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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.
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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Source Transformation01:15

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Source transformation is a fundamental technique employed in circuit analysis, offering a valuable tool for simplifying complex electrical circuits. This technique involves the replacement of either a voltage source in series with a resistor by a current source in parallel with a resistor, or vice versa. The key concept here is that when the original sources are deactivated (turned off), the equivalent resistance at the circuit's end terminals remains the same.
It is essential to note that when...
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The Ideal Transformer01:26

The Ideal Transformer

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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...
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Types Of Transformers01:16

Types Of Transformers

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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.
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Stereotype Content Model02:16

Stereotype Content Model

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The Stereotype Content Model (SCM) was first proposed by Susan Fiske and her colleagues (Fiske, Cuddy, Glick & Xu, 2002; see also Fiske, 2012 and Fiske, 2017). The SCM specifies that when someone encounters a new group, they will stereotype them based on two metrics: warmth—or that group’s perceived intent, and how likely they are to provide help or inflict harm—and competence—or their ability to carry out that objective. Depending on the warmth-competence...
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Transformers in Distribution System01:27

Transformers in Distribution System

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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.
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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Display-Semantic Transformer for Scene Text Recognition.

Xinqi Yang1,2,3, Wushour Silamu1,2,3, Miaomiao Xu1,2,3

  • 1College of Computer Science and Technology, Xinjiang University, No. 777 Huarui Street, Urumqi 830017, China.

Sensors (Basel, Switzerland)
|October 14, 2023
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Summary
This summary is machine-generated.

This study introduces the Display-Semantic Transformer (DST) to improve scene text recognition by integrating linguistic knowledge. DST enhances visual features with semantic information, boosting accuracy and robustness, especially in noisy images.

Keywords:
cross-modal attentionlinguistic knowledgescene text recognitiontransformervisual information

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Area of Science:

  • Computer Vision
  • Natural Language Processing
  • Artificial Intelligence

Background:

  • Scene text recognition models often struggle with noisy images due to a lack of linguistic understanding.
  • Existing visual models focus solely on visual textures, neglecting semantic context.
  • This limitation leads to reduced recognition accuracy in challenging visual conditions.

Purpose of the Study:

  • To enhance scene text recognition by integrating linguistic and visual information.
  • To improve model robustness against image noise and distortions.
  • To develop a more accurate and efficient scene text recognition system.

Main Methods:

  • Building upon the Vision Transformer architecture.
  • Introducing the Display-Semantic Transformer (DST) model.
  • Incorporating a masked language model and a semantic visual interaction module.

Main Results:

  • The DST model significantly improves recognition accuracy on benchmark datasets, achieving nearly 2% higher average accuracy.
  • The semantic visual interaction module effectively enhances visual features with deep semantic information.
  • The model demonstrates improved robustness in recognizing text in noisy images.

Conclusions:

  • The Display-Semantic Transformer (DST) offers a superior approach to scene text recognition by effectively combining visual and semantic information.
  • DST achieves a better balance between accuracy and inference speed with a small parameter count.
  • This approach enhances model robustness and recognition performance, particularly in challenging real-world scenarios.