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Transformers with Off-Nominal Turns Ratios01:25

Transformers with Off-Nominal Turns Ratios

182
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...
182
Types Of Transformers01:16

Types Of Transformers

1.0K
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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Related Experiment Video

Updated: Jul 24, 2025

A Swin Transformer-Based Model for Thyroid Nodule Detection in Ultrasound Images
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A reliable anchor regenerative-based transformer model for x-small and dense objects recognition.

Ponduri Vasanthi1, Laavanya Mohan1

  • 1Vignan's Foundation for Science, Technology, and Research, Guntur, Andhra Pradesh, India.

Neural Networks : the Official Journal of the International Neural Network Society
|July 7, 2023
PubMed
Summary

This study introduces an anchor regenerative transformer module to improve the detection of small and dense objects. The novel approach enhances feature extraction, leading to superior accuracy in object detection tasks.

Keywords:
Auto-anchorMulti-head-self-attentionObject detectionSpatial pyramid pooling-fasterYOLOv5

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Area of Science:

  • Computer Vision
  • Deep Learning

Background:

  • Deep learning models excel at object detection but struggle with x-small and dense objects.
  • Existing models suffer from feature extraction limitations and anchor box misalignments, causing score-position discrepancies.

Purpose of the Study:

  • To address the limitations in detecting x-small and dense objects.
  • To propose a novel feature refinement network using an anchor regenerative-based transformer module.

Main Methods:

  • Introduced an anchor regenerative module to generate adaptive anchor scales based on image semantic statistics.
  • Integrated a Multi-Head Self-Attention (MHSA) transformer module for in-depth feature map analysis.
  • Experimentally validated the model on VisDrone, VOC, and SKU-110K datasets.

Main Results:

  • The proposed model demonstrated higher mean Average Precision (mAP), precision, and recall compared to existing methods.
  • Achieved superior performance in detecting x-small and dense objects across multiple datasets.
  • Showcased excellent fit for VOC and SKU-110K datasets based on accuracy, kappa coefficient, and ROC metrics.

Conclusions:

  • The anchor regenerative-based transformer module effectively overcomes limitations in detecting small and dense objects.
  • The model's ability to generate dataset-specific anchor scales significantly improves detection accuracy.
  • The proposed approach represents a significant advancement in object detection for challenging scenarios.