| Project/Area Number |
18K11477
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| Research Category |
Grant-in-Aid for Scientific Research (C)
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| Allocation Type | Multi-year Fund |
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| Section | 一般 |
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| Review Section |
Basic Section 61040:Soft computing-related
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| Research Institution | Kogakuin University |
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Principal Investigator |
Tachibana Kanta 工学院大学, 情報学部(情報工学部), 准教授 (20402539)
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| Project Period (FY) |
2018-04-01 – 2023-03-31
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| Project Status |
Completed (Fiscal Year 2022)
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| Budget Amount *help |
¥4,290,000 (Direct Cost: ¥3,300,000、Indirect Cost: ¥990,000)
Fiscal Year 2022: ¥910,000 (Direct Cost: ¥700,000、Indirect Cost: ¥210,000)
Fiscal Year 2021: ¥1,170,000 (Direct Cost: ¥900,000、Indirect Cost: ¥270,000)
Fiscal Year 2020: ¥910,000 (Direct Cost: ¥700,000、Indirect Cost: ¥210,000)
Fiscal Year 2019: ¥650,000 (Direct Cost: ¥500,000、Indirect Cost: ¥150,000)
Fiscal Year 2018: ¥650,000 (Direct Cost: ¥500,000、Indirect Cost: ¥150,000)
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| Keywords | 機械学習 / 超複素数 / 動作認識 / 活動認識 / 時系列空間情報 / ロボットヨット / 自動操縦 / 運動学習 / クリフォード代数 / 実機制御 / 無人帆走 / ASV / ASSV / 計算知能 / 強化学習 / 合議アルゴリズム |
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| Outline of Final Research Achievements |
This research has led to new discoveries and understanding regarding human activity recognition, wind prediction, emotional similarity in music, and novel applications of Clifford algebras. The research has led to the development of highly accurate activity recognition methods, potential efficiency gains in the fields of wind energy and unmanned sailing, new perspectives on machine learning models of emotion understanding, contributions to improved human behavior recognition and interaction, new applications of Clifford algebra, contributions to reduced workload and improved safety in nursing homes, and contributions to improved motion performance and obstacle avoidance capabilities of autonomous sailing robots. Contributions to the improvement of motion performance and obstacle avoidance capabilities of autonomous sailing robots were suggested.
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| Academic Significance and Societal Importance of the Research Achievements |
私たちの研究では、人間の活動認識、風予測、音楽の感情類似性、Clifford代数の新規応用に関して重要な成果を得ました。これにより、高精度な活動認識手法の開発、風力エネルギーや無人帆走分野の効率向上、感情理解のための機械学習モデルの進化、人間と機械の相互作用の改善、Clifford代数の新たな応用、介護現場の作業負担軽減と安全性向上、自律的な帆走ロボットの動作性能向上などの社会的な恩恵が期待されます。これらの成果は、私たちの生活や産業において新たな進歩と解決策をもたらし、社会全体の発展に寄与することが期待されます。
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