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Related Concept Videos

Equivalent Circuits for Practical Transformers01:28

Equivalent Circuits for Practical Transformers

1.3K
The practical equivalent circuits of single-phase two-winding transformers exhibit significant deviations from their idealized versions due to the inherent properties of winding resistance and finite core permeability. These properties result in real and reactive power losses, affecting the transformer's performance. Understanding these deviations is crucial for designing more efficient transformers.
In a practical transformer, each winding exhibits resistance and leakage reactance. The...
1.3K
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 in Distribution System01:27

Transformers in Distribution System

475
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...
475
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
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
Transformers with Off-Nominal Turns Ratios01:25

Transformers with Off-Nominal Turns Ratios

491
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...
491

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Related Experiment Video

Updated: Jan 8, 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

Published on: April 21, 2023

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Classification of current density vector map using transformer hybrid residual network.

Lihui Zhu1,2, Yunfeng Yang1, Wenyue Yu1

  • 1School of Physics, Zhejiang University of Technology, Hangzhou, Zhejiang, China.

Plos One
|December 16, 2025
PubMed
Summary
This summary is machine-generated.

This study introduces a deep learning model for classifying cardiac current density vector maps (CDVM) from magnetocardiogram (MCG) data. The novel approach achieves 97.52% accuracy, overcoming data scarcity challenges for improved cardiac assessment.

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Last Updated: Jan 8, 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

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Area of Science:

  • Cardiology
  • Biomedical Engineering
  • Artificial Intelligence

Background:

  • Magnetocardiogram (MCG) derived Current Density Vector Maps (CDVM) are crucial for cardiac function assessment.
  • Limited MCG data availability and analysis complexity hinder clinical application.
  • Computer-aided diagnostics are increasingly vital for interpreting complex cardiac data.

Purpose of the Study:

  • To develop a deep learning model for accurate CDVM classification.
  • To address data scarcity issues in MCG research through advanced augmentation techniques.
  • To enhance the precision and efficiency of cardiac state assessment using CDVM.

Main Methods:

  • Data augmentation using noise addition, ARIMA models, and interpolation to overcome data scarcity.
  • Implementation of a transformer hybrid residual network with transfer learning.
  • Utilization of the self-attention mechanism for enhanced feature extraction from CDVM.

Main Results:

  • Achieved a classification accuracy of 97.52% for CDVM across categories 0 to 4.
  • Demonstrated superior performance compared to existing deep learning methods.
  • Exhibited high precision, efficiency, and scalability for expanding CDVM datasets.

Conclusions:

  • The proposed deep learning approach effectively classifies CDVM with high accuracy.
  • The method successfully addresses data limitations in MCG analysis.
  • This scalable solution offers a promising tool for physicians in clinical cardiac diagnosis.