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Transformers with Off-Nominal Turns Ratios01:25

Transformers with Off-Nominal Turns Ratios

139
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...
139
Types Of Transformers01:16

Types Of Transformers

948
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...
948
Energy Losses in Transformers01:21

Energy Losses in Transformers

834
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...
834
The Ideal Transformer01:26

The Ideal Transformer

356
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...
356
Transformers in Distribution System01:27

Transformers in Distribution System

98
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...
98
Three-Winding Transformers01:19

Three-Winding Transformers

200
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...
200

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Related Experiment Video

Updated: Jun 7, 2025

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique
04:48

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Published on: July 5, 2024

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ETFT: Equiangular Tight Frame Transformer for Imbalanced Semantic Segmentation.

Seonggyun Jeong1, Yong Seok Heo1,2

  • 1Department of Artificial Intelligence, Ajou University, Suwon 16499, Republic of Korea.

Sensors (Basel, Switzerland)
|November 9, 2024
PubMed
Summary

This study introduces the Equiangular Tight Frame Transformer (ETFT) to address class imbalance in semantic segmentation. The novel model dynamically generates classifiers, improving performance on imbalanced datasets.

Keywords:
class imbalanceneural collapsesemantic segmentationtransformer

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Area of Science:

  • Computer Vision
  • Machine Learning
  • Deep Learning

Background:

  • Semantic segmentation faces challenges with class imbalance, where label distribution is uneven.
  • Existing methods use neural collapse and Equiangular Tight Frames (ETF) to improve minor class discrimination, but struggle with class correlation.
  • Current approaches result in fixed classifiers, limiting adaptability to diverse input images.

Purpose of the Study:

  • To propose a novel transformer-based model, the Equiangular Tight Frame Transformer (ETFT), for imbalanced semantic segmentation.
  • To dynamically generate classifiers as a function of input, balancing class discrimination and correlation.
  • To enhance the adaptability and performance of semantic segmentation models on datasets with uneven class distributions.

Main Methods:

  • The ETFT model jointly processes features and classifiers using an ETF structure within a transformer architecture.
  • Input patch tokens and the ETF-initialized classifier are processed together during the attention mechanism.
  • The classifier is dynamically adjusted based on input correlations and combined with a fixed ETF classifier.

Main Results:

  • The ETFT model achieves improved discriminability between classes while preserving contextual correlation.
  • The dynamically generated classifier adapts to input, enhancing performance.
  • Experiments show the proposed method outperforms state-of-the-art approaches on ADE20K and Cityscapes datasets for imbalanced semantic segmentation.

Conclusions:

  • The ETFT model effectively addresses the challenge of class imbalance in semantic segmentation.
  • Dynamic classifier generation offers a significant advantage over fixed classifiers for varying input data.
  • The proposed approach demonstrates superior performance and robustness in imbalanced semantic segmentation tasks.