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相关概念视频

Uniform Depth Channel Flow01:27

Uniform Depth Channel Flow

167
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...
167
Uniform Depth Channel Flow: Problem Solving01:18

Uniform Depth Channel Flow: Problem Solving

126
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...
126
Rapidly Varying Flow01:24

Rapidly Varying Flow

140
Rapidly varying flow (RVF) in open channels is characterized by abrupt changes in flow depth over a short distance, with the rate of depth change relative to distance often approaching unity. These flows are inherently complex due to their transient and multi-dimensional nature, making exact analysis difficult. However, approximate solutions using simplified models provide valuable insights into their behavior.Key Features of Rapidly Varying FlowRVF is commonly observed in scenarios involving...
140
Plane Potential Flows01:23

Plane Potential Flows

453
Plane potential flows simplify fluid motion by assuming the fluid to be irrotational and incompressible. These characteristics allow these flows to be described by a velocity potential function, ϕ, representing the flow speed in a given direction, and a stream function, ψ, that visualizes the flow path, both governed by Laplace's equation. These parameters help in estimating flow patterns, velocity distributions, and pressure fields around various hydraulic structures.
Uniform...
453
Eulerian and Lagrangian Flow Descriptions01:22

Eulerian and Lagrangian Flow Descriptions

1.5K
Fluid flow analysis is critical in many scientific and engineering disciplines, and two principal approaches are used to describe this flow: the Eulerian and Lagrangian methods. These methods offer different perspectives on monitoring and analyzing the motion of fluids, each with distinct advantages depending on the scenario.
The Eulerian method focuses on fixed points in space where fluid properties, such as velocity, pressure, and temperature, are observed as the fluid moves between these...
1.5K
Turbulent Flow01:24

Turbulent Flow

281
Turbulent flow is characterized by unpredictable fluctuations in velocity and pressure, which result in a chaotic fluid movement distinct from the orderly patterns of laminar flow. While laminar flow is governed by smooth, parallel layers with minimal mixing, turbulent flow exhibits highly irregular, three-dimensional patterns. This behavior arises due to instabilities in the fluid's velocity profile, and amplifies as the flow velocity increases. Minor disturbances, known as turbulent...
281

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相关实验视频

Updated: Sep 13, 2025

Spatial Temporal Analysis of Fieldwise Flow in Microvasculature
09:39

Spatial Temporal Analysis of Fieldwise Flow in Microvasculature

Published on: November 18, 2019

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从空中观看区域人群流量的估计.

Huibin Wei1, Qi Li2, Xindai Lin2

  • 1Fujian Police College, Fuzhou, Fujian, China.

Neural networks : the official journal of the International Neural Network Society
|August 1, 2025
PubMed
概括
此摘要是机器生成的。

这项研究引入了一种新的空中人群流量估计模型. 它准确地跟踪人群进入和离开地区的流动,改进了现有的方法,以便更好地进行人群分析.

关键词:
人群流量估计人群流量估计基于无人机的人群计数.以视频为基础的人群计数.

更多相关视频

Determining 3D Flow Fields via Multi-camera Light Field Imaging
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Determining 3D Flow Fields via Multi-camera Light Field Imaging

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Three-dimensional Particle Tracking Velocimetry for Turbulence Applications: Case of a Jet Flow
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Three-dimensional Particle Tracking Velocimetry for Turbulence Applications: Case of a Jet Flow

Published on: February 27, 2016

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相关实验视频

Last Updated: Sep 13, 2025

Spatial Temporal Analysis of Fieldwise Flow in Microvasculature
09:39

Spatial Temporal Analysis of Fieldwise Flow in Microvasculature

Published on: November 18, 2019

5.9K
Determining 3D Flow Fields via Multi-camera Light Field Imaging
14:25

Determining 3D Flow Fields via Multi-camera Light Field Imaging

Published on: March 6, 2013

16.7K
Three-dimensional Particle Tracking Velocimetry for Turbulence Applications: Case of a Jet Flow
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科学领域:

  • 计算机视觉 计算机视觉
  • 人工智能的人工智能
  • 数据科学数据科学数据科学

背景情况:

  • 群众分析对于现实世界的应用至关重要.
  • 现有的空中人群流程方法缺乏灵活性,简单性和准确性,特别是上下视图.
  • 清晰的人群外观特征在空中视角中经常缺失.

研究的目的:

  • 提出一种新的挑战,用于从空中观测大规模人群场景.
  • 开发一个人群流量估计模型,以准确地测量随着时间的推移进入和离开特定地区的流量.
  • 通过解决现有的上下视图方法的局限性,增强人群流量估计.

主要方法:

  • 一个双流网络共同回归人群密度和个体速度.
  • 一个局部受限的注意模块通过考虑局部关系来增强流量估计.
  • 反向时间损失在上下视图中被纳入时间空间规范化.

主要成果:

  • 拟议的模型准确地估计了每个地点的瞬间人群流动.
  • 局部受限的注意模块可以提高流量估计的准确性.
  • 反时间损失增强了空中视图的时空规则化.

结论:

  • 开发的人群流量估计模型在性能上超过了先前的方法.
  • 这种方法对于基于无人机的空中人群视频是有效的.
  • 该模型在各种人群分析任务中具有潜在的应用,以了解社会动态.