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Related Concept Videos

Types Of Transformers01:16

Types Of Transformers

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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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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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Transformers with Off-Nominal Turns Ratios01:25

Transformers with Off-Nominal Turns Ratios

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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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Transformation01:26

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Microbial communities are dynamic environments where cell lysis releases free DNA into the surroundings. Other cells can take up this extracellular DNA through a process known as transformation.When a cell incorporates this foreign DNA into its genome, resulting in genetic modification, the process is known as transformation. Cells capable of this process are termed competent. Competence can be natural, as observed in certain bacteria and archaea, or artificially induced in the...
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Related Experiment Videos

Template-Aware Transformer for Person Reidentification.

Yanwei Zheng1, Zengrui Zhao1, Xiaowei Yu2

  • 1School of Computer Science and Technology, Shandong University, Qingdao 266237, China.

Computational Intelligence and Neuroscience
|April 11, 2022
PubMed
Summary
This summary is machine-generated.

This study introduces a template-aware transformer (TAT) to improve person reidentification (ReID) by focusing on distinct features and ignoring backgrounds. The TAT method enhances accuracy in computer vision tasks where faces are unavailable.

Related Experiment Videos

Area of Science:

  • Computer Vision
  • Artificial Intelligence
  • Machine Learning

Background:

  • Person reidentification (ReID) is crucial for identifying individuals without facial data.
  • Indistinguishable backgrounds and occlusions in images hinder ReID model performance.
  • Backgrounds captured by the same camera are often similar, while different cameras present distinct backgrounds.

Purpose of the Study:

  • To develop a novel method for enhancing person reidentification (ReID) performance.
  • To address the challenge of background interference in ReID tasks.
  • To improve the model's ability to focus on discriminative features despite varying backgrounds.

Main Methods:

  • Proposed a template-aware transformer (TAT) method incorporating a learnable template.
  • The template guides the transformer to reduce attention on low-discrimination regions like backgrounds and occlusions.
  • Increased the number of templates using side information to adapt to diverse camera backgrounds.

Main Results:

  • The TAT method effectively learns inter-sample indistinguishable features.
  • Attention gradually shifts towards distinguishable features as encoder depth increases.
  • Achieved competitive results on various public datasets through quantitative evaluation.

Conclusions:

  • The template-aware transformer (TAT) is a valid and effective approach for person reidentification.
  • The method successfully mitigates the negative impact of backgrounds and occlusions.
  • TAT demonstrates strong performance and adaptability across different ReID scenarios.