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相关概念视频

Types Of Transformers01:16

Types Of Transformers

951
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...
951
Transformers in Distribution System01:27

Transformers in Distribution System

99
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...
99
Vector Algebra: Graphical Method01:10

Vector Algebra: Graphical Method

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Vectors can be multiplied by scalars, added to other vectors, or subtracted from other vectors. The vector sum of two (or more) vectors is called the resultant vector or, for short, the resultant.
We use the laws of geometry to construct resultant vectors, followed by trigonometry to find vector magnitudes and directions. For a geometric construction of the sum of two vectors in a plane, we follow the parallelogram rule. Suppose two vectors are at arbitrary positions. Translate either one of...
11.9K
Energy Losses in Transformers01:21

Energy Losses in Transformers

839
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...
839
Transformers01:26

Transformers

1.1K
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...
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Equivalent Circuits for Practical Transformers01:28

Equivalent Circuits for Practical Transformers

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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...
398

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  2. Grakerformer:一个带有图核的变压器,用于无监督的图形表示学习.

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    GraKerformer通过将图形神经网络和最短路径图形内核集成到变压器架构中来增强无监督图形表示学习. 这种方法捕捉了全面的图形结构,比传统方法提高了性能.

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    科学领域:

    • 人工智能的人工智能
    • 机器学习 机器学习
    • 图形表示学习学习学习图形表示学习

    背景情况:

    • 变压器模型在NLP中表现出色,但在无监督图形表示学习 (UGRL) 中扎.
    • 现有的UGRL方法往往侧重于局部图形结构,限制了它们捕获全球信息的能力,并导致了糟糕的泛化.
    • 需要先进的模型,可以有效地表示复杂的图形结构.

    研究的目的:

    • 介绍GraKerformer,一种基于Transformer的新型模型,旨在改善无监督图形表示学习.
    • 解决传统的UGRL方法在捕获全面的图形结构信息方面的局限性.
    • 在UGRL任务中增强模型的性能和概括能力.

    主要方法:

    • 提出了GraKerformer,这是一个修改过的变压器架构,包含了图形神经网络.
    • 利用最短路径图核 (SPGK) 在变压器内权重注意力得分.
    • 集成SPGK与图形神经网络编码细微的图形结构信息.

    主要成果:

    • 在无监督图表表示学习任务中,GraKerformer表现出卓越的性能.
    • 该模型有效地捕获了图形的全面结构信息,克服了以局部结构为重点的方法的局限性.
  • 对基准图形分类数据集的评估验证了增强的性能.
  • 结论:

    • 在无监督图表表示学习中,GraKerformer提供了显著的进步.
    • 在变压器框架内集成SPGK和GNN有效编码复杂的图形结构.
    • 拟议的模型实现了最先进的性能,为更强大的基于图形的深度学习应用铺平了道路.