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Energy Losses in Transformers01:21

Energy Losses in Transformers

978
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...
978
Reconstruction of Signal using Interpolation01:10

Reconstruction of Signal using Interpolation

345
Signal processing techniques are essential for accurately converting continuous signals to digital formats and vice versa. When a continuous signal is sampled with a period T, the resulting sampled signal exhibits replicas of the original spectrum in the frequency domain, spaced at intervals equal to the sampling frequency. To handle this sampled signal, a zero-order hold method can be applied, which creates a piecewise constant signal by retaining each sample's value until the next...
345
Reducing Line Loss01:18

Reducing Line Loss

194
In a three-phase circuit, line loss is an indicator of energy dissipated as heat due to the resistance of transmission lines. To address this, incorporating transformers into the system—a step-up transformer at the source and a step-down transformer at the load—is a strategic solution. Two three-phase transformers are introduced to improve this.
With a step-up transformer at the source, the voltage is increased, thereby reducing the current in the transmission lines since power loss...
194
Equivalent Circuits for Practical Transformers01:28

Equivalent Circuits for Practical Transformers

800
The practical equivalent circuits of single-phase two-winding transformers exhibit significant deviations from their idealized versions due to the inherent properties of winding resistance and finite core permeability. These properties result in real and reactive power losses, affecting the transformer's performance. Understanding these deviations is crucial for designing more efficient transformers.
In a practical transformer, each winding exhibits resistance and leakage reactance. The...
800
The Ideal Transformer01:26

The Ideal Transformer

905
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...
905
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

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Updated: Sep 13, 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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学习适应性稀疏变压器,以实现高效的图像恢复.

Shihao Zhou, Jinshan Pan, Jufeng Yang

    IEEE transactions on pattern analysis and machine intelligence
    |August 1, 2025
    PubMed
    概括
    此摘要是机器生成的。

    本研究介绍了自适应光束变压器版本2 (AST-v2),这是一个高效的图像恢复模型. AST-v2减少了噪音交互和功能冗余,在六个常见的图像恢复任务中提高了性能.

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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
    • 人工智能的人工智能
    • 机器学习 机器学习

    背景情况:

    • 变压器模型通过捕捉远程依赖来卓越于图像恢复.
    • 现有的高效变压器在与冗余信息和来自无关图像区域的杂交互作斗争.

    研究的目的:

    • 开发一个改进的变压器模型,即适应性稀疏变压器版本2 (AST-v2),用于增强的图像恢复.
    • 为了解决当前变压器架构中计算强度和噪声相互作用的局限性.

    主要方法:

    • AST-v2使用具有双分支设计的自适应性稀疏自我注意 (ASSA) 块来引导注意力重量并减少无关紧要的令牌交互.
    • 使用特征精制传送网络 (FRFN) 来消除跨道的特征冗余.

    主要成果:

    • AST-v2在六个不同的图像修复任务中展示了竞争力的性能:雨纹清除,雾清除,影子清除,雪除除,模糊清除和低光增强.
    • 拟议的方法有效地减轻了噪音交互和特征冗余,从而导致更清晰的图像恢复.

    结论:

    • AST-v2为各种图像恢复挑战提供了高效有效的解决方案.
    • 适应性稀疏注意力和特征精细化机制有助于优越的图像质量和模型效率.