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

Predicting Molecular Geometry02:27

Predicting Molecular Geometry

45.7K
VSEPR Theory for Determination of Electron Pair Geometries
45.7K
Prediction Intervals01:03

Prediction Intervals

3.4K
The interval estimate of any variable is known as the prediction interval. It helps decide if a point estimate is dependable.
However, the point estimate is most likely not the exact value of the population parameter, but close to it. After calculating point estimates, we construct interval estimates, called confidence intervals or prediction intervals. This prediction interval comprises a range of values unlike the point estimate and is a better predictor of the observed sample value, y. 
3.4K
End Point Prediction: Gran Plot01:07

End Point Prediction: Gran Plot

1.2K
A Gran plot is used to predict the equivalence volume or endpoint of a potentiometric or acid-base titration without reaching the endpoint. Typically, titration data is collected as a function of the titrant's volume up to a point less than the equivalence volume and then transformed into a linear format. The straight line is extended to the x-axis, indicating the necessary titrant volume to achieve the equivalence point.
For potentiometric titration, the Gran plot is created by plotting...
1.2K
Sensitivity, Specificity, and Predicted Value01:13

Sensitivity, Specificity, and Predicted Value

1.3K
In healthcare diagnostics, laboratory tests play a crucial role in identifying and diagnosing a wide range of medical conditions. However, interpreting test results is not always straightforward. An abnormal test result does not always confirm the presence of a disease, just as a normal result does not guarantee its absence. To assess the reliability of these diagnostic tools, healthcare practitioners rely on two key statistical indicators: sensitivity and specificity.
Sensitivity is the...
1.3K
Predicting Reaction Outcomes02:24

Predicting Reaction Outcomes

10.8K
Kinetics describes the rate and path by which a reaction occurs. In contrast, thermodynamics deals with state functions and describes the properties, behavior, and components of a system. It is not concerned with the path taken by the process and cannot address the rate at which a reaction occurs. Although it does provide information about what can happen during a reaction process, it does not describe the detailed steps of what appears on an atomic or a molecular level. On the other hand,...
10.8K
Predicting Products: Substitution vs. Elimination02:52

Predicting Products: Substitution vs. Elimination

14.8K
When a nucleophile and an alkyl halide react, nucleophilic substitution and β-elimination reactions compete to generate products.
The following factors can influence the mechanisms competing against each other:
14.8K

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

Augmented intelligence with voice assistance and automated machine learning in Industry 5.0.

Frontiers in artificial intelligence·2025
Same author

Permissioned blockchain network for proactive access control to electronic health records.

BMC medical informatics and decision making·2024
Same author

Editorial: Human-Centered Artificial Intelligence in Industry 5.0.

Frontiers in artificial intelligence·2024
Same author

Modelling and Predictive Monitoring of Business Processes under Uncertainty with Reinforcement Learning.

Sensors (Basel, Switzerland)·2023
Same author

Enterprise Integration and Interoperability for Big Data-Driven Processes in the Frame of Industry 4.0.

Frontiers in big data·2021
Same author

NEURON: enabling autonomicity in wireless sensor networks.

Sensors (Basel, Switzerland)·2012

相关实验视频

Updated: Jan 29, 2026

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma
04:09

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

Published on: October 10, 2018

8.7K

健康状况预测与强化学习用于预测性维护.

Anastasis Aglogallos1, Alexandros Bousdekis1, Stefanos Kontos1

  • 1Information Management Unit (IMU), Institute of Communication and Computer Systems (ICCS), School of Electrical and Computer Engineering, National Technical University of Athens (NTUA), Athens, Greece.

Frontiers in artificial intelligence
|January 28, 2026
PubMed
概括
此摘要是机器生成的。

强化学习 (RL) 为预测性维护提供了传统机器学习的强大替代方案,在条件不断变化的场景中表现出色. 靠近政策优化 (PPO) 和软行为者批判 (SAC) 证明了CNC机床磨损预测中最有效和最稳定的性能.

关键词:
工业4.0 工业4.0 工业4.0 工业4.0 工业4.0 是什么?深度学习是一种深度学习.降解降解预测的预测.机器学习是机器学习.预测性维护是指预测性维护.强化学习是一种强化学习.

更多相关视频

A Protocol for Computer-Based Protein Structure and Function Prediction
16:41

A Protocol for Computer-Based Protein Structure and Function Prediction

Published on: November 3, 2011

69.8K
Comparison of Predictive Performance of Three Lymph Node Staging Systems in Colorectal Signet Ring Cell Carcinoma Based on Machine Learning Model
07:13

Comparison of Predictive Performance of Three Lymph Node Staging Systems in Colorectal Signet Ring Cell Carcinoma Based on Machine Learning Model

Published on: April 18, 2025

509

相关实验视频

Last Updated: Jan 29, 2026

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma
04:09

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

Published on: October 10, 2018

8.7K
A Protocol for Computer-Based Protein Structure and Function Prediction
16:41

A Protocol for Computer-Based Protein Structure and Function Prediction

Published on: November 3, 2011

69.8K
Comparison of Predictive Performance of Three Lymph Node Staging Systems in Colorectal Signet Ring Cell Carcinoma Based on Machine Learning Model
07:13

Comparison of Predictive Performance of Three Lymph Node Staging Systems in Colorectal Signet Ring Cell Carcinoma Based on Machine Learning Model

Published on: April 18, 2025

509

科学领域:

  • 制造业 制造技术 制造技术
  • 人工智能的人工智能
  • 机器学习 机器学习

背景情况:

  • 预测性维护对于工业4.0至关重要,但传统方法在标记数据和适应性方面扎.
  • 强化学习 (RL) 通过互动学习最佳策略,绕过标记的数据需求并处理设备退化动态.

研究的目的:

  • 评估在制造业中用于预测性维护的无模型RL算法.
  • 在不同环境中比较近接政策优化 (PPO),优势行为者-关键 (A2C),深度决定性政策梯度 (DDPG) 和软行为者-关键 (SAC) 的表现.

主要方法:

  • 制定了CNC机床磨损预测作为马尔科夫决策过程 (MDP).
  • 实现并测试了四个无模型的RL算法:PPO,A2C,DPG和SAC.
  • 在四个自定义环境中验证了性能,分析了学习动态,融合和概括.

主要成果:

  • PPO和SAC表现出最稳定和最有效的表现.
  • 在结构化环境中,SAC表现出色,而PPO表现出强大的泛化能力.
  • A2C显示了持续的长期学习;由于探索有限,DDPG表现不佳.

结论:

  • RL显示了先进的预测性维护应用的巨大潜力.
  • 算法选择应与特定的环境特征和奖励结构保持一致,以获得最佳的结果.