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相关概念视频

Types Of Transformers01:16

Types Of Transformers

1.4K
Transformers can provide desired voltages to a circuit by modifying the number of turns in the secondary windings.
If the ratio of the number of turns in the secondary winding to that of the primary winding is greater than one, then the transformer is said to be a step-up transformer. In a step-up transformer, the voltage at the secondary winding is greater than the voltage applied at the primary winding.
However, if this ratio is less than one, the transformer is said to be a step-down...
1.4K
Transformers in Distribution System01:27

Transformers in Distribution System

467
Transformers in distribution systems can be broadly categorized into distribution substation transformers and other distribution transformers. They are crucial for stepping down high transmission voltages to levels suitable for distribution and end-user applications.
Distribution substation transformers come in various ratings and typically use mineral oil for insulation and cooling. To prevent moisture and air from entering the oil, some transformers use an inert gas like nitrogen to fill the...
467
The Retina01:32

The Retina

74.0K
The retina is a layer of nervous tissue at the back of the eye that transduces light into neural signals. This process, called phototransduction, is carried out by rod and cone photoreceptor cells in the back of the retina.
74.0K
Transformers01:26

Transformers

1.7K
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...
1.7K
Vision01:24

Vision

59.2K
Vision is the result of light being detected and transduced into neural signals by the retina of the eye. This information is then further analyzed and interpreted by the brain. First, light enters the front of the eye and is focused by the cornea and lens onto the retina—a thin sheet of neural tissue lining the back of the eye. Because of refraction through the convex lens of the eye, images are projected onto the retina upside-down and reversed.
59.2K
Transformers with Off-Nominal Turns Ratios01:25

Transformers with Off-Nominal Turns Ratios

486
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...
486

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相关实验视频

Updated: Jan 7, 2026

A Swin Transformer-Based Model for Thyroid Nodule Detection in Ultrasound Images
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A Swin Transformer-Based Model for Thyroid Nodule Detection in Ultrasound Images

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超越卷曲和使用变压器监督学习以及用于视网膜图像分析的表示学习.

Yue Wu1, Cecilia S Lee2, Aaron Y Lee2

  • 1Department of Ophthalmology, University of Washington, Seattle, WA, United States of America; Roger and Angie Karalis Johnson Retina Center, Seattle, WA, United States of America.

Progress in retinal and eye research
|December 6, 2025
PubMed
概括

视网膜图像分析的最新进展利用无标签方法和视觉变换器,超越传统的监督人工智能 (AI). 这种转变使强大的基础和多模式模型能够用于增强的诊断.

关键词:
在这里,我们可以看到AIAIAI.深度学习是一种深度学习.基金会模型 基金会模型图像分析 图像分析视网膜成像 视网膜成像自主监督学习学习半监督学习 半监督学习

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Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique
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Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

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相关实验视频

Last Updated: Jan 7, 2026

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科学领域:

  • 眼科医生 眼科 眼科
  • 计算机视觉 计算机视觉
  • 人工智能的人工智能

背景情况:

  • 由于计算机视觉技术,视网膜图像分析已经显著进步.
  • 之前的审查主要集中在监督学习或眼科医学的特定AI应用上.
  • 一个值得注意的趋势是,人工智能对视网膜成像的无标签方法的转变.

研究的目的:

  • 要总结最近在视网膜图像分析方面的进展.
  • 为了突出从监督到无标签方法的转变.
  • 讨论视觉转换器的出现及其影响.

主要方法:

  • 在视网膜图像分析中对人工智能和计算机视觉的文献综述.
  • 专注于代表性学习和无标签技术.
  • 探索视觉转换器作为卷积神经网络的替代品.

主要成果:

  • 该领域正在向半监督和自我监督的学习过渡.
  • 视觉转换器正在成为图像分析的强大工具.
  • 这些进步导致了基础,视觉语言和多模式模型的发展.

结论:

  • 无标签的代表性学习和视觉转换器正在改变视网膜图像分析.
  • 新的人工智能模型提供了超越传统方法的增强功能.
  • 人工智能在眼科的未来需要更复杂,数据效率更高的方法.