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

Transformers in Distribution System01:27

Transformers in Distribution System

127
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...
127
Instrument Transformers01:23

Instrument Transformers

108
Instrument transformers, comprising voltage transformers (VTs) and current transformers (CTs), play crucial roles in power substations by providing isolated replicas of current or voltage for measurement and protection purposes. Voltage transformers reduce the primary voltage to levels suitable for relay operation and measurement, while current transformers scale down the primary current. The primary winding of a current transformer often consists of a single turn, achieved by threading the...
108
Transformers with Off-Nominal Turns Ratios01:25

Transformers with Off-Nominal Turns Ratios

181
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...
181

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

A Label-Free Ultraviolet Photoacoustic Microscopy Enables Nuclear Imaging and Toxicity Assessment in an Intact Brain Organoid.

Small (Weinheim an der Bergstrasse, Germany)·2026
Same author

Deciphering Interfacial Electronic Interactions Between Iridium Oxide and Ni-Based Supports for High-Current-Density Anion Exchange Membrane Water Electrolysis.

Small (Weinheim an der Bergstrasse, Germany)·2026
Same author

Photoactivatable Oligoelectrolytes Engendering Pyroptotic Vesicles.

Journal of the American Chemical Society·2026
Same author

Exciton-plasmon coupling in tBLG-Au nanodisks for ultrasensitive miRNA sensing.

National science review·2025
Same author

Review of Linear-Array-Transducer-Based Volumetric Ultrasound Imaging Techniques and Their Biomedical Applications.

Bioengineering (Basel, Switzerland)·2025
Same author

Review on Multispectral Photoacoustic Imaging Using Stimulated Raman Scattering Light Sources.

Sensors (Basel, Switzerland)·2025

相关实验视频

Updated: Jul 24, 2025

Measuring the Structure, Composition, and Change of Underwater Environments with Large-area Imaging
09:19

Measuring the Structure, Composition, and Change of Underwater Environments with Large-area Imaging

Published on: April 18, 2025

633

跨网:基于变压器的点云采样网络.

Hookyung Lee1, Jaeseung Jeon1, Seokjin Hong1

  • 1Graduate School of Automotive Engineering, Kookmin University, Seoul 02707, Republic of Korea.

Sensors (Basel, Switzerland)
|July 11, 2023
PubMed
概括

本研究介绍了TransNet,这是一个基于变压器的新型网络,用于高效的点云下载采样. 通过学习特定任务的抽样,TransNet提高了深度学习的性能,特别是在高抽样比率的稀疏数据中.

关键词:
这是分类分类的分类.深度学习是一种深度学习.下方采样采样 下方采样采样多头注意力多头注意力网络 网络 网络 网络 网络 网络一个点云,一个点云.自己注意力自我注意力变压器变压器变压器变压器

更多相关视频

Author Spotlight: Innovative Device Development for Advancing Dendroecology and Wood Anatomy Research
07:05

Author Spotlight: Innovative Device Development for Advancing Dendroecology and Wood Anatomy Research

Published on: September 27, 2024

2.7K
Three-dimensional Particle Tracking Velocimetry for Turbulence Applications: Case of a Jet Flow
13:02

Three-dimensional Particle Tracking Velocimetry for Turbulence Applications: Case of a Jet Flow

Published on: February 27, 2016

12.3K

相关实验视频

Last Updated: Jul 24, 2025

Measuring the Structure, Composition, and Change of Underwater Environments with Large-area Imaging
09:19

Measuring the Structure, Composition, and Change of Underwater Environments with Large-area Imaging

Published on: April 18, 2025

633
Author Spotlight: Innovative Device Development for Advancing Dendroecology and Wood Anatomy Research
07:05

Author Spotlight: Innovative Device Development for Advancing Dendroecology and Wood Anatomy Research

Published on: September 27, 2024

2.7K
Three-dimensional Particle Tracking Velocimetry for Turbulence Applications: Case of a Jet Flow
13:02

Three-dimensional Particle Tracking Velocimetry for Turbulence Applications: Case of a Jet Flow

Published on: February 27, 2016

12.3K

科学领域:

  • 计算机视觉 计算机视觉
  • 机器学习 机器学习
  • 几何深度学习 几何深度学习

背景情况:

  • 点云处理对于深度学习至关重要,但计算复杂性是一个挑战.
  • 传统的下方采样方法是无关任务的,限制了性能,尤其是在高采样比率下.
  • 实际应用需要有效和精确的点云采样.

研究的目的:

  • 提出一种新的基于变压器的点云采样网络 (TransNet),以实现高效的下方采样.
  • 开发一个以任务为导向的采样方法,提高精度,处理稀疏数据.
  • 提高处理点云数据的深度学习网络的性能.

主要方法:

  • 开发了TransNet,一个基于变压器的网络,利用自我注意力和完全连接的层.
  • 实施注意力机制以提取有意义的特征并理解点云关系.
  • 设计了一种以任务为导向的采样方法,用于对点云进行下方采样.

主要成果:

  • 与最先进的模型相比,TransNet显示出更高的准确性.
  • 拟议的网络擅长从稀疏的数据中生成点数,特别是在高采样比率下.
  • 实现了高效的低采样,精确度提高.

结论:

  • 跨网为点云下载采样任务提供了一个有前途的解决方案.
  • 面向任务的方法显著提高了采样性能.
  • 基于变压器的方法可以有效地解决点云处理中的计算复杂性.