연구동향 분석

인공지능과 빅데이터를 활용한 예지 정비 적용 방안에 관한 연구

홍창우 2022년

논문상세정보
인용/피인용
' 인공지능과 빅데이터를 활용한 예지 정비 적용 방안에 관한 연구' 의 주제별 논문영향력
논문영향력 선정 방법
논문영향력 요약
주제
  • Artificial intelligence
  • Big data
  • Predictive maintenance
  • 빅데이터
  • 예지정비
  • 인공지능
동일주제 총논문수 논문피인용 총횟수 주제별 논문영향력의 평균
6,916 0

0.0%

' 인공지능과 빅데이터를 활용한 예지 정비 적용 방안에 관한 연구' 의 참고문헌

  • 인수위, 국방부 업무보고...인공지능 컨트롤타워 ‘합동인공지능센터’ 구축및 신속한 국방 AI 도입 방안 논의
  • Understanding AI technology
    Allen, G [2020]
  • Supporting group maintenance through prognostics-enhanced dynamic dependability prediction
  • Remaining useful life predictions for turbofan engine degradation usingsemi-supervised deep architecture
  • Remaining useful life predictions for turbofan engine degradation based on concurrent semi-supervised model
    Wang, T [2021]
  • Remaining useful life estimation in prognostics using deep convolution neural networks
    Li, X [2018]
  • Remaining useful life estimation in prognostics using deep bidirectional lstm neural network
    Wang, J [2018]
  • Prognostics and Health Management of Engineering Systems
    Kim, N. H [2017]
  • Mastering the game of go without human knowledge
    Silver, D [2017]
  • Long short-term memory network for remaining useful life estimation
    Zheng, S [2017]
  • Long short-term Memory
  • Gradient-based learning applied to document recognition
    LeCun, Y. [1998]
  • Finding Artificial Intelligence Money in the Fisical 2020 Budget
  • Extracting value from chaos
    Gantz, J. [2011]
  • Deep learningand its applications to machine health monitoring
    Zhao, R [2019]
  • Deep convolutional neural network based regression approach for estimation of remaining useful life
    Babu, G. S [2016]
  • Convolutional, long short-term memory, fully connected deep neural networks
  • Condition based maintenance plus DoD guidebook
    Bell, J [2008]
  • China In Race To Overtake U.S. Military in AI Warfare
  • Air Force Expands AI-Based Predictive Maintenance
  • A directed acyclic graph network combined with CNN and LSTM for remaining useful life prediction
    Li, J [2019]