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

84
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...
84
Difference from Background: Limit of Detection01:05

Difference from Background: Limit of Detection

6.4K
The limit of detection (LOD) is the smallest amount of analyte that can be distinguished from the background noise. The LOD value corresponds to the concentration at which the analyte signal is three times larger than the standard deviation of the blank signal. Below this value, the analyte signal cannot be differentiated from the background noise. It is calculated by dividing the calibration slope by 3 times the standard deviation of the blank signals.
The LOD indicates the presence or absence...
6.4K

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

PainFedMVL: A Federated Multi-View Learning Approach for Multi-Level Pain Recognition.

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society·2026
Same author

Calcitriol protects against diabetic kidney disease by alleviating ferroptosis in renal tubular epithelial cells via JUN/ATF3 pathway.

Biochemical pharmacology·2026
Same author

Effect of psyllium husk gel addition on the quality of whole wheat steamed bread: insights from rheological properties and protein structural changes.

Food chemistry·2026
Same author

A NIR Molecular-level Framework Tracks Mercury Ions via Fast and Real-time Determination in Real Samples and Live Cells.

Journal of fluorescence·2026
Same author

Wavelet-Transformer Attention Network for Accurate Fetal ECG Estimation from Multi-Channel Abdominal Signals.

IEEE journal of biomedical and health informatics·2026
Same author

An Efficient Regenerated Cross-Modal Hashing: Improving Existing Hash Codes with the Arbitrary Length.

IEEE transactions on pattern analysis and machine intelligence·2026

相关实验视频

Updated: Jun 29, 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

530

在城市森林环境中,基于BDS NLOS自编码器的高效卷积无化自编码检测方法.

Yahang Qin1,2, Zhenni Li1,2, Shengli Xie3,4

  • 1School of Automation, Guangdong University of Technology, Guangzhou 510006, China.

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

本研究介绍了CDAENet,这是一种新的深度学习模型,用于检测北斗导航卫星系统 (BDS) 定位中的非视线信号. 该方法在复杂的城市环境中显著提高了准确性,提高了自动驾驶的可靠性.

关键词:
这是BDS BDS的意思.在NLOS的NLOS.拒绝使用自动编码器.时间序列的特征 时间序列的特征城市森林是城市森林.

更多相关视频

Author Spotlight: UAV Remote Sensing for Efficient Invasive Plant Biomass Estimation
08:47

Author Spotlight: UAV Remote Sensing for Efficient Invasive Plant Biomass Estimation

Published on: February 9, 2024

1.4K
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

9.0K

相关实验视频

Last Updated: Jun 29, 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

530
Author Spotlight: UAV Remote Sensing for Efficient Invasive Plant Biomass Estimation
08:47

Author Spotlight: UAV Remote Sensing for Efficient Invasive Plant Biomass Estimation

Published on: February 9, 2024

1.4K
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

9.0K

科学领域:

  • 卫星导航系统 卫星导航系统
  • 深度学习应用程序
  • 信号处理 信号处理

背景情况:

  • 北斗导航卫星系统 (BDS) 对于自动驾驶至关重要,但由于非视线 (NLOS) 信号,城市环境会导致定位错误.
  • 目前用于NLOS检测的深度学习 (DL) 方法面临着大型标记数据集和噪声敏感性的挑战,限制了准确性和概括性.
  • 对于在复杂环境中可靠的自动驾驶车辆导航,需要强大而准确的NLOS检测至关重要.

研究的目的:

  • 提出一种新的深度神经架构,CDAENet,用于在城市森林环境中有效地检测NLOS信号.
  • 解决监督学习的局限性,提高DL模型对噪音数据的稳定性.
  • 提高北斗导航卫星系统 (BDS) 在具有挑战性的城市环境中的定位精度.

主要方法:

  • 开发了一种卷积无声自编码网络 (CDAENet),利用无监督深度学习进行信号无声和特征提取.
  • 实现了一种无噪声自动编码器,以减少时间序列信号的维度,并增强对输入噪声的稳定性.
  • 集成了一个多层感知器 (MLP) 算法,用于识别BDS信号中的非线性特征.

主要成果:

  • 在真实的城市森林数据集上,CDAENet模型实现了超过95%的卫星检测准确度.
  • 与现有的机器学习方法相比,其准确度大约提高了8%.
  • 与目前基于深度学习的NLOS检测方法相比,显示了大约3%的精度提升.

结论:

  • 拟议的CDAENet模型在复杂的城市环境中有效检测NLOS信号,显著提高了BDS定位精度.
  • 无监督学习和无噪声自动编码器提高了模型的稳定性,并减少了对大型标记数据集的依赖.
  • 通过减轻NLOS信号引起的定位错误,CDAENet为可靠的自动驾驶提供了一个有前途的解决方案.