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相关概念视频

Transformers with Off-Nominal Turns Ratios01:25

Transformers with Off-Nominal Turns Ratios

139
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...
139
Types Of Transformers01:16

Types Of Transformers

948
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...
948
Energy Losses in Transformers01:21

Energy Losses in Transformers

834
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...
834
The Ideal Transformer01:26

The Ideal Transformer

356
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...
356
Transformers in Distribution System01:27

Transformers in Distribution System

98
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...
98
Three-Winding Transformers01:19

Three-Winding Transformers

200
Three identical single-phase transformers can be configured to form a three-phase transformer connection, which involves high-voltage and low-voltage windings. The high-voltage windings are denoted by capital letters A-B-C, while the low-voltage windings are labeled with lowercase letters a-b-c, representing their respective phases. This notation helps distinguish between the high and low voltage sides of the transformer.
In the per-unit equivalent circuit of a grounded Y-Y three-phase...
200

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Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique
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ETFT:用于不平衡的语义细分的等角窄框变压器.

Seonggyun Jeong1, Yong Seok Heo1,2

  • 1Department of Artificial Intelligence, Ajou University, Suwon 16499, Republic of Korea.

Sensors (Basel, Switzerland)
|November 9, 2024
PubMed
概括

本研究介绍了等角紧框变压器 (ETFT),以解决语义细分中的类失衡问题. 新型模型动态生成分类器,改善不平衡数据集的性能.

科学领域:

  • 计算机视觉 计算机视觉
  • 机器学习 机器学习
  • 深度学习 (Deep Learning) 是一种深度学习.

背景情况:

  • 语义细分面临着阶级不平衡的挑战,标签分配不均.
  • 现有的方法使用神经崩和等角紧框架 (ETF) 来改善小阶级歧视,但与阶级相关性作斗争.
  • 当前的方法导致固定的分类器,限制适应不同输入图像的适应性.

研究的目的:

  • 提出一种基于变压器的新型模型,即平角紧框变压器 (ETFT),用于不平衡的语义细分.
  • 作为输入的函数,动态生成分类器,平衡类歧视和相关性.
  • 增强语义细分模型在数据集上的适应性和性能,其类分布不均.

主要方法:

  • ETFT模型通过在变压器架构中的ETF结构共同处理特征和分类器.
  • 输入补丁令牌和ETF初始化分类器在注意力机制期间一起处理.
  • 该分类器根据输入相关性进行动态调整,并与固定ETF分类器相结合.

主要成果:

  • 通过ETFT模型,可以提高阶级之间的区别,同时保持上下文相关性.
  • 动态生成的分类器适应输入,提高性能.
  • 实验表明,拟议的方法优于ADE20K和Cityscapes数据集上的最新方法,用于不平衡的语义细分.
关键词:
阶级不平衡 阶级不平衡神经崩的神经崩语义细分 语义细分 语义细分 语义细分变压器变压器变压器变压器

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结论:

  • ETFT模型有效地解决了语义细分中的类不平衡的挑战.
  • 动态分类器生成为不同的输入数据提供了相对于固定分类器的显著优势.
  • 提出的方法在不平衡的语义细分任务中表现出卓越的性能和稳定性.