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相关概念视频

Load-frequency control01:28

Load-frequency control

Load-frequency control (LFC) is vital for maintaining power system stability, ensuring that frequency and power flows remain within acceptable limits during load changes. Turbine-governor control eliminates rotor accelerations and decelerations following load changes. However, a steady-state frequency error persists when the change in the turbine-governor reference setting is zero. In an interconnected power system, each area agrees to export or import a scheduled amount of power through...

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相关实验视频

Updated: Jul 19, 2026

Characterizing Far-infrared Laser Emissions and the Measurement of Their Frequencies
09:38

Characterizing Far-infrared Laser Emissions and the Measurement of Their Frequencies

Published on: December 18, 2015

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用于激光频率稳定的人工神经网络.

Lisa Winkler, Christian Nölleke

    Optics express
    |October 20, 2023
    PubMed
    概括

    一个人工神经网络可靠地识别了来自光谱的激光频率稳定引用. 在模拟数据上训练,它在现实世界的测量中准确地识别吸收线,在各种条件下证明强大.

    科学领域:

    • 原子,分子和化学物理学
    • 激光物理 激光物理
    • 计算科学 计算科学

    背景情况:

    • 稳定激光发射频率对于精密应用至关重要.
    • 绝对引用,如分子吸收线,通常用于频率稳定.
    • 从光谱数据中自动识别这些参考线是一个关键的挑战.

    研究的目的:

    • 开发一种自动化方法,可靠地识别激光光谱中的分子吸收线.
    • 为了证明人工神经网络对这一光谱分析任务的有效性.
    • 用光谱作为案例研究来验证神经网络的性能.

    主要方法:

    • 一个人工神经网络 (ANN) 被设计和训练.
    • 该ANN仅在模拟的光谱数据上接受训练.
    • 然后,训练有素的ANN在实验测量的光谱数据上进行了测试,特别针对吸收线.

    主要成果:

    • 人工神经网络成功地从光谱中确定了所需的吸收线.
    • 该网络表现出强性,尽管运营和环境条件的变化很大,但仍能准确地执行.
    • 模拟数据用于培训的使用在现实世界应用中被证明是有效的.

    结论:

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    • 人工神经网络提供了一种可行的自动化解决方案,用于识别激光频率稳定中的光谱参考.
    • 用模拟数据训练ANN是一个实用的方法,可以很好地将测量数据概括为测量数据.
    • 开发的方法提高了激光频率稳定系统的可靠性和自动化.