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相关概念视频

Estimation of the Physical Quantities01:05

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On many occasions, physicists, other scientists, and engineers need to make estimates of a particular quantity. These are sometimes referred to as guesstimates, order-of-magnitude approximations, back-of-the-envelope calculations, or Fermi calculations. The physicist Enrico Fermi was famous for his ability to estimate various kinds of data with surprising precision. Estimating does not mean guessing a number or a formula at random. Instead, estimation means using prior experience and sound...
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Linear Approximation in Frequency Domain01:26

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Linear systems are characterized by two main properties: superposition and homogeneity. Superposition allows the response to multiple inputs to be the sum of the responses to each individual input. Homogeneity ensures that scaling an input by a scalar results in the response being scaled by the same scalar.
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Electronic Distance Measuring Instruments (EDMs) are essential tools in modern surveying, offering precise distance measurements by emitting electromagnetic signals and calculating the time required for these signals to travel to a target and return. Two primary types of signals are used in EDMs — light waves and microwaves — each suited to specific environmental and distance requirements. Light-wave-based EDMs utilize either infrared or laser light, providing high accuracy over...
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Cortical Source Analysis of High-Density EEG Recordings in Children
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基于物理的机器学习用于匹配的场源范围估计)

Yongsung Park1

  • 1Applied Ocean Physics and Engineering, Woods Hole Oceanographic Institution, Woods Hole, Massachusetts 02540, USA.

The Journal of the Acoustical Society of America
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概括
此摘要是机器生成的。

一个新的基于物理的机器学习框架,使用匹配的现场处理,准确地定位水下声音源. 这种方法通过将物理整合到AI模型中,在数据有限的场景中增强了海洋声学定位.

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科学领域:

  • 海洋声学 海洋声学
  • 机器学习是机器学习.
  • 信号处理 信号处理

背景情况:

  • 海洋声源的定位对于水下监测和研究至关重要.
  • 传统的匹配场处理 (MFP) 方法通常需要大量的环境数据,并且对不匹配很敏感.
  • 纯粹基于数据的机器学习 (ML) 方法可能缺乏物理一致性.

研究的目的:

  • 为海洋声源定位开发基于物理的机器学习 (ML) 框架.
  • 将物理信息神经网络 (PINNs) 集成到匹配场处理 (MFP) 方案中.
  • 为了实现精确的源接收器范围估计,采用稀疏测量和减少环境特征.

主要方法:

  • 使用物理信息神经网络 (PINN) 来从稀疏测量和已知的声速概况 (SSP) 中预测声压场.
  • PINN预测的复制字段被集成到MFP算法中.
  • 该框架使用1996年浅水评估细胞实验 (SWellEx-96) 的实验数据进行了验证.

主要成果:

  • 拟议的方法实现了精确的源接收器范围估计,即使在最接近点等具有挑战性的场景中也是如此.
  • 该框架证明了对稀疏阵列配置和中等声速概况 (SSP) 不匹配的稳定性.
  • 在训练期间排除的阵列元素深度的性能保持不变,显示出良好的插值/外推能力.

结论:

  • 基于物理学的ML提供了一种强大的方法,用于在现实的,数据有限的环境中定位海洋声学.
  • 这种方法克服了传统基于模型的MFP的局限性,通过减少对环境的依赖和减轻不匹配效应.
  • 将物理整合到机器学习模型中可以产生物理一致的预测,提高本地化准确性和通用性.