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Uniform Depth Channel Flow: Problem Solving01:18

Uniform Depth Channel Flow: Problem Solving

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To calculate the flow rate for a trapezoidal channel, first, identify the bottom width, side slope, and flow depth of the channel. The cross-sectional area (A) corresponding to the depth of flow (y), channel bottom width (B), and side slope (θ) is determined by:Next, calculate the wetted perimeter, which includes the bottom width and the sloped side lengths in contact with the water. Using the values of the cross-sectional area and the wetted perimeter, determine the hydraulic radius by...
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Uniform Depth Channel Flow01:27

Uniform Depth Channel Flow

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Uniform depth channel flow keeps fluid depth consistent along channels such as irrigation canals. In natural channels, such as rivers, approximate uniform flow is often assumed. This condition occurs when the channel’s bottom slope matches the energy slope, balancing potential energy lost from gravity with head loss due to shear stress. This balance prevents depth changes along the channel length, resulting in a steady, uniform flow.Uniform flow in open channels with a constant cross-section...
144
Transformers in Distribution System01:27

Transformers in Distribution System

156
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...
156
Transformers with Off-Nominal Turns Ratios01:25

Transformers with Off-Nominal Turns Ratios

205
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...
205
Energy Losses in Transformers01:21

Energy Losses in Transformers

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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...
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Fast Decoupled and DC Powerflow01:24

Fast Decoupled and DC Powerflow

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The fast decoupled power flow method addresses contingencies in power system operations, such as generator outages or transmission line failures. This method provides quick power flow solutions, essential for real-time system adjustments. Fast decoupled power flow algorithms simplify the Jacobian matrix by neglecting certain elements, leading to two sets of decoupled equations:
283

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Video Experimental Relacionado

Updated: Sep 10, 2025

A Swin Transformer-Based Model for Thyroid Nodule Detection in Ultrasound Images
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UTN: red de estimación de flujo óptico sin supervisión basada en transformador

Xiaochen Liu1, Tao Zhang2, Mingming Liu3

  • 1State Key Laboratory of Extreme Environment Optoelectronic Dynamic Measurement Technology and Instrument, North University of China, Taiyuan 030051, China; School of Instrument and Electronics, North University of China, Tai Yuan 030051, China.

Neural networks : the official journal of the International Neural Network Society
|August 26, 2025
PubMed
Resumen
Este resumen es generado por máquina.

Este estudio introduce un nuevo marco para la estimación de flujo óptico sin supervisión utilizando transformadores y redes de pirámides de características. El método propuesto mejora significativamente la precisión del flujo al incorporar módulos avanzados y una pérdida de flujo óptico estático.

Palabras clave:
Red neuronal convolucionalEstimación del flujo ópticoEl transformadorAprendizaje sin supervisión

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Área de la Ciencia:

  • Visión por computadora
  • Aprendizaje profundo
  • Aprendizaje automático

Sus antecedentes:

  • La estimación precisa del flujo óptico es crucial para varias tareas de visión por computadora.
  • Los métodos no supervisados son deseables para reducir la dependencia de los datos etiquetados.

Objetivo del estudio:

  • Desarrollar un marco escalable para la estimación del flujo óptico sin supervisión.
  • Mejorar la precisión de la estimación del flujo en píxeles utilizando arquitecturas de aprendizaje profundo.

Principales métodos:

  • Un codificador de transformador-CNN captura las características de la imagen global y local.
  • Un decodificador de red de pirámide de características (FPN) integra módulos de correlación cruzada normalizada (NCCM) y estimación de flujo intermedio basada en la atención (AIFE).
  • Se introduce una pérdida de flujo óptico estático para mejorar el entrenamiento.

Principales resultados:

  • El marco logró ganancias sustanciales de rendimiento en conjuntos de datos de referencia (FlyingChairs, MPI-Sintel, KITTI).
  • Se observó una reducción significativa en el error de punto final (EPE) en MPI-Sintel en comparación con ARFlow (24,27% limpio, 28,01% final).
  • Los estudios de ablación confirmaron la eficacia de NCCM, AIFE y la pérdida de flujo óptico estático.

Conclusiones:

  • El marco transformador-FPN propuesto ofrece una solución escalable y eficaz para la estimación del flujo óptico sin supervisión.
  • Los nuevos módulos y la función de pérdida contribuyen al estado de la técnica en la precisión del flujo óptico.