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Related Concept Videos

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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Types Of Transformers01:16

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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.
However, if this ratio is less than one, the transformer is said to be a step-down...
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Source Transformation for AC Circuits01:11

Source Transformation for AC Circuits

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The process of source transformation in the frequency domain entails the conversion of a voltage source, positioned in series with an impedance, into a current source that is parallel to an impedance, or the other way around. It is essential to maintain the following relationships while transitioning from one source type to another.
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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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Transformers with Off-Nominal Turns Ratios01:25

Transformers with Off-Nominal Turns Ratios

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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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Related Experiment Video

Updated: May 5, 2026

X-ray Dose Reduction through Adaptive Exposure in Fluoroscopic Imaging
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ACformer: A unified transformer for arbitrary-frame image exposure correction.

Chao Li1, Yuchen Yang1, Qiujia He1

  • 1School of Statistics and Data Science, Nankai University, Tianjin, 300071, China.

Neural Networks : the Official Journal of the International Neural Network Society
|January 24, 2025
PubMed
Summary

This study unifies single-image exposure correction (SEC) and multi-image exposure fusion (MEF) into arbitrary-frame exposure correction (AF-EC). The proposed ACformer model effectively handles any number of input frames, improving performance on both SEC and MEF tasks.

Keywords:
Arbitrary-frame exposure correctionMulti-exposure image fusionSelf-attentionSingle image exposure correction

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Area of Science:

  • Computer Vision
  • Image Processing
  • Deep Learning

Background:

  • Single-image exposure correction (SEC) and multi-image exposure fusion (MEF) address image under/overexposure.
  • Existing methods are specialized for either SEC or MEF, neglecting their underlying task correlation.
  • A unified approach is needed to leverage commonalities between SEC and MEF.

Purpose of the Study:

  • To unify single-image exposure correction (SEC) and multi-image exposure fusion (MEF) into a single "Arbitrary-Frame Exposure Correction" (AF-EC) task.
  • To develop an integrated deep learning model capable of handling an arbitrary number of input frames for exposure correction.
  • To demonstrate mutually boosted performance on both SEC and MEF tasks using the unified framework.

Main Methods:

  • Proposed an Arbitrary-Frame Exposure Correction Transformer (ACformer) model for the unified AF-EC task.
  • Introduced a Parallel Feature Fusion and Correction (PFFC) module within ACformer.
  • Employed Spatial, Channel, and Temporal Self-Attention mechanisms for feature-level correction and fusion across multiple frames.

Main Results:

  • The ACformer model achieved superior performance compared to specialized SEC and MEF methods.
  • Experiments demonstrated significant improvements in both objective metrics and subjective visual quality.
  • The unified approach showed enhanced capabilities in handling diverse exposure correction scenarios.

Conclusions:

  • The proposed ACformer effectively unifies SEC and MEF tasks under the AF-EC framework.
  • The integrated model demonstrates the benefits of addressing exposure correction holistically.
  • Future work can explore further advancements in arbitrary-frame image processing.