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相关概念视频

Types Of Transformers01:16

Types Of Transformers

1.4K
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...
1.4K
Transformers in Distribution System01:27

Transformers in Distribution System

475
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...
475
The Ideal Transformer01:26

The Ideal Transformer

1.3K
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 tangential...
1.3K
Transformers with Off-Nominal Turns Ratios01:25

Transformers with Off-Nominal Turns Ratios

491
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...
491
Transformers01:26

Transformers

1.7K
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...
1.7K
Energy Losses in Transformers01:21

Energy Losses in Transformers

1.3K
In an ideal transformer, it is assumed that there are no energy losses, and, hence, all the power at the primary winding is transferred to the secondary winding. However, in reality,  the transformers always have some energy losses, and, hence, the output power obtained at the secondary winding is less than the input power at the primary winding due to energy losses.
There are four main reasons for energy losses in transformers.
The first cause can be  the high resistance of the...
1.3K

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Updated: Jan 9, 2026

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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通过检测变压器实现高效的半监控物体检测.

Jiacheng Zhang, Jiaming Li, Xiangru Lin

    IEEE transactions on pattern analysis and machine intelligence
    |December 10, 2025
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    概括
    此摘要是机器生成的。

    使用检测变压器 (DETR) 的半监控物体检测 (SSOD) 得到了半DETR++的改进. 它解决了杂的伪标签,并增强了解码器规范化,以提高训练效率和性能.

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    Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
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    相关实验视频

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    A Swin Transformer-Based Model for Thyroid Nodule Detection in Ultrasound Images
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    Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
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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
    • 人工智能的人工智能

    背景情况:

    • 半监控对象检测 (SSOD) 通过使用未标记的数据来降低注释成本.
    • 检测变压器 (DETR) 提供端到端的对象检测,没有非最大抑制 (NMS).
    • 现有的SSOD方法没有针对DETR架构进行优化,留下了一个研究缺口.

    研究的目的:

    • 系统地研究和改进DETR模型的半监督学习.
    • 解决基于DETR的SSOD的挑战,包括对伪标签和规范化困难的敏感性.
    • 提出一个新的框架,Semi-DETR++,用于高效和有效的SSOD与DETRs.

    主要方法:

    • 引入了一个阶段性混合匹配策略,将一对多和一对一的分配结合起来,以获得伪标签的稳定性.
    • 开发了一种重新解码查询一致性培训方法,以根据其层wise行为规范DETR解码器.
    • 保留了DETR模型固有的无NMS推理.

    主要成果:

    • 半DETR++在各种DETR架构中展示了更高效的半监督学习.
    • 拟议的框架显著优于现有的SSOD方法.
    • 半DETR++的组件显示了多功能性,并且很好地将其概括为半监督的细分任务.

    结论:

    • 半DETR++有效地解决了半监督DETR培训中的关键挑战.
    • 这些新方法提高了伪标签的稳定性,并改善了解码器规范化.
    • 该框架为SSOD提供了一个可扩展和高性能解决方案,在相关任务中具有潜在的应用.