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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...
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A hydraulic jump is a sudden rise in fluid depth in open channels, occurring when high-velocity (supercritical) flow transitions to low-velocity (subcritical) flow. This phenomenon requires an upstream Froude number greater than 1, as flows with Fr1<1 remain subcritical, making a hydraulic jump impossible due to the need for negative head loss, which violates thermodynamic principles.The characteristics of a hydraulic jump depend on the upstream Froude number and are classified as...
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Related Experiment Video

Updated: Sep 20, 2025

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
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Long Jump Action Recognition Based on Deep Convolutional Neural Network.

Zhiteng Wang1

  • 1Fujian Normal University, Fuzhou 350108, Fujian, China.

Computational Intelligence and Neuroscience
|June 9, 2022
PubMed
Summary
This summary is machine-generated.

This study introduces a deep convolutional neural network for recognizing long jump movements in students. The method accurately assesses lower limb strength by analyzing spatiotemporal features, improving physical health monitoring.

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Area of Science:

  • Sports Science
  • Computer Vision
  • Biomechanical Analysis

Background:

  • Long jump performance is a key indicator of student lower limb strength in physical health monitoring.
  • Technical deficiencies in long jump movements can lead to inaccurate assessments of students' physical condition.
  • Accurate and rapid feedback on long jump technique is crucial for effective student training and evaluation.

Purpose of the Study:

  • To develop a deep convolutional neural network-based method for recognizing long jump movements.
  • To improve the accuracy of long jump performance assessment in students.
  • To provide rapid diagnostic feedback on students' long jump technique.

Main Methods:

  • Utilized 3D convolutional neural networks to extract spatiotemporal features from video data.
  • Employed multi-modal data fusion (RGB and depth images) for enhanced feature representation.
  • Investigated different feature fusion strategies (tandem, maximum, multiplicative) in the scoring layer.

Main Results:

  • Achieved a highest recognition accuracy of 82.3% using tandem feature fusion across three modalities.
  • Demonstrated that joint training of RGB and depth data accelerates convergence and improves accuracy.
  • Validated the effectiveness of the proposed deep learning approach for long jump movement recognition.

Conclusions:

  • The developed deep convolutional neural network method effectively recognizes long jump movements.
  • Multi-modal data fusion and strategic feature integration significantly enhance recognition accuracy.
  • This approach offers a promising tool for objective and accurate assessment of student physical health through long jump analysis.