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

Reducing Line Loss01:18

Reducing Line Loss

530
In a three-phase circuit, line loss is an indicator of energy dissipated as heat due to the resistance of transmission lines. To address this, incorporating transformers into the system—a step-up transformer at the source and a step-down transformer at the load—is a strategic solution. Two three-phase transformers are introduced to improve this.
With a step-up transformer at the source, the voltage is increased, thereby reducing the current in the transmission lines since power loss...
530
Three-Winding Transformers01:19

Three-Winding Transformers

1.0K
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...
1.0K
Transformers with Off-Nominal Turns Ratios01:25

Transformers with Off-Nominal Turns Ratios

754
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...
754

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

Interpretable Machine Learning to Predict Anxiety or Depression in Acute Ischemic Stroke after IV Thrombolysis: A Retrospective Cohort Study.

Current neurovascular research·2026
Same author

Accuracy of machine learning in predicting recurrence risk of lumbar disc herniation after percutaneous endoscopic lumbar discectomy: a systematic review and meta-analysis.

Journal of orthopaedic surgery and research·2026
Same author

Is ambulatory flexible ureteroscopic lithotripsy safe and feasible? evidence from a propensity score-matched cohort.

BMC urology·2026
Same author

Three-dimensional dendrite-free and anti-corrosion aluminum foam anode via hetero-metallic bismuth interface engineering for high-performance aqueous batteries.

Journal of colloid and interface science·2026
Same author

Computational fluid dynamics-based flow field simulation and optimization of negative-pressure stone removal: stone size, position, and sheath geometry.

Scientific reports·2026
Same author

Two-Dimensional Crystalline Alkyne-Rich Conjugated Carbonaceous Framework for Extremely Fast-Charging Lithium-Ion Batteries.

Advanced materials (Deerfield Beach, Fla.)·2026

相关实验视频

Updated: May 5, 2026

A MRI-Based Toolbox for Neurosurgical Planning in Nonhuman Primates
08:41

A MRI-Based Toolbox for Neurosurgical Planning in Nonhuman Primates

Published on: July 17, 2020

4.9K

通过层间维度关系建模,用于视觉变压器的头部内部修剪.

Peng Zhang1, Cong Tian1, Liang Zhao1

  • 1School of Computer Science and Technology, Xidian University, No. 2 South Taibai Road, Xi'an, 710071, PR China.

Neural networks : the official journal of the International Neural Network Society
|June 12, 2025
PubMed
概括

这项研究引入了头部内部修剪 (IHP),以降低视觉变压器的计算成本. 这种新的技术有效地在最小的精度损失下修剪变压器模型,提高了硬件有限的平台的效率.

科学领域:

  • 计算机视觉 计算机视觉
  • 深度学习 (Deep Learning) 是一种深度学习.
  • 人工智能的人工智能

背景情况:

  • 变压器模型在NLP和计算机视觉中实现了高性能,但产生了相当大的计算成本.
  • 对于变压器而言,现有的头部修剪方法往往会导致由于粗细粒度和忽视层间依赖性而导致准确性损失.
  • 变压器网络的有效压缩需要解决这些局限性,以便在实践中部署.

研究的目的:

  • 为视觉转换器提供一种新的头部内部修剪 (IHP) 技术,以进行高效的稀疏训练.
  • 开发一种方法,尽量减少计算成本,同时保持网络准确性.
  • 为了克服变压器模型中现有的修剪策略的局限性.

主要方法:

  • 引入了一个可训练行参数,用于变压器头内的稀疏训练.
  • 开发了一个关系矩阵来指导组分修剪过程以消除组件.
  • 确保连续消除多余元件,以保持结构和功能完整性.

主要成果:

  • 在基准数据集 (CIFAR-10/100,ImageNet-1K) 上,对视觉转换器 (如Deit,Swin Transformer和CCT) 的计算成本显著降低.
  • 在测试模型中实现了最小的精度下降.
关键词:
模型的压缩压缩.模型修剪 模型修剪视觉变压器 视觉变压器

更多相关视频

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

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Published on: July 5, 2024

378

相关实验视频

Last Updated: May 5, 2026

A MRI-Based Toolbox for Neurosurgical Planning in Nonhuman Primates
08:41

A MRI-Based Toolbox for Neurosurgical Planning in Nonhuman Primates

Published on: July 17, 2020

4.9K
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
Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique
04:48

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Published on: July 5, 2024

378
  • 在ILSVRC-12中,IHP提高了Deit-tiny的Top-1精度0.47%,相比于先进方法,FLOP减少了46.20%.

    • 结论:

    • 头部内部修剪 (IHP) 为压缩视觉变压器提供了一种有效的策略.
    • 拟议的方法平衡了计算效率和准确性保护.
    • IHP为在资源有限的设备上部署变压器模型提供了可行的解决方案.