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相关概念视频

Types Of Transformers01:16

Types Of Transformers

983
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...
983
Equivalent Circuits for Practical Transformers01:28

Equivalent Circuits for Practical Transformers

437
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...
437
Transformers with Off-Nominal Turns Ratios01:25

Transformers with Off-Nominal Turns Ratios

160
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...
160
The Ideal Transformer01:26

The Ideal Transformer

399
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...
399
Transformers in Distribution System01:27

Transformers in Distribution System

103
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...
103
Energy Losses in Transformers01:21

Energy Losses in Transformers

880
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...
880

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Updated: Jul 10, 2025

A Swin Transformer-Based Model for Thyroid Nodule Detection in Ultrasound Images
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RST:用于点云学习的粗集变压器.

Xinwei Sun1, Kai Zeng1

  • 1Faculty of Information Engineering and Automation, Kunming University of Science and Technology, Kunming 650500, China.

Sensors (Basel, Switzerland)
|November 25, 2023
PubMed
概括
此摘要是机器生成的。

本研究介绍了一种新的Rough Set Transformer (RST) 网络,通过处理数据不确定性来改善点云学习. 该RST网络增强了3D传感任务,如分类和细分.

关键词:
3D传感器 3D传感器在点云学习中学习点云.一个粗略的设置.变压器的变压器是一个变压器.

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科学领域:

  • 计算机视觉 计算机视觉
  • 人工智能的人工智能
  • 机器学习 机器学习

背景情况:

  • 来自LiDAR的点云数据对于3D传感应用至关重要.
  • 变压器模型在点云任务中表现出色,但与数据不确定性作斗争.
  • 现有的方法受限于点产品注意力机制的精度.

研究的目的:

  • 为点云学习开发一种新的全球指导方法,容忍不确定性.
  • 为更可靠的点云处理引入一个粗略的基于集的注意力机制.
  • 介绍粗体集合变压器 (RST) 网络,将粗体集合理论与变压器集成在一起.

主要方法:

  • 使用邻近粗略集合理论重新定义颗粒和低近似运算符.
  • 专门为点云数据开发一个粗略的基于集的注意力机制.
  • 实施粗集变压器 (RST) 网络,利用代币集群进行概念近似.

主要成果:

  • 该RST网络在点云分类和细分任务中表现出卓越的性能.
  • 该方法有效地处理点云数据中的不确定性,提高了注意力机制的可靠性.
  • 实验结果验证了将粗略的集合理论和变压器网络用于点云学习的有效性.

结论:

  • 粗集变压器 (RST) 网络为点云学习挑战提供了强大的解决方案,特别是数据不确定性.
  • 这种粗略的集合理论和变压器的开创性融合为3D传感提供了一个新的范式.
  • 该方法基于近似的概念探索提高了点云分析的可靠性和性能.