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相关概念视频

Transformers with Off-Nominal Turns Ratios01:25

Transformers with Off-Nominal Turns Ratios

523
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...
523
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
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
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
Time-Series Graph00:54

Time-Series Graph

5.0K
A time-series graph is a line graph with repeated measurements taken at successive intervals of time. It is also called a time series chart. To construct a time-series graph, one must look at both pieces of a paired data set. The horizontal axis is used to plot the time increments, and the vertical axis is used to plot the values of the variable that one is measuring. By using the axes in this way, each point on the graph will correspond to time and a measured quantity. The points on the graph...
5.0K
Per-Unit Sequence Models01:26

Per-Unit Sequence Models

431
An ideal Y-Y transformer, grounded through neutral impedances, displays per-unit sequence networks akin to those of a single-phase ideal transformer when subjected to balanced positive- or negative-sequence currents. These currents do not produce neutral currents, and their associated voltage drops.
Zero-sequence currents, which are identical in magnitude and phase, generate a neutral current, resulting in voltage drops across the neutral impedance and the low-voltage winding. If the...
431

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相关实验视频

Updated: Jan 17, 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

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用变压器模型和RNNs进行时间序列预测的深度学习.

Rogerio Pereira Dos Santos1, João P Matos-Carvalho1,2,3, Valderi R Q Leithardt1,4

  • 1COPELABS, Universidade Lusófona de Humanidades e Technologias, Lisboa, Portugal.

PeerJ. Computer science
|September 24, 2025
PubMed
概括

变压器神经网络擅长长长期天气预报,优于循环神经网络 (RNN). 像Informer和iTransformer这样的模型对复杂的时间序列数据显示出卓越的准确性,提高了预测能力.

关键词:
预测的准确性 在预测的准确性.深度学习是一种深度学习.神经网络的神经网络的神经网络预测应用程序的应用程序.经常性神经网络 (RNNs) 是指经常性神经网络.变压器模型变压器模型

相关实验视频

Last Updated: Jan 17, 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

2.3K

科学领域:

  • 人工智能的人工智能
  • 气象学 天气学
  • 数据科学数据科学数据科学

背景情况:

  • 准确的天气预报至关重要.
  • 包括变压器和RNN在内的神经网络显示出时间序列模式识别的前景.

研究的目的:

  • 评估14个神经网络模型用于天气变量预报.
  • 为了比较变压器和RNN模型在不同预测时间的性能.

主要方法:

  • 在天气预报任务中应用了14个神经网络模型.
  • 使用以下指标评估模型:中位数AbsE,中位数AbsE,最大AbsE,RMSPE和RMSE.
  • 将变压器模型 (Informer,iTransformer,Former,PatchTST) 与RNN模型 (TCN,BiTCN) 进行比较.

主要成果:

  • 变压器模型在长期模式捕获方面表现出卓越的准确性,而Informer表现最好.
  • RNN模型更适合短期预测,但容易产生更高的错误.
  • 转换器实现了特定的错误指标:中位数AbsE 1.21,中位数AbsE 1.24,最大AbsE 2.86,RMSPE 0.66,RMSE 1.43.

结论:

  • 神经网络,特别是变压器,为改善天气预报的准确性提供了巨大的潜力.
  • 该研究为根据预测需求选择适合天气预测应用程序的模型提供了基础.