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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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Related Experiment Video

Updated: Sep 27, 2025

Author Spotlight: Analgesic Effect of Tuina on Rat Models with Compression of the Dorsal Root Ganglion Pain
05:49

Author Spotlight: Analgesic Effect of Tuina on Rat Models with Compression of the Dorsal Root Ganglion Pain

Published on: July 14, 2023

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Model Compression Based on Differentiable Network Channel Pruning.

Yu-Jie Zheng, Si-Bao Chen, Chris H Q Ding

    IEEE Transactions on Neural Networks and Learning Systems
    |April 15, 2022
    PubMed
    Summary
    This summary is machine-generated.

    We introduce a differentiable network channel pruning (DNCP) method to compress neural networks for mobile devices. This approach efficiently finds optimal network structures using gradient descent, reducing computational costs.

    Related Experiment Videos

    Last Updated: Sep 27, 2025

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    1.6K

    Area of Science:

    • Computer Science
    • Artificial Intelligence
    • Machine Learning

    Background:

    • Deep neural networks (DNNs) excel in many areas but face limitations on mobile devices due to high computational and storage demands.
    • Model compression techniques, such as neural network pruning, are crucial for reducing DNN size and enhancing efficiency.

    Purpose of the Study:

    • To propose a novel differentiable network channel pruning (DNCP) method for efficient neural network model compression.
    • To enable efficient searching for optimal network substructures that meet resource constraints like FLOPs.

    Main Methods:

    • The DNCP method assigns learnable probabilities to channel counts in each network layer.
    • It uses gradient descent for end-to-end optimization of these probabilities, relaxing channel selection via softmax.
    • The network is pruned based on optimized probabilities to achieve the best substructure.

    Main Results:

    • DNCP efficiently searches for optimal substructures without extensive sampling, meeting resource constraints.
    • Experiments on ResNet and MobileNet V2 across CIFAR, Tiny ImageNet, and ImageNet datasets demonstrate DNCP's effectiveness and efficiency.

    Conclusions:

    • The proposed DNCP method offers an efficient and effective solution for neural network model compression.
    • This technique facilitates the deployment of complex neural networks on resource-constrained mobile devices.