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相关概念视频

Types Of Transformers01:16

Types Of Transformers

1.0K
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...
1.0K
Transformers in Distribution System01:27

Transformers in Distribution System

127
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...
127
Transformers with Off-Nominal Turns Ratios01:25

Transformers with Off-Nominal Turns Ratios

181
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...
181
The Ideal Transformer01:26

The Ideal Transformer

434
In single-phase two-winding transformers, two windings are coiled around a magnetic core characterized by cross-sectional area A and magnetic permeability μ. A phasor current i1 enters the left winding while i2 exits the right winding, establishing the fundamental working of the transformer through electromagnetic principles.
Ampere's Law forms the basis of understanding the magnetic field within the transformer. It states that the integral of the magnetic field intensity's...
434
Vision01:24

Vision

53.6K
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.
53.6K
Energy Losses in Transformers01:21

Energy Losses in Transformers

904
In an ideal transformer, it is assumed that there are no energy losses, and, hence, all the power at the primary winding is transferred to the secondary winding. However, in reality,  the transformers always have some energy losses, and, hence, the output power obtained at the secondary winding is less than the input power at the primary winding due to energy losses.
There are four main reasons for energy losses in transformers.
The first cause can be  the high resistance of the...
904

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相关实验视频

Updated: Jul 23, 2025

A Methodology for Capturing Joint Visual Attention Using Mobile Eye-Trackers
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A Methodology for Capturing Joint Visual Attention Using Mobile Eye-Trackers

Published on: January 18, 2020

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有效的视觉变压器通过代币合并并购.

Zhanzhou Feng, Shiliang Zhang

    IEEE transactions on image processing : a publication of the IEEE Signal Processing Society
    |July 13, 2023
    PubMed
    概括
    此摘要是机器生成的。

    视觉转换器 (ViT) 通过合并冗余的图像令牌来加速计算机视觉任务. 这个令牌合并模块显著减少了令牌,并提高了推断速度,以最小的准确性损失.

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    Author Spotlight: Insights into Visual Cortex Research Through Wide-View fMRI Mapping
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    Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique
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    科学领域:

    • 计算机视觉 计算机视觉
    • 机器学习 机器学习
    • 人工智能的人工智能

    背景情况:

    • 视觉转换器 (ViTs) 通过将图像分成固定大小的补丁来处理图像,从而创建多个令牌.
    • 这种代币化策略可能导致语义和视觉冗余,影响效率.
    • 减少令牌数量的现有方法可能会损害重要的上下文信息.

    研究的目的:

    • 为了引入一个新的模块,令牌合并,加速视觉变换器.
    • 通过将它们合并成一个紧的表示来解决ViTs中冗余令牌的问题.
    • 提高ViT的效率,同时保持语义意义和上下文线索.

    主要方法:

    • 提出了代币合并,这是一个识别和合并语义上相似的代币的模块.
    • 在合并过程中使用元标记来表示关键的图像内容线索.
    • 介绍了可学习的门,以控制不同ViT层的自适应性代币合并比率.
    • 设计为一个plug-and-play模块,以便轻松集成到现有的ViT架构中.

    主要成果:

    • 代币合并有效地合并了多余的代币,创建了一个保持清晰语义的紧集.
    • 通过减少令牌数量 (例如95%),实现了推断速度的显著加速 (例如62%).
    • 保持高精度,只有轻微的下降 (例如,在ImageNet分类上为0.4%).
    • 与代币修剪和其他降低样本方法相比,表现出更高的性能和概括性.

    结论:

    • 代币合并提供了一个有效的解决方案,通过智能合并冗余代币来加速视觉变压器.
    • 该模块保留了关键的上下文信息,从而提高了性能和概括性.
    • 为视觉任务提供了计算效率和模型准确性之间的有利权衡.