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Related Concept Videos

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.
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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.
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A Y-connected synchronous generator, grounded through a neutral impedance, is designed to produce balanced internal phase voltages with only positive-sequence components. The generator's sequence networks include a source voltage that is exclusively in the positive-sequence network. The sequence components of line-to-ground voltages at the generator terminals illustrate this configuration.
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Transformers can provide desired voltages to a circuit by modifying the number of turns in the secondary windings.
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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...
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Vector Algebra: Graphical Method01:10

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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.
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Graph representation learning via enhanced GNNs and transformers.

Hongrui Mu1, Chengchen Zhou1, Qiancheng Yu2,3

  • 1School of Computer Science and Engineering, North Minzu University, Yinchuan, 750021, China.

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|August 6, 2025
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Summary
This summary is machine-generated.

This study introduces EHDGT, a novel graph representation learning method that enhances graph neural networks (GNNs) and Transformers. EHDGT improves local feature learning and edge utilization, outperforming existing methods and boosting wine industry knowledge graph quality.

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Area of Science:

  • Graph representation learning
  • Artificial intelligence
  • Machine learning

Background:

  • Graph transformers (GTs) show promise but struggle with local feature learning and edge information.
  • Existing graph neural networks (GNNs) and Transformers have limitations in capturing complex graph structures.

Purpose of the Study:

  • To propose EHDGT, an enhanced graph representation learning method addressing GT deficiencies.
  • To improve local feature learning and edge information utilization in graph data.
  • To enhance the quality and practical value of the wine industry knowledge graph.

Main Methods:

  • Enhanced graph representation learning using GNNs and Transformers (EHDGT).
  • Superimposed edge-level positional encoding and subgraph encoding for GNNs.
  • Incorporated edge information and linear attention mechanism for Transformers.
  • Gate-based fusion mechanism for dynamic integration of GNN and Transformer outputs.

Main Results:

  • EHDGT significantly outperforms traditional message-passing networks.
  • EHDGT achieves strong performance compared to existing graph transformers.
  • Application to wine industry knowledge graph using link prediction shows excellent results, enhancing completeness and semantic quality.

Conclusions:

  • EHDGT offers a superior approach to graph representation learning by effectively integrating GNNs and Transformers.
  • The method enhances the utilization of local and global graph features.
  • EHDGT significantly improves the wine industry knowledge graph, demonstrating practical applicability and value in digitalization efforts.