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Name NAKANO Hidehiro
Official Title Professor
Affiliation Tokyo City University
E-mail hnakano@tcu.ac.jp
Web
  1. http://www.risys.gl.tcu.ac.jp/Main.php?action=profile&type=detail&tchCd=5001614
Profile Hidehiro Nakano received the B.E., M.E., and Ph.D. degrees in electrical engineering, all from Hosei University, Tokyo, Japan, in 1999, 2001, and 2004, respectively. He is currently a professor with the department of computer science, Tokyo City University, Tokyo, Japan. His research interests include optimization algorithms, neural networks, chaotic circuits, and computer systems.
Research Field(Keyword & Summary)
  1. (1) Optimization algorithms

    (1) We develop optimization algorithms to solve various optimization problems including engineering design problems. Especially, we focus on the meta-heuristic algorithms including swarm intelligence and their implementation on hardware systems.

  2. (2) Neural Networks

    (2) We study learning algorithms for deep neural networks considering the improvement of the computation cost and the learning accuracy. We also study learning algorithms for binary neural networks suitable for digital hardware implementation.

  3. (3) Chaotic Circuits

    (3) Nonlinear circuits can exhibit rich phenomena including chaos, synchronization, and so on. We study synthesis and analysis of the nonlinear circuits and their applications to optimization algorithms and neural networks.
Representative Papers
  1. [1] CycleGAN using Semi-Supervised Learning, The Australian Journal of Intelligent Information Processing Systems, Vol.15, No.2, pp.10-19, 2019.
  2. [2] Analysis and Investigation of Frame Invariance and Particle Behavior for Piecewise-Linear Particle Swarm Optimizer, IEICE Trans. Funds., Vol.E102-A, No.12, pp.1956-1967, 2019.
  3. [3] Deterministic particle swarm optimizer with the convergence and divergence dynamics, IEICE Trans. Funds., Vol.E100-A, No.5, pp.1244-1247, 2017.
  4. [4] Particle Swarm Optimizer Networks with Stochastic Connection for Improvement of Diversity Search Ability to Solve Multimodal Optimization Problems, IEICE Trans. Funds., Vol.E100-A, No.4, pp.996-1007, 2017.
  5. [5] Improvement of the Solving Performance by the Networking of Particle Swarm Optimization, IEICE Trans. Funds., Vol.E98-A, No.8, pp.1777-1786, 2015.
  6. [6] A Competitive Particle Swarm Optimizer and its Application to Wireless Sensor Networks, IEEJ Trans. EEE, Vol.7, No.S1, pp.S52-S58, 2012.
  7. [7] Chaos Synchronization-Based Data Transmission Scheme in Multiple Sink Wireless Sensor Networks, International Journal of Innovative Computing Information and Control, Vol.7, No.4, pp.1983-1994, 2011.
  8. [8] A Parallel Distributed Structure Learning Method of Dynamic Bayesian Networks Using Discrete Particle Swarm Optimization, Artificial Life and Robotics, Vol.16, No.3, pp.329-332, 2011.
  9. [9] Grouping of Mobile Nodes in Manet Based on Location and Mobility Information Using an Art Network, International Journal of Innovative Computing Information and Control Vol.5, No.11b, pp.4357-4365, 2009.
  10. [10] Data Gathering Scheme Using Chaotic Pulse-Coupled Neural Networks for Wireless Sensor Networks, IEICE Trans. Funds., Vol.E92-A, No.2, pp.459-466, 2009.
Grant-in-Aid for Scientific Research Support: Japan Society for Promotion of Science (JSPS) https://nrid.nii.ac.jp/en/nrid/1000010386360/
Recruitment of research assistant(s) No
Affiliated academic society (Membership type) IEICE (Member), IEEE (Member)
Education Field (Undergraduate level) Computer Systems, Computer Architecture, Artificial Intelligence
Education Field (Graduate level) Adv. Computer Engineering., Adv. Artificial Intelligence

Affiliation