Jove
Visualize
联系我们
JoVE
x logofacebook logolinkedin logoyoutube logo
关于 JoVE
概览领导团队博客JoVE 帮助中心
作者
出版流程编辑委员会范围与政策同行评审常见问题投稿
图书馆员
用户评价订阅访问资源图书馆顾问委员会常见问题
研究
JoVE JournalMethods CollectionsJoVE Encyclopedia of Experiments存档
教育
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab Manual教师资源中心教师网站
使用条款与条件
隐私政策
政策

相关概念视频

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
Transformers with Off-Nominal Turns Ratios01:25

Transformers with Off-Nominal Turns Ratios

162
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...
162
Transformers in Distribution System01:27

Transformers in Distribution System

104
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...
104
Three-Winding Transformers01:19

Three-Winding Transformers

237
Three identical single-phase transformers can be configured to form a three-phase transformer connection, which involves high-voltage and low-voltage windings. The high-voltage windings are denoted by capital letters A-B-C, while the low-voltage windings are labeled with lowercase letters a-b-c, representing their respective phases. This notation helps distinguish between the high and low voltage sides of the transformer.
In the per-unit equivalent circuit of a grounded Y-Y three-phase...
237
Per-Unit Sequence Models01:26

Per-Unit Sequence Models

77
An ideal Y-Y transformer, grounded through neutral impedances, displays per-unit sequence networks akin to those of a single-phase ideal transformer when subjected to balanced positive- or negative-sequence currents. These currents do not produce neutral currents, and their associated voltage drops.
Zero-sequence currents, which are identical in magnitude and phase, generate a neutral current, resulting in voltage drops across the neutral impedance and the low-voltage winding. If the...
77
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

您也可能阅读

相关文章

通过共同作者、期刊和引用图与本文相关的文章。

排序
Same author

Cardiovascular-kidney-metabolic syndrome stage modifies the efficacy of intensive blood pressure control on cognitive outcomes: A post hoc analysis of SPRINT MIND.

Alzheimer's & dementia : the journal of the Alzheimer's Association·2026
Same author

A ROS-responsive supramolecular peptide hydrogel attenuates rheumatoid arthritis by modulating synoviocyte activity and inflammatory microenvironments.

Journal of materials chemistry. B·2026
Same author

Association of glycolipid metabolism 6 factors index with cardiovascular and metabolic diseases, and its predictive value in disease progression.

BMC endocrine disorders·2026
Same author

NaOH-urea pretreatment of corn stover under outdoor cold conditions for enhanced biodegradability and anaerobic methane yield.

Bioprocess and biosystems engineering·2026
Same author

Enhancing methane production from corn stover through thermophilic anaerobic digestion and digestate recirculation: performance and the microbial community.

Bioprocess and biosystems engineering·2026
Same author

TAFNet: Trusted Multiview Associative Fusion Neural Networks for Analyzing Dynamic Brain Networks.

IEEE transactions on neural networks and learning systems·2026
Same journal

CAFF-CIL: Causality-Aware Freedom Forgetting Approach for Class-Incremental Learning.

IEEE transactions on neural networks and learning systems·2026
Same journal

Harmonic Autoencoding Framework for Multiple Tasks in Magnetic Particle Imaging Reconstruction.

IEEE transactions on neural networks and learning systems·2026
Same journal

A Survey on Human-Centric Voice-Face Multimodal Learning.

IEEE transactions on neural networks and learning systems·2026
Same journal

Vision-Assisted Foundation Model for Solving Multitask Vehicle Routing Problems.

IEEE transactions on neural networks and learning systems·2026
Same journal

FP3O: Enabling Proximal Policy Optimization in Multiagent Cooperation With Parameter-Sharing Versatility.

IEEE transactions on neural networks and learning systems·2026
Same journal

Hierarchical Semantic Concept Modeling for Generalizable Myocardial Pathology Segmentation on Multisequence CMR Images.

IEEE transactions on neural networks and learning systems·2026
查看所有相关文章

相关实验视频

Updated: Jul 11, 2025

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

1.9K

MGRW-变压器:用于可解释学习的多粒度随机步行变压器模型.

Weiping Ding, Yu Geng, Jiashuang Huang

    IEEE transactions on neural networks and learning systems
    |November 8, 2023
    PubMed
    概括
    此摘要是机器生成的。

    我们开发了一种新的深度学习模型,即多粒度随机步行变压器 (MGRW-Transformer),用于可解释的医学图像识别. 该模型通过可视化医疗专业人员的决策过程来提高可解释性.

    更多相关视频

    Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique
    04:48

    Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

    Published on: July 5, 2024

    435
    Using a Split-belt Treadmill to Evaluate Generalization of Human Locomotor Adaptation
    08:04

    Using a Split-belt Treadmill to Evaluate Generalization of Human Locomotor Adaptation

    Published on: August 23, 2017

    8.3K

    相关实验视频

    Last Updated: Jul 11, 2025

    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

    1.9K
    Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique
    04:48

    Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

    Published on: July 5, 2024

    435
    Using a Split-belt Treadmill to Evaluate Generalization of Human Locomotor Adaptation
    08:04

    Using a Split-belt Treadmill to Evaluate Generalization of Human Locomotor Adaptation

    Published on: August 23, 2017

    8.3K

    科学领域:

    • 人工智能的人工智能
    • 医学成像分析 医学成像分析
    • 计算机视觉 计算机视觉

    背景情况:

    • 深度学习模型擅长图像识别,但往往作为"黑子",缺乏语义解释.
    • 视觉转换器 (ViT) 通过自我注意机制提供了更好的解释性.
    • 由于病变的大小和位置各不相同,将深度学习应用于医学图像仍然存在挑战,这阻碍了准确和可解释的结论.

    研究的目的:

    • 开发一个可解释的深度学习模型用于医学图像识别.
    • 解决当前模型在为其决策提供语义解释方面的局限性.
    • 通过提供对分类过程的视觉洞察力,增强AI在医学诊断中的应用.

    主要方法:

    • 提出了一种多粒度随机步行变压器 (MGRW-Transformer) 模型,将注意力机制与随机步行方法相结合.
    • 将医疗图像划分为子图像块,由ViT模块处理以进行分类.
    • 将注意力矩阵与多粒度随机步行模块融合在一起,构建了一个图形,图像块是节点,注意力指导步行.

    主要成果:

    • MGRW-变压器模型提供了决策过程的语义解释和可视化.
    • 实验结果表明,与现有方法相比,医疗图像的分类性能得到了提高.
    • 该模型提供了对结论的得出方式的明确见解,帮助医疗专业人员.

    结论:

    • 该MGRW-变压器模型成功地提高了医学图像识别中的解释性.
    • 拟议的方法通过提供可解释的AI洞察力,为医疗专业人员提供了有价值的工具.
    • 这种方法通过提高透明度,促进了深度学习在临床实践中的整合.