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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...
1.7K
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...
1.4K
Transformers in Distribution System01:27

Transformers in Distribution System

470
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...
470
Deconvolution01:20

Deconvolution

520
Deconvolution, also known as inverse filtering, is the process of extracting the impulse response from known input and output signals. This technique is vital in scenarios where the system's characteristics are unknown, and they must be inferred from the observable signals.
Deconvolution involves several mathematical techniques to derive the impulse response. One common approach is polynomial division. In this method, the input and output sequences are treated as coefficients of...
520
Transformers with Off-Nominal Turns Ratios01:25

Transformers with Off-Nominal Turns Ratios

490
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...
490
Three-Winding Transformers01:19

Three-Winding Transformers

648
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...
648

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Video Experimental Relacionado

Updated: Jan 8, 2026

A Swin Transformer-Based Model for Thyroid Nodule Detection in Ultrasound Images
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A Swin Transformer-Based Model for Thyroid Nodule Detection in Ultrasound Images

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Un método de explicabilidad de la visión para la generación de subtítulos de imágenes utilizando mapas de atención

Meena Kowshalya1, Suchitra2, Rajesh Kumar Dhanaraj3

  • 1Department of Computer Science and Engineering, Government College of Technology, Coimbatore, India.

MethodsX
|December 24, 2025
PubMed
Resumen
Este resumen es generado por máquina.

Este estudio presenta un marco de subtitulado de imágenes explicable que utiliza un codificador de red neuronal convolucional y un decodificador Transformer. Mejora la transparencia en la toma de decisiones de la IA para aplicaciones confiables.

Palabras clave:
red neuronal convolucionalIA explicablesubtitulado de imágenesmodelos Transformermapas de atención visual

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Last Updated: Jan 8, 2026

A Swin Transformer-Based Model for Thyroid Nodule Detection in Ultrasound Images
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A Swin Transformer-Based Model for Thyroid Nodule Detection in Ultrasound Images

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Área de la Ciencia:

  • Inteligencia Artificial
  • Visión por Computadora
  • Procesamiento del Lenguaje Natural

Sus antecedentes:

  • Los modelos de generación de subtítulos de imágenes a menudo funcionan como cajas negras, careciendo de transparencia en sus procesos de toma de decisiones.
  • Existe la necesidad de IA explicable (XAI) en modelos de visión y lenguaje para generar confianza y fiabilidad.

Objetivo del estudio:

  • Desarrollar un marco novedoso y explicable para la generación de subtítulos de imágenes que integre la explicabilidad visual.
  • Abordar la brecha de transparencia en los modelos actuales de visión y lenguaje.

Principales métodos:

  • Se utilizó un codificador de red neuronal convolucional (CNN) y una arquitectura de decodificador Transformer.
  • Se integraron mapas de calor basados en la atención para proporcionar explicaciones visuales de los subtítulos generados.
  • Se evaluó el rendimiento y la interpretabilidad en el conjunto de datos MS COCO utilizando métricas estándar (BLEU, METEOR, CIDER, SPICE).

Principales resultados:

  • El marco propuesto ofrece transparencia en el proceso de toma de decisiones de la generación de subtítulos de imágenes.
  • Los mapas de calor basados en la atención resaltan de manera efectiva las características visuales que influyen en la generación de subtítulos.
  • El método equilibra la calidad de la generación de subtítulos con una mayor interpretabilidad.

Conclusiones:

  • El marco desarrollado aumenta la confiabilidad y la transparencia en los sistemas de IA.
  • Este enfoque es adecuado para aplicaciones críticas en atención médica, educación, seguridad y pronósticos.
  • Contribuye al avance de la IA explicable al tender un puente entre el rendimiento y la interpretabilidad en los modelos de visión y lenguaje.