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Related Concept Videos

Types Of Transformers01:16

Types Of Transformers

1.1K
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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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...
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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...
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The Ideal Transformer01:26

The Ideal Transformer

950
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...
950
Transformers with Off-Nominal Turns Ratios01:25

Transformers with Off-Nominal Turns Ratios

227
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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Transformers in Distribution System01:27

Transformers in Distribution System

173
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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Related Experiment Video

Updated: Oct 4, 2025

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
03:31

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Published on: December 15, 2023

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Dual Global Enhanced Transformer for image captioning.

Tiantao Xian1, Zhixin Li1, Canlong Zhang1

  • 1Guangxi Key Lab of Multi-source Information Mining and Security, Guangxi Normal University, Guilin 541004, China.

Neural Networks : the Official Journal of the International Neural Network Society
|February 8, 2022
PubMed
Summary
This summary is machine-generated.

Dual Global Enhanced Transformer (DGET) improves image captioning by integrating global visual and textual information. This approach enhances attention mechanisms for more accurate scene understanding and word generation.

Keywords:
Global informationImage captioningReinforcement learningTransformerVisual attention

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Area of Science:

  • Computer Vision
  • Artificial Intelligence
  • Natural Language Processing

Background:

  • Transformer architectures excel at image captioning by modeling interactions.
  • Current methods often overlook global scene context in attention calculations.
  • Explicitly incorporating global information is crucial for understanding scene content.

Purpose of the Study:

  • To propose the Dual Global Enhanced Transformer (DGET) model.
  • To integrate global visual and textual information into both encoding and decoding stages.
  • To enhance the accuracy and relevance of generated image captions.

Main Methods:

  • Developed a novel Global Enhanced Encoder (GEE) to fuse grid features (visual global information) with region features.
  • Introduced a Global Enhanced Decoder (GED) that utilizes textual global information via a context vector.
  • Employed a context encoder to process existing captions and guide word generation.

Main Results:

  • DGET achieved superior performance on the MS COCO image captioning dataset.
  • The model demonstrated improved ability to understand scene content and generate accurate captions.
  • Extensive experiments validated the effectiveness of the proposed global enhancement techniques.

Conclusions:

  • Integrating global visual and textual information significantly enhances transformer-based image captioning.
  • DGET offers a novel and effective approach to address limitations in current attention mechanisms.
  • The proposed methods provide a strong foundation for future advancements in image captioning research.