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相关概念视频

Design Example: Measuring Distance Between Two Points with Obstructions01:10

Design Example: Measuring Distance Between Two Points with Obstructions

386
When measuring distances in areas with physical obstructions, such as a lake in a field, surveyors must employ techniques to calculate accurate lengths without direct line measurements. One effective method is the offset technique, which allows for precise distance estimation over inaccessible stretches.In this scenario, a surveyor must measure a side of an area that crosses a lake. Since the measuring tape cannot span the lake, the surveyor begins by establishing a baseline that aligns with...
386
Uniform Depth Channel Flow: Problem Solving01:18

Uniform Depth Channel Flow: Problem Solving

426
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...
426

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Updated: May 3, 2026

Spatial Multiobjective Optimization of Agricultural Conservation Practices using a SWAT Model and an Evolutionary Algorithm
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通过未经监督的对抗性部分域调整来缓解负面转移,用于估计水下源范围.

Jianbo Zhou1,2, Runling Long3, Yixin Yang1,2

  • 1School of Marine Science and Technology, Northwestern Polytechnical University, Xi'an 710072, China.

The Journal of the Acoustical Society of America
|October 28, 2025
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概括

本研究引入了一种使用部分域适应 (PDA) 的新水下源范围估计方法. 它通过对模拟数据进行加权来改进无监督域适应 (UDA),有效地减轻实际场景中的性能损失.

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科学领域:

  • 海洋声学 海洋声学
  • 信号处理 信号处理
  • 机器学习 机器学习

背景情况:

  • 水下源的定位对于各种应用至关重要.
  • 现有的无监督域适应 (UDA) 方法在测量和模拟范围不同的情况下,与实际数据作斗争.
  • 这种性能降低被称为负转移.

研究的目的:

  • 提出使用无监督部分域适应 (PDA) 的改进的水下源范围估计方法.
  • 为了解决UDA中的负传输问题,当测量和模拟的数据范围空间不相同时.
  • 为了提高在具有挑战性的现实世界条件下水下来源定位的准确性.

主要方法:

  • 开发了一个部分域调整 (PDA) 框架,用于估计水下源范围.
  • 将基于PDA的模拟数据权重机制纳入了基于PDA的UDA框架.
  • 利用深度神经网络处理测量数据并指导模拟数据的权重用于域区分.

主要成果:

  • 拟议的基于PDA的方法在测量和模拟数据范围空间不同时显著优于UDA.
  • 该方法在各种场景中显示出与传统匹配场处理相比更高的性能.
  • 在不同的适应数据采样策略和环境参数不匹配中验证了有效性.

结论:

  • 基于PDA的权重机制有效地减轻了对范围估计无监督域适应的负转移.
  • 这种方法为水下来源定位提供了强大的解决方案,特别是在处理域移动时.
  • 该方法在改善海洋环境中声源定位的准确性和可靠性方面显示出显著的前景.