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相关概念视频

Equivalent Circuits for Practical Transformers01:28

Equivalent Circuits for Practical Transformers

1.3K
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...
1.3K
Types Of Transformers01:16

Types Of Transformers

1.4K
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...
1.4K
Transformers in Distribution System01:27

Transformers in Distribution System

475
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...
475
Transformers01:26

Transformers

1.7K
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...
1.7K
Energy Losses in Transformers01:21

Energy Losses in Transformers

1.3K
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...
1.3K
Transformers with Off-Nominal Turns Ratios01:25

Transformers with Off-Nominal Turns Ratios

491
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...
491

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相关实验视频

Updated: Jan 8, 2026

A Swin Transformer-Based Model for Thyroid Nodule Detection in Ultrasound Images
04:23

A Swin Transformer-Based Model for Thyroid Nodule Detection in Ultrasound Images

Published on: April 21, 2023

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使用变压器混合残余网络进行电流密度向量图的分类.

Lihui Zhu1,2, Yunfeng Yang1, Wenyue Yu1

  • 1School of Physics, Zhejiang University of Technology, Hangzhou, Zhejiang, China.

PloS one
|December 16, 2025
PubMed
概括

本研究引入了一种深度学习模型,用于从磁心图 (MCG) 数据对心脏电流密度向量图 (CDVM) 进行分类. 这种新的方法实现了97.52%的准确性,克服了数据稀缺的挑战,以改善心脏评估.

相关实验视频

Last Updated: Jan 8, 2026

A Swin Transformer-Based Model for Thyroid Nodule Detection in Ultrasound Images
04:23

A Swin Transformer-Based Model for Thyroid Nodule Detection in Ultrasound Images

Published on: April 21, 2023

2.2K

科学领域:

  • 心脏病学 心脏病学
  • 生物医学工程 生物医学工程
  • 人工智能的人工智能

背景情况:

  • 磁心电图 (MCG) 衍生出的电流密度向量图 (CDVM) 对于心脏功能评估至关重要.
  • 有限的MCG数据可用性和分析复杂性阻碍了临床应用.
  • 计算机辅助诊断对于解释复杂的心脏数据越来越重要.

研究的目的:

  • 为准确的CDVM分类开发一个深度学习模型.
  • 通过先进的增强技术来解决MCG研究中的数据稀缺问题.
  • 使用CDVM提高心脏状况评估的精度和效率.

主要方法:

  • 使用噪声加值,ARIMA模型和插值来克服数据稀缺性的数据增强.
  • 实现一个变压器混合残余网络与转移学习.
  • 利用自我注意机制,从CDVM中提取增强的特征.

主要成果:

  • 在0至4类的CDVM中实现了97.52%的分类准确度.
  • 与现有的深度学习方法相比,表现出卓越的性能.
  • 展示了用于扩展CDVM数据集的高精度,效率和可扩展性.

结论:

  • 提出的深度学习方法有效地以高准确度对CDVM进行分类.
  • 该方法成功地解决了MCG分析中的数据限制.
  • 这种可扩展的解决方案为临床心脏诊断中的医生提供了一个有前途的工具.