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相关概念视频

Stress Concentrations01:24

Stress Concentrations

Stress concentration is when stress intensifies near discontinuities such as holes or abrupt cross-sectional changes in a structural member. This localized stress can often surpass the average stress within the member. The stress distribution in flat bars, either with a circular hole or varying widths connected by fillets, can be determined experimentally using a photoelastic method. The results are based on ratios of geometric parameters like the ratio of the hole's radius to the smaller width...
Elasticity in Concrete01:20

Elasticity in Concrete

Upon subjecting concrete to moderate or high uniaxial compressive or tensile stresses, the strain response is non-linear relative to the stress applied. As the stress is removed, the resulting stress-strain curve deviates from the original path traced during loading, creating a hysteresis loop, indicative of the concrete's non-linear and non-elastic properties. Typically, a material's modulus of elasticity, which is a measure of the material's stiffness, is inferred from the linear portion of...
Dynamic Modulus of Elasticity of Concrete01:16

Dynamic Modulus of Elasticity of Concrete

The dynamic modulus of elasticity assesses how a concrete structure deforms under impact or dynamic loads. It is typically higher than the static modulus of elasticity, measured under slow, steady loading conditions.
The sonic test is a common method to determine the dynamic modulus. In this test, a concrete beam, sized either 6 x 6 x 30 inches or 4 x 4 x 20 inches, is clamped at its center. Vibrations are initiated at one end of the beam by an electromagnetic exciter unit powered by a...
Influence of Earth's Curvature and Atmospheric Refraction on Leveling01:26

Influence of Earth's Curvature and Atmospheric Refraction on Leveling

During leveling, the Earth's curvature and atmospheric refraction introduce deviations in the line of sight from a true horizontal reference. When the line of sight is leveled, it remains perpendicular to the plumb line only at a single point. Beyond this, it deviates due to the Earth’s curvature, represented by the correction C. For a sight distance D, the deviation can be derived using the relationship:This relationship shows that the deviation increases quadratically with distance. Over a...
Design Example: Analyzing Capacity Contours for Flood Risk Assessment01:17

Design Example: Analyzing Capacity Contours for Flood Risk Assessment

Flood risk assessment involves careful planning and analysis to ensure the safety of communities near water retention structures. Capacity contours are a vital tool in this process, as they illustrate the potential spread of water at specific levels in a given area. In the context of building a bund across a small valley, these contours play a critical role in evaluating the safety of nearby residential areas.In this example, the bund is intended to store stormwater in the valley. The engineers...
Linear Approximations01:23

Linear Approximations

For a differentiable function of two variables, linear approximation estimates values near a known point by replacing the curved surface with its tangent plane. Consider the function\begin{equation*}f(x,y)=x^2+3y^2\end{equation*}near the point (2, 1). The exact value at this point is f(2, 1) = 22 + 3(1)2 = 4 + 3 = 7.The linear approximation of f(x, y)) near (a, b) is\begin{equation*}L(x,y)=f(a,b)+f_x(a,b)(x-a)+f_y(a,b)(y-b)\end{equation*}First, compute the partial derivatives: fx(x, y) = 2x and...

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相关实验视频

Updated: Jun 20, 2026

Sit-to-stand-and-walk from 120% Knee Height: A Novel Approach to Assess Dynamic Postural Control Independent of Lead-limb
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PCA预规范化方法对地面反应力估计精度的影响.

Amal Kammoun1,2, Philippe Ravier1, Olivier Buttelli1,3

  • 1PRISME Laboratory, University of Orleans, 12 Rue de Blois, 45100 Orleans, France.

Sensors (Basel, Switzerland)
|February 24, 2024
PubMed
概括

选择正确的预规范化方法对于使用机器学习 (PCA-ML) 主成分分析准确估计地面反应力 (GRF) 是至关重要的. 最好的方法完全取决于所采用的特定机器学习技术.

关键词:
电力电网组件估计的部分.在PCA前规范化前进行PCA规范化.强力板测量测量方法内尺寸测量 在内尺寸测量.机器学习是机器学习.标准化方法 标准化方法

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Last Updated: Jun 20, 2026

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科学领域:

  • 生物力学 生物力学
  • 运动科学 运动科学 运动科学
  • 生物医学工程 生物医学工程

背景情况:

  • 鞋底压力传感器可以估计地面反应力 (GRF) 组件.
  • 主要组件分析与机器学习 (PCA-ML) 结合在一起是此估计的常见方法.
  • PCA需要预规范化,其对GRF估计准确性的影响尚未完全理解.

研究的目的:

  • 评估12种预规范化方法对GRF组件估计准确性的影响.
  • 为了比较三个PCA-ML方法 (PCA-ANN,PCA-LS,PCA-SVR) 与不同规范化的性能.
  • 在GRF估计中确定特定PCA-ML技术的最佳规范化策略.

主要方法:

  • 评估了12种用于内底压力传感器数据的预规范化方法.
  • 使用了三种PCA-ML方法:人工神经网络 (ANN),最小平方 (LS) 和支持向量回归 (SVR).
  • 评估准确性与九个受试者在行走时的金标准力板测量.

主要成果:

  • 在不同的ML技术中,规范化方法的性能差异很大.
  • 体重正常化对于PCA-ANN来说是最佳的,但对于PCA-SVR来说是最差的.
  • 建议对PCA-ANN和PCA-LS进行矢量标准化,而对于PCA-SVR则建议使用平均方法.

结论:

  • 预规范化方法的选择严重取决于所选择的机器学习算法.
  • 选择独立于ML技术的规范化方法可能会导致低于最佳或不准确的GRF估计.
  • 为PCA-ANN,PCA-LS和PCA-SVR提供了具体的规范化建议,以提高GRF估计的准确性.