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

Range00:59

Range

The range is one of the measures of variation. It can be defined as the difference between a dataset's highest and lowest values. For example, in the study of seven 16-ounce soda cans, the filled volume of soda was measured, thus producing the following amount (in ounces) of soda:
15.9; 16.1; 15.2; 14.8; 15.8; 15.9; 16.0; 15.5
Measurements of the amount of soda in a 16-ounce can vary since different subjects record these measurements or since the exact amount - 16 ounces of liquid, was not...
Wald-Wolfowitz Runs Test I01:17

Wald-Wolfowitz Runs Test I

The Wald-Wolfowitz test, also known as the runs test, is a nonparametric statistical test used to assess the randomness of a sequence of two different types of elements (e.g., positive/negative values, successes/failures). It examines whether the order of the elements in a sequence is random or if there is a pattern or trend present. This nonparametric test applies to any ordered data despite the population and sample data distribution, even if a higher sample size is available.
The test works...
Statistical Methods to Analyze Parametric Data: Student t-Test and Goodness-of-Fit Test01:09

Statistical Methods to Analyze Parametric Data: Student t-Test and Goodness-of-Fit Test

In parametric statistics, two fundamental tests stand out for their utility and wide application: the Student's t-test and goodness-of-fit tests. These tests provide researchers with a robust method for drawing insights from data, testing hypotheses, and making informed decisions based on their findings.
The Student's t-test is a statistical test that examines if there is a statistically significant difference between the means of two groups. This test is instrumental when dealing with data...
Interpreting Run Charts01:25

Interpreting Run Charts

Run charts, essentially line graphs plotted over time, serve as fundamental yet effective tools for process analysis. They chronicle data sequentially, facilitating the identification of trends, shifts, or cyclical movements. This graphical representation is instrumental in determining whether a process is stable or exhibits signs of potential instability indicative of special cause variation. In the healthcare domain, run charts depict infection rates over time, enabling hospitals to monitor...

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 Performance Study of Deep Neural Network Representations of Interpretable ML on Edge Devices with AI Accelerators.

Sensors (Basel, Switzerland)·2025
Same author

Robust Distribution-Aware Ensemble Learning for Multi-Sensor Systems.

Sensors (Basel, Switzerland)·2025
Same author

Towards Interpretable Machine Learning for Automated Damage Detection Based on Ultrasonic Guided Waves.

Sensors (Basel, Switzerland)·2022
Same author

Metal Oxide Nanolayer-Decorated Epitaxial Graphene: A Gas Sensor Study.

Nanomaterials (Basel, Switzerland)·2020
Same author

Graphene Decorated with Iron Oxide Nanoparticles for Highly Sensitive Interaction with Volatile Organic Compounds.

Sensors (Basel, Switzerland)·2019
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: May 11, 2026

Fiber Optic Distributed Sensors for High-resolution Temperature Field Mapping
09:48

Fiber Optic Distributed Sensors for High-resolution Temperature Field Mapping

Published on: November 7, 2016

12.4K

实用测试时间域调整用于通过利用正常类数据来监测工业条件.

Payman Goodarzi1, Andreas Schütze1

  • 1Laboratory for Measurement Technology, Saarland University, 66123 Saarbrücken, Germany.

Sensors (Basel, Switzerland)
|December 31, 2025
PubMed
概括
此摘要是机器生成的。

本研究介绍了用于工业条件监测的正常类测试时间域调整 (NC-TTDA). 该框架将机器学习模型调整为使用正常类样本的新数据分布,在域更换下提高性能.

关键词:
在AutoML中使用AutoML.状态监控 状态监控 状态监控深度学习是一种深度学习.域名适应 域名适应域名转移 域名转移 域名转移检测故障的检测故障检测.多传感器多传感器

更多相关视频

Design and Application of a Fault Detection Method Based on Adaptive Filters and Rotational Speed Estimation for an Electro-Hydrostatic Actuator
06:45

Design and Application of a Fault Detection Method Based on Adaptive Filters and Rotational Speed Estimation for an Electro-Hydrostatic Actuator

Published on: October 28, 2022

2.1K

相关实验视频

Last Updated: May 11, 2026

Fiber Optic Distributed Sensors for High-resolution Temperature Field Mapping
09:48

Fiber Optic Distributed Sensors for High-resolution Temperature Field Mapping

Published on: November 7, 2016

12.4K
Design and Application of a Fault Detection Method Based on Adaptive Filters and Rotational Speed Estimation for an Electro-Hydrostatic Actuator
06:45

Design and Application of a Fault Detection Method Based on Adaptive Filters and Rotational Speed Estimation for an Electro-Hydrostatic Actuator

Published on: October 28, 2022

2.1K

科学领域:

  • 机器学习 机器学习
  • 工业物联网工业物联网工业物联网
  • 数据科学数据科学数据科学

背景情况:

  • 机器学习模型随着领域的转变而退化,影响工业条件监测.
  • 现有的领域适应方法不适合于现实世界的工业环境.

研究的目的:

  • 开发一个实用的领域适应框架,用于工业条件监测.
  • 为了解决传感器数据分布式转移造成的性能下降.

主要方法:

  • 引入了一个正常类测试时间域调整 (NC-TTDA) 框架.
  • 框架检测转移,并通过使用没有标记目标数据的正常类样本来调整模型.
  • 与自动机器学习 (AutoML) 集成,用于端到端优化.

主要成果:

  • 在域名转移下,在六个条件监控数据集中实现了强大的泛化.
  • 获得了超过99%的AUROC平均得分,虚假阳性率低.
  • 在现实世界的工业监控场景中证明了有效性.

结论:

  • 在条件监测中,NC-TTDA为域调整提供了实用解决方案.
  • 该框架提高了机器学习模型在工业应用中的可靠性和通用性.