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相关概念视频

Depth Perception and Spatial Vision01:15

Depth Perception and Spatial Vision

631
Depth perception is the ability to perceive objects three-dimensionally. It relies on two types of cues: binocular and monocular. Binocular cues depend on the combination of images from both eyes and how the eyes work together. Since the eyes are in slightly different positions, each eye captures a slightly different image. This disparity between images, known as binocular disparity, helps the brain interpret depth. When the brain compares these images, it determines the distance to an object.
631
Three-Dimensional Force System01:30

Three-Dimensional Force System

2.0K
In mechanical engineering, a three-dimensional force system is a system of forces acting in three dimensions, with forces applied along the x, y, and z coordinate axes. The three-dimensional force system is an important concept in mechanical engineering, as it allows engineers to understand and analyze the behavior of objects and structures in three dimensions. By understanding the forces acting on a system, engineers can design more efficient and effective mechanical systems that can withstand...
2.0K
Three-Dimensional Force System:Problem Solving01:30

Three-Dimensional Force System:Problem Solving

663
A three-dimensional force system refers to a scenario in which three forces act simultaneously in three different directions. This type of problem is commonly encountered in physics and engineering, where it is necessary to calculate the resultant force on the system, which can then be used to predict or analyze the behavior of the object or structure under consideration.
To solve a three-dimensional force system, first resolve each force into its respective scalar components. Do this using...
663
Three-Winding Transformers01:19

Three-Winding Transformers

222
Three identical single-phase transformers can be configured to form a three-phase transformer connection, which involves high-voltage and low-voltage windings. The high-voltage windings are denoted by capital letters A-B-C, while the low-voltage windings are labeled with lowercase letters a-b-c, representing their respective phases. This notation helps distinguish between the high and low voltage sides of the transformer.
In the per-unit equivalent circuit of a grounded Y-Y three-phase...
222
Transformers01:26

Transformers

1.1K
A device that transforms voltages from one value to another using induction is called a transformer. A transformer consists of two separate coils, or windings, wrapped around the same soft iron core. However, they are electrically insulated from each other.
The iron core has a substantial relative permeability. Therefore, the magnetic field lines generated due to the current in one winding are almost entirely confined within the core, such that the same magnetic flux permeates each turn of both...
1.1K
Transformers with Off-Nominal Turns Ratios01:25

Transformers with Off-Nominal Turns Ratios

149
In scenarios involving parallel transformers with disparate ratings, developing per-unit models requires accommodating off-nominal turns ratios. This situation arises when the selected base voltages are not proportional to the transformer’s voltage ratings. Consider a transformer where the rated voltages are related by the term a. If the chosen voltage bases satisfy a relationship involving term b, term c is defined as the ratio of these bases. This ratio is then substituted into the...
149

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相关实验视频

Updated: Jun 24, 2025

Recording Ultra-Realistic Full-Color Analog Holograms for Use in a Moving Hologram Display
09:04

Recording Ultra-Realistic Full-Color Analog Holograms for Use in a Moving Hologram Display

Published on: January 14, 2020

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视觉转换器赋予了物理驱动的深度学习,用于全方位的三维全息图.

Zhongwei Jin, Qiuyu Ren, Tao Chen

    Optics express
    |June 11, 2024
    PubMed
    概括

    这项研究引入了一种新的深度学习方法,使用视觉转换器来创建高质量的,全向的3D全息图. 这种方法显著减少了交叉声波,并改善了高级全息显示器的轴分辨率.

    科学领域:

    • 光子学 是一个光子学.
    • 计算机视觉 计算机视觉
    • 全息影像的使用方法.

    背景情况:

    • 三维全息图面临诸如平面间交叉声和有限的轴分辨率等挑战.
    • 目前减少交叉通话的方法往往导致单向全息图或斑点噪声.
    • 现有的深度学习方法,主要是使用CNN,在全息显示质量和轴向分辨率方面扎.

    研究的目的:

    • 开发一种先进的方法来生成高质量的,全向的3D全息图.
    • 为了克服全息显示技术现有技术的局限性.
    • 在重建的3D全息图中改进平面间交叉声和轴分辨率.

    主要方法:

    • 一个由视觉变压器 (ViT) 支持的物理驱动深度神经网络被开发出来.
    • 在ViT中固有的全球注意力机制被利用来应对全息显示的挑战.
    • 该模型的重点是生成全方位的3D计算机生成全息图 (CGH).

    主要成果:

    • 提出的方法成功生成了全向的3D全息图.
    • 使用ViT导致飞机间交叉声响显著减少.
    • 在重建的3D全息图中实现了高轴分辨率.

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    结论:

    • 视觉变压器支持的网络为高质量的3D全息显示提供了一个新的解决方案.
    • 这种方法通过最大限度地减少交叉声和改善轴分辨率来提高全息显示性能.
    • 这项工作为光子学中复杂的反向设计应用铺平了道路.