Publication in fiscal year 2016 (Apr. 2016 – Mar. 2017)

Journal Paper

  1. J.E. Rodriguez Ramirez, Y. Minami, and K. Sugimoto,
    “Design of Finite-Level Dynamic Quantizers by using Covariance Matrix Adaptation Evolution Strategy”,
    International Journal of Innovative Computing, Information and Control, Vol. 12, No. 3, pp. 795–808 (2016)
  2. D. Tanaka, T. Matsubara and K. Sugimoto,
    “Input-Output Manifold Learning with State Space Models”,
    IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences, Vol. E99-A, No. 6, pp. 1179–1187
  3. T. Hasegawa, T. Matsubara, and K. Sugimoto,
    “Reinforcement Learning of Shared Control Policies for Dexterous Telemanipulation : Application to a Page Turning Task”,
    Transactions of the Institute of Systems, Control and Information Engineer, Vol.29, No.8, pp.346–354 (2016) (in Japanese)
  4. Y. Minami and T. Muromaki,
    “Differential Evolution Based Synthesis of Dynamic Quantizers with Fixed-Structures”,
    International Journal of Computational Intelligence and Applications, Vol. 15, Issue 2, 1650008 (17 pages) (2016)
  5. H. Okajima, Y. Minami, and N. Matsunaga,
    “A Control Structure with High Design Degree of Freedom for Networked Control Systems”,
    Transactions of the Society of Instrument and Control Engineers, Vol. 52, No. 7, pp. 393-400 (2016) (in Japanese)
  6. 室巻孝郎,南裕樹,徳永泰伸:ロボティック照明システムの分散型照射角度制御,
    電気学会論文誌 C,Vol. 137, No. 1, pp. XXX–XXX (2016)
  7. Yunduan Cui, Takamitsu Matsubara, and Kenji Sugimoto,
    “Pneumatic artificial muscle-driven robot control using local update reinforcement learning”,
    Advanced Robotics (2017): 1-16

International Conference

  1. K. Hatada, K. Hirata and T. Sato,
    “Energy-Efficient Power Assist Control with Periodic Disturbance Observer and Frequency Estimator,”
    2016 IEEE 14th International Workshop on Advanced Motion Control (AMC16), pp. 384-389, Auckland, New Zealand, Apr., 2016.
  2. T. Muromaki, Y. Minami, A. Suda, and K. Hanahara,
    “An Improvement of Stress-Ratio Method by Using Excess and Deficiency Information on the Stress”,
    Asian Congress of Structural and Multidisciplinary Optimization 2016, p. 65, Nagasaki (2016.5)
  3. Y. Minami and K. Kashima,
    “Dynamic Quantizer Design based on Serial System Decomposition”,
    22nd International Symposium on Mathematical Theory of Networks and Systems, pp. 577–579, Minneapolis, 2016.7.12-15 (7.14)
  4. Y. Minami, Y. Shimizu, and K. Sugimoto,
    “Distributed Cooperative ON/OFF Control of Photovoltaic Generation Systems”,
    6th IFAC Workshop on Distributed Estimation and Control in Networked Systems, Tokyo, 2016.9.8-9 (9.8)
  5. H. Okajima, Y. Minami, and N. Matsunaga,
    “Unilateral Control Structure Under Communication Rate Constraint”,
    6th IFAC Workshop on Distributed Estimation and Control in Networked Systems, Tokyo, 2016.9.8-9 (9.9)
  6. W. Fukumi, Y. Minami, T. Matsubara and K. Sugimoto,
    “Reinforcement-learning-based Quantizer Design for Discrete-valued Input Control”,
    SICE Annual Conference 2016, pp. 609–611, Tsukuba, 2016.9.20-23 (2016.9.21)
  7. H. Tachibana, Y. Minami, and K. Sugimoto,
    “Tracking Control of Ball and Beam Systems by Prediction Governors”,
    SICE Annual Conference 2016, pp. 1293–1295, Tsukuba, 2016.9.20-23 (2016.9.23)
  8. K. Sugimoto and K. Noto,
    “An Approach to Irregular Sampling State Observer Design”,
    SICE Annual Conference 2016, pp. 1724–1726, 2016.9.20-23 (2016.9.23)
  9. K. Sugimoto and K. Noto,
    “Design of Switching State Observer for Irregular Delayed Measurement”,
    The 16th International Conference on Control, Automation, and Systems (ICCAS2016), pp. 675-678 (TC03-1), 2016.10.16-19 (2016.10.18)
  10. Yunduan Cui, Takamitsu Matsubara and Kenji Sugimoto,
    “Kernel Dynamic Policy Programming: Practical Reinforcement Learning for High-dimensional Robots,”
    2016 IEEE-RAS 16th International Conference on Humanoid Robots (Humanoids 2016), pp. 662-667, Cancun, Mexico, 2016.11.15-17 (2016.11.17)
  11. Yunduan Cui, James Poon, Takamitsu Matsubara, Jaime Valls Miro, Kenji Sugimoto and Kimitoshi Yamazaki,
    “Environment-adaptive Interaction Primitives for Human-Robot Motor Skill Learning,”
    2016 IEEE-RAS 16th International Conference on Humanoid Robots (Humanoids 2016), pp. 712-717, Cancun, Mexico, 2016.11.15-17 (2016.11.17)
  12. T. Ota, K. Ohara, A. Ichikawa, T. Kobayashi,
    Y. Hasegawa and T. Fukuda, “Modeling of the High-Speed Running Humanoid Robot,”
    27th 2016 International Symposium on Micro-NanoMechatronics and Human Science (MHS 2016), pp. 112-113, Nagoya, Japan, 2016.11.28-30 (2016.11.28)