コーディング

Research

Nuclear security
(insider detection)

Deep learning is used to detect malicious acts (sabotage) by insiders in nuclear facilities.

Reference

  1. Daisuke Miki, Shi Chen, Kazuyuki Demachi, "Weakly Supervised Graph Convolutional Neural Network for Human Action Localization", The IEEE Winter Conference on Applications of Computer Vision.  March 1-5, 2020. Colorado, United States.

  2. Daisuke Miki, Shinya Abe, Shi Chen, and Kazuyuki Demachi. "Robust human motion recognition from wide-angle images for video surveillance in nuclear power plants", Mechanical Engineering Journal (2020): 19-00533.

  3. Daisuke Miki, Kazuyuki Demachi. "Bearing fault diagnosis using weakly supervised long short-term memory", Journal of Nuclear Science and Technology (2020): 1-10

  4. Daisuke Miki, Kazuyuki Demachi. "Weakly Supervised Deep Neural Network for Bearing Fault Diagnosis", 2020 International Conference on Nuclear Engineering (ICONE2020), August 4 – 5, 2020.

  5. Shi Chen, and Kazuyuki Demachi. "Proposal of an insider sabotage detection method for nuclear security using deep learning." Journal of Nuclear Science and Technology 56.7 (2019): 599-607.

  6. Shi Chen and Kazuyuki Demachi. "Insider Sabotage Detection for Nuclear Facilities using Deep Learning",  The 27th International Conference on Nuclear Engineering (ICONE27), Tsukuba, Japan (2019/5/19-24)

  7. Shi Chen, Ryo Kubota, Kazuyuki Demachi, "Skeleton based Hand Motion Recognition using Convolutional Neural Network",  2019 IEICE General Conference, Tokyo, Mar. 2019.

  8. Daisuke Miki, Shinya Abe, Shi Chen and Kazuyuki Demachi. "Robust Human Motion Recognition from Distorted Wide-Angle Images for Video Surveillance",  The 27th International Conference on Nuclear Engineering (ICONE27), Tsukuba, Japan (2019/5/19-24)

  9. Daisuke Miki, Shinya Abe, Shi Chen, and Kazuyuki Demachi. "Robust human pose estimation from distorted wide-angle images through iterative search of transformation parameters." Signal, Image and Video Processing (2019): 1-8.

  10. Kazuyuki Demachi, Daisuke Miki, Shi Chen, “Development of Sabotage Detection Technology using Deep Learning Model”, The 9th ESARDA Workshop(2019)

  11. Kazuyuki DEMACHI, Shi CHEN, "DEVELOPMENT OF MALICIOUS HAND BEHAVIORS DETECTION METHOD BY MOVIE ANALYSIS", International Conference on Nuclear Engineering (ICONE26) (2018/7/17-20) 

  12. Kazuyuki DEMACHI, Shi CHEN, "Development of Sabotage Hand Behaviors Detection Method by Image Analysis", 59th Annual Meeting of Institute of Nuclear Materials Management (Baltimore, USA) (accepted) (2018/7/22-26)

  13. Shi CHEN, Kazuyuki DEMACHI, Tomoyuki FUJITA, Yutaro NAKASHIMA, Yusuke KAWASAKI, “Insider Malicious Behaviors Detection and Prediction Technology for Nuclear Security”, E-journal of Advanced Maintenance (EJAM), Vol.9, No.3, pp. 66-71, 2017.

  14. Shi CHEN, Kazuyuki DEMACHI, “Vision-Based Hand Motion Recognition for Insider Sabotage Detection using Deep Learning”, International Conference on Physical Protection of Nuclear Material and Nuclear Facilities, Vienna, Austria, Nov. 2017.

  15. Shi CHEN, Kazuyuki DEMACHI, Tomoyuki FUJITA, Yutaro NAKASHIMA, Yusuke KAWASAKI、Insider Malicious Behaviors Detection and Prediction Technology for Nuclear Security、2016 International Conference on Maintenance Science and Technology、2016/11/1~4

Access

Demachi laboratory

Department of Nuclear Engineering and Management

The University of Tokyo

7-3-1 Hongo, Bunkyo-ku, Tokyo 113-8656, JAPAN

Professor's room: Room 812, Engineering building 8

Students' room: Room 802, Engineering building 8

Tel:03-5841-8630

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