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

Multi-input and Multi-variable systems01:22

Multi-input and Multi-variable systems

93
Cruise control systems in cars are designed as multi-input systems to maintain a driver's desired speed while compensating for external disturbances such as changes in terrain. The block diagram for a cruise control system typically includes two main inputs: the desired speed set by the driver and any external disturbances, such as the incline of the road. By adjusting the engine throttle, the system maintains the vehicle's speed as close to the desired value as possible.
In the absence...
93
Distribution Reliability and Automation01:25

Distribution Reliability and Automation

104
Distribution reliability in electrical power systems is critical for ensuring an uninterrupted power supply to consumers at minimal cost. According to IEEE Standard Terms, reliability is the probability that a device will function without failure over a specified time period or amount of usage. For electric power distribution, this translates to maintaining continuous power supply and addressing customer concerns over power outages. Several indices, as defined by IEEE Standard 1366-2012, are...
104
Classification of Systems-II01:31

Classification of Systems-II

133
Continuous-time systems have continuous input and output signals, with time measured continuously. These systems are generally defined by differential or algebraic equations. For instance, in an RC circuit, the relationship between input and output voltage is expressed through a differential equation derived from Ohm's law and the capacitor relation,
133
Classification of Systems-I01:26

Classification of Systems-I

167
Linearity is a system property characterized by a direct input-output relationship, combining homogeneity and additivity.
Homogeneity dictates that if an input x(t) is multiplied by a constant c, the output y(t) is multiplied by the same constant. Mathematically, this is expressed as:
167
Observational Learning01:12

Observational Learning

123
Albert Bandura's observational learning, also known as imitation or modeling, occurs when a person observes and imitates another's behavior. It is a quicker process than operant conditioning. A well-known example is the Bobo doll study, where children who saw an adult acting aggressively towards the doll were more likely to act aggressively when left alone, compared to those who observed a nonaggressive adult. Many psychologists view observational learning as a form of latent learning...
123
Propagation of Uncertainty from Systematic Error01:10

Propagation of Uncertainty from Systematic Error

455
The atomic mass of an element varies due to the relative ratio of its isotopes. A sample's relative proportion of oxygen isotopes influences its average atomic mass. For instance, if we were to measure the atomic mass of oxygen from a sample, the mass would be a weighted average of the isotopic masses of oxygen in that sample. Since a single sample is not likely to perfectly reflect the true atomic mass of oxygen for all the molecules of oxygen on Earth, the mass we obtain from this...
455

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

Practical Test-Time Domain Adaptation for Industrial Condition Monitoring by Leveraging Normal-Class Data.

Sensors (Basel, Switzerland)·2025
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

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 28, 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

2.6K

对于多传感器系统的强大的分布意识集体学习.

Payman Goodarzi1, Julian Schauer1, Andreas Schütze1

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

Sensors (Basel, Switzerland)
|February 13, 2025
PubMed
概括
此摘要是机器生成的。

本研究引入了一种新的自动化机器学习 (AutoML) 框架,用于强大的多传感器数据分析. 它可以有效地检测工业监控中的分配转移,提高准确性和降低成本.

关键词:
在AutoML中使用AutoML.检测异常检测异常检测状态监控 状态监控 状态监控深度集体学习 (Deep Ensemble Learning) 是一种深度集体学习.域名适应 域名适应销售之外 (OOD) 检测检测预后和健康管理 (PHM)基于传感器的系统结构健康监测 结构健康监测

更多相关视频

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: 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

455

相关实验视频

Last Updated: May 28, 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

2.6K
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: 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

455

科学领域:

  • 机器学习 机器学习
  • 数据科学数据科学数据科学
  • 传感器网络 传感器网络

背景情况:

  • 传感器数据的分布和域移动对工业监控系统构成重大挑战.
  • 适应不知不觉的变化对于在关键应用中可靠的决策至关重要.

研究的目的:

  • 引入一种新的,强大的多传感器整体框架,集成自动机器学习 (AutoML),以解决传感器数据的领域转移和变化.
  • 提高适应无人注意到的分销转移的能力,并降低组合模型的培训成本.

主要方法:

  • 一个多传感器整体框架,利用多种模型架构,超参数和决策聚合策略.
  • 整合超参数优化和模型选择,以实现高效的组合训练.
  • 对五个公开可用的数据集进行评估,用于监督和无监督的轮班检测.

主要成果:

  • 该框架显示了对不同数据属性的未被注意到的分布转移的增强适应性.
  • 与单一模型基线相比,共同评估指标的显著改善.
  • 对于分类任务和有效的分配转移识别的近乎完美的测试准确性 (90%的AUROC,20%的FPR95).

结论:

  • 拟议的AutoML集成框架为面对现实世界传感器数据挑战的工业应用提供了实用,分布意识的解决方案.
  • 该方法在监督和无监督的分配转移检测场景中显著提高了性能.
  • 这种方法代表了朝着更具弹性和适应性的工业监测系统迈出的新步骤.