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相关概念视频

Transformers in Distribution System01:27

Transformers in Distribution System

167
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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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...
1.1K
Vision01:24

Vision

55.4K
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.
55.4K
Depth Perception and Spatial Vision01:15

Depth Perception and Spatial Vision

972
Depth perception is the ability to perceive objects three-dimensionally. It relies on two types of cues: binocular and monocular. Binocular cues depend on the combination of images from both eyes and how the eyes work together. Since the eyes are in slightly different positions, each eye captures a slightly different image. This disparity between images, known as binocular disparity, helps the brain interpret depth. When the brain compares these images, it determines the distance to an object.
972
Transformers01:26

Transformers

1.2K
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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Source Transformation01:15

Source Transformation

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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...
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相关实验视频

Updated: Sep 18, 2025

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

Published on: July 5, 2024

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MAPE-ViT:多式模式场景理解与新浪波段增强视觉转换器

Muhammad Waqas Ahmed1, Touseef Sadiq2, Hameedur Rahman1

  • 1Department of Computer Science, Air University, Islamabad, Pakistan.

PeerJ. Computer science
|June 26, 2025
PubMed
概括

本研究介绍了多模拟自适应补丁嵌入与视觉变压器 (MAPE-ViT) 进行强大的RGB-D场景分类. 它克服了传感器噪声和边界问题,在具有挑战性的条件下显著提高了准确性.

关键词:
深度学习是一种深度学习.多式联络是多式联络.模式识别可以识别模式.场景的分类 场景的分类视觉变压器 视觉变压器

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A Swin Transformer-Based Model for Thyroid Nodule Detection in Ultrasound Images
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A Swin Transformer-Based Model for Thyroid Nodule Detection in Ultrasound Images

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相关实验视频

Last Updated: Sep 18, 2025

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A Swin Transformer-Based Model for Thyroid Nodule Detection in Ultrasound Images
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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
  • 图像处理 图像处理

背景情况:

  • RGB-D场景分类面临诸如传感器错位和深度噪声等挑战.
  • 保持对象边界对于准确的场景理解至关重要.
  • 现有的方法与传感器工件和噪音作斗争.

研究的目的:

  • 引入一种新的RGB-D场景分类方法.
  • 为了应对传感器错位,深度噪声和物体边界保护的挑战.
  • 为了提高特征歧视和分类准确性.

主要方法:

  • 集成最大稳定的极端区域 (MSER) 与波形系数用于补丁嵌入.
  • 使用带有注意力机制的视觉变压器 (ViT) 来进行高级特征提取.
  • 采用灰狼算法来优化功能,并使用双流架构 (极端学习机器和条件随机场) 来进行分类.

主要成果:

  • 与现有方法相比,在分类准确度方面取得了显著的改进.
  • MAPE-ViT有效地处理传感器错位,深度噪声,并保留对象边界.
  • 该方法在具有挑战性的RGB-D场景理解场景中显示出强度.

结论:

  • MAPE-ViT为RGB-D场景分类提供了强大而有效的解决方案.
  • 提出的方法优于传统方法,特别是在杂的环境下.
  • 这一框架推动了多式联运场景理解领域的发展.