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The Ideal Transformer01:26

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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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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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Transformers can provide desired voltages to a circuit by modifying the number of turns in the secondary windings.
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Related Experiment Video

Updated: Jun 21, 2025

Design and Use of an Apparatus for Presenting Graspable Objects in 3D Workspace
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A Novel Part Refinement Tandem Transformer for Human-Object Interaction Detection.

Zhan Su1, Hongzhe Yang1

  • 1School of Computer Science and Technology, Dalian University of Technology, Dalian 116024, China.

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

This study introduces a new Part Refinement Tandem Transformer (PRTT) for improved human-object interaction (HOI) detection. The PRTT method enhances interaction recognition and classification accuracy in images.

Keywords:
HOI detectiondeep learningvision transformervisual relationship

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Area of Science:

  • Computer Vision
  • Artificial Intelligence

Background:

  • Human-object interaction (HOI) detection is crucial for understanding complex scenes.
  • Existing Transformer-based methods face challenges due to image complexity.

Purpose of the Study:

  • To propose a novel Part Refinement Tandem Transformer (PRTT) for enhanced HOI detection.
  • To improve the accuracy of recognizing interacting instances and classifying interaction categories.

Main Methods:

  • PRTT utilizes multiple decoders for HOI prediction elements.
  • Introduces a Part State Feature Extraction (PSFE) module for better classification.
  • Employs Prior Feature Integrated Cross-Attention (PFIC) to guide queries with fine-grained features.

Main Results:

  • The PRTT method significantly improves HOI detection performance.
  • Validated on V-COCO and HICO-DET datasets, outperforming state-of-the-art models.
  • Enhanced classification accuracy for human-object interactions.

Conclusions:

  • The proposed PRTT model offers a significant advancement in HOI detection.
  • The integration of PSFE and PFIC modules contributes to superior performance.
  • PRTT demonstrates effectiveness on benchmark datasets for complex interaction recognition.