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相关概念视频

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
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

489
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...
489
Sequence Networks of Rotating Machines01:24

Sequence Networks of Rotating Machines

477
A Y-connected synchronous generator, grounded through a neutral impedance, is designed to produce balanced internal phase voltages with only positive-sequence components. The generator's sequence networks include a source voltage that is exclusively in the positive-sequence network. The sequence components of line-to-ground voltages at the generator terminals illustrate this configuration.
Zero-sequence current induces a voltage drop across the generator's neutral impedance and other...
477
Observational Learning01:12

Observational Learning

804
Albert Bandura's observational learning, also known as imitation or modeling, occurs when a person observes and imitates another's behavior. It is a quicker process than operant conditioning. A well-known example is the Bobo doll study, where children who saw an adult acting aggressively towards the doll were more likely to act aggressively when left alone, compared to those who observed a nonaggressive adult. Many psychologists view observational learning as a form of latent learning...
804
Causes of Similarity-Dissimilarity Effect01:26

Causes of Similarity-Dissimilarity Effect

245
The similarity-dissimilarity effect, a fundamental concept in social psychology, explains how interpersonal similarities and differences influence attraction and social interactions. This effect is supported by three key psychological perspectives: balance theory, social comparison theory, and consensual validation.Balance Theory and Cognitive ConsistencyBalance theory, developed by Fritz Heider, posits that individuals seek cognitive consistency in their relationships. When two people share...
245

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相关实验视频

Updated: Jan 12, 2026

A Swin Transformer-Based Model for Thyroid Nodule Detection in Ultrasound Images
04:23

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

Published on: April 21, 2023

2.3K

SPAN:学习场景图和图像与变压器之间的相似性.

Yuren Cong, Wentong Liao, Bodo Rosenhahn

    IEEE transactions on pattern analysis and machine intelligence
    |November 7, 2025
    PubMed
    概括
    此摘要是机器生成的。

    我们介绍了SPAN,这是一个用于测量场景图形和图像相似性的新框架. 这种方法改善了场景图生成评估,并使下游应用程序更好.

    相关实验视频

    Last Updated: Jan 12, 2026

    A Swin Transformer-Based Model for Thyroid Nodule Detection in Ultrasound Images
    04:23

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

    Published on: April 21, 2023

    2.3K

    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:

    • 场景图表生成对人工智能应用至关重要,但缺乏有效的评估指标.
    • 像Recall@K这样的当前指标对噪音敏感,不能捕捉整体语义差异.
    • 这限制了下游任务中生成场景图的实际使用.

    研究的目的:

    • 提出第一个框架,SPAN (Scene graPh-imAge coNtrastive学习),用于学习场景图和图像之间的相似性.
    • 引入一个新的评估指标,R-Precision,用于场景图表生成.
    • 建立新的基准来评估场景图形图像相似性.

    主要方法:

    • 开发了一个图形转换器和一个图像转换器,以在共享的潜空间中对齐场景图形和图像.
    • 引入了一种新的图形序列化技术,将场景图形转换为具有结构编码的序列.
    • 建议R-Precision,一个图像检索准确度指标,用于场景图表生成评估.

    主要成果:

    • 该SPAN框架有效地测量场景图和图像相似性.
    • 作为场景图表生成的评估指标,R-Precision展示了卓越的性能.
    • 在视觉基因组和开放图像数据集上建立了新的基准,验证了SPAN的有效性.

    结论:

    • SPAN提供了一种强大的方法来学习场景图像相似性,解决现有指标的局限性.
    • 拟议的R-Precision指标为场景图表生成提供了更可靠的评估.
    • SPAN显示出作为各种AI应用程序的场景图形编码器的巨大潜力.