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相关概念视频

Transformers with Off-Nominal Turns Ratios01:25

Transformers with Off-Nominal Turns Ratios

182
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...
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Vision01:24

Vision

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Vision is the result of light being detected and transduced into neural signals by the retina of the eye. This information is then further analyzed and interpreted by the brain. First, light enters the front of the eye and is focused by the cornea and lens onto the retina—a thin sheet of neural tissue lining the back of the eye. Because of refraction through the convex lens of the eye, images are projected onto the retina upside-down and reversed.
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Transformers01:26

Transformers

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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...
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Transformers in Distribution System01:27

Transformers in Distribution System

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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...
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Transformation01:26

Transformation

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Microbial communities are dynamic environments where cell lysis releases free DNA into the surroundings. Other cells can take up this extracellular DNA through a process known as transformation.When a cell incorporates this foreign DNA into its genome, resulting in genetic modification, the process is known as transformation. Cells capable of this process are termed competent. Competence can be natural, as observed in certain bacteria and archaea, or artificially induced in the...
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Types Of Transformers01:16

Types Of Transformers

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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...
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相关实验视频

Updated: Jul 24, 2025

Capturing Dynamic Finger Gesturing with High-resolution Surface Electromyography and Computer Vision
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HGR-ViT:用视觉变压器识别手势

Chun Keat Tan1, Kian Ming Lim1, Roy Kwang Yang Chang1

  • 1Faculty of Information Science and Technology, Multimedia University, Jalan Ayer Keroh Lama, Melaka 75450, Malaysia.

Sensors (Basel, Switzerland)
|July 8, 2023
PubMed
概括
此摘要是机器生成的。

本研究介绍了HGR-ViT,这是用于高级手势识别 (HGR) 的视觉转换器模型. 这种新方法显著提高了在各种数据集中识别手势的准确性,增强了人机交互.

关键词:
这里是ViT ViT ViT关注注意力注意力注意力注意力手的手势识别手势识别标志性语言识别 标志性语言识别视觉变压器 视觉变压器

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科学领域:

  • 计算机视觉 计算机视觉
  • 机器学习 机器学习
  • 人与计算机的交互

背景情况:

  • 手势识别 (HGR) 对沟通和人机交互至关重要.
  • 现有的深度学习模型往往忽略了关键的手指方向和位置信息.
  • 需要强大的HGR模型来捕捉空间细节.

研究的目的:

  • 提出HGR-ViT,一个新的视觉转换器 (ViT) 模型,用于准确的手势识别.
  • 为了解决先前的HGR方法中编码手的位置和方向的局限性.
  • 通过基于注意力的机制来提高HGR系统的性能.

主要方法:

  • 拟议的HGR-ViT模型使用视觉变压器架构.
  • 手势图像被分为固定大小的补丁,并添加了位置嵌入.
  • 一个标准的变压器编码器处理这些嵌入,然后由一个多层感知子进行分类.

主要成果:

  • 在ASL数据集上,HGR-ViT实现了非常高的准确性:99.98%.
  • 该模型在数字 (99.36%) 和NUS数据集 (99.85%) 的ASL上显示出高性能.
  • 包括位置嵌入式有效地捕获空间手信息.

结论:

  • HGR-ViT显著提高了手势识别的准确性.
  • 模型编码位置信息的能力是其卓越性能的关键.
  • 这种方法为改善人机交互和手语识别提供了一个有希望的方向.