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Related Concept Videos

Types Of Transformers01:16

Types Of Transformers

1.0K
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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Energy Losses in Transformers01:21

Energy Losses in Transformers

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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...
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Equivalent Circuits for Practical Transformers01:28

Equivalent Circuits for Practical Transformers

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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...
488
The Ideal Transformer01:26

The Ideal Transformer

448
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...
448
Transformers in Distribution System01:27

Transformers in Distribution System

134
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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Transformers01:26

Transformers

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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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Related Experiment Video

Updated: Aug 3, 2025

A Swin Transformer-Based Model for Thyroid Nodule Detection in Ultrasound Images
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Consformer: Consciousness Detection Using Transformer Networks With Correntropy-Based Measures.

Xuyun Sun, Yu Qi, Xiulin Ma

    IEEE Transactions on Neural Systems and Rehabilitation Engineering : a Publication of the IEEE Engineering in Medicine and Biology Society
    |April 7, 2023
    PubMed
    Summary

    We developed Consformer, a novel transformer network using electroencephalography (EEG) signals, to detect consciousness in disorders of consciousness (DOC). This method accurately differentiates minimally conscious state (MCS) from vegetative state (VS).

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    Area of Science:

    • Neuroscience and Biomedical Engineering
    • Computational Neuroscience
    • Medical Signal Processing

    Background:

    • Accurate diagnosis and treatment of disorders of consciousness (DOC) are critical.
    • Electroencephalography (EEG) signals offer valuable insights into brain activity for consciousness evaluation.
    • Existing methods require improvement for reliable consciousness detection in DOC patients.

    Purpose of the Study:

    • To introduce novel EEG measures, spatiotemporal correntropy and neuromodulation intensity, for assessing brain signal complexity.
    • To develop an advanced deep learning model, Consformer, for enhanced consciousness detection in DOC.
    • To improve the discrimination between minimally conscious state (MCS) and vegetative state (VS) using EEG features.

    Main Methods:

    • Proposed two novel EEG measures: spatiotemporal correntropy and neuromodulation intensity.
    • Constructed a comprehensive pool of EEG measures including spectral, complexity, and connectivity features.
    • Developed Consformer, a transformer network utilizing an attention mechanism for adaptive feature optimization across subjects.

    Main Results:

    • Consformer achieved high performance in discriminating between MCS and VS using resting-state EEG data from 280 DOC patients.
    • The model demonstrated an accuracy of 85.73% and an F1-score of 86.95%.
    • These results represent a significant advancement, achieving state-of-the-art performance in consciousness detection.

    Conclusions:

    • The proposed novel EEG measures and the Consformer model show significant potential for accurate consciousness detection in DOC.
    • Consformer's adaptive feature learning capability enhances its effectiveness across diverse patient data.
    • This approach offers a promising tool for improving the diagnosis and management of disorders of consciousness.