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

End Point Prediction: Gran Plot01:07

End Point Prediction: Gran Plot

1.5K
A Gran plot is used to predict the equivalence volume or endpoint of a potentiometric or acid-base titration without reaching the endpoint. Typically, titration data is collected as a function of the titrant's volume up to a point less than the equivalence volume and then transformed into a linear format. The straight line is extended to the x-axis, indicating the necessary titrant volume to achieve the equivalence point.
For potentiometric titration, the Gran plot is created by plotting...
1.5K

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

Gestational Trichloroacetic Acid Exposure Induces Miscarriage by Disrupting Iron Homeostasis in Trophoblasts via the KEAP1-NRF2 Pathway.

Environment & health (Washington, D.C.)·2026
Same author

Endo-DET: A Domain-Specific Detection Framework for Multi-Class Endoscopic Disease Detection.

Journal of imaging·2026
Same author

3D-guided planning enhances safety of laparoscopic resection for special liver segments: A propensity score-matched study.

American journal of surgery·2026
Same author

CDsyn: A comprehensive database for deleterious human synonymous variation prediction.

iScience·2026
Same author

Characteristics and significance of bronchiolitis obliterans syndrome after hematopoietic stem cell transplantation at bronchoscopy.

Respiratory research·2026
Same author

TDCIPP disrupts decidual macrophage function to induce miscarriage through ferroptosis-mediated DNA damage.

Journal of hazardous materials·2026

相关实验视频

Updated: Apr 30, 2026

Examining Local Network Processing using Multi-contact Laminar Electrode Recording
13:40

Examining Local Network Processing using Multi-contact Laminar Electrode Recording

Published on: September 8, 2011

13.2K

空间增强的尖端神经网络,用于高效的点云分析.

Yijie Lu1, Zhiyi Pan2, Renrui Zhang3

  • 1School of Electronic and Computer Engineering, Shenzhen Graduate School, Peking University, China.

Neural networks : the official journal of the International Neural Network Society
|October 29, 2025
PubMed
概括
此摘要是机器生成的。

尖端神经网络 (SNN) 显示出对3D点云分析的前景. 新的方法提高空间感知,在分类和细分等任务中实现最先进的性能和低能耗.

关键词:
大脑启发的计算深度神经网络是一个神经网络.能源效率 能源效率是指能源的使用效率.神经形态计算是一种神经形态计算.分析点云分析点云分析尖的神经网络的神经网络.

更多相关视频

A Simple Stimulatory Device for Evoking Point-like Tactile Stimuli: A Searchlight for LFP to Spike Transitions
07:34

A Simple Stimulatory Device for Evoking Point-like Tactile Stimuli: A Searchlight for LFP to Spike Transitions

Published on: March 25, 2014

10.3K
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

1.0K

相关实验视频

Last Updated: Apr 30, 2026

Examining Local Network Processing using Multi-contact Laminar Electrode Recording
13:40

Examining Local Network Processing using Multi-contact Laminar Electrode Recording

Published on: September 8, 2011

13.2K
A Simple Stimulatory Device for Evoking Point-like Tactile Stimuli: A Searchlight for LFP to Spike Transitions
07:34

A Simple Stimulatory Device for Evoking Point-like Tactile Stimuli: A Searchlight for LFP to Spike Transitions

Published on: March 25, 2014

10.3K
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

1.0K

科学领域:

  • 人工智能的人工智能
  • 计算机视觉 计算机视觉
  • 神经形态计算是一种神经形态计算.

背景情况:

  • 尖端神经网络 (SNN) 在2D任务中的效率得到了认可.
  • 由于无序,复杂的空间数据,3D点云处理带来了独特的挑战.
  • 现有的SNN需要改进,以便有效地进行3D空间特征建模.

研究的目的:

  • 增强SNN用于计算密集的3D点云分析.
  • 为了应对在无序点云中建模空间信息的挑战.
  • 开发一种新的SNN框架,用于改善空间感知的3D任务.

主要方法:

  • 引入了无参数的Spiking空间位置编码 (SSPE) 来提供本地位置信息.
  • 集成的 Spiking 交叉特征图形位置编码 (SCGPE) 用于全球空间关系.
  • 开发了Spiking 3D网络 (S3DNet) 框架,利用Spiking完全连接的层.

主要成果:

  • 在3D点云任务中,S3DNet在SNN中实现了最先进的性能.
  • 证明能耗低,分类准确度高 (在ModelNet40上为92.34%).
  • 在点云细分方面实现了高精度 (85.0%在ShapeNetPart上),这是SNNs的新探索.

结论:

  • 增强的空间编码显著提高了SNN在3D点云分析中的性能.
  • S3DNet展示了SNN在复杂的3D视觉任务中的潜力.
  • 提出的方法为使用SNN的3D点云处理提供了高效和有效的解决方案.