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Transformers01:26

Transformers

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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
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
The Ideal Transformer01:26

The Ideal Transformer

890
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...
890
Transformers with Off-Nominal Turns Ratios01:25

Transformers with Off-Nominal Turns Ratios

205
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...
205
Improving Translational Accuracy02:07

Improving Translational Accuracy

11.9K
Base complementarity between the three base pairs of mRNA codon and the tRNA anticodon is not a failsafe mechanism. Inaccuracies can range from a single mismatch to no correct base pairing at all. The free energy difference between the correct and nearly correct base pairs can be as small as 3 kcal/ mol. With complementarity being the only proofreading step, the estimated error frequency would be one wrong amino acid in every 100 amino acids incorporated. However, error frequencies observed in...
11.9K
Transformers in Distribution System01:27

Transformers in Distribution System

156
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...
156

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Updated: Sep 11, 2025

A Swin Transformer-Based Model for Thyroid Nodule Detection in Ultrasound Images
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一个具有针对节点分类优化注意力得分的图形变压器.

Yu Zhang1, Xin Li1, Yaoqun Xu2

  • 1School of Computer Science and Information Engineering, Harbin University of Commerce, Harbin, 150028, China.

Scientific reports
|August 16, 2025
PubMed
概括
此摘要是机器生成的。

本研究介绍了OGFormer,这是一种新的图形转换器模型,可以增强节点嵌入式表示学习. OGFormer提高了图形神经网络 (GNN) 上的全球依赖性捕获和节点分类性能.

关键词:
图表注意力注意力.图表神经网络的神经网络图形变压器 图形变压器节点的分类 节点的分类

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科学领域:

  • 图形神经网络的神经网络
  • 变压器架构 变压器架构
  • 机器学习 机器学习

背景情况:

  • 通过图表传递信息在地方结构中表现出色,但在全球信息方面却扎不.
  • 变压器模型虽然在非局部建模方面强大,但在节点级预测任务中表现不佳.
  • 现有的研究专注于接近变压器,忽视了它们对节点嵌入的潜力.

研究的目的:

  • 介绍OGFormer,一个新的图形转换器模型,具有优化的注意力得分.
  • 解决在图形数据中捕捉全球依赖关系和复杂关系方面的局限性.
  • 增强节点嵌入式表示学习,以改进图形分析.

主要方法:

  • 开发了OGFormer,这是一个带有简化单头自我注意机制的图形转换器.
  • 实现了一个端到端的注意力评分优化损失函数来改进连接权重.
  • 将结构编码策略集成到注意力计算中,以优先考虑关键的本地依赖关系.

主要成果:

  • 在基准数据集中,OGFormer在节点分类任务中展示了竞争性性能.
  • 在同型和异型图表测试中取得了卓越的结果.
  • 超越了当前主流图形神经网络 (GNN) 方法的性能.

结论:

  • 在图表中,OGFormer有效地捕捉了全球依赖关系和本地结构.
  • 提议的注意力优化和结构编码增强了节点嵌入表示.
  • OGFormer代表了使用基于变压器的图形模型进行节点分类的重大进步.