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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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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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A Multi-Level Relation-Aware Transformer model for occluded person re-identification.

Guorong Lin1, Zhiqiang Bao2, Zhenhua Huang3

  • 1School of Artificial Intelligence, South China Normal University, Foshan 528225, China.

Neural Networks : the Official Journal of the International Neural Network Society
|May 18, 2024
PubMed
Summary
This summary is machine-generated.

This study introduces a novel Multi-Level Relation-Aware Transformer (MLRAT) model to improve occluded person re-identification (Re-ID) by learning relationships between image patches and samples, outperforming existing methods on occluded datasets.

Keywords:
Occluded pedestrianPerson Re-IDSelf-distillationTransformer

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Area of Science:

  • Computer Vision
  • Artificial Intelligence
  • Machine Learning

Background:

  • Occluded person re-identification (Re-ID) is a significant challenge due to occlusions from objects or other pedestrians.
  • Existing Re-ID methods often rely on auxiliary models like pose estimation, which can be unreliable with occlusions.
  • Previous approaches frequently learn features from single images, neglecting inter-sample relationships crucial for robust Re-ID.

Purpose of the Study:

  • To develop a novel Multi-Level Relation-Aware Transformer (MLRAT) model for enhanced occluded person Re-ID.
  • To address limitations of auxiliary models and single-image feature learning in existing Re-ID techniques.
  • To improve the accuracy and robustness of person Re-ID in scenarios with significant occlusions.

Main Methods:

  • Introduced the Multi-Level Relation-Aware Transformer (MLRAT) model, comprising Patch-Level Relation-Aware (PLRA) and Sample-Level Relation-Aware (SLRA) modules.
  • PLRA utilizes a Graph Convolutional Network (GCN) to model structural relations between key image patches, bypassing auxiliary models.
  • SLRA employs a Relation-Aware Transformer (RAT) block and self-distillation to model inter-sample relationships and transfer knowledge.

Main Results:

  • The MLRAT model demonstrated significant performance improvements over existing baselines on four occluded person Re-ID datasets.
  • The model maintained competitive performance on partial and holistic person Re-ID datasets.
  • PLRA effectively learned local features by modeling patch relations, while SLRA captured discriminative sample-level features.

Conclusions:

  • The proposed MLRAT model effectively addresses the challenges of occluded person Re-ID by leveraging multi-level relational modeling.
  • The novel PLRA and SLRA modules provide a robust framework for learning discriminative features without reliance on auxiliary models.
  • MLRAT offers a superior solution for occluded person Re-ID, showing strong generalization across various dataset types.