SOLAR PROPELLED AIRCRAFT STRUCTURE AND SOLAR PANELS CONTROL METHOD
    2.
    发明申请
    SOLAR PROPELLED AIRCRAFT STRUCTURE AND SOLAR PANELS CONTROL METHOD 有权
    太阳能螺旋桨飞机结构和太阳能电池板控制方法

    公开(公告)号:US20170029129A1

    公开(公告)日:2017-02-02

    申请号:US15292563

    申请日:2016-10-13

    Inventor: Hyo Jung AHN

    Abstract: A solar propelled aircraft which has a wing having solar cell modules mounted therein includes: first solar cell modules which are positioned in a wing or a tail wing of the aircraft and receive solar energy directly from the sun; second solar cell modules which are positioned in a main wing or a tail wing of the aircraft and supplied with directed energy from the earth; and rotating shafts which rotate the first solar cell modules and the second solar cell modules so that the first solar cell modules and the second solar cell modules correspond to each other in both directions. The first solar cell module at the upper surface obtains solar energy from the sun, and the second solar cell module at the lower surface obtains directed energy transferred from the earth.

    Abstract translation: 具有安装有太阳能电池模块的机翼的太阳能推进飞机包括:第一太阳能电池模块,其位于飞行器的机翼或尾翼中并直接从太阳接收太阳能; 第二太阳能电池模块,其位于飞行器的主翼或尾翼中,并且从地球提供定向能量; 以及旋转轴,其使第一太阳能电池模块和第二太阳能电池模块旋转,使得第一太阳能电池模块和第二太阳能电池模块在两个方向上彼此对应。 上表面的第一个太阳能电池模块从太阳获得太阳能,而在下表面的第二个太阳能电池模块获得从地球转移的定向能量。

    METHOD AND APPARATUS FOR ANOMALY DETECTION FOR INDIVIDUAL VEHICLES IN SWARM SYSTEM

    公开(公告)号:US20250165013A1

    公开(公告)日:2025-05-22

    申请号:US18946598

    申请日:2024-11-13

    Inventor: Hyo Jung AHN

    Abstract: A method for detecting anomalies in a swarm system comprises: collecting first movement data from multiple vehicles moving as a swarm in a first scenario; generating first training data based on positioning data and second training data based on multi-channel inertial sensor data from the first movement data; training a first learning model using the first training data and multiple second learning models using the second training data for each vehicle; receiving real-time second movement data from vehicles moving as a swarm in a second scenario; generating first input data based on positioning data from the second movement data; inputting the first input data into the first learning model to detect abnormal vehicles in real-time; generating second input data for abnormal vehicles based on inertial sensor data from the second movement data; and inputting the second input data into the corresponding second learning model to identify abnormal channels in the inertial measurement unit of abnormal vehicles.

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