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

Uncertainty: Confidence Intervals00:54

Uncertainty: Confidence Intervals

4.1K
The confidence interval is the range of values around the mean that contains the true mean. It is expressed as a probability percentage. The interpretation of a 95% confidence interval, for instance, is that the statistician is 95% confident that the true mean falls within the interval. The upper and lower limits of this range are known as confidence limits. The confidence limits for the true mean are estimated from the sample's mean, the standard deviation, and the statistical factor...
4.1K
Prediction Intervals01:03

Prediction Intervals

2.2K
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. 
2.2K
Uncertainty: Overview00:59

Uncertainty: Overview

549
In analytical chemistry, we often perform repetitive measurements to detect and minimize inaccuracies caused by both determinate and indeterminate errors. Despite the cares we take, the presence of random errors means that repeated measurements almost never have exactly the same magnitude. The collective difference between these measurements - observed values - and the estimated or expected value is called uncertainty. Uncertainty is conventionally written after the estimated or expected value.
549
Propagation of Uncertainty from Random Error00:59

Propagation of Uncertainty from Random Error

677
An experiment often consists of more than a single step. In this case, measurements at each step give rise to uncertainty. Because the measurements occur in successive steps, the uncertainty in one step necessarily contributes to that in the subsequent step. As we perform statistical analysis on these types of experiments, we must learn to account for the propagation of uncertainty from one step to the next. The propagation of uncertainty depends on the type of arithmetic operation performed on...
677
Propagation of Uncertainty from Systematic Error01:10

Propagation of Uncertainty from Systematic Error

516
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...
516
Response Surface Methodology01:16

Response Surface Methodology

117
Response Surface Methodology (RSM) is a collection of statistical and mathematical techniques used to develop, improve, and optimize processes. It is particularly valuable when many input variables or factors potentially influence a response variable.
The process of RSM involves several key steps:
117

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 Novel Method for Early Gear Pitting Fault Diagnosis Using Stacked SAE and GBRBM.

Sensors (Basel, Switzerland)·2019
Same author

A monitoring-modeling approach to SO<sub>4</sub><sup>2-</sup> and NO<sub>3</sub><sup>-</sup> secondary conversion ratio estimation during haze periods in Beijing, China.

Journal of environmental sciences (China)·2019
Same author

Short Versus Long Cephalomedullary Nails for Fixation of Stable Versus Unstable Intertrochanteric Femur Fractures at a Level 1 Trauma Center.

Orthopedics·2019
Same author

E-cadherin is Required for the Homeostasis of Lgr5<sup>+</sup> Gastric Antral Stem Cells.

International journal of biological sciences·2019
Same author

Development of a real-time nucleic acid sequence-based amplification assay for the rapid detection of Salmonella spp. from food.

Brazilian journal of microbiology : [publication of the Brazilian Society for Microbiology]·2019
Same author

Conjugated Microporous Polymers with Tunable Electronic Structure for High-Performance Potassium-Ion Batteries.

ACS nano·2019

相关实验视频

Updated: Jun 23, 2025

Surrogate Model Development for Digital Experiments in Welding
09:17

Surrogate Model Development for Digital Experiments in Welding

Published on: March 28, 2025

767

一个先进的工具,使用贝叶斯推理和支向量回归的不确定性量化预测技术.

Zhiming Rong1, Yuxiong Li2, Li Wu2

  • 1Applied Technology College, Dalian Ocean University, Dalian 116023, China.

Sensors (Basel, Switzerland)
|June 19, 2024
PubMed
概括

本研究引入了一种用于预测切削工具磨损的新方法,该方法包含不确定性量化. 该方法提高了工业应用的预测准确性和稳定性.

关键词:
贝叶斯人的推理.布朗运动是什么意思 布朗运动切割工具的磨损预测和预测.支持向量的回归.不确定性量化不确定性量化

更多相关视频

Author Spotlight: Efficient Image Recognition Using Directional Gradient Histogram Technique and Support Vector Machines
08:27

Author Spotlight: Efficient Image Recognition Using Directional Gradient Histogram Technique and Support Vector Machines

Published on: January 5, 2024

1.0K
Split Point Analysis and Uncertainty Quantification of Thermal-Optical Organic/Elemental Carbon Measurements
10:22

Split Point Analysis and Uncertainty Quantification of Thermal-Optical Organic/Elemental Carbon Measurements

Published on: September 7, 2019

8.2K

相关实验视频

Last Updated: Jun 23, 2025

Surrogate Model Development for Digital Experiments in Welding
09:17

Surrogate Model Development for Digital Experiments in Welding

Published on: March 28, 2025

767
Author Spotlight: Efficient Image Recognition Using Directional Gradient Histogram Technique and Support Vector Machines
08:27

Author Spotlight: Efficient Image Recognition Using Directional Gradient Histogram Technique and Support Vector Machines

Published on: January 5, 2024

1.0K
Split Point Analysis and Uncertainty Quantification of Thermal-Optical Organic/Elemental Carbon Measurements
10:22

Split Point Analysis and Uncertainty Quantification of Thermal-Optical Organic/Elemental Carbon Measurements

Published on: September 7, 2019

8.2K

科学领域:

  • 制造业 工程 制造工程
  • 机械工程 机械工程
  • 数据科学数据科学数据科学

背景情况:

  • 工具磨损预测对于工业生产效率至关重要.
  • 现有的机器学习方法在工具磨损估计中经常忽略随机不确定性因素.
  • 需要更强大,更准确的工具磨损预测技术.

研究的目的:

  • 提出一种用于预测具有不确定性量化的切削工具磨损的新方法.
  • 为了解决目前基于机器学习的工具磨损预测方法的局限性.
  • 提前提高工具磨损预测的准确性和稳定性.

主要方法:

  • 使用布朗运动随机过程建模退化特征.
  • 训练一个支向量回归 (SVR) 模型,将特征映射到工具磨损.
  • 采用贝叶斯推理来进行在线参数更新和未来特征趋势估计.
  • 使用模拟样本预测工具磨损作为分布密度.

主要成果:

  • 拟议的方法有效地模拟了不确定性的工具磨损退化.
  • 预先预测切割刀具磨损是以分布密度的形式实现的.
  • 与现有方法相比,实验验证证明了更高的预测准确性和稳定性.

结论:

  • 这种新方法在切割工具磨损预测方面取得了重大进展.
  • 结合随机过程和贝叶斯推理可以提高预测可靠性.
  • 该方法为主动维护和优化制造流程提供了宝贵的见解.