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相关概念视频

Types Of Transformers01:16

Types Of Transformers

943
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...
943
Transformers01:26

Transformers

1.0K
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...
1.0K
Deformations in a Transverse Cross Section01:21

Deformations in a Transverse Cross Section

165
When a material is subjected to uniaxial stress, it elongates or contracts in the direction of the applied force, and also undergoes changes in the perpendicular directions. This behavior is crucial for understanding how materials behave under stress and is governed by mechanical properties such as Poisson's ratio v, which measures the ratio of transverse strain to axial strain.
As the material stretches, it expands or contracts in orthogonal directions to the load. This phenomenon varies...
165
The Ideal Transformer01:26

The Ideal Transformer

339
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...
339
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
Transformation of Plane Strain01:12

Transformation of Plane Strain

148
When analyzing elongated structures like bars subjected to uniformly distributed loads, it is essential to understand the transformation of plane strain when coordinate axes are rotated. This transformation helps to assess how material deformation characteristics vary with orientation, which is crucial in materials science and structural engineering.
Under plane strain conditions, typical for members where one dimension significantly exceeds the others, deformations and resultant strains are...
148

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Updated: May 22, 2025

Three-Dimensional Shape Modeling and Analysis of Brain Structures
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Three-Dimensional Shape Modeling and Analysis of Brain Structures

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可变形的图形变压器

Jinyoung Park, Seongjun Yun, Hyeonjin Park

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    此摘要是机器生成的。

    可变形图形转换器 (DGT) 解决了当前图形模型的局限性,通过使用稀疏的注意力来有效地学习大规模图形上的表示. 这种方法显著降低了计算成本,同时在基准数据集上保持了卓越的性能.

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    科学领域:

    • 图形神经网络 图形神经网络
    • 机器学习 机器学习
    • 人工智能的人工智能

    背景情况:

    • 变压器模型对图形表示学习有希望.
    • 变压器中的全注意力机制导致二次复杂性,限制了大图的可扩展性.
    • 来自无关节点的消息聚合进一步阻碍了复杂图形结构的性能.

    研究的目的:

    • 提出一种基于变压器的新型模型,即可变形图形变压器 (DGT),用于高效和可扩展的图形表示学习.
    • 在大尺度图表上克服现有的变压器模型的计算限制.
    • 通过专注于相关节点来提高图形表示学习的性能.

    主要方法:

    • 开发了可变形图形变压器 (DGT),使用的注意力很少.
    • 构建了多个节点序列,考虑到结构和语义上的接近性.
    • 集成可学习的卡茨定位编码来增强稀疏注意力机制.
    • 实现了关于节点数的线性复杂性,以实现高效的计算.

    主要成果:

    • 在7个图表基准数据集中,DGT表现出卓越的表现.
    • 实现了显著的计算成本降低,比全注意力变压器模型少2.5至449倍.
    • 成功地通过稀疏的注意力高效地处理大规模图表.

    结论:

    • 可变形图形变压器 (DGT) 提供了一种高效且可扩展的解决方案,用于在大规模图形上的表示学习.
    • 建议的稀疏注意力机制有效地减少了计算复杂性,同时提高了性能.
    • 与现有的基于变压器的图形模型相比,DGT代表了显著的进步.