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相关概念视频

Sequence Networks of Rotating Machines01:24

Sequence Networks of Rotating Machines

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A Y-connected synchronous generator, grounded through a neutral impedance, is designed to produce balanced internal phase voltages with only positive-sequence components. The generator's sequence networks include a source voltage that is exclusively in the positive-sequence network. The sequence components of line-to-ground voltages at the generator terminals illustrate this configuration.
Zero-sequence current induces a voltage drop across the generator's neutral impedance and other...
103
Force Classification01:22

Force Classification

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Forces play a crucial role in the study of physics and engineering. They are essential in describing the motion, behavior, and equilibrium of objects in the physical world. Forces can be classified based on their origin, type, and direction of action.
Contact and non-contact forces are two of the most widely used categories of forces. As the name suggests, contact forces require physical contact between two objects to act upon each other. Examples of contact forces include frictional,...
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Conservation of Angular Momentum: Application01:18

Conservation of Angular Momentum: Application

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A system's total angular momentum remains constant if the net external torque acting on the system is zero. Examples of such systems include a freely spinning bicycle tire that slows over time due to torque arising from friction, or the slowing of Earth's rotation over millions of years due to frictional forces exerted on tidal deformations. However in the absence of a net external torque, the angular momentum remains conserved. The conservation of angular momentum principle requires a...
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Improving Translational Accuracy02:07

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Base complementarity between the three base pairs of mRNA codon and the tRNA anticodon is not a failsafe mechanism. Inaccuracies can range from a single mismatch to no correct base pairing at all. The free energy difference between the correct and nearly correct base pairs can be as small as 3 kcal/ mol. With complementarity being the only proofreading step, the estimated error frequency would be one wrong amino acid in every 100 amino acids incorporated. However, error frequencies observed in...
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Conservation of Angular Momentum01:09

Conservation of Angular Momentum

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A system's total angular momentum remains constant if the net external torque acting on the system is zero. Considering a system that consists of n tiny particles, the angular momentum of any tiny particle may change, but the system's total angular momentum would remain constant. The principle of conservation of angular momentum only considers the net external torque acting on the system. While there are internal forces exerted by different particles within the system that also produce...
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Angular Momentum about an Arbitrary Axis01:11

Angular Momentum about an Arbitrary Axis

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Imagine a rigid body with a mass denoted as 'm', which has its center of mass at point G and is rotating around an inertial reference frame. The angular momentum at an arbitrary point P can be calculated by taking the cross product of the position vector and linear momentum vector for each individual mass element.
The velocity of a mass element comprises its translational velocity and the relative velocity instigated by the body's rotation. Substituting the velocity equation into...
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轨道角动量介导的机器学习用于高精度的模式特征编码.

Xinyuan Fang1, Xiaonan Hu2,3, Baoli Li2

  • 1Institute of Photonic Chips, University of Shanghai for Science and Technology, Shanghai, 200093, China. xinyuan.fang@usst.edu.cn.

Light, science & applications
|February 14, 2024
PubMed
概括
此摘要是机器生成的。

这项研究引入了机器学习的轨道角动量 (OAM),使全光卷积神经网络 (CNN) 在MNIST数据集上达到97.2%的准确性. 这一进步提高了光学信息处理和安全性.

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科学领域:

  • 光电学是指光电子产品.
  • 机器学习 机器学习
  • 信息光学是指信息光学.

背景情况:

  • 光学神经网络通过利用光学维度提供高速和低能耗.
  • 由于特征提取的局限性,利用神经网络节点的轨道角动量 (OAM) 状态仍然是一个挑战.
  • 拉盖尔-高斯 (Laguerre-Gaussian,LG) 波束模式提供了在光学信息处理中增加自由度的潜力.

研究的目的:

  • 通过全光卷积神经网络 (CNN) 使用轨道角动量 (OAM) 演示机器学习.
  • 利用OAM状态在光学神经网络中进行增强的特征提取和分类.
  • 探索安全通信和异常检测中的应用,使用基于OAM的光学机器学习.

主要方法:

  • 开发一种全光学CNN,使用拉盖尔-高斯 (Laguerre-Gaussian,LG) 束模式.
  • 实现可训练的OAM模式分散脉冲作为特征提取的卷积内核.
  • 利用深度学习的衍射层进行分类,并使用编码的OAM模式的能量加权系数进行检测.

主要成果:

  • 在MNIST数据库上通过OAM模式分散选择性实现了97.2%的准确性,用于信息模式特征编码.
  • 在点对点自由空间传输中表现出对窃听的抵抗力.
  • 实现全光学尺寸缩小,用于使用多重 OAM 状态检测异常,精度为 85%.

结论:

  • 通过OAM介导的机器学习为光学信息处理提供了一种新的方法.
  • 拟议的全光学CNN架构有效地提取特征,并使用OAM状态对数据进行分类.
  • 这项研究为改进机器视觉任务和安全的光通信系统开辟了道路.