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Updated: Jun 10, 2025

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通过基于时间延迟的深度学习方法,定位水分网络的声学泄漏.

Rongsheng Liu1, Tarek Zayed1, Rui Xiao2

  • 1Department of Building and Real Estate, The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong.

Water research
|October 16, 2024
PubMed
概括

本研究介绍了一种深度学习方法,用于定位分布网络中的水泄漏. Res1D-CNN模型在噪音条件下表现出强的性能,提高了泄漏检测的准确性.

关键词:
卷积神经网络 (CNN) 是一种神经网络.泄漏局部化泄漏情况剩余的区块块是剩余的时间延迟估计时间延迟估计水分网络的水分网络.

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Deriving the Speed of Sound in a Liquid01:09

Deriving the Speed of Sound in a Liquid

As with waves on a string, the speed of sound or a mechanical wave in a fluid depends on the fluid's elastic modulus and inertia. The two relevant physical quantities are the bulk modulus and the density of the material. Indeed, it turns out that the relationship between speed and the bulk modulus and density in fluids is the same as that between the speed and the Young's modulus and density in solids.
The speed of sound in fluids can be derived by considering a mechanical wave...
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