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相关概念视频

Types Of Transformers01:16

Types Of Transformers

971
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...
971
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
Energy Losses in Transformers01:21

Energy Losses in Transformers

866
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...
866
Equivalent Circuits for Practical Transformers01:28

Equivalent Circuits for Practical Transformers

419
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...
419
The Ideal Transformer01:26

The Ideal Transformer

381
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...
381
Convolution Properties II01:17

Convolution Properties II

197
The important convolution properties include width, area, differentiation, and integration properties.
The width property indicates that if the durations of input signals are T1 and T2, then the width of the output response equals the sum of both durations, irrespective of the shapes of the two functions. For instance, convolving two rectangular pulses with durations of 2 seconds and 1 second results in a function with a width of 3 seconds.
The area property asserts that the area under the...
197

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相关实验视频

Updated: Jun 28, 2025

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

Published on: April 21, 2023

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变压器与差异卷积网络用于轻量级的通用边界检测.

Mingchun Li1, Yang Liu1, Dali Chen2

  • 1College of Information Engineering, Shenyang University, Shenyang, China.

PloS one
|April 16, 2024
PubMed
概括
此摘要是机器生成的。

一个新的轻量级深度学习模型,差异卷积网络 (TDCN) 的变压器,通过更少的参数在数据集中实现了通用边界检测. 这种方法可以降低计算成本,同时保持高性能,而不需要重新培训.

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科学领域:

  • 计算机视觉 计算机视觉
  • 人工智能的人工智能
  • 深度学习 (Deep Learning) 是一种深度学习.

背景情况:

  • 深度学习在边界检测方面表现出色,但需要大型模型和数据集,导致高计算成本.
  • 现有的方法往往缺乏跨不同数据集的通用性,需要对数据集进行特定的再培训.
  • 需要使用更少参数的高效,通用边界检测模型.

研究的目的:

  • 开发一种使用混合卷积变压器架构的轻量级,通用边界检测方法.
  • 为了研究一个单一模型的能力,在没有重新训练的情况下进行跨数据集边界检测.
  • 在基于深度学习的边界检测中减少计算功耗.

主要方法:

  • 介绍了变压器与差异卷积网络 (TDCN),集成卷积和变压器组件.
  • 采用带有边缘运算符的卷积网络进行多尺度差异特征提取.
  • 在变压器内开发了一个边界意识的自我注意力机制,以及一个包含边界方向的注意力丧失功能.

主要成果:

  • 在多个公共数据集上,TDCN在模型参数显著减少的情况下实现了竞争性表现.
  • 证明了通用预测能力,在未见的数据集上有效执行,无需重新训练.
  • 验证了模型在实现高性能,低参数边界检测方面的有效性.

结论:

  • 拟议的TDCN为通用边界检测提供了一个高效和有效的解决方案.
  • 混合架构和边界意识的注意力机制有助于改进跨数据集的概括.
  • 这项研究通过使计算成本低廉,可适应的边界检测模型能够推进低水平视觉任务.