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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...
1.0K
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 with Off-Nominal Turns Ratios01:25

Transformers with Off-Nominal Turns Ratios

184
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...
184
Per-Unit Sequence Models01:26

Per-Unit Sequence Models

105
An ideal Y-Y transformer, grounded through neutral impedances, displays per-unit sequence networks akin to those of a single-phase ideal transformer when subjected to balanced positive- or negative-sequence currents. These currents do not produce neutral currents, and their associated voltage drops.
Zero-sequence currents, which are identical in magnitude and phase, generate a neutral current, resulting in voltage drops across the neutral impedance and the low-voltage winding. If the...
105
Equivalent Circuits for Practical Transformers01:28

Equivalent Circuits for Practical Transformers

489
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...
489
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...
134

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Updated: Aug 4, 2025

A Swin Transformer-Based Model for Thyroid Nodule Detection in Ultrasound Images
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TransZero++: Cross Attribute-Guided Transformer for Zero-Shot Learning.

Shiming Chen, Ziming Hong, Wenjin Hou

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    Summary
    This summary is machine-generated.

    TransZero++ enhances zero-shot learning (ZSL) by using cross attribute-guided Transformers to improve visual feature understanding and attribute localization. This semantic-augmented approach achieves state-of-the-art results in recognizing novel classes.

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    Area of Science:

    • Computer Vision
    • Machine Learning
    • Artificial Intelligence

    Background:

    • Zero-shot learning (ZSL) aims to recognize novel classes by transferring knowledge from seen classes using semantic attributes.
    • Existing attention-based ZSL models often struggle with effective visual feature representation and attribute localization.
    • Unidirectional attention mechanisms limit the discriminative power of visual features for semantic knowledge transfer.

    Purpose of the Study:

    • To propose TransZero++, a novel cross attribute-guided Transformer network for ZSL.
    • To refine visual features and learn accurate attribute localization for key semantic knowledge representation in ZSL.
    • To improve the visual-semantic interaction for effective knowledge transfer in ZSL.

    Main Methods:

    • TransZero++ utilizes an attribute → visual Transformer (AVT) and a visual → attribute Transformer (VAT) sub-network.
    • These transformers learn attribute-based visual features and visual-based attribute features, respectively.
    • Semantical collaborative learning, including feature-level and prediction-level losses, enhances semantic augmentation.

    Main Results:

    • TransZero++ achieves new state-of-the-art results on three standard ZSL benchmarks.
    • The model demonstrates superior performance on the large-scale ImageNet dataset.
    • The proposed method effectively refines visual features and improves attribute localization.

    Conclusions:

    • TransZero++ significantly advances the field of zero-shot learning through improved visual-semantic interaction.
    • The cross attribute-guided Transformer approach offers a powerful framework for semantic knowledge representation in ZSL.
    • The model's success highlights the importance of bidirectional attribute-visual feature learning for ZSL.