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相关概念视频

Transformers with Off-Nominal Turns Ratios01:25

Transformers with Off-Nominal Turns Ratios

213
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...
213
Types Of Transformers01:16

Types Of Transformers

1.1K
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...
1.1K
Transformers in Distribution System01:27

Transformers in Distribution System

165
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...
165
Difference from Background: Limit of Detection01:05

Difference from Background: Limit of Detection

7.1K
The limit of detection (LOD) is the smallest amount of analyte that can be distinguished from the background noise. The LOD value corresponds to the concentration at which the analyte signal is three times larger than the standard deviation of the blank signal. Below this value, the analyte signal cannot be differentiated from the background noise. It is calculated by dividing the calibration slope by 3 times the standard deviation of the blank signals.
The LOD indicates the presence or absence...
7.1K
Transformers01:26

Transformers

1.2K
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...
1.2K
Differential Relays01:20

Differential Relays

268
Differential relays are used to protect generators, buses, and transformers by comparing electrical quantities at different points. When a fault occurs, the difference in current between the two points triggers the relay to operate, opening the circuit breaker. Under normal conditions, the current entering (i1) and leaving (i2) a generator are equal. When a fault occurs, however, these currents become unequal, and the difference current flows in the relay operating coil, causing the relay to...
268

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Updated: Sep 18, 2025

A Swin Transformer-Based Model for Thyroid Nodule Detection in Ultrasound Images
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基于统计差异表示的变压器用于异质变化检测.

Xinhui Cao1,2, Minggang Dong3, Xingping Liu4

  • 1School of Artificial Intelligence, Guangzhou Huashang College, Guangzhou 511300, China.

Sensors (Basel, Switzerland)
|June 27, 2025
PubMed
概括
此摘要是机器生成的。

本研究引入了一种新的弱监督方法,用于检测异质变化,使用结构相似性生成可靠的标签. 该方法有效地检测来自不同传感器的图像的变化,克服数据限制.

关键词:
异质变化检测检测异质变化检测遥感图像来自远程传感.统计差异的统计差异结构上的相似性.变压器的变压器是一个变压器.

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Author Spotlight: Efficient Image Recognition Using Directional Gradient Histogram Technique and Support Vector Machines
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Author Spotlight: Efficient Image Recognition Using Directional Gradient Histogram Technique and Support Vector Machines

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相关实验视频

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Author Spotlight: Efficient Image Recognition Using Directional Gradient Histogram Technique and Support Vector Machines
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科学领域:

  • 遥感 遥感 遥感 遥感
  • 计算机视觉 计算机视觉
  • 人工智能的人工智能

背景情况:

  • 异质变化检测比较来自不同传感器的图像随着时间的推移.
  • 深度学习和域调整方法是当前的主流方法.
  • 缺乏可信的标签阻碍了现有方法的实际应用.

研究的目的:

  • 为异质变化检测开发一个监管较弱的框架.
  • 为了克服标签数据不足的局限性.
  • 提高异质图像中变化检测的准确性和稳定性.

主要方法:

  • 提出了一个结构相似性引导的样本生成 (S3G2) 策略,用于可靠的伪标签生成.
  • 引入了一个统计差异表示变压器 (SDFormer) 来减轻模式差异.
  • 雇员差异结构相似性,用于先前获取信息.

主要成果:

  • 拟议的S3G2战略反复生成可靠的伪标签.
  • SDFormer有效地减少了模态差异在比特时异质图像中的影响.
  • 实验结果表明,与最先进的方法相比,其性能具有竞争力.

结论:

  • 开发的监管薄弱的框架解决了在异质变化检测中有限的标记数据的挑战.
  • 提出的方法显示了对现实世界应用的巨大潜力,这些应用需要从各种图像来源检测变化.
  • 对参数影响的进一步调查证实了该方法的稳定性.