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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

130
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...
130
Transformers with Off-Nominal Turns Ratios01:25

Transformers with Off-Nominal Turns Ratios

182
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...
182
Equivalent Circuits for Practical Transformers01:28

Equivalent Circuits for Practical Transformers

481
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...
481
Source Transformation01:15

Source Transformation

6.6K
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...
6.6K
Energy Losses in Transformers01:21

Energy Losses in Transformers

911
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...
911

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Updated: Jul 27, 2025

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique
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一个补丁多样性变压器,用于域泛化语义细分领域的多样性变压器.

Pei He, Licheng Jiao, Ronghua Shang

    IEEE transactions on neural networks and learning systems
    |June 6, 2023
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    概括
    此摘要是机器生成的。

    域泛化 (DG) 通过新的补丁多样性转换器 (PDTrans) 方法得到改进. 通过学习域不变的上下文,PDTrans增强了深度学习模型,使其在未知领域有效执行.

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    科学领域:

    • 计算机科学 计算机科学
    • 人工智能的人工智能
    • 机器学习 机器学习

    背景情况:

    • 域泛化 (DG) 对于深度学习模型在未见的环境中可靠执行至关重要.
    • 代表域不变语境 (DIC) 是实现有效的总干事的关键挑战.
    • 变压器由于其全球上下文理解能力,显示了学习通用特征的前景.

    研究的目的:

    • 提出一种新的方法,补丁多样性转换器 (PDTrans),以增强DG的场景细分.
    • 改善全球多领域语义关系的学习,以便更好地泛化.
    • 为应对有效地代表域不变语境的挑战.

    主要方法:

    • 引入了使用自我注意机制的补丁多样性变压器 (PDTrans).
    • 开发了补丁光度干扰 (PPP) 技术,以在全球范围内增强多域表示.
    • 拟议的补丁统计扰动 (PSP) 在域移动下建模补丁特征统计,编码域不变语义特征.

    主要成果:

    • 在补丁和功能层面上,PDTrans使源域多样化.
    • 该方法有效地学习跨不同补丁的上下文.
    • 实验结果显示,PDTrans的性能比现有的最先进的DG方法有显著的改进.

    结论:

    • PDTrans提供了一种强大的方法来改善深度学习中的域概括.
    • 建议的扰动 (PPP和PSP) 有效地提高了模型的概括性.
    • 在需要域概括的场景分割任务中,PDTrans表现出卓越的性能.