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相关概念视频

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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In fluid mechanics, buoyancy and stability are key concepts for understanding the behavior of submerged and floating bodies. When a stationary body is fully or partially submerged in a fluid, the fluid exerts a force on the body known as the buoyant force. This force acts vertically upward through a point called the center of buoyancy, which is the center of the displaced fluid volume. According to Archimedes' principle, the magnitude of the buoyant force is equal to the weight of the fluid...
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In signal processing, signals are classified based on various characteristics: continuous-time versus discrete-time, periodic versus aperiodic, analog versus digital, and causal versus noncausal. Each category highlights distinct properties crucial for understanding and manipulating signals.
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半监督图表学习使用潜水源定位机会船谱

Jhon A Castro-Correa1, Mohsen Badiey1, Jhony H Giraldo2

  • 1Department of Electrical and Computer Engineering, University of Delaware, Newark, Delaware 19716, USA.

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这项研究引入了一种新的图形学习方法,用于使用船舶噪声谱的水下来源定位. 该方法有效地捕获数据相关性,即使有有限的标记数据,也提高了准确性.

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科学领域:

  • 声信号处理
  • 水下声学
  • 机器学习用于本地化

背景情况:

  • 传统的水下来源定位方法难以充分利用关键的数据相关性.
  • 基于图形的方法可以在声学数据中捕捉空间关系.
  • 对于声学定位的监督学习来说,有限的标记数据是一个重大挑战.

研究的目的:

  • 开发一种新的图形学习模块,用于准确地定位水下源.
  • 为了利用机会船的光谱图进行声学信号分析.
  • 解决声学定位任务中有限的标记数据的挑战.

主要方法:

  • 一种两步的方法,将预训练的卷积神经网络 (CNN) 结合为特征提取和图形神经网络 (GNN) 进行本地化.
  • 自主监督学习用于CNN特征提取,半监督学习用于GNN培训.
  • 在CNN从船舶噪声谱图中提取的特征上使用k-最近的邻居进行图形构造.

主要成果:

  • 拟议的图形学习框架实现了与传统的监督学习模型相提并论的性能.
  • 这种方法通过图形表示有效地利用数据相关性.
  • 在合成和测量数据上展示了概括能力.

结论:

  • 这种新型图形学习模块为水下源地定位提供了有效的解决方案,
  • 整合CNN和GNN为声信号分析提供了一个强大的框架.
  • 这种方法在水下声学监测和导航中具有前景.