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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
Transformers with Off-Nominal Turns Ratios01:25

Transformers with Off-Nominal Turns Ratios

511
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...
511
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

491
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...
491
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 14, 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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Graph Transformers: A Survey.

Ahsan Shehzad, Feng Xia, Shagufta Abid

    IEEE Transactions on Neural Networks and Learning Systems
    |January 12, 2026
    PubMed
    Summary
    This summary is machine-generated.

    Graph transformers combine graph learning and transformer models for powerful performance on graph data. This survey reviews their progress, design, applications, and challenges in machine learning.

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    A Swin Transformer-Based Model for Thyroid Nodule Detection in Ultrasound Images
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    Area of Science:

    • Machine Learning
    • Artificial Intelligence
    • Graph Theory

    Background:

    • Graph-structured data is prevalent in many domains.
    • Traditional models struggle with complex graph relationships.
    • Transformers excel at sequence modeling but need adaptation for graphs.

    Purpose of the Study:

    • To provide a comprehensive review of graph transformers.
    • To analyze design principles and integration of graph features.
    • To classify existing graph transformer models and identify future research directions.

    Main Methods:

    • Review of foundational concepts in graph learning and transformers.
    • Analysis of architectural designs integrating graph inductive biases and attention.
    • Development of a taxonomy for classifying graph transformers.
    • Discussion of applications and challenges.

    Main Results:

    • Graph transformers show strong performance across node, edge, and graph-level tasks.
    • Key design considerations include inductive biases and attention mechanisms.
    • A taxonomy based on depth, scalability, and pretraining is proposed.
    • Identified challenges include scalability, robustness, and interpretability.

    Conclusions:

    • Graph transformers represent a significant advancement in machine learning for graph data.
    • Further research is needed to address challenges in scalability, generalization, and interpretability.
    • The field holds great potential for diverse applications.