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What do you think is the single most influential factor in determining with whom you become friends and whom you form romantic relationships? You might be surprised to learn that the answer is simple: the people with whom you have the most contact. This most important factor is proximity. You are more likely to be friends with people you have regular contact with. For example, there are decades of research that shows that you are more likely to become friends with people who live in your dorm,...
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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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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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Updated: Sep 1, 2025

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Multi-Level Transformer-Based Social Relation Recognition.

Yuchen Wang1, Linbo Qing1, Zhengyong Wang1

  • 1College of Electronics and Information Engineering, Sichuan University, Chengdu 610065, China.

Sensors (Basel, Switzerland)
|August 12, 2022
PubMed
Summary
This summary is machine-generated.

This study introduces a novel Multi-level Transformer-Based Social Relation Recognition (MT-SRR) framework. MT-SRR effectively orchestrates multi-scale features for improved social relation recognition and addresses data imbalance challenges.

Keywords:
data drivensocial intelligencesocial relation recognitiontransformer

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Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Human-Computer Interaction

Background:

  • Social relationships are crucial for understanding human behavior and developing intelligent systems.
  • Existing social relation recognition (SRR) methods struggle to comprehensively integrate features of varying importance.
  • A need exists for advanced SRR frameworks that can effectively fuse multi-scale information.

Purpose of the Study:

  • To propose a novel Multi-level Transformer-Based Social Relation Recognition (MT-SRR) framework.
  • To enhance the orchestration of multi-scale features for more accurate SRR.
  • To address the challenge of data imbalance in social relation recognition tasks.

Main Methods:

  • Utilized a Vision Transformer (ViT) for global feature extraction.
  • Introduced an Intra-Relation Transformer (Intra-TRM) for dynamic feature fusion.
  • Employed an Inter-Relation Transformer (Inter-TRM) to leverage logical constraints among relationships.
  • Incorporated a margin-based loss function to mitigate data imbalance.

Main Results:

  • The MT-SRR framework demonstrated superior fusion of multi-scale features.
  • The model effectively ameliorated the negative impact of data imbalance.
  • Experimental results showed significant improvements over state-of-the-art methods on benchmark datasets.

Conclusions:

  • MT-SRR offers a robust approach for social relation recognition by effectively integrating multi-level features.
  • The proposed framework advances the development of more sophisticated social intelligent systems.
  • MT-SRR provides a significant performance improvement in recognizing social relationships, even with imbalanced data.