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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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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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Consider a control volume, such as a pipe with solid boundaries, through which fluid flows and changes direction due to the impulse exerted by the resulting force from the pipe walls. In steady flow, the mass of fluid entering the control volume at a given time, t, with velocity v1, is equal to the mass leaving after infinitesimal time dt, with velocity v2.
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The brain processes sensory information rapidly due to parallel processing, which involves sending data across multiple neural pathways at the same time. This method allows the brain to manage various sensory qualities, such as shapes, colors, movements, and locations, all concurrently. For instance, when observing a forest landscape, the brain simultaneously processes the movement of leaves, the shapes of trees, the depth between them, and the various shades of green. This enables a quick and...
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Enhanced Action Recognition Using Multiple Stream Deep Learning with Optical Flow and Weighted Sum.

Hyunwoo Kim1, Seokmok Park1, Hyeokjin Park1

  • 1Department of Image, Graduate School of Advanced Imaging Science, Multimedia and Film, Chung-Ang University, Seoul 06974, Korea.

Sensors (Basel, Switzerland)
|July 17, 2020
PubMed
Summary
This summary is machine-generated.

This study introduces an improved action recognition method using enhanced optical flow and a weighted score fusion technique. The novel approach boosts accuracy by focusing on main object movements and stream reliability.

Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Machine Learning

Background:

Keywords:
action recognitiondeep learningmulti-streamscore fusion

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  • Existing action recognition methods using 3D convolution and multi-stream structures are sensitive to background noise and optical flow inaccuracies.
  • These limitations hinder the accurate learning of main object actions within video frames.
  • Current methods lack mechanisms to dynamically weigh the accuracy of individual streams during fusion.