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Related Concept Videos

Transformers in Distribution System01:27

Transformers in Distribution System

140
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...
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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...
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Signal Flow Graphs01:18

Signal Flow Graphs

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Signal-flow graphs offer a streamlined and intuitive approach to representing control systems, providing an alternative to traditional block diagrams. These graphs use branches to symbolize systems and nodes to represent signals, effectively illustrating the relationships and interactions within the system.
In a signal-flow graph, branches denote the system's transfer functions, while nodes represent the signals. The direction of signal flow is indicated by arrows, with the corresponding...
284
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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The Ideal Transformer01:26

The Ideal Transformer

453
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...
453
Transformers with Off-Nominal Turns Ratios01:25

Transformers with Off-Nominal Turns Ratios

189
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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DGTR: Dynamic graph transformer for rumor detection.

Siqi Wei1, Bin Wu1, Aoxue Xiang2

  • 1Beijing Key Laboratory of Intelligence Telecommunication Software and Multimedia, Beijing University of Posts and Telecommunications, Beijing, China.

Frontiers in Research Metrics and Analytics
|January 30, 2023
PubMed
Summary
This summary is machine-generated.

This study introduces DGTR, a transformer-based approach for rumor identification by analyzing dynamic graph representations. DGTR effectively captures temporal and structural dependencies in news propagation, outperforming existing methods.

Keywords:
dynamic graphneural networkrumor detectionrumor propagationtransformer

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Area of Science:

  • Computer Science
  • Social Network Analysis
  • Artificial Intelligence

Background:

  • Social media rumors can negatively impact public perception and societal advancement.
  • Detecting rumors is crucial, with news propagation patterns offering key insights.
  • Current rumor detection methods often overlook temporal dynamics, relying solely on static graph structures.

Purpose of the Study:

  • To develop a novel approach for rumor identification that effectively models both structural and temporal information in news propagation.
  • To address the limitations of existing dynamic graph representation learning methods in capturing long-range dependencies and semantic associations.

Main Methods:

  • A transformer-based dynamic graph representation learning approach named DGTR was developed.
  • A specialized position embedding format was designed to adapt transformer models for dynamic graph data.
  • The self-attention mechanism within the transformer architecture captures long-range structural and temporal dependencies.

Main Results:

  • DGTR demonstrated superior performance in rumor identification compared to state-of-the-art methods.
  • The model effectively captures long-range structural reliance between nodes and temporal dependencies across snapshots.
  • The use of the CLS token aids in modeling semantic relationships between the overall graph and its components.

Conclusions:

  • The proposed DGTR model offers a significant advancement in rumor detection by leveraging dynamic graph representations.
  • Transformer-based architectures are effective for modeling complex temporal and structural patterns in social media news dissemination.
  • DGTR provides a robust framework for identifying social media rumors, enhancing the reliability of information ecosystems.