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相关概念视频

Types Of Transformers01:16

Types Of Transformers

1.1K
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...
1.1K
Transformers with Off-Nominal Turns Ratios01:25

Transformers with Off-Nominal Turns Ratios

213
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...
213
The Ideal Transformer01:26

The Ideal Transformer

909
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...
909
Transformers01:26

Transformers

1.2K
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...
1.2K
Source Transformation01:15

Source Transformation

9.4K
Source transformation is a fundamental technique employed in circuit analysis, offering a valuable tool for simplifying complex electrical circuits. This technique involves the replacement of either a voltage source in series with a resistor by a current source in parallel with a resistor, or vice versa. The key concept here is that when the original sources are deactivated (turned off), the equivalent resistance at the circuit's end terminals remains the same.
It is essential to note that when...
9.4K
Transformers in Distribution System01:27

Transformers in Distribution System

162
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...
162

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Updated: Sep 15, 2025

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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通过可学习的令牌合并来实现高效的视觉转换器.

Yancheng Wang, Yingzhen Yang

    IEEE transactions on pattern analysis and machine intelligence
    |July 15, 2025
    PubMed
    概括
    此摘要是机器生成的。

    本研究介绍了可学习的标记合并 (LTM) 变压器,这是视觉变压器的一个新块. 通过执行可学习的令牌合并,LTM-Transformer 提高了计算机视觉任务的效率和准确性.

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    Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique
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    相关实验视频

    Last Updated: Sep 15, 2025

    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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    科学领域:

    • 计算机视觉 计算机视觉
    • 深度学习 (Deep Learning) 是一种深度学习.
    • 人工智能的人工智能

    背景情况:

    • 变压器和自我注意力在深度学习中很普遍,导致计算机视觉的视觉变压器.
    • 现有的视觉转换器可以是计算密集型,激励研究更高效的架构.

    研究的目的:

    • 提出一种新而紧的变压器块,即具有可学习令牌合并 (LTM) 的变压器.
    • 降低视觉变压器的计算成本 (FLOP) 和推断时间,同时保持或提高准确性.

    主要方法:

    • 开发了一个可学习的代币合并方案的LTM-Transformer块.
    • 获得了信息瓶 (IB) 损失的新型变化上限.
    • 在LTM块内设计了一个面罩模块,以最大限度地减少衍生的IB损失上限.

    主要成果:

    • 在MobileViT,EfficientViT,ViT和Swin中用LTM-变压器块取代了变压器块.
    • 在各种视觉变压器骨干中实现了FLOP和推断时间的显著减少.
    • 在计算机视觉任务中证明了可比或改进的预测准确性.

    结论:

    • 通过LTM-Transformer,可以创建紧且高效的视觉变压器.
    • 拟议的方法提供了一种可行的方法来提高性能和减少计算机视觉深度学习模型中的计算需求.