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相关概念视频

Deriving the Speed of Sound in a Liquid01:09

Deriving the Speed of Sound in a Liquid

594
As with waves on a string, the speed of sound or a mechanical wave in a fluid depends on the fluid's elastic modulus and inertia. The two relevant physical quantities are the bulk modulus and the density of the material. Indeed, it turns out that the relationship between speed and the bulk modulus and density in fluids is the same as that between the speed and the Young's modulus and density in solids.
The speed of sound in fluids can be derived by considering a mechanical wave...
594
Perceiving Loudness, Pitch, and Location01:21

Perceiving Loudness, Pitch, and Location

424
The human brain perceives pitch through two primary mechanisms reflected in place theory and frequency theory. Each mechanism describes how sound waves are interpreted as specific pitches by the brain, offering insights into the intricate processes of auditory perception.
Place theory, or place coding, suggests that different pitches are heard because various sound waves activate specific locations along the cochlea's basilar membrane. The brain determines the pitch of a sound by...
424
Perception of Sound Waves01:01

Perception of Sound Waves

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The human ear is not equally sensitive to all frequencies in the audible range. It may perceive sound waves with the same pressure but different frequencies as having different loudness. Moreover, the perception of sound waves depends on the health of an individual's ears, which decays with age. The health of one's ears may also be affected by regular exposure to loud noises.
The pitch of a sound depends on the frequency and the pressure amplitude of the source. Two sounds of the same...
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Uniform Depth Channel Flow: Problem Solving01:18

Uniform Depth Channel Flow: Problem Solving

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To calculate the flow rate for a trapezoidal channel, first, identify the bottom width, side slope, and flow depth of the channel. The cross-sectional area (A) corresponding to the depth of flow (y), channel bottom width (B), and side slope (θ) is determined by:Next, calculate the wetted perimeter, which includes the bottom width and the sloped side lengths in contact with the water. Using the values of the cross-sectional area and the wetted perimeter, determine the hydraulic radius by...
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Linear Approximation in Frequency Domain01:26

Linear Approximation in Frequency Domain

131
Linear systems are characterized by two main properties: superposition and homogeneity. Superposition allows the response to multiple inputs to be the sum of the responses to each individual input. Homogeneity ensures that scaling an input by a scalar results in the response being scaled by the same scalar.
In contrast, nonlinear systems do not inherently possess these properties. However, for small deviations around an operating point, a nonlinear system can often be approximated as linear....
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Sound Waves01:01

Sound Waves

9.5K
Sound waves can be thought of as fluctuations in the pressure of a medium through which they propagate. Since the pressure also makes the medium's particles vibrate along its direction of motion, the waves can be modeled as the displacement of the medium's particles from their mean position.
Sound waves are longitudinal in most fluids because fluids cannot sustain any lateral pressure. In solids, however, shear forces help in propagating the disturbance in the lateral direction as well....
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相关实验视频

Updated: Sep 12, 2025

Sound Source Localization Testing in Single-sided Deafness Following Bone Conduction Intervention
04:32

Sound Source Localization Testing in Single-sided Deafness Following Bone Conduction Intervention

Published on: December 20, 2024

434

在浅海中使用深度学习方法定位源,采用范围依赖的声音速度配置模型建模.

Jing Guo1,2, Juan Zeng1

  • 1Key Laboratory of Underwater Acoustic Environment, Institute of Acoustics, Chinese Academy of Sciences, Beijing 100190, China.

JASA express letters
|August 8, 2025
PubMed
概括

以模型为基础的源本地化深度学习与声音速度配置不匹配作斗争. 整合一个取决于范围的模型和转移学习提高了性能,即使在有限的浅水实验数据.

科学领域:

  • 声学信号处理 声学信号处理
  • 机器学习在海洋学中的应用.

背景情况:

  • 基于模型的深度学习为源本地化中的有限训练数据提供了解决方案.
  • 音速概况 (SSP) 的不匹配,特别是在带内波的浅水中,会降低性能.

研究的目的:

  • 将范围依赖的SSP模型集成到深度学习中,以改进源本地化.
  • 为了解决SSP在水下声学变化引起的性能恶化.

主要方法:

  • 开发了一种深度学习方法,包括一个简单的范围依赖SSP模型.
  • 在使用范围依赖SSP模型生成的模拟数据上训练网络.
  • 应用转移学习使用有限的实验数据进行概括.

主要成果:

  • 该网络在验证数据方面表现良好.
  • 该模型在转移学习后有效地对实验测试数据进行了概括.
  • 成功地缓解了由于SSP在浅水环境中的不匹配而导致的性能下降.

结论:

  • 整合范围依赖的SSP模型可以增强源本地化深度学习.
  • 转移学习是有效的适应模型,以现实世界的实验数据与有限的样本.

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  • 拟议的方法显示了强大的水下声源定位的前景.