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Related Concept Videos

Reducing Line Loss01:18

Reducing Line Loss

152
In a three-phase circuit, line loss is an indicator of energy dissipated as heat due to the resistance of transmission lines. To address this, incorporating transformers into the system—a step-up transformer at the source and a step-down transformer at the load—is a strategic solution. Two three-phase transformers are introduced to improve this.
With a step-up transformer at the source, the voltage is increased, thereby reducing the current in the transmission lines since power loss...
152
Equivalent Circuits for Practical Transformers01:28

Equivalent Circuits for Practical Transformers

422
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...
422
Types Of Transformers01:16

Types Of Transformers

976
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...
976
Energy Losses in Transformers01:21

Energy Losses in Transformers

873
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...
873
Transformers with Off-Nominal Turns Ratios01:25

Transformers with Off-Nominal Turns Ratios

152
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...
152
Transformers in Distribution System01:27

Transformers in Distribution System

102
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...
102

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Related Experiment Video

Updated: Jul 2, 2025

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CESA-MCFormer: An Efficient Transformer Network for Hyperspectral Image Classification by Eliminating Redundant

Shukai Liu1, Changqing Yin1, Huijuan Zhang1

  • 1School of Software, Tongji University, Shanghai 201800, China.

Sensors (Basel, Switzerland)
|February 24, 2024
PubMed
Summary

Hyperspectral image classification is improved by the novel CESA-MCFormer model. This transformer-based approach effectively extracts key spatial and spectral features, achieving high accuracy with minimal data.

Keywords:
hyperspectral image classificationmorphological convolutionspatial attentiontransformer

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Area of Science:

  • Remote Sensing
  • Computer Vision
  • Machine Learning

Background:

  • Hyperspectral image (HSI) classification faces challenges due to spectral and spatial redundancy.
  • Applications in agriculture and infrastructure detection require robust feature extraction.

Purpose of the Study:

  • To propose an innovative model, CESA-MCFormer, for effective HSI classification.
  • To address information redundancy in HSI data for improved classification accuracy.

Main Methods:

  • Developed the CESA-MCFormer model based on the transformer architecture.
  • Introduced the Center Enhanced Spatial Attention (CESA) module for prior spatial information.
  • Incorporated Morphological Convolution (MC) for feature extraction and merging.

Main Results:

  • CESA-MCFormer achieved high classification performance on IP, UP, and Chikusei datasets.
  • Kappa coefficients reached 96.38%, 98.24%, and 99.53% respectively.
  • The model demonstrated precise classification with minimal samples, without PCA.

Conclusions:

  • The CESA-MCFormer model offers a powerful solution for HSI classification.
  • The integration of CESA and MC modules enhances feature processing capabilities.
  • This method provides a significant advancement in HSI analysis for various applications.