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相关概念视频

Transformers01:26

Transformers

1.1K
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...
1.1K
Source Transformation01:15

Source Transformation

6.5K
Source transformation is a fundamental technique employed in circuit analysis, offering a valuable tool for simplifying complex electrical circuits. This technique involves the replacement of either a voltage source in series with a resistor by a current source in parallel with a resistor, or vice versa. The key concept here is that when the original sources are deactivated (turned off), the equivalent resistance at the circuit's end terminals remains the same.
It is essential to note that when...
6.5K
The Ideal Transformer01:26

The Ideal Transformer

407
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...
407
Types Of Transformers01:16

Types Of Transformers

987
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...
987
Stereotype Content Model02:16

Stereotype Content Model

14.7K
The Stereotype Content Model (SCM) was first proposed by Susan Fiske and her colleagues (Fiske, Cuddy, Glick & Xu, 2002; see also Fiske, 2012 and Fiske, 2017). The SCM specifies that when someone encounters a new group, they will stereotype them based on two metrics: warmth—or that group’s perceived intent, and how likely they are to provide help or inflict harm—and competence—or their ability to carry out that objective. Depending on the warmth-competence...
14.7K
Transformers in Distribution System01:27

Transformers in Distribution System

107
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...
107

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A Swin Transformer-Based Model for Thyroid Nodule Detection in Ultrasound Images
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显示语义转换器用于场景文本识别.

Xinqi Yang1,2,3, Wushour Silamu1,2,3, Miaomiao Xu1,2,3

  • 1College of Computer Science and Technology, Xinjiang University, No. 777 Huarui Street, Urumqi 830017, China.

Sensors (Basel, Switzerland)
|October 14, 2023
PubMed
概括
此摘要是机器生成的。

本研究介绍了显示语义转换器 (DST),通过整合语言知识来改善场景文本识别. DST通过语义信息增强视觉特征,提高准确性和稳定性,特别是在噪音较大的图像中.

关键词:
跨模式的注意力.语言知识语言知识知识.场景文本识别 场景文本识别变压器的变压器是一个变压器.视觉信息是一种视觉信息.

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科学领域:

  • 计算机视觉 计算机视觉
  • 自然语言处理自然语言处理.
  • 人工智能的人工智能

背景情况:

  • 场景文本识别模型经常因缺乏语言理解而与杂的图像作斗争.
  • 现有的视觉模型只关注视觉纹理,忽视语义上下文.
  • 这种限制导致在具有挑战性的视觉条件下识别精度降低.

研究的目的:

  • 通过整合语言和视觉信息来增强场景文本识别.
  • 提高模型对抗图像噪声和扭曲的稳定性.
  • 开发一个更准确,更有效的场景文本识别系统.

主要方法:

  • 在Vision Transformer架构的基础上构建.
  • 介绍显示语义变压器 (DST) 模型.
  • 整合了一个面具语言模型和一个语义视觉交互模块.

主要成果:

  • DST模型显著提高了基准数据集的识别精度,平均精度高出近2%.
  • 语义视觉交互模块有效地通过深层次的语义信息来增强视觉特征.
  • 该模型在噪音图像中识别文本方面表现出更好的稳定性.

结论:

  • 显示语义转换器 (DST) 通过有效地结合视觉和语义信息,为场景文本识别提供了卓越的方法.
  • 通过小的参数数量,DST在准确性和推断速度之间实现了更好的平衡.
  • 这种方法提高了模型的稳定性和识别性能,特别是在具有挑战性的现实场景中.