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

Stereotype Content Model02:16

Stereotype Content Model

14.0K
The Stereotype Content Model (SCM) was first proposed by Susan Fiske and her colleagues (Fiske, Cuddy, Glick & Xu, 2002; see also Fiske, 2012 and Fiske, 2017). The SCM specifies that when someone encounters a new group, they will stereotype them based on two metrics: warmth—or that group’s perceived intent, and how likely they are to provide help or inflict harm—and competence—or their ability to carry out that objective. Depending on the warmth-competence...
14.0K

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 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

相关实验视频

Updated: Jun 17, 2025

Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention
06:37

Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention

Published on: December 15, 2023

2.7K

MF-Match:人类行动识别的半监督模型

Tianhe Yun1, Zhangang Wang1

  • 1School of Information and Communication Engineering, Beijing Information Science and Technology University, Beijing 100101, China.

Sensors (Basel, Switzerland)
|August 10, 2024
PubMed
概括
此摘要是机器生成的。

本研究介绍了MF-Match,这是一种使用雷达进行人类行动识别 (HAR) 的半监督学习算法. 它有效地使用未标记的数据来提高准确性,解决基于雷达的HAR中的数据稀缺性挑战.

关键词:
交叉域名跨域名人类行动承认承认雷达 雷达 雷达 雷达 是一个半监督学习 半监督学习

更多相关视频

Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment
08:25

Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment

Published on: May 7, 2019

8.9K
Evaluation of a Smartphone-based Human Activity Recognition System in a Daily Living Environment
06:49

Evaluation of a Smartphone-based Human Activity Recognition System in a Daily Living Environment

Published on: December 11, 2015

8.9K

相关实验视频

Last Updated: Jun 17, 2025

Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention
06:37

Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention

Published on: December 15, 2023

2.7K
Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment
08:25

Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment

Published on: May 7, 2019

8.9K
Evaluation of a Smartphone-based Human Activity Recognition System in a Daily Living Environment
06:49

Evaluation of a Smartphone-based Human Activity Recognition System in a Daily Living Environment

Published on: December 11, 2015

8.9K

科学领域:

  • 雷达信号处理是指处理雷达信号的过程.
  • 机器学习用于传感.
  • 人与计算机的互动.

背景情况:

  • 使用雷达的人类行动识别 (HAR) 提供了保护隐私的,独立于照明的非接触式传感.
  • 对于HAR的深度学习模型需要大型标记数据集,而雷达数据很少.
  • 这种数据稀缺性阻碍了基于雷达的HAR技术的进步.

研究的目的:

  • 为了应对基于雷达的HAR中有限的标记数据的挑战.
  • 提出一个半监督学习算法,利用未标记的雷达数据.
  • 提高人类行动识别模型的准确性和稳定性.

主要方法:

  • 开发了MF-Match,这是一个半监督的算法,用于生成无监督雷达数据的伪标签.
  • 集成的对比学习来完善伪标签的质量并最大限度地减少错误标签的影响.
  • 应用算法从雷达信号中提取嵌入式人类行为信息.

主要成果:

  • 在两个雷达数据集上实现了86.69%和91.48%的高动作识别准确度.
  • 使用仅10%的标记数据证明有效性,显著减少数据要求.
  • 验证了算法的能力来提高HAR性能,尽管有限的标记数据.

结论:

  • 在基于雷达的HAR中,MF-Match有效地克服了数据稀缺的问题.
  • 拟议的方法通过使用未标记的数据和对比学习来提高HAR的准确性.
  • 这种方法显著提高了基于深度模型的大规模HAR系统的可行性.