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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...
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Depth Perception and Spatial Vision01:15

Depth Perception and Spatial Vision

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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.
653
Types Of Transformers01:16

Types Of Transformers

976
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...
976
The Ideal Transformer01:26

The Ideal Transformer

394
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...
394
Source Transformation01:15

Source Transformation

6.4K
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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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...
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A Swin Transformer-Based Model for Thyroid Nodule Detection in Ultrasound Images
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结构意识的交叉模式变压器用于深度完成.

Linqing Zhao, Yi Wei, Jiaxin Li

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    此摘要是机器生成的。

    本研究介绍了一个结构意识的交叉模式变压器 (SCMT),以改进深度完成. 这种新的方法有效地利用了稀疏深度数据的3D结构,增强了特征表示,以准确地重建场景.

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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
    • 三维重建的3D重建

    背景情况:

    • 现有的深度完成方法往往忽略了稀疏深度数据中的3D结构信息.
    • 依靠2D图像纹理限制了在纹理差地区的性能.
    • 缺乏明确的3D线索妨碍了准确的前景和背景特征区分.

    研究的目的:

    • 开发一种新的深度完成方法,充分利用固有的3D结构.
    • 用3D几何先验来增强2D特征的表示.
    • 提高深度完成精度,特别是在具有挑战性的,纹理有限的环境中.

    主要方法:

    • 提出了一个结构意识的跨模态变压器 (SCMT),利用一个双流网络来提取2D和3D特征.
    • 层次的3D场景结构从RGB-D输入中脱而出.
    • 跨模式变压器在2D特征流中自适应地集成了多尺度的3D结构特征.

    主要成果:

    • 在基准数据集 (KITTI,VOID,NYU) 上,SCMT展示了最先进的性能.
    • 该方法成功地结合了3D结构先验,改善了深度边界和对象形状轮预测.
    • 增强的2D功能导致更准确的深度完成,特别是在纹理有限的区域.

    结论:

    • 拟议的SCMT有效地捕获和利用3D结构,以获得更高的深度完成.
    • 整合3D信息显著克服了基于纹理的方法的局限性.
    • SCMT提供了一个强大的解决方案,可以从稀疏的深度数据中准确地理解3D场景.