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Related Concept Videos

Types Of Transformers01:16

Types Of Transformers

1.1K
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...
1.1K
The Ideal Transformer01:26

The Ideal Transformer

913
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...
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Transformers in Distribution System01:27

Transformers in Distribution System

165
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...
165
Transformers01:26

Transformers

1.2K
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...
1.2K
Three-Winding Transformers01:19

Three-Winding Transformers

315
Three identical single-phase transformers can be configured to form a three-phase transformer connection, which involves high-voltage and low-voltage windings. The high-voltage windings are denoted by capital letters A-B-C, while the low-voltage windings are labeled with lowercase letters a-b-c, representing their respective phases. This notation helps distinguish between the high and low voltage sides of the transformer.
In the per-unit equivalent circuit of a grounded Y-Y three-phase...
315
Energy Losses in Transformers01:21

Energy Losses in Transformers

982
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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Updated: Sep 17, 2025

Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention
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SEEG Emotion Recognition Based on Transformer Network With Channel Selection and Explainability.

Zhuobin Yang, Xiaopeng Si, Weipeng Jin

    IEEE Journal of Biomedical and Health Informatics
    |July 3, 2025
    PubMed
    Summary
    This summary is machine-generated.

    A novel Spatial Transformer-based Hybrid Network (STHN) effectively recognizes emotions using stereo-electroencephalography (SEEG) data. This brain-computer interface (BCI) technology shows promise for treating emotional disorders.

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    Area of Science:

    • Neuroscience
    • Biomedical Engineering
    • Artificial Intelligence

    Background:

    • Brain-computer interface (BCI) technology is crucial for treating refractory emotional disorders.
    • Stereo-electroencephalography (SEEG) offers precise neural activity recording from deep brain structures and the cortex.
    • SEEG has significant potential for developing emotion recognition BCIs.

    Purpose of the Study:

    • To develop and evaluate a novel algorithm for emotion recognition using SEEG data.
    • To construct an emotion dataset from SEEG recordings of nine subjects.
    • To assess the performance of the proposed Spatial Transformer-based Hybrid Network (STHN) against existing methods.

    Main Methods:

    • Collected SEEG data from nine subjects to create an emotion dataset.
    • Developed a Spatial Transformer-based Hybrid Network (STHN) for SEEG emotion recognition.
    • Evaluated STHN's performance against baseline methods like EEGNet, TSception, and deep convolution neural networks.

    Main Results:

    • STHN achieved a triple-classification accuracy of 83.56%, outperforming baseline methods.
    • STHN demonstrated channel weighting and selection capabilities, identifying key brain regions for emotion recognition.
    • High-weight channels were predominantly in emotion-associated areas like the frontal lobe, temporal lobe, and hippocampus, indicating model explainability.

    Conclusions:

    • The developed SEEG emotion recognition algorithm using STHN is effective and offers a degree of explainability.
    • This novel approach holds significant potential for monitoring and treating patients with refractory emotional disorders.
    • This study represents the first development of an SEEG-based emotion recognition algorithm.