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

Survival Tree01:19

Survival Tree

73
Survival trees are a non-parametric method used in survival analysis to model the relationship between a set of covariates and the time until an event of interest occurs, often referred to as the "time-to-event" or "survival time." This method is particularly useful when dealing with censored data, where the event has not occurred for some individuals by the end of the study period, or when the exact time of the event is unknown.
 Building a Survival Tree
Constructing a...
73

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 Machine Learning Ensemble Framework for Carbon Price Prediction and Decision Support Under Information Structure Heterogeneity in Regional Carbon Markets in China.

Entropy (Basel, Switzerland)·2026
Same author

A Novel Simulation Method for 3D Digital-Image Correlation: Combining Virtual Stereo Vision and Image Super-Resolution Reconstruction.

Sensors (Basel, Switzerland)·2024
Same author

A Contact-Mode Triboelectric Nanogenerator for Energy Harvesting from Marine Pipe Vibrations.

Sensors (Basel, Switzerland)·2021
Same author

[Lung protection of recruitment maneuver].

Zhongguo dang dai er ke za zhi = Chinese journal of contemporary pediatrics·2008
Same author

Evaluation of triple anti-platelet therapy by modified thrombelastography in patients with acute coronary syndrome.

Chinese medical journal·2008
Same author

Four novel mutations of TYR gene in Chinese OCA1 patients.

Journal of dermatological science·2008
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 13, 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

487

自主监督的水变形异常检测基于时间空间对比学习

Yu Wang1, Guohua Liu1

  • 1College of Civil Engineering and Architecture, Zhejiang University, Hangzhou 310058, China.

Sensors (Basel, Switzerland)
|September 14, 2024
PubMed
概括
此摘要是机器生成的。

检测水变形异常对于结构完整性至关重要. 一种新的时空对比学习预训练 (STCLP) 方法有效地从未标记的数据中提取特征,以改善大健康监测.

关键词:
检测异常检测异常检测大的变形.大健康监测监测大健康监测自主监督学习学习

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

1.0K
Visualization Method for Proprioceptive Drift on a 2D Plane Using Support Vector Machine
07:05

Visualization Method for Proprioceptive Drift on a 2D Plane Using Support Vector Machine

Published on: October 27, 2016

9.2K

相关实验视频

Last Updated: Jun 13, 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

487
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

1.0K
Visualization Method for Proprioceptive Drift on a 2D Plane Using Support Vector Machine
07:05

Visualization Method for Proprioceptive Drift on a 2D Plane Using Support Vector Machine

Published on: October 27, 2016

9.2K

科学领域:

  • 地质技术工程 地质技术工程
  • 结构健康监测 结构健康监测
  • 机器学习 机器学习

背景情况:

  • 检测水变形异常对于结构完整性和水健康监测 (DHM) 的早期预警至关重要.
  • 传统的方法需要大量的标记数据,这是昂贵和劳动密集型的.
  • 由于数据采集挑战,未标记和半标记的数据方法正在获得引力.

研究的目的:

  • 引入一种新的空间时间对比学习预训练 (STCLP) 策略,从未标记的变形数据中提取歧视性特征.
  • 提出一种有效的异常检测方法,利用开发的STCLP策略来检测水变形的异常.
  • 通过参数转移和先前知识微调来增强下游分类任务.

主要方法:

  • 开发了一个空间时间对比学习预训练 (STCLP) 策略,将空间和时间对比学习结合起来.
  • 从未标记的水变形数据集中提取了歧视性特征.
  • 通过将STCLP预训练的参数转移到下游任务并根据先前知识进行微调来实施异常检测方法.

主要成果:

  • 提出的基于STCLP的异常检测方法在涉及形大的案例研究中表现出色.
  • 该方法在大变形异常检测方面明显优于其他基准模型.
  • 使用STCLP战略验证了利用空间和时间特征的有效性.

结论:

  • 该STCLP战略提供了一种强大的方法,用于使用未标记的数据进行大健康监测.
  • 拟议的方法为检测水变形异常提供了强大而高效的解决方案.
  • 这项研究有助于推进关键基础设施结构健康监测的数据驱动技术.