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

Methods of Classification and Identification01:28

Methods of Classification and Identification

Bacterial identification relies on a diverse array of techniques to classify and understand microorganisms, each tailored to uncover specific characteristics. Traditional morphological approaches, while still valuable, are limited for closely related or structurally simple organisms. Modern methods integrate biochemical, serological, genetic, and advanced molecular tools to achieve greater accuracy.Morphological and Biochemical TechniquesMorphological characteristics, such as cell shape and...

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

Machine Learning Methods for Predicting Syncope Severity in the Emergency Department: A Retrospective Analysis.

Health science reports·2025
Same author

Building an Open Health Data Analytics Platform: a Case Study Examining Relationships and Trends in Seniority and Performance in Healthcare Providers.

Journal of healthcare informatics research·2022
Same author

Rising Mental Health Incidence Among Adolescents in Westchester, NY.

Community mental health journal·2021
Same author

An oscillatory neural network model that demonstrates the benefits of multisensory learning.

Cognitive neurodynamics·2018

相关实验视频

Updated: May 11, 2026

Design and Analysis for Fall Detection System Simplification
08:05

Design and Analysis for Fall Detection System Simplification

Published on: April 6, 2020

10.6K

深度学习系统用于使用门把手上的传感器识别用户.

Jesús Vegas1, A Ravishankar Rao2, César Llamas1

  • 1Escuela de Ingeniería Informática, Universidad de Valladolid, Paseo de Belén 15, 47011 Valladolid, Spain.

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

这项研究引入了一种使用门交互的运动模式来控制门户访问的新方法. 深度学习准确地根据用户独特的运动活动识别用户,增强物理安全.

关键词:
这就是为什么物联网物联网物联网.访问控制 访问控制 访问控制机器学习是机器学习.传感器 传感器 传感器用户识别用户识别

更多相关视频

Author Spotlight: AI-Driven Trypanosome Species Detection from Microscopic Images
08:20

Author Spotlight: AI-Driven Trypanosome Species Detection from Microscopic Images

Published on: October 27, 2023

1.4K
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

相关实验视频

Last Updated: May 11, 2026

Design and Analysis for Fall Detection System Simplification
08:05

Design and Analysis for Fall Detection System Simplification

Published on: April 6, 2020

10.6K
Author Spotlight: AI-Driven Trypanosome Species Detection from Microscopic Images
08:20

Author Spotlight: AI-Driven Trypanosome Species Detection from Microscopic Images

Published on: October 27, 2023

1.4K
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

科学领域:

  • 生物识别信息 生物识别信息
  • 人与计算机的交互
  • 安全系统安全系统

背景情况:

  • 门口访问控制对于物理安全至关重要.
  • 目前的系统通常依赖于传统的身份验证方法.
  • 准确性和速度等性能指标对于访问控制系统至关重要.

研究的目的:

  • 研究一种用于门门访问控制中的用户识别的新方法.
  • 使用用户与门把手的交互模式进行身份验证.
  • 将深度学习算法应用于用于行为生物识别的传感器数据.

主要方法:

  • 使用嵌入式加速度计和陀螺仪传感器测量用户与门把手的互动.
  • 应用基于深度学习的算法来分析收集的传感器数据.
  • 评估不同用户群体和样本持续时间的识别准确性.

主要成果:

  • 在47名参与者中,获得了90.2%的整体用户识别准确率.
  • 用户识别的准确性达到97.0%的女性和89.8%的男性.
  • 以0.5秒的短样本持续时间 (68.5%准确度) 证明识别用户的可行性.

结论:

  • 通过运动活动模式识别用户是可行的访问控制.
  • 这种方法提供了一种新的行为生物识别技术,用于增强身体安全.
  • 该方法为传统的身份验证方法提供了替代方案.