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

Structural Classification of Joints01:20

Structural Classification of Joints

3.5K
Joints, also known as articulations, are classified based on their structural characteristics, i.e., based on whether the articulating surfaces of the adjacent bones are directly connected by fibrous connective tissue or cartilage, or whether the articulating surfaces contact each other within a fluid-filled joint cavity. These differences serve to divide the joints of the body into three structural classifications.
A fibrous joint is where the adjacent bones are united by fibrous connective...
3.5K

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

CT-Malaria Detection via Adaptive-Weighted Deep Learning Models.

Biomedicines·2026
Same author

Level-crossing processing and deep convolutional neural network for arrhythmia classification in telehealth services.

Physical and engineering sciences in medicine·2025
Same author

Survey on Blockchain-Based Data Storage Security for Android Mobile Applications.

Sensors (Basel, Switzerland)·2023
Same author

ECG based apnea detection by multirate processing hybrid of wavelet-empirical decomposition Hjorth features extraction and neural networks.

PloS one·2023
Same author

Interoperable IoMT Approach for Remote Diagnosis with Privacy-Preservation Perspective in Edge Systems.

Sensors (Basel, Switzerland)·2023
Same author

Identification, 3D-Reconstruction, and Classification of Dangerous Road Cracks.

Sensors (Basel, Switzerland)·2023
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: Jul 20, 2025

Surrogate Model Development for Digital Experiments in Welding
09:17

Surrogate Model Development for Digital Experiments in Welding

Published on: March 28, 2025

1.0K

使用X射线图像增强和卷积神经网络的多类接缺陷的自动分类.

Dalila Say1, Salah Zidi1, Saeed Mian Qaisar2,3

  • 1Hatem Bettaher Laboratory, IResCoMath, University of Gabes, Gabes 6029, Tunisia.

Sensors (Basel, Switzerland)
|July 29, 2023
PubMed
概括
此摘要是机器生成的。

本研究介绍了一种使用数据增强和卷积神经网络 (CNN) 来检测X射线图像中的多类接缺陷的自动化方法. 该方法实现了92%的准确性,为工业检查提供了有前途的解决方案.

关键词:
在美国,CNN是CNN.数据增强数据增强深度学习是一种深度学习.多个类别的分类分类.细分化 细分化的细分化接缺陷 接缺陷 接缺陷

更多相关视频

Author Spotlight: Efficient Image Recognition Using Directional Gradient Histogram Technique and Support Vector Machines
08:27

Author Spotlight: Efficient Image Recognition Using Directional Gradient Histogram Technique and Support Vector Machines

Published on: January 5, 2024

1.2K
Author Spotlight: Advancing CBCT and Digital Dental Image Integration with AI-Assisted Digitization
05:49

Author Spotlight: Advancing CBCT and Digital Dental Image Integration with AI-Assisted Digitization

Published on: February 23, 2024

887

相关实验视频

Last Updated: Jul 20, 2025

Surrogate Model Development for Digital Experiments in Welding
09:17

Surrogate Model Development for Digital Experiments in Welding

Published on: March 28, 2025

1.0K
Author Spotlight: Efficient Image Recognition Using Directional Gradient Histogram Technique and Support Vector Machines
08:27

Author Spotlight: Efficient Image Recognition Using Directional Gradient Histogram Technique and Support Vector Machines

Published on: January 5, 2024

1.2K
Author Spotlight: Advancing CBCT and Digital Dental Image Integration with AI-Assisted Digitization
05:49

Author Spotlight: Advancing CBCT and Digital Dental Image Integration with AI-Assisted Digitization

Published on: February 23, 2024

887

科学领域:

  • 工业非破坏性测试 工业非破坏性测试
  • 机器学习用于质量控制.
  • 图像处理用于缺陷检测和检测.

背景情况:

  • 手动X射线检查接缺陷是昂贵的,耗时的,容易出现人为错误.
  • 在制造业中,需要自动化,可靠和高效的方法来识别各种接缺陷的需求至关重要.

研究的目的:

  • 开发一种自动化系统,从X射线图像中识别和分类多种类型的接缺陷.
  • 利用数据增强和卷积神经网络 (CNN) 来提高缺陷检测的准确性.

主要方法:

  • 实施了一种混合方法,将先进的数据增强技术 (旋转,剪切,放大,亮度,翻转) 与CNN模型相结合.
  • 在一个工业数据集上训练和评估该模型,该数据集包括6个缺陷类别和正常样本中的4479张X射线图像.

主要成果:

  • 该自动化系统在检测和分类各种接缺陷时平均达到92%的准确性.
  • 数据增强策略改善了CNN模型的泛化能力,用于多类缺陷识别.

结论:

  • 拟议的自动化方法表明,在工业环境中,可靠和高效的接缺陷检测具有显著的潜力.
  • 这种基于CNN的方法为传统的手动检查提供了可行的替代方案,提高了准确性并降低了运营成本.