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相关概念视频

Vector Algebra: Method of Components01:08

Vector Algebra: Method of Components

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It is cumbersome to find the magnitudes of vectors using the parallelogram rule or using the graphical method to perform mathematical operations like addition, subtraction, and multiplication. There are two ways to circumvent this algebraic complexity. One way is to draw the vectors to scale, as in navigation, and read approximate vector lengths and angles (directions) from the graphs. The other way is to use the method of components.
In many applications, the magnitudes and directions of...
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Vector Algebra: Graphical Method01:10

Vector Algebra: Graphical Method

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Vectors can be multiplied by scalars, added to other vectors, or subtracted from other vectors. The vector sum of two (or more) vectors is called the resultant vector or, for short, the resultant.
We use the laws of geometry to construct resultant vectors, followed by trigonometry to find vector magnitudes and directions. For a geometric construction of the sum of two vectors in a plane, we follow the parallelogram rule. Suppose two vectors are at arbitrary positions. Translate either one of...
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Uniform Depth Channel Flow: Problem Solving01:18

Uniform Depth Channel Flow: Problem Solving

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To calculate the flow rate for a trapezoidal channel, first, identify the bottom width, side slope, and flow depth of the channel. The cross-sectional area (A) corresponding to the depth of flow (y), channel bottom width (B), and side slope (θ) is determined by:Next, calculate the wetted perimeter, which includes the bottom width and the sloped side lengths in contact with the water. Using the values of the cross-sectional area and the wetted perimeter, determine the hydraulic radius by...
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Turbulent Flow: Problem Solving01:09

Turbulent Flow: Problem Solving

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Carbonation is a process used to dissolve carbon dioxide gas in a liquid, commonly used in the production of carbonated beverages. Achieving efficient carbonation requires careful control of temperature, pressure, and flow conditions. By adjusting these parameters, carbonation efficiency can be maximized, producing a higher concentration of CO2 in the liquid.
Temperature is a key factor in CO2 solubility. In this case, the CO2 gas and the liquid are cooled to 20°C. Lower temperatures...
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Eulerian and Lagrangian Flow Descriptions01:22

Eulerian and Lagrangian Flow Descriptions

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Fluid flow analysis is critical in many scientific and engineering disciplines, and two principal approaches are used to describe this flow: the Eulerian and Lagrangian methods. These methods offer different perspectives on monitoring and analyzing the motion of fluids, each with distinct advantages depending on the scenario.
The Eulerian method focuses on fixed points in space where fluid properties, such as velocity, pressure, and temperature, are observed as the fluid moves between these...
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Divergence and Curl of Electric Field01:25

Divergence and Curl of Electric Field

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The divergence of a vector is a measure of how much the vector spreads out (diverges) from a point. For example, an electric field vector diverges from the positive charge and converges at the negative charge. The divergence of an electric field is derived using Gauss's law and is equal to the charge density divided by the permittivity of space. Mathematically, it is expressed as
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相关实验视频

Updated: Jun 5, 2025

Experimental Investigation of the Flow Structure over a Delta Wing Via Flow Visualization Methods
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Experimental Investigation of the Flow Structure over a Delta Wing Via Flow Visualization Methods

Published on: April 23, 2018

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基于深度学习的旋分解和切换基于光纤矢量固有模式.

Mengdie Hou1, Mengjun Xu1, Jiangtao Xu1

  • 1The Key Lab of Specialty Fiber Optics and Optical Access Network, Joint International Research Laboratory of Specialty Fiber Optics and Advanced Communication, Shanghai University, Shanghai 200444, China.

Nanophotonics (Berlin, Germany)
|December 5, 2024
PubMed
概括

研究人员使用深度学习开发了一种新的方法,可以准确地分解和控制结构化的光学场,如圆柱向量 (CV) 和轨道角动量 (OAM) 模式,实现高纯度和快速切换能力.

关键词:
深度学习是一种深度学习.模式分解分解模式矢量固有模式 矢量固有模式转换旋转的转换方式

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Experimental Investigation of Secondary Flow Structures Downstream of a Model Type IV Stent Failure in a 180° Curved Artery Test Section
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Preparation of Free-Surface Hyperbolic Water Vortices
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Preparation of Free-Surface Hyperbolic Water Vortices

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相关实验视频

Last Updated: Jun 5, 2025

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科学领域:

  • 光学和光子学 在光学和光子学.
  • 量子信息科学 量子信息科学

背景情况:

  • 结构化光学场,包括圆柱向量 (CV) 和轨道角动量 (OAM) 模式,由于其独特的极化和相位特性,对于先进的光学应用至关重要.
  • 智能生成和精确控制这些复杂的光学模式仍然是该领域的一个重大挑战.

研究的目的:

  • 展示一种新的模拟和实验方法来分解CV和OAM模式.
  • 通过使用多视图强度预测和深度学习,实现结构化光学场的准确和高效的表征.

主要方法:

  • 利用预测强度分布的多视图图像来重建光场属性.
  • 采用基于深度学习的随机平行梯度下降 (SPGD) 算法进行模态分解和现场检索.
  • 集成的干扰模式和四分之一波板用于相位确认和偏振分析.

主要成果:

  • 成功分解了CV和OAM模式,具有高准确度 (平均误差为0.416%) 和效率 (检索时间为1.32秒).
  • 实现了高纯度的分解CV模式,达到高达99.5%.
  • 通过电气控制偏振,证明了模式的快速切换,从而实现了多样化的CV模式生成.

结论:

  • 开发的方法提供了一种方便而准确的方法来表征结构光学场,包括它们的模态比例.
  • 这些发现有助于更深入地了解CV和OAM模式,在信息编码和量子计算方面有潜在的应用.