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

Distributed Loads01:19

Distributed Loads

Distributed loads are a common type of load that engineers and scientists encounter in various practical situations. Distributed loads often refer to a type of load spread over a surface or a structure and can be modeled as continuous force per unit area.
For example, consider a bookshelf filled with books stacked vertically adjacent to each other. The weight of the books is evenly distributed over the length of the shelf. As a result, the pressure at different locations on the surface of the...
Reducing Line Loss01:18

Reducing Line Loss

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 in...
Transformers in Distribution System01:27

Transformers in Distribution System

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...
Fast Decoupled and DC Powerflow01:24

Fast Decoupled and DC Powerflow

The fast decoupled power flow method addresses contingencies in power system operations, such as generator outages or transmission line failures. This method provides quick power flow solutions, essential for real-time system adjustments. Fast decoupled power flow algorithms simplify the Jacobian matrix by neglecting certain elements, leading to two sets of decoupled equations:

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

Spatiotemporal profiling of white matter lesions and their contribution in the pathologies of Parkinson's disease animal models.

GeroScience·2026
Same author

Effect of navigated transcranial magnetic stimulation for glioma surgery outcomes: a systematic review and meta-analysis.

Open medicine (Warsaw, Poland)·2026
Same author

The Associations of Emotional Intelligence, AI Self-Efficacy, and AI Literacy Among Nursing Undergraduates Under the NUR.S.E.S. Framework: Network Analysis.

JMIR nursing·2026
Same author

Bezafibrate-associated rhabdomyolysis in a patient with diabetic kidney disease: A case report and successful transition to tafolecimab.

Medicine·2026
Same author

Association between the systemic inflammation response index and serum uric acid in acute traumatic brain injury: a cross-sectional study.

Frontiers in neurology·2026
Same author

Kosmotrope-Promoted Proton Hopping in Supramolecular Conductors.

Journal of the American Chemical Society·2026

相关实验视频

Updated: Jun 28, 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

1.8K

一个高效的点云语义细分网络与多尺度超级补丁变压器.

Yongwei Miao1, Yuliang Sun2, Yimin Zhang3

  • 1School of Information Science and Technology, Hangzhou Normal University, Hangzhou, 311121, China.

Scientific reports
|June 25, 2024
PubMed
概括

本研究介绍了一种新型的多尺度超补丁变压器网络 (MSSPTNet),用于大规模3D点云场景的高效语义细分. 该网络显著加快了培训速度,以数量级超越现有方法.

更多相关视频

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
03:31

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Published on: December 15, 2023

517
Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique
04:48

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Published on: July 5, 2024

386

相关实验视频

Last Updated: Jun 28, 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

1.8K
Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
03:31

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Published on: December 15, 2023

517
Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique
04:48

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Published on: July 5, 2024

386

科学领域:

  • 计算机视觉 计算机视觉
  • 3D数据处理 3D数据处理
  • 深度学习 (Deep Learning) 是一种深度学习.

背景情况:

  • 大规模3D点云的语义细分对于环境感知至关重要.
  • 挑战包括庞大的数据大小,高效的深度学习培训,以及处理对象多样性/封闭.

研究的目的:

  • 提出一个高效有效的深度学习模型,用于大规模点云场景的语义细分.
  • 为了应对计算方面的挑战,并改善各种3D对象的表示.

主要方法:

  • 介绍了使用场景超级补丁作为数据表示的多尺度超级补丁变压器网络 (MSSPTNet).
  • 开发了一个多级超补丁局部聚合 (MSSPLA) 模块和一个超补丁变压器 (SPT) 模块.
  • 采用动态区域增长算法进行超级补丁提取和特征学习的自我注意.

主要成果:

  • MSSPTNet有效地从点云数据中学习本地和全球特征.
  • 在网络培训中显著提高了效率,比现有方法快几十到几百倍.
  • 在S3DIS数据集上取得了强的表现,特别是在具有重复结构的室内场景中.

结论:

  • MSSPTNet提供了一个高效的解决方案,用于大规模点云场景的语义细分.
  • 超级补丁表示和变压器架构有效地捕获上下文信息和补丁之间的关系.
  • 拟议的方法显著减少了培训时间,使大规模的3D场景理解更容易获得.