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

972
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...
972
Transformers in Distribution System01:27

Transformers in Distribution System

102
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...
102
Energy Losses in Transformers01:21

Energy Losses in Transformers

867
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...
867
The Ideal Transformer01:26

The Ideal Transformer

387
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...
387
Transformers with Off-Nominal Turns Ratios01:25

Transformers with Off-Nominal Turns Ratios

151
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...
151
Vector Algebra: Graphical Method01:10

Vector Algebra: Graphical Method

12.1K
Vectors can be multiplied by scalars, added to other vectors, or subtracted from other vectors. The vector sum of two (or more) vectors is called the resultant vector or, for short, the resultant.
We use the laws of geometry to construct resultant vectors, followed by trigonometry to find vector magnitudes and directions. For a geometric construction of the sum of two vectors in a plane, we follow the parallelogram rule. Suppose two vectors are at arbitrary positions. Translate either one of...
12.1K

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

LoRASculpt: Harmonious Low-Rank Adaptation for Multimodal Large Language Models.

IEEE transactions on pattern analysis and machine intelligence·2026
Same author

Towards clinical-level interpretation of dental panoramic radiography using an instance-guided vision-language model.

Nature biomedical engineering·2026
Same author

Systemic immune-inflammation index predicts post-thrombectomy outcomes and reveals a mediating role in the association between neurocardiac stress and prognosis: a multicenter study.

Frontiers in neurology·2026
Same author

Holistic Invariant Retracing for Distortion-Resilient Multi-Modal Learning in Spatial Transcriptomics.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2026
Same author

Differentiable Clustering Graph Convolutional Network for Hyperspectral Unmixing: Methodology and Benchmark.

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

MUP-SAM: Multi-scale vision mamba UNet prompt generation for SAM in multi-organ medical image segmentation.

Neural networks : the official journal of the International Neural Network Society·2026
Same journal

Exploiting audio-visual modalities in videos: Object detection via multi-stage bilateral coupling network.

Neural networks : the official journal of the International Neural Network Society·2026
Same journal

Reliability-aware modality completion with cross-modal distillation for federated learning with missing modalities.

Neural networks : the official journal of the International Neural Network Society·2026
Same journal

IGFD-Net: Illumination-guided frequency decoupling for polarization image fusion.

Neural networks : the official journal of the International Neural Network Society·2026
Same journal

Multiple-Strategies dung beetle optimizer and its applications in engineering optimization and bankruptcy prediction.

Neural networks : the official journal of the International Neural Network Society·2026
Same journal

Aggregating global-scale pixel-wise forgery cues within a graph.

Neural networks : the official journal of the International Neural Network Society·2026
Same journal

Finite-Time intermittent control for secure synchronization of Neutral-Type stochastic delayed neural networks under aperiodic DoS attacks.

Neural networks : the official journal of the International Neural Network Society·2026
查看所有相关文章

相关实验视频

Updated: Jun 29, 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

1.8K

探索图形变压器中的稀疏性

Chuang Liu1, Yibing Zhan2, Xueqi Ma3

  • 1School of Computer Science, Wuhan University, Wuhan, China.

Neural networks : the official journal of the International Neural Network Society
|March 29, 2024
PubMed
概括
此摘要是机器生成的。

本研究介绍了一个图形变压器散散化 (GTSP) 框架,以降低图形变压器 (GT) 的计算成本. 实际上,GTSP有效地削减了GT,实现了大幅减少操作,对准确度的影响最小.

关键词:
图形分类的图形分类.图表稀疏的培训 稀疏的培训图形变压器 图形变压器模型修剪 模型修剪

更多相关视频

Modeling the Functional Network for Spatial Navigation in the Human Brain
05:55

Modeling the Functional Network for Spatial Navigation in the Human Brain

Published on: October 13, 2023

1.1K
Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique
04:48

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Published on: July 5, 2024

395

相关实验视频

Last Updated: Jun 29, 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

1.8K
Modeling the Functional Network for Spatial Navigation in the Human Brain
05:55

Modeling the Functional Network for Spatial Navigation in the Human Brain

Published on: October 13, 2023

1.1K
Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique
04:48

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Published on: July 5, 2024

395

科学领域:

  • 人工智能的人工智能
  • 机器学习 机器学习
  • 图形神经网络的神经网络

背景情况:

  • 图形变压器 (GTs) 在与图形相关的任务中表现出高性能.
  • 由于GT的大量计算费用限制了其实际应用,特别是在资源有限的环境中.
  • 现有的GT车型表现出冗余性,这表明了优化潜力.

研究的目的:

  • 调查散射图形变压器的可行性和有效性.
  • 提出一个全面的框架来减少GTs的计算复杂性.
  • 为了使GT在资源有限的环境中能够有效地部署.

主要方法:

  • 开发了一个图形转换器散散化 (GTSP) 框架,针对四个关键领域:输入图形数据,注意力头,模型层和模型重量.
  • 每个可压缩元件内置的可差异化口罩,以方便端到端的修剪.
  • 在已建立的GT模型上进行了广泛的实验,如GraphTrans,Graphormer和GraphGPS.

主要成果:

  • GTSP显著降低了计算成本,OGBG-HIV数据集的浮点运算减少了30%.
  • 实现了边际准确性损失,在某些情况下,准确性得到了改善,例如曲线下面面积增加了1.8%.
  • 提供了对注意力头特征和注意力机制行为的新见解.

结论:

  • 拟议的GTSP框架有效地减少了图形变压器的计算复杂性.
  • 稀疏化提供了一种可行的策略,可以在不大幅降低性能的情况下提高GT的效率.
  • 这些发现为未来研究高效的图形变压器架构提供了有价值的方向.