Current Affiliation Organization 【 display / non-display 】

Current Affiliation Organization 【 display / non-display 】

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University 【 display / non-display 】

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Academic degree 【 display / non-display 】

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External Career 【 display / non-display 】

Affiliated academic organizations 【 display / non-display 】

Affiliated academic organizations 【 display / non-display 】

Research Interests 【 display / non-display 】

Research Interests 【 display / non-display 】

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Research Areas 【 display / non-display 】

Acquisition of a qualification 【 display / non-display 】

Acquisition of a qualification 【 display / non-display 】

Published Papers 【 display / non-display 】

Published Papers 【 display / non-display 】

• Adaptive Control Simulation of AUV Using Neural Controllers Trained by GA

  Kinjo Hiroshi, Nakazono Kunihiko, Uezato Eiho, Oshiro Naoki

  IEEJ Transactions on Electronics, Information and Systems ( The Institute of Electrical Engineers of Japan )  144 ( 3 ) 169 - 178   2024.03 [ Peer Review Accepted ]

  Type of publication: Research paper (scientific journal)

  　View Summary

  <p>In this research, we propose a method to adaptively generate AUV trajectories using a neural controller that learns by applying a genetic algorithm. The method uses two neuro-controllers. One is learned in advance as a controller. The other is learning at the same time as redesigning a new trajectory when the AUV deviates from the planned trajectory during navigation. The two neural controllers are evolutionarily learned using genetic algorithms. In the navigation simulation, the multi-point search problem of AUV under the tidal current environment was taken up. Numerical experiments have shown the effectiveness of the proposed method.</p>

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• Performance evaluation of schedule plan for cuckoo search applied to the neural network controller of a rotary crane

  Rui Kinjo, Kunihiko Nakazono, Naoki Oshiro, Hiroshi Kinjo

  Artifcial Life and Robotics ( Springer Nature )  29 ( 1 ) 129 - 135   2023.11 [ Peer Review Accepted ]

  Type of publication: Research paper (scientific journal)

  　View Summary

  Here, an optimized neural network controller (NC) was developed with the cuckoo search (CS) method. This was inspired by the mending behavior of the cuckoo bird, which lays eggs similar to those of their putative parents in their nests and allows the putative parents to raise them. CS is an evolutionary computation algorithm that mimics the ecological behavior of organisms to optimize a controller. Previous studies have demonstrated good evolutionary processes for NCs when the value of the scaling index varies in steps during a scheduled period. Therefore, the proposed CS scheduling plan adjusts the scaling index as a linear function, nonlinear function, or stairs. Computer simulations demonstrated that an NC optimized with the scheduled CS method had superior control performance compared to the original CS method. The best results were obtained when the schedule plan was set to a linear or nonlinear function rather than a stair plan.

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• Fuzzy controller for AUV robots based on machine learning and genetic algorithm

  Yamada, T; Kinjo, H; Nakazono, K; Oshiro, N; Uezato, E

  ARTIFICIAL LIFE AND ROBOTICS ( Artificial Life and Robotics )  28 ( 3 ) 632 - 641   2023.08 [ Peer Review Accepted ]

  Type of publication: Research paper (scientific journal)

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• Design of Machine Learning Solutions to Post-Harvest Classification of Vegetal Species

  Papa Moussa Diop, Naoki Oshiro, Morikazu Nakamura, Jin Takamoto, Yuji Nakamura

  AgriEngineering   5 ( 2 ) 1005 - 1019   2023.06 [ Peer Review Accepted ]

  Type of publication: Research paper (scientific journal)

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• Study on evolutionary performance of NC optimized by scheduled Cuckoo Search for rotary

  Kunihiko Nakazono, Naoki Oshiro, and Hiroshi Kinjo

  Proceedings of the Joint Symposium of The Twenty-Seventh International Symposium on Artificial Life and Robotics (AROB 27th 2022), The Seventh International Symposium on BioComplexity 2022 (ISBC 7th 2022), The Fifth International Symposium on Swarm Behavior and Bio-Inspired Robotics 2022 (SWARM 5th 2022) ( その他の出版社 )    889 - 892   2022.01 [ Peer Review Accepted ]

  Type of publication: Research paper (scientific journal)

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Academic Awards 【 display / non-display 】

Academic Awards 【 display / non-display 】

• Best Paper Award

  2006   International Symposium on Artificial Life and Robotics