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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
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
The Ideal Transformer01:26

The Ideal Transformer

915
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...
915
Transformers with Off-Nominal Turns Ratios01:25

Transformers with Off-Nominal Turns Ratios

213
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...
213
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
Energy Losses in Transformers01:21

Energy Losses in Transformers

985
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...
985

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Related Experiment Video

Updated: Sep 17, 2025

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

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Published on: July 5, 2024

529

CTFS: A consolidated transformer framework for instance and semantic segmentation tasks.

Kun Dai1, Fuyuan Qiu2, Hongbo Gao2

  • 1State Key Laboratory of Robotics and System, Harbin Institute of Technology, Harbin, 150006, China; Yangtze River Delta HIT Robot Technology Research Institute, Wuhu, 241000, China.

Neural Networks : the Official Journal of the International Neural Network Society
|June 27, 2025
PubMed
Summary
This summary is machine-generated.

This study introduces a consolidated Transformer framework (CTFS) for simultaneous instance and semantic segmentation. CTFS employs novel strategies to optimize shared parameters and resolve gradient conflicts, enhancing computer vision model performance.

Keywords:
Gradient conflictsInstance segmentationMulti-task learningSemantic segmentation

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Area of Science:

  • Computer Vision
  • Deep Learning
  • Machine Learning

Background:

  • Instance and semantic segmentation are key computer vision tasks.
  • Existing unified transformer frameworks struggle with concurrent multi-task optimization and gradient conflicts.

Purpose of the Study:

  • To develop a consolidated Transformer framework (CTFS) for efficient simultaneous instance and semantic segmentation.
  • To address challenges in optimizing shared parameters and mitigating gradient conflicts in multi-task learning.

Main Methods:

  • Introduced an affinity-guided sharing strategy (AGSS) for staged learning of task-shared parameter proportions and distributions.
  • Proposed a fine-grained gradient rectification strategy (FGRS) to resolve element-wise gradient conflicts during backpropagation.
  • Built the CTFS framework upon the standard Swin Transformer architecture.

Main Results:

  • CTFS achieved impressive performance on instance segmentation (COCO dataset) and semantic segmentation (ADE20K dataset).
  • The AGSS strategy reduced the difficulty of network optimization by using shared parameter proportions as prior knowledge.
  • The FGRS strategy effectively mitigated gradient conflicts at the element level.

Conclusions:

  • The consolidated Transformer framework (CTFS) offers an effective solution for simultaneous instance and semantic segmentation.
  • The proposed AGSS and FGRS strategies significantly improve network optimization and gradient conflict resolution.
  • CTFS demonstrates strong performance without complicating the underlying Swin Transformer architecture.