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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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Aggregate classification is generally based on its size, petrographic characteristics, weight, and source. Size classification ranges from coarse to fine aggregates, defined by the size of the particles. Coarse aggregates are particles that do not pass through ASTM sieve No. 4, and aggregates that pass through the sieve are fine aggregates.
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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.
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Related Experiment Video

Updated: Jun 28, 2025

A Swin Transformer-Based Model for Thyroid Nodule Detection in Ultrasound Images
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Dual-Dependency Attention Transformer for Fine-Grained Visual Classification.

Shiyan Cui1,2,3,4, Bin Hui1,2,3

  • 1Key Laboratory of Opto-Electronic Information Processing, Chinese Academy of Sciences, Shenyang 110016, China.

Sensors (Basel, Switzerland)
|April 13, 2024
PubMed
Summary
This summary is machine-generated.

This study introduces a dual-dependency attention transformer for fine-grained visual classification (FGVC). The new model achieves linear complexity, improving performance on FGVC tasks.

Keywords:
deep learningfine-grained visual classificationvision transformer

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Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Machine Learning

Background:

  • Visual Transformers (ViTs) are prevalent in visual tasks like fine-grained visual classification (FGVC).
  • The core self-attention mechanism in ViTs incurs quadratic computational and memory costs.
  • Existing sparse-attention and local-attention methods are inadequate for FGVC's need for dense features and global dependency modeling.

Purpose of the Study:

  • To develop an efficient attention mechanism for Visual Transformers suitable for fine-grained visual classification.
  • To address the computational and memory complexity limitations of standard self-attention in ViTs.
  • To enhance the modeling of discriminative cues for improved FGVC performance.

Main Methods:

  • Proposing a dual-dependency attention transformer that decouples global token interactions.
  • Implementing a position-dependency attention pathway using grouped attention.
  • Utilizing a semantic dependency attention pathway with dynamic central aggregation.
  • Developing discriminative enhancement strategies for sensitive cue tracking using knowledge-based representations.

Main Results:

  • The dual-dependency attention transformer achieves linear computational complexity.
  • The model demonstrates suitability for fine-grained image classification tasks.
  • Experimental results on NABIRDS, CUB, and DOGS datasets validate the proposed approach.

Conclusions:

  • The proposed dual-dependency attention transformer offers an effective solution for fine-grained visual classification.
  • This method balances computational efficiency with high performance in FGVC.
  • The approach enhances semantic modeling and discriminative cue tracking, outperforming existing methods for FGVC.