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相关概念视频

Uniform Depth Channel Flow: Problem Solving01:18

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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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Uniform depth channel flow keeps fluid depth consistent along channels such as irrigation canals. In natural channels, such as rivers, approximate uniform flow is often assumed. This condition occurs when the channel’s bottom slope matches the energy slope, balancing potential energy lost from gravity with head loss due to shear stress. This balance prevents depth changes along the channel length, resulting in a steady, uniform flow.Uniform flow in open channels with a constant cross-section...
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In fluid mechanics, buoyancy and stability are key concepts for understanding the behavior of submerged and floating bodies. When a stationary body is fully or partially submerged in a fluid, the fluid exerts a force on the body known as the buoyant force. This force acts vertically upward through a point called the center of buoyancy, which is the center of the displaced fluid volume. According to Archimedes' principle, the magnitude of the buoyant force is equal to the weight of the fluid...
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Depth perception is the ability to perceive objects three-dimensionally. It relies on two types of cues: binocular and monocular. Binocular cues depend on the combination of images from both eyes and how the eyes work together. Since the eyes are in slightly different positions, each eye captures a slightly different image. This disparity between images, known as binocular disparity, helps the brain interpret depth. When the brain compares these images, it determines the distance to an object.
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相关实验视频

Updated: Jan 13, 2026

Development of New Methods for Quantifying Fish Density Using Underwater Stereo-video Tools
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DL-SDE:一个深度学习框架,用于在浅水中使用垂直线性阵列进行源深度估计.

Zhen Li1,2,3, Shengchun Piao1,2,3, Jiankang Zhan1,2,3

  • 1Country National Key Laboratory of Underwater Acoustic Technology, Harbin Engineering University, Harbin 150001, China.

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概括
此摘要是机器生成的。

一个新的深度学习框架通过分析声干扰模式,准确地估计了水下源的深度. 这种方法比传统技术提供了更好的稳定性和性能,增强了声学定位能力.

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科学领域:

  • 水下声学 水下声学
  • 信号处理 信号处理
  • 机器学习是机器学习.

背景情况:

  • 准确的源深度估计至关重要,但在水下声学方面具有挑战性.
  • 垂直线阵列跨光谱密度矩阵中的干扰模式包含深度敏感信息.
  • 像匹配场处理 (MFP) 这样的现有方法可能对环境不匹配很敏感.

研究的目的:

  • 开发一个强大而准确的基于深度学习的源深度估计 (DL-SDE) 框架.
  • 为了提高深度估计,利用多规模的本地和非均的全球干扰模式.
  • 为了证明框架的优越性比传统的方法,如MFP.

主要方法:

  • 提出了一个基于深度学习的源深度估计 (DL-SDE) 框架.
  • 集成了一个多尺度卷积模块来捕获局部干扰模式.
  • 整合了一个剩余的多头自我注意模块来建模全球干扰关系.

主要成果:

  • 对于环境不匹配,DL-SDE表现出比MFP更强大的稳定性.
  • 在100Hz以上的频率和覆盖至少50%水柱的阵列深度下观察到稳定的性能.
  • 萨克兰特1993年实验显示,平均绝对误差减少了11.63m,可信本地化概率比MFP增加了71%.

结论:

  • 拟议的DL-SDE框架有效地利用物理引导的组件从多尺度干扰模式中学习.
  • DL-SDE为水下源深度估计提供了强大而准确的解决方案.
  • 该框架在具有挑战性的声学环境中明显优于传统的MFP.