Jove
Visualize
联系我们
JoVE
x logofacebook logolinkedin logoyoutube logo
关于 JoVE
概览领导团队博客JoVE 帮助中心
作者
出版流程编辑委员会范围与政策同行评审常见问题投稿
图书馆员
用户评价订阅访问资源图书馆顾问委员会常见问题
研究
JoVE JournalMethods CollectionsJoVE Encyclopedia of Experiments存档
教育
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab Manual教师资源中心教师网站
使用条款与条件
隐私政策
政策

相关概念视频

Reducing Line Loss01:18

Reducing Line Loss

In a three-phase circuit, line loss is an indicator of energy dissipated as heat due to the resistance of transmission lines. To address this, incorporating transformers into the system—a step-up transformer at the source and a step-down transformer at the load—is a strategic solution. Two three-phase transformers are introduced to improve this.
With a step-up transformer at the source, the voltage is increased, thereby reducing the current in the transmission lines since power loss in...

您也可能阅读

相关文章

通过共同作者、期刊和引用图与本文相关的文章。

排序
Same author

Targeting metabolism and membrane: Neferine synergizes with gentamicin against <i>Escherichia coli</i> by disrupting FNR/ArcA-Mediated homeostasis.

Virulence·2026
Same author

Digitally encoded dual-narrowband photodetectors for secure optical wireless communication.

Nature communications·2026
Same author

Single-source full-duplex UWOC system using a Bessel beam in bubble-disturbed channels.

Applied optics·2026
Same author

Underwater computational ghost imaging LiDAR for multi-target detection with a super-low sampling ratio.

Applied optics·2026
Same author

The dual effects of anesthetics on glial cells: a review of neuroprotection and neurotoxicity.

Frontiers in pharmacology·2026
Same author

Photonic-waveguide-enabled femtosecond dissipative solitons in mode-locked lasers.

Optics letters·2026

相关实验视频

Updated: Jun 30, 2026

Fabrication And Characterization Of Photonic Crystal Slow Light Waveguides And Cavities
11:08

Fabrication And Characterization Of Photonic Crystal Slow Light Waveguides And Cavities

Published on: November 30, 2012

19.0K

机器学习代优化所有偏振维护线性腔 Er:光纤激光器.

Minghe Zhao, Xuanyi Liu, H Y Fu

    Optics letters
    |September 14, 2023
    PubMed
    概括
    此摘要是机器生成的。

    这项研究展示了一种强大的全极化维护 (PM) 纤维激光器. 一种机器学习方法快速优化了模式锁定,在超快激光脉冲生成模拟中实现了实验一致性.

    更多相关视频

    Automation of Mode Locking in a Nonlinear Polarization Rotation Fiber Laser through Output Polarization Measurements
    14:18

    Automation of Mode Locking in a Nonlinear Polarization Rotation Fiber Laser through Output Polarization Measurements

    Published on: February 28, 2016

    11.5K
    Low-cost Custom Fabrication and Mode-locked Operation of an All-normal-dispersion Femtosecond Fiber Laser for Multiphoton Microscopy
    08:48

    Low-cost Custom Fabrication and Mode-locked Operation of an All-normal-dispersion Femtosecond Fiber Laser for Multiphoton Microscopy

    Published on: November 22, 2019

    7.6K

    相关实验视频

    Last Updated: Jun 30, 2026

    Fabrication And Characterization Of Photonic Crystal Slow Light Waveguides And Cavities
    11:08

    Fabrication And Characterization Of Photonic Crystal Slow Light Waveguides And Cavities

    Published on: November 30, 2012

    19.0K
    Automation of Mode Locking in a Nonlinear Polarization Rotation Fiber Laser through Output Polarization Measurements
    14:18

    Automation of Mode Locking in a Nonlinear Polarization Rotation Fiber Laser through Output Polarization Measurements

    Published on: February 28, 2016

    11.5K
    Low-cost Custom Fabrication and Mode-locked Operation of an All-normal-dispersion Femtosecond Fiber Laser for Multiphoton Microscopy
    08:48

    Low-cost Custom Fabrication and Mode-locked Operation of an All-normal-dispersion Femtosecond Fiber Laser for Multiphoton Microscopy

    Published on: November 22, 2019

    7.6K

    科学领域:

    • 光学和光子学 在光学和光子学.
    • 超快的激光技术 超快的激光技术
    • 机器学习应用 机器学习应用

    背景情况:

    • 保持极化 (PM) 的光纤激光器为超高速应用提供了紧性和环境稳定性.
    • 了解模式锁定形成对于优化激光性能至关重要.

    研究的目的:

    • 为了实验性地演示一个全PM线性腔模式锁定光纤激光器.
    • 使用机器学习代优化方法调查模式锁定形成.
    • 分析内腔动力学和极化效应.

    主要方法:

    • 一个全PM线性腔纤维激光器的实验设置.
    • 基于高斯过程的机器学习用于模式锁定参数的代优化.
    • 数字模拟用于模拟脉冲生成和内腔动力学.
    • 分析小组速度不匹配和跨相调制效应.

    主要成果:

    • 机器学习优化算法的快速融合在30次运行内.
    • 模拟的输出频谱和脉冲能量与实验结果密切匹配.
    • 洞内动态演变的详细描述,包括组速度不匹配.
    • 由于交叉相调节导致过度补偿的时间同步,证明脉冲捕获.

    结论:

    • 全PM线性腔纤维激光器是一种可行的超快激光源.
    • 机器学习为优化光纤激光器模式锁定提供了一种有效的方法.
    • 内腔动力学,包括脉冲捕获,显著影响超快脉冲特征.