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相关概念视频

The Ideal Transformer01:26

The Ideal Transformer

423
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...
423
Three-Winding Transformers01:19

Three-Winding Transformers

256
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...
256
Instrument Transformers01:23

Instrument Transformers

106
Instrument transformers, comprising voltage transformers (VTs) and current transformers (CTs), play crucial roles in power substations by providing isolated replicas of current or voltage for measurement and protection purposes. Voltage transformers reduce the primary voltage to levels suitable for relay operation and measurement, while current transformers scale down the primary current. The primary winding of a current transformer often consists of a single turn, achieved by threading the...
106
Energy Losses in Transformers01:21

Energy Losses in Transformers

901
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...
901
Transformers in Distribution System01:27

Transformers in Distribution System

123
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...
123
Types Of Transformers01:16

Types Of Transformers

1.0K
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...
1.0K

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How to Use the H1 Deep Transcranial Magnetic Stimulation Coil for Conditions Other than Depression
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深度引导变压器排气网络的深导变压器

Shengdong Zhang1,2, Liping Zhao2, Keli Hu2

  • 1Key Laboratory of Intelligent Informatics for Safety and Emergency of Zhejiang Province, Wenzhou University, Education Park Zone, Wenzhou City, 325035, Zhejiang Province, People's Republic of China.

Scientific reports
|September 15, 2023
PubMed
概括
此摘要是机器生成的。

本研究介绍了一种深度引导变压器脱气网络,以改善单图像的脱气. 这种新型模型结合了变压器和引导过器,以克服卷积限制,实现更好的雾清除结果.

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Method for Simultaneous fMRI/EEG Data Collection during a Focused Attention Suggestion for Differential Thermal Sensation
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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
  • 图像处理 图像处理

背景情况:

  • 深度学习模型已经进行了先进的单一图像消毒.
  • 在dehazing中,卷积神经网络 (CNN) 受到局部受体场的限制.
  • 捕捉远程依赖对于准确的雾密度估计至关重要.

研究的目的:

  • 开发一种新的深度学习模型,用于单个图像dehazing.
  • 解决现有的基于CNN的方法中局部特征提取的局限性.
  • 为了提高图像去的准确性和速度.

主要方法:

  • 设计了一个新的深度引导变压器脱气网络 (DGTRAN).
  • 一个基于变压器的子网络被用来捕捉全球雾信息的远程依赖.
  • 用CNN子网络进行本地细节恢复,并集成引导过器以加快处理.

主要成果:

  • 拟议的DGTRAN模型在自然和模拟模糊图像上都表现出卓越的性能.
  • 实验结果显示,与最先进的除烟方法相比,这种方法得到了持续的改进.
  • 集成引导过器有效地提高了除气速度,而不会影响质量.

结论:

  • 该DGTRAN模型成功地克服了传统的卷积方法在形象破坏中的局部局限性.
  • 结合全球环境的变压器和当地细节的CNN,为有效和高效的脱气提供了强大的解决方案.
  • 拟议的方法在单一图像消毒研究方面取得了重大进展.