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

Transformers with Off-Nominal Turns Ratios

519
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...
519
The Ideal Transformer01:26

The Ideal Transformer

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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
Transformers in Distribution System01:27

Transformers in Distribution System

497
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...
497
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...
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Related Experiment Video

Updated: Jan 16, 2026

Dynamic Inter-subject Functional Connectivity Reveals Moment-to-Moment Brain Network Configurations Driven by Continuous or Communication Paradigms
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Dynamic Inter-subject Functional Connectivity Reveals Moment-to-Moment Brain Network Configurations Driven by Continuous or Communication Paradigms

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MASKED MOMENTUM CONTRASTIVE DYNAMIC TRANSFORMER FOR SELF-SUPERVISED FUNCTIONAL CONNECTIVITY REPRESENTATION LEARNING.

Jiale Cheng1,2, Dan Hu1, Zhengwang Wu1

  • 1Department of Radiology and Biomedical Research Imaging Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.

Proceedings. IEEE International Symposium on Biomedical Imaging
|October 6, 2025
PubMed
Summary

This study introduces a new deep learning method using functional MRI (fMRI) data to improve predictions of behavior and demographics. The Masked Momentum Contrastive Dynamic Transformer enhances accuracy by analyzing subject-specific functional connectivity patterns.

Keywords:
Functional ConnectivitySelf-Supervised LearningTransformer

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Modeling the Functional Network for Spatial Navigation in the Human Brain
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Modeling the Functional Network for Spatial Navigation in the Human Brain
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Area of Science:

  • Neuroimaging
  • Machine Learning
  • Computational Neuroscience

Background:

  • Functional connectivity (FC) from functional MRI (fMRI) shows potential for predicting behavior and demographics with deep learning.
  • Vertex-wise FC maps offer detailed neural activity insights but face challenges due to fMRI data limitations and noise.
  • High-dimensional cortical vertex data requires advanced methods for reliable pattern identification.

Purpose of the Study:

  • To develop a novel deep learning framework, the Masked Momentum Contrastive Dynamic Transformer, for enhanced prediction accuracy using fMRI data.
  • To leverage subject-specific features and temporal dynamics of functional connectivity for improved predictive modeling.
  • To address the challenges of limited and noisy fMRI data in identifying neural patterns.

Main Methods:

  • Utilized masked momentum contrastive pre-training to learn subject-specific representations from vertex-wise FC maps.
  • Treated vertex-wise FCs from different runs as distinct views to maximize affinity and learn robust representations.
  • Employed a vertex-wise masking strategy to enhance learning from limited fMRI data.
  • Incorporated a dynamic transformer to leverage the temporal dynamics of functional connectivity.

Main Results:

  • The proposed Masked Momentum Contrastive Dynamic Transformer demonstrated superior performance in gender classification and cognition prediction tasks.
  • The framework effectively learned subject-specific representations by maximizing the affinity of different views of vertex-wise FCs.
  • The vertex-wise masking strategy proved beneficial for learning from data-limited scenarios.
  • Experiments were validated on the Human Connectome Project dataset, confirming the model's effectiveness.

Conclusions:

  • The novel Masked Momentum Contrastive Dynamic Transformer significantly enhances prediction accuracy for behavioral and demographic traits using fMRI data.
  • The framework's ability to capture subject-specific features and temporal dynamics of functional connectivity offers a promising approach for neuroimaging analysis.
  • This method effectively addresses the inherent limitations and noise in fMRI data, paving the way for more reliable predictions.