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相关概念视频

Sample Size Calculation01:19

Sample Size Calculation

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Knowledge of the sample size is the first requirement to conduct random sampling or an experiment. The sample size is the total number of units, observations, or groups (in some cases) used to get the data to estimate a population parameter. As the name suggests, the sample size is that of the sample drawn from the population and differs from the population size.
The sample size for the given experiment or sampling effort is fundamental to any study design. Sample size decides the number of...
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Estimation of the Physical Quantities01:05

Estimation of the Physical Quantities

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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...
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How Data are Classified: Numerical Data00:59

How Data are Classified: Numerical Data

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Data that are countable or measurable in specific units are called numerical or quantitative data. Quantitative data are always numbers. Quantitative data are the result of counting or measuring the attributes of a population. Amount of money, pulse rate, weight, number of people living in a town, and number of students who opt for statistics are examples of quantitative data.
Quantitative data may be either discrete or continuous. All quantitative data that take on only specific numerical...
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Dimensional Analysis01:23

Dimensional Analysis

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Dimensional analysis is a powerful tool that is used in physics and engineering to understand and predict the behavior of physical systems. The basic idea behind dimensional analysis is to express physical quantities in terms of fundamental dimensions such as the mass, length, and time. Derived dimensions like the velocity, acceleration, and force are derived from the combinations of these fundamental dimensions.
Dimensional analysis allows us to analyze and compare physical quantities on a...
923
Sampling Distribution01:12

Sampling Distribution

13.2K
Given simple random samples of size n from a given population with a measured characteristic such as mean, proportion, or standard deviation for each sample, the probability distribution of all the measured characteristics is called a sampling distribution. How much the statistic varies from one sample to another is known as the sampling variability of a statistic. You typically measure the sampling variability of a statistic by its standard error. The standard error of the mean is an example...
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Base Quantities and Derived Quantities01:14

Base Quantities and Derived Quantities

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In any system of units, the units for some physical quantities must be specified through a measurement process. These measurements are the base quantities of the system, and their units are the base units of the system. The algebraic combinations of the base values can then be used to express all other physical quantities. Each of these physical quantities is then referred to as a derived quantity, with each unit being referred to as a derived unit.
The International Organization for...
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Updated: Jul 26, 2025

Three-Dimensional Particle Shape Analysis Using X-ray Computed Tomography: Experimental Procedure and Analysis Algorithms for Metal Powders
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在材料科学中用于机器学习的数据量治理.

Yue Liu1,2, Zhengwei Yang1, Xinxin Zou1

  • 1School of Computer Engineering and Science, Shanghai University, Shanghai200444, China.

National science review
|June 16, 2023
PubMed
概括
此摘要是机器生成的。

材料科学中的机器学习 (ML) 在有限的数据上扎. 本研究回顾了数据治理策略,并提出了一个领域知识集成的方法,以改善加速材料发现的ML模型性能.

关键词:
数据治理数据治理数据量 数据量 数据量机器学习是机器学习.材料科学 材料科学 材料科学

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科学领域:

  • 材料科学 材料科学 材料科学
  • 数据科学数据科学数据科学
  • 机器学习 机器学习

背景情况:

  • 机器学习 (ML) 对材料科学至关重要,有助于结构-活动关系分析,性能优化和材料设计.
  • 将ML应用于材料科学的一个重大挑战是数据稀缺,导致特征空间维度和样本大小之间或模型参数和样本大小之间不匹配.
  • 这种数据限制往往导致ML模型性能差.

研究的目的:

  • 审查现有策略,以解决材料科学ML中的数据限制,包括特征减少,样本增量和专门的ML方法.
  • 强调在数据量治理中平衡样本大小与特征维度或模型参数的重要性.
  • 提出一个新的协同效应的数据量治理框架,结合材料领域的知识.

主要方法:

  • 对解决材料数据稀缺问题的技术的文献综述 ML.
  • 分析样本大小,特征空间和模型参数之间的相互作用.
  • 开发一个数据量治理流程,整合材料领域的知识.

主要成果:

  • 讨论了现有的方法,如特征减少和样本增量,以减轻数据限制的方法.
  • 强调需要仔细考虑数据量与模型复杂性之间的平衡.
  • 将材料领域的知识纳入机器学习数据治理方案显示出显著的优势.

结论:

  • 有效的数据量治理对于材料科学中的ML应用成功至关重要.
  • 结合数据治理与材料领域知识的协同方法可以显著提高ML模型的性能.
  • 这项工作为生成高质量的数据提供了一条途径,加速了基于ML的材料设计和发现.