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Transformers

Transformers

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...

Types Of Transformers

Types Of Transformers

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...

Energy Losses in Transformers

Energy Losses in Transformers

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 copper windings...

The Ideal Transformer

The Ideal Transformer

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 component...

Transformers in Distribution System

Transformers in Distribution System

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...

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Related Experiment Video

Updated: Jul 8, 2026

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

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

Published on: April 21, 2023

Lightweight and hybrid transformer-based solution for quick and reliable deepfake detection.

Geeta Rani¹, Atharv Kothekar¹, Shawn George Philip¹

¹Manipal University Jaipur, Jaipur, Rajasthan, India.

Frontiers in Big Data

|April 28, 2025

Summary

A new hybrid deepfake detection model combines transformer and Linformer architectures for high accuracy and efficiency. This advanced technique combats realistic fake media, preventing public harm and reputational damage.

Keywords:

blackmail computation deepfake generative social safety transformer

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Area of Science:

Computer Science
Artificial Intelligence
Cybersecurity

Background:

Rapid advancements in AI generate realistic fake images and videos (deepfakes).
Deepfakes are used for blackmail and damaging public figures' reputations.
Existing deepfake detection methods are often computationally intensive and vary in accuracy.

Purpose of the Study:

To propose a novel hybrid deepfake detection architecture.
To enhance the accuracy and reduce computational intensity of deepfake detection.
To provide a robust solution against sophisticated fake media.

Main Methods:

A hybrid architecture combining transformer and Linformer models is proposed.
Images are converted into patches with position encoding to preserve spatial information.
The model utilizes Gaussian Error Linear Unit to mitigate vanishing gradient issues.

Main Results:

The hybrid model achieves a high accuracy of 98.9%.
The Linformer component reduces execution time by half without compromising accuracy.
Increased patch size (e.g., 11) improves model performance and feature extraction.

Conclusions:

The proposed hybrid model offers a computationally efficient and accurate deepfake detection solution.
The model's robustness and generalization are enhanced by combining transformer and Linformer strengths.
This technique has significant real-time applicability for preventing deepfake-related harm.