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可解释的深度学习用于在水分系统中检测声泄漏

Ziyang Xu1, Haixing Liu1, Guangtao Fu2

  • 1School of Hydraulic Engineering, Dalian University of Technology, Dalian, Liaoning 116024, PR China.

Water research
|January 5, 2025
PubMed
概括

本研究介绍了一种多通道卷积神经网络 (MCNN),用于改善水系统的泄漏检测. 与现有方法相比,MCNN模型表现出卓越的性能和可解释性,提高了泄漏声学信号的准确性和理解性.

关键词:
关键签名识别认可 关键签名识别可解释的深度学习泄漏检测器可以检测泄漏.在MGrad-CAM上进行.多通道卷积神经网络多通道卷积神经网络

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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...
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