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Updated: Sep 15, 2025

Low-Cost Automated Flight Intercept Trap for the Temporal Sub-Sampling of Flying Insects Attracted to Artificial Light at Night
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Published on: December 29, 2021

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在城市环境中利用深度强化学习优化城市空气流动的低噪音轨迹)

Younghoon Kim1, Jeongwoo Ko2, Kukhwan Yu3

  • 1Department of Aerospace Engineering, Seoul National University, Seoul, Republic of Korea.

The Journal of the Acoustical Society of America
|July 16, 2025
PubMed
概括

本研究使用深度强化学习 (DRL) 来为城市空中移动 (UAM) 飞机创建更安静的飞行路径. DRL方法成功地减少了城市环境中的噪音影响,为更安静的UAM操作铺平了道路.

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