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Related Concept Videos

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...
1.4K
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...
1.7K
Transformers in Distribution System01:27

Transformers in Distribution System

494
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...
494
Transformers with Off-Nominal Turns Ratios01:25

Transformers with Off-Nominal Turns Ratios

517
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...
517
The Ideal Transformer01:26

The Ideal Transformer

1.4K
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.
Ampere's Law forms the basis of understanding the magnetic field within the transformer. It states that the integral of the magnetic field intensity's tangential...
1.4K
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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Updated: Jan 15, 2026

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

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Object Detection with Transformers: A Review.

Tahira Shehzadi1,2,3, Khurram Azeem Hashmi1,2,3, Marcus Liwicki4

  • 1Department of Computer Science, Technical University of Kaiserslautern, 67663 Kaiserslautern, Germany.

Sensors (Basel, Switzerland)
|October 16, 2025
PubMed
Summary
This summary is machine-generated.

Detection Transformers (DETR) leverage transformer models for computer vision, improving object detection by treating it as a set prediction problem. This review covers 25 advancements enhancing DETR

Keywords:
DETRcomputer visiondeep neural networksobject detectiontransformer

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Area of Science:

  • Computer Vision
  • Artificial Intelligence
  • Machine Learning

Background:

  • Transformers have revolutionized Natural Language Processing (NLP).
  • Detection Transformer (DETR) applies transformers to object detection, framing it as a set prediction problem.
  • Initial DETR versions faced challenges with slow convergence and detecting small objects.

Purpose of the Study:

  • To provide a comprehensive review of 25 recent advancements in Detection Transformers (DETR).
  • To analyze foundational DETR modules and recent enhancements.
  • To encourage further research in transformer-based object detection.

Main Methods:

  • Review of 25 recent DETR advancements.
  • Analysis of modifications to backbone structure, query design, and attention mechanisms.
  • Comparative analysis of various detection transformers' performance and architectures.

Main Results:

  • Numerous improvements have addressed DETR's initial limitations, leading to state-of-the-art performance.
  • DETR advancements include backbone modifications, refined query strategies, and improved attention mechanisms.
  • Comparative analysis highlights performance and architectural differences across various DETR models.

Conclusions:

  • DETR has evolved significantly, overcoming initial performance hurdles.
  • This review offers a structured overview of DETR's progress and challenges.
  • Future research directions are identified to further advance transformer applications in object detection.