Search research articles

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 Journal Methods Collections JoVE Encyclopedia of Experiments 存档

教育

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual 教师资源中心教师网站

使用条款与条件

Search research articles

相关实验视频

Updated: May 10, 2025

Automated Analysis of Dynamic Ca2+ Signals in Image Sequences

Automated Analysis of Dynamic Ca2+ Signals in Image Sequences

Published on: June 16, 2014

使用深度学习和波形转换对板状结构的影响定位的新方法.

Asaad Migot^1,2, Ahmed Saaudi^2,3, Victor Giurgiutiu²

¹Department of Petroleum and Gas Engineering, College of Engineering, University of Thi-Qar, Nasiriyah 64001, Iraq.

Sensors (Basel, Switzerland)

|April 28, 2025

概括

相关概念视频

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

Fatigue Crack Length Estimation Using Acoustic Emissions Technique-Based Convolutional Neural Networks.

Sensors (Basel, Switzerland)·2026

Same author

Exploring Piezoelectric Actuation towards Its Applications in Laser Powder Bed Fusion Additive Manufacturing.

Sensors (Basel, Switzerland)·2024

Same author

Piezoelectric Transducer-Based Structural Health Monitoring.

Sensors (Basel, Switzerland)·2024

Same author

Delamination Depth Detection in Composite Plates Using the Lamb Wave Technique Based on Convolutional Neural Networks.

Sensors (Basel, Switzerland)·2024

Same author

Piezoelectric Wafer Active Sensor Transducers for Acoustic Emission Applications.

Sensors (Basel, Switzerland)·2023

Same author

Crack-Length Estimation for Structural Health Monitoring Using the High-Frequency Resonances Excited by the Energy Release during Fatigue-Crack Growth.

Sensors (Basel, Switzerland)·2021

这项研究引入了一种使用二维卷积神经网络 (CNN) 和压电晶片活性传感器 (PWAS) 精确定位对结构的影响的新方法. 该模型在冲击定位方面实现了高精度,证明了其在结构健康监测方面的有效性.

科学领域:

结构健康监测结构健康监测
信号处理信号处理
机器学习应用机器学习应用

背景情况:

对板状结构的撞击事件会产生由传感器检测到的反射波.
准确地定位冲击对于评估结构完整性和损坏至关重要.
现有的方法在区分类似的信号特征时可能面临挑战.

研究的目的:

开发和评估一种用于定位板状结构上的冲击事件的新方法.
使用二维卷积神经网络 (CNN) 来处理冲击信号.
评估拟议的CNN模型在冲击本地化准确性方面的表现.

主要方法:

使用压电晶片活性传感器 (PWAS) 的网络来获取撞击信号.
接收到的信号经过基于波形变换 (WT) 的时间频率分析.
经过处理的WT图表被用作图像数据集来训练和测试2D CNN模型.
研究了两种传感器放置场景,影响的次数各不相同.

主要成果:

拟议的CNN模型在冲击定位方面表现出了卓越的表现.
在第一个场景 (两个传感器) 中,所有撞击点都被准确地定位.
该模型在第二种情景 (四个传感器) 中实现了99%的局部冲击精度.

关键词:

在PWAS中,PWAS是PWAS.卷积神经网络 (CNN) 是一种神经网络.深度学习 (DL) 是指深度学习.影响本地化影响本地化.波形变换 (WT) 是一个波形变换.

更多相关视频

Picometer-Precision Atomic Position Tracking through Electron Microscopy

Picometer-Precision Atomic Position Tracking through Electron Microscopy

Published on: July 3, 2021

Control of Cell Adhesion using Hydrogel Patterning Techniques for Applications in Traction Force Microscopy

Control of Cell Adhesion using Hydrogel Patterning Techniques for Applications in Traction Force Microscopy

Published on: January 29, 2022

相关实验视频

Last Updated: May 10, 2025

Automated Analysis of Dynamic Ca2+ Signals in Image Sequences

Automated Analysis of Dynamic Ca2+ Signals in Image Sequences

Published on: June 16, 2014

Picometer-Precision Atomic Position Tracking through Electron Microscopy

Picometer-Precision Atomic Position Tracking through Electron Microscopy

Published on: July 3, 2021

Control of Cell Adhesion using Hydrogel Patterning Techniques for Applications in Traction Force Microscopy

Control of Cell Adhesion using Hydrogel Patterning Techniques for Applications in Traction Force Microscopy

Published on: January 29, 2022

结论:

开发的基于CNN的方法非常有效地将撞击事件定位在板状结构上.
该研究强调了基于WT的信号处理和CNN用于结构性健康监测的潜力.
未来的工作可能将重点放在解决与细分和影响近距离产生的差异化类似信号特征相关的挑战上.