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

517
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...
517

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 Novel Machine Learning Approach for Predicting Prognosis of SFTS Patients in the Early Stages of Disease.

The Canadian journal of infectious diseases & medical microbiology = Journal canadien des maladies infectieuses et de la microbiologie medicale·2026
Same author

Distinct Longitudinal Trajectories of SLEDAI-2K Scores Predict Prognosis in Systemic Lupus Erythematosus Based on Group-Based Trajectory Modeling.

Journal of immunology research·2026
Same author

T Follicular Helper Cell Immune Signatures Associated With Disease Severity in Severe Fever With Thrombocytopenia Syndrome.

Journal of immunology research·2026
Same author

Beyond hepatitis C: clinical spectrum and demographic characteristics of anti-rods and rings antibodies in a large ANA-positive cohort.

Frontiers in immunology·2026
Same author

Eating Difficulties, Psychological Distress, and Self-Management in Colorectal Cancer Patients Undergoing Chemotherapy: A Qualitative Study.

Psycho-oncology·2026
Same author

Andrographolide Suppresses Head and Neck Squamous Cell Carcinoma Progression via EGR1-ACSL4 Axis-Mediated Ferroptosis.

The American journal of Chinese medicine·2026
Same journal

RETRACTED: Zhang et al. A Novel Framework for Reconstruction and Imaging of Target Scattering Centers via Wide-Angle Incidence in Radar Networks. <i>Sensors</i> 2025, <i>25</i>, 6802.

Sensors (Basel, Switzerland)·2026
Same journal

Enhancing Unsupervised Multi-Source Domain Adaptation for Person Re-Identification via Mixture of Experts and Graph-Based Relation.

Sensors (Basel, Switzerland)·2026
Same journal

Development of an Instrumented Glove for Palmar Pressure Assessment in Kayakers.

Sensors (Basel, Switzerland)·2026
Same journal

Development and Experimental Validation of an Autonomous IoT-Based Monitoring System for Real-Time Water Quality Assessment in the Amazon River.

Sensors (Basel, Switzerland)·2026
Same journal

Semi-Supervised Adversarial Learning Framework for Controller Area Network Bus Intrusion Detection.

Sensors (Basel, Switzerland)·2026
Same journal

Smart Optimization Method for Safety Signs in Innovative Manufacturing Environments Integrating Industrial Field IoT Sensors and Knowledge Graphs.

Sensors (Basel, Switzerland)·2026
查看所有相关文章

相关实验视频

Updated: Jun 11, 2025

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

483

冲击负载定位基于多尺度特征融合卷积神经网络

Shiji Wu1,2, Xiufeng Huang1,2, Rongwu Xu1,2

  • 1Laboratory of Vibration and Noise, Naval University of Engineering, Wuhan 430033, China.

Sensors (Basel, Switzerland)
|September 28, 2024
PubMed
概括
此摘要是机器生成的。

这项研究引入了一种新的多尺度特征融合卷积神经网络 (MSFF-CNN),用于在船只等复杂结构中准确地定位冲击负载. 该方法实现了94.29%的准确性,在识别和定位撞击事件方面超过了传统的CNN.

关键词:
卷积神经网络是一种卷积神经网络.冲击负荷的影响负荷.多个尺度的多个尺度.冲击源的定位和位置

更多相关视频

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

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Published on: July 5, 2024

376
Automated Midline Shift and Intracranial Pressure Estimation based on Brain CT Images
14:08

Automated Midline Shift and Intracranial Pressure Estimation based on Brain CT Images

Published on: April 13, 2013

42.5K

相关实验视频

Last Updated: Jun 11, 2025

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

483
Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique
04:48

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Published on: July 5, 2024

376
Automated Midline Shift and Intracranial Pressure Estimation based on Brain CT Images
14:08

Automated Midline Shift and Intracranial Pressure Estimation based on Brain CT Images

Published on: April 13, 2013

42.5K

科学领域:

  • 工程 工程师 工程师 工程师
  • 机器学习 机器学习
  • 结构健康监测 结构健康监测

背景情况:

  • 冲击负荷定位对于船只等复杂结构至关重要.
  • 传统方法通常需要手动提取特征,这可能是复杂和耗时的.

研究的目的:

  • 为自动冲击负载定位提出一个多级特征融合卷积神经网络 (MSFF-CNN).
  • 提高冲击载荷在船舶结构中识别和定位的准确性和效率.

主要方法:

  • 一个端到端的机器学习模型直接处理原始振动信号.
  • 使用四个独立的卷积层,具有不同的内核大小,用于自动功能学习和连接.
  • 使用数据规范化和L2规范化来增强数据并防止过拟合.
  • 软max分类层用于分类和定位.

主要成果:

  • 在船的船尾模型上,MSFF-CNN实现了94.29%的分类和定位准确度.
  • 演示了改进的特征提取能力,结合了本地感知和全球视野.
  • 超过了传统的卷积神经网络 (CNN) 方法.

结论:

  • 多元框架-CNN方法有效地提高了对冲击负载的分类能力.
  • 该方法显示了结构健康监测中的实际工程应用的巨大潜力.