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RIKEN Center for Advanced Intelligence Project

Disaster Resilience Science Team

Team Leader: Naonori Ueda (D.Eng.)
Naonori  Ueda(D.Eng.)

In recent years, natural disasters such as a major earthquake and the accompanying big tsunami that cause enormous damage occur. It takes a huge amount of time and cost to restore a social system that suffered greatly every time. In our research team, in the event of a natural disaster that brings about such a serious disaster, we aim to research and develop artificial intelligence technologies that enable to reduce the damage as much as possible, restore the social system once damaged efficiently and effectively, and resume social and economic activities promptly.

Main Research Field

Computer Science

Related Research Fields

Geosciences

Research Subjects

  • Ambient intelligence CPS by autonomous learning
  • Earthquake occurrence prediction model
  • Ground motion prediction model

Selected Publications

Papers with an asterisk(*) are based on research conducted outside of RIKEN.
  1. *Ueda, N.
    "Proactive People-flow Navigation Based on Spatio-temporal Prediction"
    Japanese Journal Applied Statistics, Vol.45, No.3, pp.89-104, 2016, (invited).
  2. *Iwata, T., Shimizu, H., Naya, F., and Ueda, N.:
    "Estimating people flow from spatio-temporal population data via collective graphical mixture models"
    ACM Transactions on Spatial Algorithms and Systems, Vol. 3, Issue 1, Article 39. 2017.
  3. *Ueda, N., Naya, F., Shimizu, H., Iwata, T., Okawa, M., and Sawada, H.:
    "Real-time and proactive navigation via spario-temporal prediction"
    Proceedings of the 2015 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2015 ACM International Symposium on Wearable Computers(UbiComp 2015), pp. 1559-1566, 2015.
  4. *Takeuchi, K., and Ueda, N.:
    "Graph regularized non-negative tensor completion for spatio-temporal data analysis"
    The Second International Workshop on Smart Cities, 2016.
  5. *Saito, K. Ohara, K. Kimura, M. and Motoda, H.:
    "An Accurate and Efficient method to Detect Critical Links to Maintain Information Flow in Network"
    Proc. of the 23th International Symposium on Methodologies for Intelligent Systems (ISMIS2017), pp.116--126, 2017.
  6. *Saito, K, Kimura, M. Ohara, K. and Motoda, H.:
    "Detecting Critical Links in Complex Network to Maintain Information Flow/Reachability"
    Proc. of the 14th Pacific Rim International Conference on Artificial Intelligence (PRICAI2016), pp.419-432, 2016.
  7. *Toda, R., Inuue, S., and Ueda, N.:
    "Mobile Activity Recognition from Training Labels with Inaccurate Activity Segments (Best paper candidate)"
    International Conference on Mobile and Ubiquitous Systems: Computing, Networking and Services (MobiQuitous) (Acceptance Rate: 30%), pp. 57-64, 2016

Contact information

NTT Communication Science Laboratories, NTT Corporation
Tel: +81-(0)774-93-5108

Email: naonori.ueda [at] riken.jp

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