지, 46(7), 1185–1198. doi: 10.7840/kics.2021.46.7.1185
  • 한국통신학회
  • View : 700
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