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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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Transformers in Distribution System01:27

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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.
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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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Beyond convolutions and supervised learning with transformers and representation learning for retinal image analysis.

Yue Wu1, Cecilia S Lee2, Aaron Y Lee2

  • 1Department of Ophthalmology, University of Washington, Seattle, WA, United States of America; Roger and Angie Karalis Johnson Retina Center, Seattle, WA, United States of America.

Progress in Retinal and Eye Research
|December 6, 2025
PubMed
Summary

Recent advances in retinal image analysis leverage label-free methods and vision transformers, moving beyond traditional supervised artificial intelligence (AI). This shift enables powerful foundation and multi-modal models for enhanced diagnostics.

Keywords:
AIDeep learningFoundation modelsImage analysisRetinal imagingSelf-supervised learningSemi-supervised learning

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Area of Science:

  • Ophthalmology
  • Computer Vision
  • Artificial Intelligence

Background:

  • Retinal image analysis has significantly advanced due to computer vision techniques.
  • Prior reviews focused on supervised learning or specific AI applications in ophthalmology.
  • A notable trend is the move towards label-free approaches in AI for retinal imaging.

Purpose of the Study:

  • To summarize recent advances in retinal image analysis.
  • To highlight the shift from supervised to label-free methods.
  • To discuss the emergence of vision transformers and their impact.

Main Methods:

  • Review of literature on artificial intelligence and computer vision in retinal image analysis.
  • Focus on representational learning and label-free techniques.
  • Exploration of vision transformers as alternatives to convolutional neural networks.

Main Results:

  • The field is transitioning towards semi-supervised and self-supervised learning.
  • Vision transformers are emerging as powerful tools for image analysis.
  • These advances have led to the development of foundation, vision-language, and multi-modal models.

Conclusions:

  • Label-free representational learning and vision transformers are transforming retinal image analysis.
  • New AI models offer enhanced capabilities beyond traditional methods.
  • The future of AI in ophthalmology involves more sophisticated, data-efficient approaches.