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Transformers in Distribution System01:27

Transformers in Distribution System

486
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.
Distribution substation transformers come in various ratings and typically use mineral oil for insulation and cooling. To prevent moisture and air from entering the oil, some transformers use an inert gas like nitrogen to fill the...
486
Transformers with Off-Nominal Turns Ratios01:25

Transformers with Off-Nominal Turns Ratios

495
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...
495
Types Of Transformers01:16

Types Of Transformers

1.4K
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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Transformers01:26

Transformers

1.7K
A device that transforms voltages from one value to another using induction is called a transformer. A transformer consists of two separate coils, or windings, wrapped around the same soft iron core. However, they are electrically insulated from each other.
The iron core has a substantial relative permeability. Therefore, the magnetic field lines generated due to the current in one winding are almost entirely confined within the core, such that the same magnetic flux permeates each turn of both...
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Three-Winding Transformers01:19

Three-Winding Transformers

666
Three identical single-phase transformers can be configured to form a three-phase transformer connection, which involves high-voltage and low-voltage windings. The high-voltage windings are denoted by capital letters A-B-C, while the low-voltage windings are labeled with lowercase letters a-b-c, representing their respective phases. This notation helps distinguish between the high and low voltage sides of the transformer.
In the per-unit equivalent circuit of a grounded Y-Y three-phase...
666
Energy Losses in Transformers01:21

Energy Losses in Transformers

1.3K
In an ideal transformer, it is assumed that there are no energy losses, and, hence, all the power at the primary winding is transferred to the secondary winding. However, in reality,  the transformers always have some energy losses, and, hence, the output power obtained at the secondary winding is less than the input power at the primary winding due to energy losses.
There are four main reasons for energy losses in transformers.
The first cause can be  the high resistance of the...
1.3K

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Related Experiment Video

Updated: Jan 11, 2026

A Swin Transformer-Based Model for Thyroid Nodule Detection in Ultrasound Images
04:23

A Swin Transformer-Based Model for Thyroid Nodule Detection in Ultrasound Images

Published on: April 21, 2023

2.3K

Transformer-based vehicle re-identification with view information.

Mingdong Zhu1,2, Qinghe Feng3

  • 1Henan Institute of Technology, School of Computer Science and Technology, Xinxiang, 453003, China.

Scientific Reports
|November 18, 2025
PubMed
Summary
This summary is machine-generated.

A new Transformer-based Vehicle Re-identification (ReID) model effectively uses view information to distinguish vehicles. This approach reduces differences between same vehicles and similarities between different vehicles, achieving state-of-the-art results.

Keywords:
Computer visionRe-identificationTransformerVehicle image

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Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
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Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

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Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
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Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Published on: December 15, 2023

999

Area of Science:

  • Computer Vision
  • Artificial Intelligence
  • Machine Learning

Background:

  • The industrial Internet of Things and smart cities necessitate advanced vehicle re-identification (ReID) systems.
  • Key challenges in ReID include intra-identity variations (different views of the same vehicle) and inter-identity similarities (similar views of different vehicles).

Purpose of the Study:

  • To propose a novel Transformer-based Vehicle ReID model with View Information (TVRVI) to address the challenges of intra-identity differences and inter-identity similarities.
  • To improve the accuracy and robustness of vehicle re-identification in large-scale datasets.

Main Methods:

  • Annotated vehicle images into five view parts and trained a parsing network to assign view labels.
  • Designed a dual-branch Transformer-based parsing network to separately extract features from different views, minimizing entanglement.
  • Utilized view information to enable the local branch to learn fine-grained view representations and the global branch to learn overall image features.

Main Results:

  • The TVRVI model effectively leverages common-view features while mitigating interference from uncommon-view features.
  • Demonstrated significant reductions in intra-identity differences and inter-identity similarities between vehicle images.
  • Achieved state-of-the-art performance on four public vehicle datasets, validated by ablation studies.

Conclusions:

  • The proposed TVRVI model, incorporating view information, significantly enhances vehicle re-identification accuracy.
  • The dual-branch Transformer architecture effectively processes multi-view features for improved ReID performance.
  • View information is crucial for overcoming common challenges in vehicle re-identification tasks.