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相关概念视频

Types Of Transformers01:16

Types Of Transformers

954
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...
954
Transformers in Distribution System01:27

Transformers in Distribution System

99
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...
99
The Ideal Transformer01:26

The Ideal Transformer

366
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...
366
Transformers with Off-Nominal Turns Ratios01:25

Transformers with Off-Nominal Turns Ratios

142
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...
142
Deconvolution01:20

Deconvolution

141
Deconvolution, also known as inverse filtering, is the process of extracting the impulse response from known input and output signals. This technique is vital in scenarios where the system's characteristics are unknown, and they must be inferred from the observable signals.
Deconvolution involves several mathematical techniques to derive the impulse response. One common approach is polynomial division. In this method, the input and output sequences are treated as coefficients of...
141
Cross Product01:25

Cross Product

235
The cross product is a fundamental concept in vector algebra that is a vector operation on two different vectors to obtain a third vector. Unlike the scalar product, the cross product results in a vector quantity perpendicular to both the original vectors.
The magnitude of the cross product is obtained by multiplying the magnitude of both the vectors and the sine of the angle between them. This means that a larger angle between the vectors will lead to a greater magnitude of the cross product.
235

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相关实验视频

Updated: Jun 14, 2025

A Swin Transformer-Based Model for Thyroid Nodule Detection in Ultrasound Images
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分离式交叉模态变压器用于引用视频对象分割.

Ao Wu1, Rong Wang1,2, Quange Tan1

  • 1School of Information and Cyber Security, People's Public Security University of China, Beijing 100038, China.

Sensors (Basel, Switzerland)
|August 29, 2024
PubMed
概括

本研究介绍了DCT,这是一种用于引用视频对象细分的新变压器模型. 通过在多个尺度上更好地整合语言和视觉数据,DCT提高了准确性.

关键词:
跨模态变压器跨模态变压器分离查询的查询.功能金字塔网络是一个特征金字塔网络.引用视频对象的细分是指视频对象的细分.

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Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique
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Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

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Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment
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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
  • 自然语言处理自然语言处理.

背景情况:

  • 引用视频对象分割 (R-VOS) 对于基于语言的视频内容的理解至关重要.
  • 现有的方法在平衡的跨模式特征融合和在多规模处理过程中信息丢失方面存在困难.
  • 挑战包括有效地将语言信息转移到视觉特征,并减轻注意力偏差.

研究的目的:

  • 提出DCT,一个端到端脱的交叉模式变压器,用于改进R-VOS.
  • 在R-VOS.中增强多模式和多规模信息的利用.
  • 解决特征融合和跨模式信息传输方面的局限性.

主要方法:

  • 开发了一个语言引导视觉增强模块 (LGVE) 用于语言信息集成.
  • 引入了解变压器解码器,并对象查询以进行独立的特征收集.
  • 实施了跨层特征金字塔网络 (CFPN),以保存多层次的视觉细节.

主要成果:

  • 在基准数据集 (A2D-Sentences,JHMDB-Sentences,Ref-Youtube-VOS) 上,DCT显示出具有竞争力的细分精度.
  • 拟议的模块有效地改善了多模式和多规模信息的利用.
  • 与现有的R-VOS方法相比,DCT实现了最先进的性能.

结论:

  • DCT为引用视频对象细分提供了一种新且有效的方法.
  • 解的交叉模式变压器架构成功地解决了以前的局限性.
  • 通过提高细分精度和功能集成,DCT在该领域取得了进展.