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相关概念视频

Sound as Pressure Waves01:17

Sound as Pressure Waves

2.6K
Sound waves, which are longitudinal waves, can be modeled as the displacement amplitude varying as a function of the spatial and temporal coordinates. As a column of the medium is displaced, its successive columns are also displaced. As the successive displacements differ relatively, a pressure difference with the surrounding pressure is created. The gauge pressure varies across the medium.
The pressure fluctuation depends on the difference in displacements between the successive points in the...
2.6K
Intensity and Pressure of Sound Waves01:05

Intensity and Pressure of Sound Waves

1.2K
The intensity of sound waves can be related to displacement and pressure amplitudes by using their wave expressions and the definition of intensity. The critical step to achieve this is to write the power delivered by the particles on the wave as the product of force and velocity and simplify the force per unit area as the pressure. The velocity of the medium's particles can be derived from the displacement.
Unlike the time average of a sinusoidal term, which is zero since it is positive...
1.2K
Measurement of Fluid Pressure01:16

Measurement of Fluid Pressure

308
Fluid pressure is commonly measured using devices called manometers, which rely on liquid columns to indicate pressure differences. The height of a liquid column in a manometer reflects the pressure exerted by the fluid, providing a simple yet effective means of measurement. Different types of manometers serve specific purposes based on their configurations and the type of fluids involved.
A basic form of manometer is the piezometer, a vertical tube open at the top and filled with the same...
308
Pressure Variation in a Fluid at Rest01:11

Pressure Variation in a Fluid at Rest

410
In a fluid at rest, the pressure at any point beneath the fluid surface depends solely on the depth, not on the container's shape or size. This principle, known as hydrostatic pressure, arises because, in stationary fluids, there is no acceleration, meaning the forces within the fluid balance out. Only vertical forces, caused by the weight of the fluid above, contribute to pressure changes with depth.
When measuring pressure at two different levels within the fluid, the difference in...
410
Properties of Laplace Transform-II01:16

Properties of Laplace Transform-II

298
Time differentiation, convolution, integration, and periodicity are fundamental concepts in analyzing functions and signals over time. Each concept provides a unique perspective on how functions evolve, interact, and repeat, offering essential tools for various scientific and engineering applications.
Time differentiation involves analyzing the rate of change of a function over time. Mathematically, it is the derivative of a function with respect to time. This concept can be likened to tracking...
298
Basic Equation for Pressure Field01:13

Basic Equation for Pressure Field

302
The basic equation for a pressure field in fluid mechanics captures the balance of forces within any segment of fluid, providing a foundational understanding of how pressure changes within fluids under various forces. Generally, two main types of forces act on any part of a fluid: surface forces and body forces. Surface forces arise from pressure differences across points within the fluid, which result in net forces that can vary depending on the local pressure gradient. Body forces, on the...
302

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相关实验视频

Updated: Sep 13, 2025

The Measurement of Unsteady Surface Pressure Using a Remote Microphone Probe
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The Measurement of Unsteady Surface Pressure Using a Remote Microphone Probe

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时间频率机器学习转移函数用于中央压力波形.

Soha Niroumandi1, Heng Wei1, Faisal Amlani2

  • 1Department of Aerospace and Mechanical Engineering, University of Southern California, 3650 McClintock Ave. Room 400, Los Angeles, CA 90089, USA.

European heart journal open
|July 28, 2025
PubMed
概括
此摘要是机器生成的。

这项研究引入了一种新的混合机器学习模型,从外围测量中准确地重建中央动脉压力波形,改善高血压和心力衰竭的诊断.

关键词:
动脉血动力学 动脉血动力学心血管传递功能的转移功能中心血压的中心血压.时间频率机器学习

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Assessing Cerebral Autoregulation via Oscillatory Lower Body Negative Pressure and Projection Pursuit Regression
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Measurements of Waves in a Wind-wave Tank Under Steady and Time-varying Wind Forcing
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相关实验视频

Last Updated: Sep 13, 2025

The Measurement of Unsteady Surface Pressure Using a Remote Microphone Probe
08:53

The Measurement of Unsteady Surface Pressure Using a Remote Microphone Probe

Published on: December 3, 2016

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Assessing Cerebral Autoregulation via Oscillatory Lower Body Negative Pressure and Projection Pursuit Regression
11:26

Assessing Cerebral Autoregulation via Oscillatory Lower Body Negative Pressure and Projection Pursuit Regression

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Measurements of Waves in a Wind-wave Tank Under Steady and Time-varying Wind Forcing
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Measurements of Waves in a Wind-wave Tank Under Steady and Time-varying Wind Forcing

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科学领域:

  • 心血管生理学心血管生理学
  • 生物医学工程 生物医学工程
  • 机器学习在医学中的应用

背景情况:

  • 脉动性血液动力学和压力波形分析对于诊断高血压和心力衰竭至关重要.
  • 一般化转移函数 (GTF) 在捕捉中心血液动力学方面存在局限性.
  • 为了改善临床评估,需要准确的中央压力波形重建.

研究的目的:

  • 开发和验证混合时频,基于机器学习的传输函数,用于从外围测量中重建中心压力波形.
  • 克服现有的GTF在捕捉中央脉动性血液动力学方面的局限性.
  • 准确捕获基于动脉波的信息,以改善心血管评估.

主要方法:

  • 利用里埃波来近似压力波形.
  • 采用了一个前神经网络 (FNN),具有自定义的时间域成本函数.
  • 通过培训,测试和验证混合FNN模型,使用来自Framingham心脏研究 (6698名参与者) 的数据.

主要成果:

  • 与GTF (0.26-0.42) 相比,混合FNN方法在心动脉波形重建 (0.09-0.10) 中实现了显著较低的正常化平均平方误差 (NMSE).
  • 与GTF (0.22-0.31) 相比,波长和振幅 (0.79-0.97) 的高相关系数得到证明.
  • 显著改善了相似性,形态和基于波的参数之间的相关性.

结论:

  • 混合的FNN传输函数可以从外围测量中对中心动脉压力波形进行可靠的计算.
  • 这种方法准确地保存了心脏周期内的关键生理序列.
  • 为改善心血管疾病的诊断和预后提供了一个有前途的工具.