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相关概念视频

Design Example: Creating a Hydraulic Model of a Dam Spillway01:21

Design Example: Creating a Hydraulic Model of a Dam Spillway

116
Scaled hydraulic models of dam spillways provide a practical way to replicate and study the intricate flow dynamics of these structures. Often built to a 1:15 ratio, these models allow for observing critical water behavior, such as velocity distribution, flow patterns, and energy dissipation.
116
Applications of GIS: Disaster Management and Emergency Response01:29

Applications of GIS: Disaster Management and Emergency Response

32
Geographic Information System (GIS) technology is essential for risk identification, action prioritization, and resource optimization in critical situations like flooding and earthquakes. By integrating spatial and demographic data, GIS provides a comprehensive framework for emergency response.GIS integrates data layers, like rainfall intensity, topography, elevation profiles, and river levels, to model high-risk flood zones. These layers assess areas susceptible to flooding based on their...
32
Design Example: Analyzing Capacity Contours for Flood Risk Assessment01:17

Design Example: Analyzing Capacity Contours for Flood Risk Assessment

36
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...
36
Rapidly Varying Flow01:24

Rapidly Varying Flow

45
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...
45
Gradually Varying Flow01:29

Gradually Varying Flow

30
Gradually varying flow (GVF) in open channels describes situations where water depth changes slowly along the channel due to factors like non-uniform bed slope, channel shape variations, or obstructions. This flow type occurs when the depth adjusts gradually to balance gravitational forces, shear forces, and energy requirements, resulting in a low rate of depth change.Characteristics of Gradually Varying FlowGVF is commonly observed in natural streams, rivers, and canals, where flow depth...
30
Hydraulic Jump: Problem Solving01:16

Hydraulic Jump: Problem Solving

43
To analyze a hydraulic jump in a rectangular channel with a flow speed of 6 meters per second, follow these steps:Calculate Effective Upstream Velocity:When the downstream gate closes, a hydraulic jump forms, traveling upstream at 2 meters per second. This wave speed combines with the initial channel flow velocity, creating an effective upstream velocity.Identify Flow Velocities Before and After the Hydraulic Jump:Upstream of the hydraulic jump, the effective flow velocity includes both the...
43

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相关实验视频

Updated: May 25, 2025

Visualization of Flow Field Around a Vibrating Pipeline Within an Equilibrium Scour Hole
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基于深度学习的垃圾流危险检测和识别系统:一个案例研究

Fei Wu1,2,3, Jianlin Zhang4,5, Dunlong Liu6

  • 1School of Electrical, Electronics and Communication Engineering, University of Chinese Academy of Sciences, Beijing, 100049, China.

Scientific reports
|February 25, 2025
PubMed
概括
此摘要是机器生成的。

本研究引入了一种深度学习系统,用于使用监控摄像头自动检测和识别碎片流. 这种新的方法实现了高精度,使得可靠的地质危险早期预警成为可能.

关键词:
卷积神经网络是一种卷积神经网络.废物流是指废物的流动.危险检测和识别 危险检测和识别转移学习转移学习

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相关实验视频

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科学领域:

  • 地质危险监测 地质危险监测
  • 计算机视觉 计算机视觉
  • 深度学习 (Deep Learning) 是一种深度学习.

背景情况:

  • 碎片流在山区构成重大威胁,因为它们具有破坏性.
  • 目前对碎片流的监控摄像头使用主要用于事件后的分析,缺乏主动监控能力.
  • 计算机视觉中的异常检测为实时危险识别提供了潜力.

研究的目的:

  • 开发一个使用深度学习的自动碎片流检测和识别系统.
  • 通过积极的实时预警能力来加强地质危险监测.
  • 为了利用计算机视觉来改善碎片流的早期预警系统.

主要方法:

  • 一个深度学习系统,包括一个用于特征提取的3D CNN,一个用于检测的MLP和另一个用于识别的CNN.
  • 使用监控摄像头的视频序列作为输入数据.
  • 关于新注释的Debrisflow23图像数据集的培训和评估.

主要成果:

  • 该系统实现了86.3%的AUC检测精度和83.7%的AUC识别精度.
  • 总体碎片流识别准确度在测试数据集上达到了88.1%的AUC.
  • 证明了准确可靠的碎片流量预警能力.

结论:

  • 拟议的深度学习系统提供了一种可靠的方法,用于自动检测和识别碎片流.
  • 这项技术可以显著改善地质危险的预警系统.
  • 提前警告可以减轻对基础设施的损害,并保护脆弱地区的人口.