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相关概念视频

Energy Losses in Transformers01:21

Energy Losses in Transformers

981
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...
981
Reducing Line Loss01:18

Reducing Line Loss

196
In a three-phase circuit, line loss is an indicator of energy dissipated as heat due to the resistance of transmission lines. To address this, incorporating transformers into the system—a step-up transformer at the source and a step-down transformer at the load—is a strategic solution. Two three-phase transformers are introduced to improve this.
With a step-up transformer at the source, the voltage is increased, thereby reducing the current in the transmission lines since power loss...
196
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

162
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...
162
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
Three-Winding Transformers01:19

Three-Winding Transformers

314
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...
314

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相关实验视频

Updated: Sep 16, 2025

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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一个轻量级的变压器边缘智能模型用于RUL预测分类.

Lilu Wang1, Yongqi Li1, Haiyuan Liu1

  • 1College of Computer Science and Technology, Beihua University, Jilin City 132013, China.

Sensors (Basel, Switzerland)
|July 12, 2025
PubMed
概括

我们开发了TBiGNet,这是一个轻量级的变压器模型,用于预测剩余使用寿命 (RUL). 它在显著降低计算负载的情况下实现了更高的准确性,使其能够在边缘设备上部署.

科学领域:

  • 工程 工程师 工程师 工程师
  • 计算机科学 计算机科学
  • 人工智能的人工智能

背景情况:

  • 其余使用寿命 (RUL) 预测对于预测性维护至关重要.
  • 当前的模型在资源有限的设备上难以获得准确性和效率.
  • 复杂的降解特征提取对现有方法构成挑战.

研究的目的:

  • 提出TBiGNet,一个轻量级的基于变压器的网络用于RUL预测.
  • 克服现有模型在平衡精度和计算成本方面的局限性.
  • 为了在边缘设备上实现有效的RUL预测.

主要方法:

  • 开发了一个编码器-解码器变压器架构 (TBiGNet).
  • 为减少记忆访问优化了注意力机制 (60%的减少).
  • 在解码器中集成了自适应功能修剪模块和BiGRU,以增强功能捕获.

主要成果:

  • 与传统的变压器相比,TBiGNet的精度超过15%.
  • 减少了超过98%的计算负载,内存访问和参数大小.
  • 在准确度方面表现出显著的改进 (6%从修剪,7%从解码器).
关键词:
这是一个巨大的BIGRU.轻量级的轻量级的轻量级的轻量级的剩余使用寿命 (RUL) 预测变压器变压器变压器变压器

相关实验视频

Last Updated: Sep 16, 2025

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

2.0K

结论:

  • 在准确性,模型大小和内存访问方面,TBiGNet提供了卓越的性能.
  • 该模型显示了重要的技术优势和边缘设备部署的潜力.
  • TBiGNet 推进了用于预测性维护应用的 RUL 预测.