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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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Topography involves measuring and mapping land elevations, natural features, and artificial structures to create accurate representations of the terrain. Topographic surveying relies on traditional and modern methods, each with distinct advantages and limitations.Traditional Surveying Methods:Transit stadia surveys and plane table surveys were widely used traditional surveying methods. These techniques relied on instruments like theodolites and stadia rods for measuring distances and angles,...
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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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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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相关实验视频

Updated: Jul 20, 2025

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
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基于视觉的污垢分布绘图使用深度学习.

Ishneet Sukhvinder Singh1,2, I D Wijegunawardana1, S M Bhagya P Samarakoon1

  • 1Engineering Product Development Pillar, Singapore University of Technology and Design, Singapore, 487372, Singapore.

Scientific reports
|August 6, 2023
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概括
此摘要是机器生成的。

本研究介绍了一种基于视觉的系统,使用YOLOv5和DeepSORT来分类机器人清洁的污垢类型. 它创建了一个污垢分布图,以提高机器人的效率,并防止不兼容的清洁任务造成损害.

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科学领域:

  • 机器人和人工智能 机器人和人工智能
  • 计算机视觉 计算机视觉

背景情况:

  • 机器人清洁系统在遇到不相容的污垢类型时面临效率问题和潜在损坏.
  • 分类清洁任务并将其分配给适当的机器人是关键的研究领域.

研究的目的:

  • 开发一种基于视觉的系统来检测和分类污垢,以优化机器人清洁操作.
  • 创建污垢分布图,以便在清洁机器人之间有效分配任务.

主要方法:

  • 使用YOLOv5进行对象检测和DeepSORT进行跟踪,以识别和分类不同类型的污垢.
  • 开发了一个污垢分布图,显示需要特定清洁行动的区域.

主要成果:

  • 拟议的基于视觉的系统在分布图上的污垢指示中实现了81.0%的高精度.
  • 污垢分布图促进了协作清洁框架,以实现不间断和节能的机器人部署.

结论:

  • 开发的系统有效地分类污垢,使机器人清洁机器人能够智能地分配任务.
  • 这种方法提高了清洁机器人的效率和运行寿命,可以在各种表面和污垢类型上使用.