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Uncertainty: Overview00:59

Uncertainty: Overview

1.5K
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

10.1K
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...
10.1K
Propagation of Uncertainty from Systematic Error01:10

Propagation of Uncertainty from Systematic Error

1.3K
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...
1.3K
Propagation of Uncertainty from Random Error00:59

Propagation of Uncertainty from Random Error

1.6K
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...
1.6K
Uncertainty in Measurement: Accuracy and Precision03:37

Uncertainty in Measurement: Accuracy and Precision

99.5K
Scientists typically make repeated measurements of a quantity to ensure the quality of their findings and to evaluate both the precision and the accuracy of their results. Measurements are said to be precise if they yield very similar results when repeated in the same manner. A measurement is considered accurate if it yields a result that is very close to the true or the accepted value. Precise values agree with each other; accurate values agree with a true value. 
99.5K
Estimation of the Physical Quantities01:05

Estimation of the Physical Quantities

7.2K
On many occasions, physicists, other scientists, and engineers need to make estimates of a particular quantity. These are sometimes referred to as guesstimates, order-of-magnitude approximations, back-of-the-envelope calculations, or Fermi calculations. The physicist Enrico Fermi was famous for his ability to estimate various kinds of data with surprising precision. Estimating does not mean guessing a number or a formula at random. Instead, estimation means using prior experience and sound...
7.2K

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相关实验视频

Updated: Jan 11, 2026

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

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在远程传感图像中半监控物体检测的不确定性量化.

Xi Yang, Penghui Li, Qiubai Zhou

    IEEE transactions on image processing : a publication of the IEEE Signal Processing Society
    |November 10, 2025
    PubMed
    概括
    此摘要是机器生成的。

    本研究引入了不确定性量化 (UNC) 用于半监督物体检测在遥感. 通过使用未标记的数据,UNC通过改进边界框和平衡类重要性来提高检测准确度.

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    Split Point Analysis and Uncertainty Quantification of Thermal-Optical Organic/Elemental Carbon Measurements
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    科学领域:

    • 计算机科学 计算机科学
    • 遥感 遥感 遥感 遥感
    • 人工智能的人工智能

    背景情况:

    • 半监控物体检测 (SSOD) 在自然场景中表现出色,但在遥感图像中尚未得到充分探索.
    • 遥感数据带来了独特的挑战:任意的对象定向,小尺度,密集的分布和模糊的伪盒边界.
    • 类不平衡和注释困难阻碍了SSOD在遥感中的性能.

    研究的目的:

    • 为SSOD提供一种针对遥感图像的新型不确定性量化 (UNC) 方法.
    • 为了应对诸如模糊边界和阶级不平衡等挑战,基于水平边界框 (HBB) 的远程传感对象检测.
    • 在远程传感物体检测任务中有效利用未标记的数据.

    主要方法:

    • UNC使用不确定性来指导网络回归和分类.
    • 语义对齐SAM校准 (SASC) 使用分段任何模型 (SAM) 改进伪盒边界.
    • 动态不确定性权重 (DUW) 根据实例可用性和不确定性调整类重点,并设置百分比值以防止过度强调.

    主要成果:

    • 在DIOR和DOTA数据集上的实验验验证了UNC在利用未标记的遥感数据方面的有效性.
    • 与监督基线相比,UNC显著提高了检测性能.
    • 在DIOR数据集上,UNC实现了12.4% (5%标记数据) 和8.6% (10%标记数据) 的mAP改进.

    结论:

    • 通过量化不确定性,UNC成功地提高了基于HBB的遥感图像的SSOD.
    • 该方法有效地解决了任意方向,小规模,密集分布和阶级不平衡的挑战.
    • UNC展示了一种强大的方法,利用未标记的数据来改进远程传感应用中的物体检测.