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Uniform Depth Channel Flow: Problem Solving

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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相关实验视频

Updated: Jul 16, 2025

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Published on: December 15, 2023

水下源源由语网络辅助的半监督学习.

Hao Wen¹, Chengzhu Yang¹, Daowei Dou¹

¹School of Information and Electronics, Beijing Institute of Technology, Beijing, 100081, People's Republic of Chinawenhao@bit.edu.cn, ycz@bit.edu.cn, ddw@bit.edu.cn, 6120210061@bit.edu.cn, jiaoyc23@bit.edu.cn.

JASA express letters

|September 15, 2023

概括

本研究引入了一种半监督学习方法,通过为未标记的数据生成伪标签来提高水下源范围的准确性. 这种方法减少了对广泛标记数据集的需求,使深度学习更有可能用于声学定位.

科学领域:

声学声学在声学上
机器学习机器学习
信号处理信号处理

背景情况:

用于水下源范围的深度学习需要大型标记数据集,这些数据集的获取是昂贵的,耗时的.
由于在声学定位任务中数据稀缺,现有的方法面临挑战.

研究的目的:

开发一种半监督式学习方法,以提高水下源范围的准确性.
为深度学习模型减少对大量标记数据的依赖.

主要方法:

利用语网络为未标记的水下声学数据生成伪标签.
提出了一个新的信任标准,包括相似性得分和样本分布,以评估伪标签的可靠性.
实现了半监督学习的包装范式,以训练具有扩展数据集的模型.

主要成果:

拟议的半监督方法有效地提高了在水下源范围内的预测准确性.
信任标准在评估伪标签方面表现出可靠性,有助于模型培训.
在SwellEx-96数据集上的实验证实了该方法的有效性.

结论:

半监督学习,特别是拟议的伪标签策略,为水下源范围的数据稀缺提供了可行的解决方案.

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Development of New Methods for Quantifying Fish Density Using Underwater Stereo-video Tools

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Last Updated: Jul 16, 2025

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Published on: December 15, 2023

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Published on: July 5, 2024

Development of New Methods for Quantifying Fish Density Using Underwater Stereo-video Tools

Development of New Methods for Quantifying Fish Density Using Underwater Stereo-video Tools

Published on: November 20, 2017

该方法通过有效利用未标记的数据,增强了深度学习在声学定位方面的实际应用.