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相关概念视频

Testing a Claim about Standard Deviation01:19

Testing a Claim about Standard Deviation

2.5K
A complete procedure to test a claim about population standard deviation or population variance is explained here.
The hypothesis testing for the claim of population standard deviation (or variance) requires the data and samples to be random and unbiased. The population distribution also must be normal. There is no specific requirement on the sample size as the estimation is based on the chi-square distribution.
As a first step, the hypothesis (null and alternative) concerning the claim about...
2.5K
Routh-Hurwitz Criterion II01:19

Routh-Hurwitz Criterion II

299
In the application of the Routh-Hurwitz criterion, two specific scenarios can arise that complicate stability analysis.
The first scenario occurs when a singular zero appears in the first column of the Routh table. This situation creates a division by zero issues. To resolve this, a small positive or negative number, denoted as epsilon (∈), is substituted for the zero. The stability analysis proceeds by assuming a sign for ∈. If ∈ is positive, any sign change in the first...
299
Mean Absolute Deviation01:13

Mean Absolute Deviation

2.7K
The mean absolute deviation is also a measure of the variability of data in a sample. It is the absolute value of the average difference between the data values and the mean.
Let us consider a dataset containing the number of unsold cupcakes in five shops: 10, 15, 8, 7, and 10. Initially, calculate the sample mean. Then calculate the deviation, or the difference, between each data value and the mean. Next, the absolute values of these deviations are added and divided by the sample size to...
2.7K
Routh-Hurwitz Criterion I01:15

Routh-Hurwitz Criterion I

286
Consider an electrical power grid, where stability is essential to prevent blackouts. The Routh-Hurwitz criterion is a valuable tool for assessing system stability under varying load conditions or faults. By analyzing the closed-loop transfer function, the Routh-Hurwitz criterion helps determine whether the system remains stable.
To apply the Routh-Hurwitz criterion, a Routh table is constructed. The table's rows are labeled with powers of the complex frequency variable s, starting from the...
286
Standard Deviation of Calculated Results01:14

Standard Deviation of Calculated Results

6.3K
Standard deviation measures the spread of data around the mean value. Many large data sets follow a Gaussian distribution, also known as a normal distribution. This distribution is bell-shaped curved, with the most frequently observed value (mean or central value) in the middle. The farther away from the central value, the greater the deviation from the central value, and the lower the frequency.
A broad Gaussian distribution curve has a wider standard deviation, representing a data set with...
6.3K
Modified Boxplots00:57

Modified Boxplots

9.8K
A standard box and whisker plot informs us about the spread of the data in a given sample. One can identify the minimum value, maximum value, first quartile value, second quartile or median value, and third quartile.
However, the box plot does not tell the reader about outliers - values that lie far from the center of the data. We can modify the standard box and whisker plot to identify the outliers and visualize the actual spread of the data in a sample.
Initially, we calculate the adjusted...
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相关实验视频

Updated: Jul 23, 2025

Evaluation of an Exclusive Spur Dike U-Turn Design with Radar-Collected Data and Simulation
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一个高效的改进的哈里斯·霍克斯优化器及其用于形成偏差区域评估的应用.

Guangshuai Liu1, Zuoxin Li1, Si Sun2

  • 1School of Mechanical Engineering, Southwest Jiaotong University, Chengdu 610031, China.

Sensors (Basel, Switzerland)
|July 14, 2023
PubMed
概括

本研究介绍了一种改进的哈里斯·霍克斯算法 (HHO),用于评估制造业和计量学中的偏差区域. 增强的HHO有效地解决了复杂的非线性问题,提高了质量控制的准确性.

关键词:
哈里斯·霍克斯优化优化形式错误 形式错误 是一个错误.最低区域评价最低区域评价萨尔普群群算法 萨尔普群群算法这是一个宽容的宽容.

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Spatial Multiobjective Optimization of Agricultural Conservation Practices using a SWAT Model and an Evolutionary Algorithm
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Design and Optimization Strategies of a High-Performance Vented Box

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相关实验视频

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科学领域:

  • 制造业和计量学 在
  • 计算智能是一种计算智能.
  • 优化算法 优化算法

背景情况:

  • 偏差区评估对于质量控制至关重要,但是一个复杂的非线性问题.
  • 传统的数值优化方法与偏差区评估的非线性性和复杂性作斗争.
  • 群体智能为此类问题提供无梯度,高质量的解决方案,更容易实现.

研究的目的:

  • 开发一个改进的群集智能算法,用于准确的偏差区评估.
  • 解决测量学中解决非线性优化问题的传统方法的局限性.
  • 增强哈里斯·霍克斯算法 (HHO) 以在质量控制应用中提供卓越的性能.

主要方法:

  • 开发了一个改进的哈里斯·霍克斯算法 (HHO),集成了Salp Swarm Algorithm (SSA) 的功能.
  • 在HHO的探索阶段,随机操作者被替换为平均适应,以减轻战略冲突.
  • SSA的非线性惯性重量和探索能力都嵌入了HHO.
  • 在基于HHO和SSA的个人之间采用了贪选择策略,以选择最佳解决方案.

主要成果:

  • 改进的HHO在基准问题上表现出有效性,与其他群集智能方法相比.
  • 实验结果显示,对原始几何学的各种形状偏差进行了准确的评估.
  • 该算法为制造业和计量学中的形状偏差区域评估提供了有效的通用解决方案.

结论:

  • 提议的改进的HHO算法为偏差区评估提供了强大而准确的解决方案.
  • 该方法通过有效处理复杂的非线性问题,提高了制造和计量质量控制.
  • 该算法为评估原始几何学上的形状偏差提供了一个可概括的方法.