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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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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

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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
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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Multi-input and Multi-variable systems01:22

Multi-input and Multi-variable systems

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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...
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Aggregates Classification01:29

Aggregates Classification

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Aggregate classification is generally based on its size, petrographic characteristics, weight, and source. Size classification ranges from coarse to fine aggregates, defined by the size of the particles. Coarse aggregates are particles that do not pass through ASTM sieve No. 4, and aggregates that pass through the sieve are fine aggregates.
Petrographic classification groups aggregates based on common mineralogical characteristics. Some of the common mineral groups found in aggregates are...
309

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相关实验视频

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UVaT:不确定性嵌入式视图识别变压器,用于强大的多视图分类.

Yapeng Li, Yong Luo, Bo Du

    IEEE transactions on image processing : a publication of the IEEE Signal Processing Society
    |September 5, 2024
    PubMed
    概括

    本研究引入了一种新的多视图分类框架,可以有效处理缺失和噪音数据视图. 这种新的方法确保了强大的性能,即使数据不完整或在不同的视图中包含错误.

    科学领域:

    • 计算机科学 计算机科学
    • 机器学习 机器学习
    • 数据科学数据科学数据科学

    背景情况:

    • 多视图分类算法通常假定所有视图中的数据都是完整和干净的.
    • 现实世界的数据在某些视图中经常显示出缺失的表示或噪声,降低了算法性能.
    • 现有的方法通常单独解决缺失或噪音视图,当两个问题并存时失败.

    研究的目的:

    • 开发一个统一的多视图分类框架,对不完整和杂的视图都可靠.
    • 创建一个灵活的模型,能够自适应地识别视图和特征意义.
    • 为了解决培训和测试阶段之间的不同视图缺失模式.

    主要方法:

    • 在一个单一的框架内集成早期和晚期聚变技术.
    • 在早期融合模块中使用了视图感知变压器来处理缺失的视图并探索视图之间的关系.
    • 整合了单一视图分类和类别一致性约束,以减轻对特定视图缺失模式的依赖.
    • 对晚期核聚变模块以整体方式量化视图不确定性,以估计噪声水平并提高稳定性.

    主要成果:

    • 拟议的框架对不完整和杂的视图表现出了同时的稳定性.
    • 对各种数据集的实验结果证实了该模型的有效性.

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  • 端到端训练的框架成功地整合了早期和晚期的核聚变战略.
  • 结论:

    • 新的框架通过处理数据缺陷,在多视图分类方面取得了重大进展.
    • 该方法为缺失或噪音视图的真实世界数据集提供了灵活和适应性的解决方案.
    • 该模型的稳定性和有效性通过全面的实验来验证.