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相关概念视频

Uncertainty: Overview00:59

Uncertainty: Overview

529
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.
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Uncertainty: Confidence Intervals00:54

Uncertainty: Confidence Intervals

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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...
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Propagation of Uncertainty from Random Error00:59

Propagation of Uncertainty from Random Error

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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...
657
Propagation of Uncertainty from Systematic Error01:10

Propagation of Uncertainty from Systematic Error

490
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...
490
Residuals and Least-Squares Property01:11

Residuals and Least-Squares Property

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The vertical distance between the actual value of y and the estimated value of y. In other words, it measures the vertical distance between the actual data point and the predicted point on the line
If the observed data point lies above the line, the residual is positive, and the line underestimates the actual data value for y. If the observed data point lies below the line, the residual is negative, and the line overestimates the actual data value for y.
The process of fitting the best-fit...
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Response Surface Methodology01:16

Response Surface Methodology

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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:
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Visualization Method for Proprioceptive Drift on a 2D Plane Using Support Vector Machine
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双支向量回归用于表征表面重建中的不确定性.

ShiCheng Yu1,2, JiaQing Miao3,4, FeiLong Qin1

  • 1School of Big Data and Artificial Intelligence, Chengdu Technological University, Chengdu, 611730, China.

Scientific reports
|August 23, 2024
PubMed
概括

这项研究引入了一种新的方法,用于使用双支持向量的回归来表征表面重建不确定性. 该方法有效地整合数据,并利用井道信息来限制不确定性,提高油气勘探等领域的准确性.

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科学领域:

  • 地质科学 地质科学
  • 数据科学数据科学数据科学
  • 工程 工程师 工程师 工程师

背景情况:

  • 表面重建对于逆向工程和石油/天然气勘探至关重要.
  • 数据错误和有限的表面信息在重建中造成不确定性.
  • 准确的不确定性可视化对于风险评估和数据采集规划至关重要.

研究的目的:

  • 提出一种不确定性表征方法,用于使用双支持向量回归 (TSVR) 来进行表面重建.
  • 有效地整合多样化的建模数据,并利用高可靠性样本.
  • 将井道数据纳入,以提高重建准确度和减少不确定性.

主要方法:

  • 利用TSVR通过量子和有限约束生成不确定性间隔.
  • 在预测点上使用不平等约束的纳入井路径点.
  • 制定了不确定性表征作为两个较小规模的二进制编程问题,以减少计算时间.

主要成果:

  • 在真实故障和合成数据集上验证了拟议的方法.
  • 证明了各种数据源的有效集成.
  • 展示了数据如何限制不确定性信封,部分减轻了重建不确定性.

结论:

  • 提出的基于TSVR的方法准确地描述和可视化了表面重建的不确定性.
  • 井道数据的整合显著限制了不确定性信封.
  • 这种方法增强了地下建模和勘探中的风险分析和规划.