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相关概念视频

Transformers with Off-Nominal Turns Ratios01:25

Transformers with Off-Nominal Turns Ratios

154
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...
154
Equivalent Circuits for Practical Transformers01:28

Equivalent Circuits for Practical Transformers

429
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...
429
Three-Winding Transformers01:19

Three-Winding Transformers

227
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...
227
Energy Losses in Transformers01:21

Energy Losses in Transformers

875
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...
875
Types Of Transformers01:16

Types Of Transformers

976
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...
976
Transformers01:26

Transformers

1.1K
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...
1.1K

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基于变压器的模型是否比基于CNN的模型更强大?

Zhendong Liu1, Shuwei Qian1, Changhong Xia1

  • 1Nanjing University, 163 Xianlin Avenue, Qixia District, Nanjing, Jiangsu 210023, China.

Neural networks : the official journal of the International Neural Network Society
|January 24, 2024
PubMed
概括
此摘要是机器生成的。

在现实世界的人工智能应用中,变压器模型与CNN相比显示出更高的稳定性. 本研究引入了新的指标和方法,以提高对数据腐败的深度学习模型的稳定性.

关键词:
数据增强数据增强深度学习是一种深度学习.图像的分类图像的分类.模型的稳固性 模型的稳固性

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科学领域:

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

背景情况:

  • 越来越多的人工智能模型的部署需要在开放环境中提供强大的性能.
  • 评估深度学习模型,特别是变压器和CNN的稳定性至关重要.
  • 现有的稳定性指标可能无法完全捕捉现实世界的性能.

研究的目的:

  • 为了比较基于变压器和基于CNN的深度学习模型的稳定性.
  • 从结构和过程的角度确定稳健性的来源.
  • 开发更好的评估指标和增强方法,以提高人工智能模型的稳定性.

主要方法:

  • 在强度指标上对变压器和CNN模型进行比较分析.
  • 通过富里埃变换和游戏相互作用分析对强度的研究.
  • 开发一个校准的评估指标和基于模糊的增强方法.

主要成果:

  • 变压器模型通常在各种指标上表现出比CNN模型更好的稳定性.
  • 分析揭示了对变压器强度的潜在机制的见解.
  • 拟议的校准度量和基于模糊的方法取得了最先进的结果.

结论:

  • 变压器模型为人工智能部署提供了增强的稳定性.
  • 新的评估和增强策略可以显著提高模型的弹性.
  • 在基准数据集 (CIFAR-10-C,CIFAR-100-C,TinyImageNet-C) 上实现了最先进的性能.