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相关概念视频

Uniform Depth Channel Flow: Problem Solving01:18

Uniform Depth Channel Flow: Problem Solving

125
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...
125
Uniform Depth Channel Flow01:27

Uniform Depth Channel Flow

144
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

969
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...
969
Fast Decoupled and DC Powerflow01:24

Fast Decoupled and DC Powerflow

283
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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Updated: Sep 10, 2025

A Swin Transformer-Based Model for Thyroid Nodule Detection in Ultrasound Images
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基于变压器的无监督光学流量估计网络

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
概括
此摘要是机器生成的。

这项研究引入了使用变压器和特征金字塔网络进行无监督光流估计的新框架. 通过结合先进的模块和静态光学流量损失,拟议的方法显著提高了流量精度.

关键词:
卷积神经网络光学流量估计变压器没有监督的学习

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科学领域:

  • 计算机视觉
  • 深度学习
  • 机器学习

背景情况:

  • 精确的光流估计对于各种计算机视觉任务至关重要.
  • 无监督的方法是可取的,以减少对标记数据的依赖.

研究的目的:

  • 为无监督光学流量估计开发一个可扩展的框架.
  • 使用深度学习架构提高像素流量估计的精度.

主要方法:

  • 一个变压器-CNN编码器捕获全球和本地图像特征.
  • 一个特征金字塔网络 (FPN) 解码器集成了标准化交叉相关性 (NCCM) 和基于注意力的中间流量估计 (AIFE) 模块.
  • 为了改善训练,引入静态光流损失.

主要成果:

  • 该框架在基准数据集 (FlyingChairs,MPI-Sintel,KITTI) 上取得了显著的性能提升.
  • 与ARFlow相比,MPI-Sintel观察到的终点误差 (EPE) 显著减少 (24. 27%,最终28. 01%).
  • 废除研究证实了NCCM,AIFE和静态光流损失的有效性.

结论:

  • 拟议的变压器-FPN框架为无监督光流估计提供了可扩展和有效的解决方案.
  • 新型模块和损失函数有助于在光流精度方面实现最先进的性能.