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

Fast Reactions01:27

Fast Reactions

Fast reactions occurring in times shorter than the time needed to mix reactants pose a unique challenge for investigation. In a liquid-phase continuous-flow system, reactants A and B are swiftly pushed into the mixing chamber, where mixing occurs within 1 ms. The reaction mixture then flows through an observation tube, and one measures light absorption to determine species concentrations at various points of the tube. This method is most appropriate when relatively large volumes of reactants...

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 light-initiated chemical reporter strategy for spatiotemporal labeling of biomolecules.

RSC chemical biology·2022
Same author

Downregulation of HULC Induces Ferroptosis in Hepatocellular Carcinoma via Targeting of the miR-3200-5p/ATF4 Axis.

Oxidative medicine and cellular longevity·2022
Same author

Effect of Programmed Nursing Plan Based on Thinking Map Guidance Mode on Hemodynamics and Intestinal Function Recovery of Patients Undergoing Endoscopic Retrograde Cholangiopancreatography.

Emergency medicine international·2022
Same author

In vivo non-invasive confocal fluorescence imaging beyond 1,700 nm using superconducting nanowire single-photon detectors.

Nature nanotechnology·2022
Same author

Primary signet ring cell carcinoma of the appendix: An interesting case.

The American journal of the medical sciences·2022
Same author

MYC drives autophagy to adapt to stress in Penaeus vannamei.

Fish & shellfish immunology·2022

相关实验视频

Updated: May 12, 2026

Fluorescence detection methods for microfluidic droplet platforms
14:16

Fluorescence detection methods for microfluidic droplet platforms

Published on: December 10, 2011

23.0K

基于低延迟网络的快速融化池状态识别方法.

Yang Lu, Xinyu Dong, Yiyue Fan

    Optics express
    |February 20, 2026
    PubMed
    概括
    此摘要是机器生成的。

    本研究介绍了一种快速,轻量级的深度学习网络,用于金属增材制造中的实时池状态识别. 新方法显著降低了延迟时间,改善了质量控制.

    更多相关视频

    Rapid and Efficient Zebrafish Genotyping Using PCR with High-resolution Melt Analysis
    06:30

    Rapid and Efficient Zebrafish Genotyping Using PCR with High-resolution Melt Analysis

    Published on: February 5, 2014

    23.1K
    Tracking Single Proteins in Lipid Bilayers Using Fluorescence Microscopy
    08:39

    Tracking Single Proteins in Lipid Bilayers Using Fluorescence Microscopy

    Published on: December 12, 2025

    935

    相关实验视频

    Last Updated: May 12, 2026

    Fluorescence detection methods for microfluidic droplet platforms
    14:16

    Fluorescence detection methods for microfluidic droplet platforms

    Published on: December 10, 2011

    23.0K
    Rapid and Efficient Zebrafish Genotyping Using PCR with High-resolution Melt Analysis
    06:30

    Rapid and Efficient Zebrafish Genotyping Using PCR with High-resolution Melt Analysis

    Published on: February 5, 2014

    23.1K
    Tracking Single Proteins in Lipid Bilayers Using Fluorescence Microscopy
    08:39

    Tracking Single Proteins in Lipid Bilayers Using Fluorescence Microscopy

    Published on: December 12, 2025

    935

    科学领域:

    • 材料科学 材料科学 材料科学
    • 制造业 工程 制造工程
    • 计算机视觉 计算机视觉

    背景情况:

    • 实时识别池状态对于高质量的金属增材制造 (MAM) 来说至关重要.
    • 现有的用于识别融化池的深度学习方法具有很高的延迟,阻碍了实时监控.
    • 需要在MAM中更快,更准确地识别池状态.

    研究的目的:

    • 开发一种低延迟,轻量级的卷积神经网络 (CNN),用于快速识别化池状态.
    • 为了提高激光化沉积 (LMD) 过程中池监测的速度和准确性.
    • 为了实现增材制造中的实时质量控制.

    主要方法:

    • 设计了一个高速基本网络单元,使用选择性卷积运算符来增强空间特征提取.
    • 开发了一个轻量级的CNN架构,将设计的网络单元纳入高效的融化池图像分析.
    • 使用定制激光融沉积增材制造系统收集了池状态的数据集.

    主要成果:

    • 拟议的低延迟网络实现了高池状态识别准确率98.50%.
    • 该方法显示,执行时间显著缩短,仅为5.26 ms.
    • 实验结果证实了拟议方法在现有方法上的优越性.

    结论:

    • 开发的轻量级CNN提供了一个卓越的解决方案,用于在MAM中实时识别池状态.
    • 该方法的低延迟和高精度对于有效的实时监控和质量保证至关重要.
    • 这一进步有助于开发更强大,更有效的增材制造工艺.