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相关概念视频

Modeling and Similitude01:12

Modeling and Similitude

Scaled modeling is a fundamental technique in engineering, enabling the study of large and complex systems by creating smaller, manageable replicas that recreate critical characteristics of the original. In hydrology and civil infrastructure, for example, scaled models of dams help analyze water flow, turbulence, and pressure. This method allows for accurate predictions of real-world behavior within a controlled environment, significantly reducing the cost and time involved in full-scale...
Typical Model Studies01:30

Typical Model Studies

Fluid mechanics model studies often utilize scaled-down systems to predict fluid behavior in full-scale environments, such as river flows, dam spillways, and structures interacting with open surfaces. Maintaining Froude number similarity in river models is crucial, as it replicates surface flow features like wave patterns and velocities.
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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相关实验视频

Updated: May 31, 2026

Long-term Behavioral Tracking of Freely Swimming Weakly Electric Fish
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Published on: March 6, 2014

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基于深度学习的水下声学目标识别:介绍最近的时间二维建模方法.

Jun Tang1, Wenbo Gao1, Enxue Ma1

  • 1School of Civil Engineering, Tianjin University, Tianjin 300072, China.

Sensors (Basel, Switzerland)
|March 13, 2024
PubMed
概括

一种新的深度学习方法结合了用于船舶辐射噪声分类的1D和2D模型. 这种方法提高了准确性并减少了模型参数,为水下目标识别提供了有效的替代方案.

关键词:
深度学习是一种深度学习.短时间的里叶变换时间2D建模水下声学目标识别系统

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Last Updated: May 31, 2026

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科学领域:

  • 信号处理 信号处理
  • 深度学习 (Deep Learning) 是一种深度学习.
  • 水下声学 水下声学

背景情况:

  • 深度学习模型越来越多地用于水下目标识别.
  • 目前的模型通常使用1D (时间域) 或2D (时间频率) 处理,每个都有局限性.

研究的目的:

  • 引入和评估一种用于船舶辐射噪声分类的新型时间二维建模方法.
  • 结合1D和2D深度学习方法,以改善水下目标识别.

主要方法:

  • 一种时间的2D建模方法适用于船只辐射噪声的分类.
  • 该方法利用时间域信号的周期性特征,将其转换为2D信号.
  • 使用2D卷积来识别长期相关性,克服1D卷积的局限性. 模型在Deepship数据库上进行了训练和测试.

主要成果:

  • 组合的1D和2D方法提高了0.9%的分类精度,并减少了30%的参数数量.
  • 在时间域信号上训练的模型显示出更高的灵敏度和30%更小的存储足迹.
  • 在时间频率表示上训练的模型实现了1-2%更高的准确性.

结论:

  • 拟议的时间2D建模方法为构建和优化用于水下目标识别的深度学习模型提供了一个新的,有效的选择.
  • 在模型大小/灵敏度 (时间域) 和精度 (时间频率) 之间存在一个权衡,允许量身定制的模型选择.