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相关概念视频

Transformers with Off-Nominal Turns Ratios01:25

Transformers with Off-Nominal Turns Ratios

129
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...
129
The Ideal Transformer01:26

The Ideal Transformer

340
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...
340
Types Of Transformers01:16

Types Of Transformers

943
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...
943
Equivalent Circuits for Practical Transformers01:28

Equivalent Circuits for Practical Transformers

376
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...
376
Energy Losses in Transformers01:21

Energy Losses in Transformers

818
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...
818
Instrument Transformers01:23

Instrument Transformers

64
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...
64

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A Swin Transformer-Based Model for Thyroid Nodule Detection in Ultrasound Images
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选择更好的学习:识别多式联机循环变压器的高质量培训数据.

Jingwei Zhang1, Zhaoyi Liu2, Christos Chatzichristos1

  • 1STADIUS Center for Dynamical Systems, Signal Processing, and Data Analytics, Department of Electrical Engineering, KU Leuven, Leuven, Belgium.

Journal of neural engineering
|March 10, 2025
PubMed
概括

这项研究引入了一种新的方法来选择高质量的数据用于训练发作检测模型,提高其性能11%. 这提高了监测的多模式系统的可靠性,并降低了中突然意外死亡等风险.

关键词:
有信心的学习学习.有噪音的标签变压器的变压器是一个变压器.

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科学领域:

  • 的研究研究.
  • 生物医学信号处理
  • 机器学习在医疗保健中的应用

背景情况:

  • 性-克隆性发作 (TCS) 构成中突然意外死亡 (SUDEP) 的风险.
  • 对TCS的准确可靠的长期监测对于患者管理至关重要.
  • 多模式发作检测系统看起来很有前途,但依赖于高质量的训练数据.

研究的目的:

  • 开发一种创新的数据选择方法,用于识别用于发作检测模型的高质量培训样本.
  • 增强多式联机检测系统的培训管道.

主要方法:

  • 提出了一种新的数据选择方法,根据学习困难评估样本质量.
  • 将较低学习困难的样本分类为更高质量的样本.
  • 引入了基于信任的方法来量化数据集中的高质量样本.

主要成果:

  • 拟议的数据选择方法提高了先进的TCS检测模型的性能11%.
  • 证明了多式联络式发作检测模型的增强培训过程.

结论:

  • 开发的数据选择方法有效地增强了多式联机检测模型的培训.
  • 这种方法有助于更可靠,更准确的长期监测性-克隆性发作.
  • 改进数据选择是推进人工智能驱动管理工具的关键.