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Related Concept Videos

Uniform Depth Channel Flow01:27

Uniform Depth Channel Flow

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

Uniform Depth Channel Flow: Problem Solving

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...
Rapidly Varying Flow01:24

Rapidly Varying Flow

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...

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Related Experiment Videos

A Multisensor Fusion-based High-Quality Depth Estimation Dataset for Dynamic Coal Flow.

Hanlin Bai1, Xin Gao1, Jianwang Gan1

  • 1School of Artificial Intelligence, China University of Mining & Technology (Beijing), Xueyuan Road, Haidian District, Beijing, 100083, Beijing, China.

Scientific Data
|July 15, 2026
PubMed
Summary

This study introduces a novel multisensor fusion dataset for high-quality depth estimation in industrial settings. This technology enhances efficiency in mining operations like extraction and processing.

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Area of Science:

  • Computer Vision
  • Robotics
  • Industrial Automation

Background:

  • Depth estimation is crucial for industrial applications like intelligent sorting and robotic grasping.
  • Existing spatial information sensors face limitations in industrial environments due to space constraints, safety, and dynamic objects.

Purpose of the Study:

  • To develop a high-quality depth estimation dataset tailored for the mining industry (coal and non-ferrous metals).
  • To address the specific challenges of depth sensing in industrial mining processes including extraction, transportation, and processing.

Main Methods:

  • Implemented a multisensor fusion approach integrating wide-field-of-view visible light sensors and active depth sensors.
  • Utilized trigger-signal generators and distributors for precise hardware synchronization.
  • Employed a reprojection method based on a time-synchronized signal system for data alignment.

Main Results:

  • Generated a high-precision dataset with spatiotemporally aligned data.
  • The dataset is specifically designed for the demanding conditions of coal and non-ferrous metal mining.
  • Demonstrated the feasibility of multisensor fusion for robust depth estimation in industrial environments.

Conclusions:

  • The developed dataset enables high-quality depth estimation for critical industrial mining applications.
  • Multisensor fusion overcomes limitations of traditional sensors in dynamic and space-constrained industrial settings.
  • This work provides a valuable resource for advancing automation and efficiency in the mining sector.