Publication in fiscal year 2018 (Apr. 2018 – Mar. 2019)

Journal Paper

  1. M. Wakaiki, M. Ogura, and J. P. Hespanha,
    LQ-optimal sample-data control under stochastic delays: gridding approach for stabilizability and detectability,”
    SIAM Journal on Control and Optimization, vol. 56, no. 4, pp. 2634–2661 (2018)
  2. J. E. Rodriguez Ramirez, Y. Minami, and K. Sugimoto,
    Synthesis of event-triggered dynamic quantizers for networked control systems,”
    Expert Systems With Applications, Vol.109, pp. 188–194 (2018)
  3. Y. Cui, J. Poon, J. Valls Miro, K. Yamazaki, K. Sugimoto, T. Matsubara
    Environment-adaptive interaction primitives through visual context for human–robot motor skill learning,”
    Autonomous Robots, Vol.??, pp. 1–16 (2018)
  4. T. Kobayashi, K. Sekiyama, Y. Hasegawa, T. Aoyama, and T. Fukuda,
    “Virtual-Dynamics-based Reference Gait Speed Generator for Limit-Cycle-based Bipedal Gait,”
    ROBOMECH Journal, vol. 5, pp. 1–17 (2018)
  5. Kenji Sugimoto and Imahayashi Wataru,
    “Left-right Polynomial Matrix Factorization for MIMO Pole/Zero Cancellation with Application to FEL,”
    to appear in Transactions of the Institute of Systems, Control and Information Engineers, Vol. 32, No. 1 (2019)
  6. Toshihide Tadenuma, Masaki Ogura, and Kenji Sugimoto,
    “Design of Gain Switching State Observer with Signal Losses,”
    to appear in Transactions of the Society of Instrument and Control Engineers, Vol. 55, No. 3 (2019) (in Japanese)
  7. W. Mei and M. Ogura,
    Kronecker weights for instability analysis of Markov jump linear systems,”
    IET Control Theory & Applications (accepted), 2018.

International Conference

  1. M. Ogura, J. Tagawa, and N. Masuda,
    Distributed agreement on activity driven networks,”
    2018 American Control Conference, pp. 4147-4152, 2018.6.27-29.
  2. M. Ogura and J. Harada,
    Resource allocation for containing epidemics from temporal network data,”
    23rd International Symposium on Mathematical Theory of Networks and Systems, pp. 537-542, Hong Kong, 2018.7.19.
  3. M. Ogura, J. Wan, and S. Kasahara,
    Model predictive control for energy-efficient operation of data centers with cold aisle containments“,
    6th IFAC Conference on Nonlinear Model Predictive Control, pp. 241-246, Madison, 2018.8.19-22.
  4. J. E. Rodriguez Ramirez, Y. Minami, and K. Sugimoto,
    “Design of Quantizers with Neural Networks: Classification Based Approach”,
    2018 International Symposium on Nonlinear Theory and Its Applications (NOLTA2018), pp. 312-315, Tarragona, 2018.9.2-6.
  5. Y. Ikawa, T. Kobayashi, and T. Matsubara,
    “Biomechanical Energy Harvester with Continuously Variable Transmission: Prototyping and Preliminary Evaluation”,
    2018 IEEE/ASME International Conference on Advanced Intelligent Mechatronics, Auckland, New Zealand, 2018.7.10-12 (7.12).
  6. T. Matsubara, Y. Norinaga, Y. Ozawa, and Y. Cui
    “Policy Transfer from Simulations to Real World by Transfer Component Analysis”,
    14th IEEE International Conference on Automation Science and Engineering (CASE2018), pp. 264–269, Munich, Germany, 2018.8.20-24.
  7. Y. Cui, L. Zhu, M. Fujisaki, H. Kanokogi, and T. Matsubara
    “Factorial Kernel Dynamic Policy Programming for Vinyl Acetate Monomer Plant Model Control”,
    14th IEEE International Conference on Automation Science and Engineering (CASE2018), pp. 304–309, Munich, Germany, 2018.8.20-24.
  8. Y. Lai, J. Poon, G. Paul, H. Han, and T. Matsubara
    “Probabilistic Pose Estimation of Deformable Linear Objects”,
    14th IEEE International Conference on Automation Science and Engineering (CASE2018), pp. 471–476, Munich, Germany, 2018.8.20-24.
  9. H. Sasaki, Y. Ozawa, T. Matsubara,
    “Variational Learning Approach for Sparse Gaussian Process Policy Search”,
    SICE Annual Conference 2018, pp. 642–645, Nara, 2018.9.11-14 (2017.9.13).
  10. T. Sugino, T. Kobayashi, K. Sugimoto,
    “Continual Learning using Modularity of Structured Reservoir Computing”,
    SICE Annual Conference 2018, pp. 650–653, Nara, 2018.9.11-14 (2017.9.13).
  11. T. Aotani, T. Kobayashi, K. Sugimoto,
    “Learning of Correlation in Decentralized Robots with Individual Tasks”,
    SICE Annual Conference 2018, pp. 662–665, Nara, 2018.9.11-14 (2018.9.13).
  12. W. Mei and M. Ogura,
    Instability analysis of Markov jump linear systems by spectral optimization,”
    SICE Annual Conference 2018, 2018, pp. 419-422.
  13. T. Kobayashi,
    “Check Regularization: Combining Modularity and Elasticity for Memory Consolidation,”
    International Conference on Artificial Neural Networks (ICANN2018), vol. 2, pp. 315–325,
    Rhodes, Greece, 2018.10.4-7 (2018.10.5).
  14. T. Kobayashi,
    “Practical Fractional-Order Neuron Dynamics for Reservoir Computing,”
    International Conference on Artificial Neural Networks (ICANN2018), vol. 3, pp. 116–125,
    Rhodes, Greece, 2018.10.4-7 (2018.10.5).
  15. T. Aotani, T. Kobayashi, K. Sugimoto,
    “Bottom-up Multi-agent Reinforcement Learning for Selective Cooperation”,
    2018 IEEE International Conference on Systems, Man, and Cybernetics (SMC2018), pp. 3580–3585, Miyazaki, 2018.10.7-10 (2018.10.10).
  16. K. Sugimoto, X. Han, and W. Imahayashi,
    “Stability of MIMO Feedback Error Learning Control under a Strictly Positive Real Condition,”
    Preprints, 5th IFAC Conference on Analysis and Control of Chaotic Systems, pp. 150–156,
    Eindhoven, The Netherlands, 2018.10.30-11.01 (2018.11.01).
  17. T. Tadenuma, M. Ogura, and K. Sugimoto,
    Sampled-Data State Observation over Lossy Networks under Round-Robin Scheduling,”
    Preprints, 5th IFAC Conference on Analysis and Control of Chaotic Systems, pp. 197–202,
    Eindhoven, The Netherlands, 2018.10.30-11.01 (2018.11.01).

Press Release

  1. Yokogawa Electric Corporation and the Nara Institute of Science and Technology
    “Development of Reinforcement Learning Algorithm Applicable to Automatic Optimization of Plant Operations”
    2018.8.22

Invited Talk

  1. M. Ogura,
    “Network epidemiology and control theory,”
    University of Hong Kong, 2018.7.19