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相关概念视频

Linear Approximation in Time Domain01:21

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Nonlinear systems often require sophisticated approaches for accurate modeling and analysis, with state-space representation being particularly effective. This method is especially useful for systems where variables and parameters vary with time or operating conditions, such as in a simple pendulum or a translational mechanical system with nonlinear springs.
For a simple pendulum with a mass evenly distributed along its length and the center of mass located at half the pendulum's length,...
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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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Field Application of Global Positioning System01:28

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The Global Positioning System (GPS) has become an indispensable tool in fieldwork, offering unparalleled precision and efficiency for surveying, navigation, and infrastructure development. By harnessing signals from a constellation of satellites, GPS receivers determine the location of objects with remarkable speed and accuracy, often completing calculations within a second.Advantages of Modern GPS TechnologyContemporary GPS receivers are designed to meet the practical demands of field...
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Calibration Curves: Linear Least Squares01:20

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A calibration curve is a plot of the instrument's response against a series of known concentrations of a substance. This curve is used to set the instrument response levels, using the substance and its concentrations as standards. Alternatively, or additionally, an equation is fitted to the calibration curve plot and subsequently used to calculate the unknown concentrations of other samples reliably.
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Residuals and Least-Squares Property01:11

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The vertical distance between the actual value of y and the estimated value of y. In other words, it measures the vertical distance between the actual data point and the predicted point on the line
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Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
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基于相关性的被动本地化:线性系统建模和精度意识优化.

Ting Zhang1, Dongyao Zhou1, Lei Cheng1

  • 1College of Information Science and Electronic Engineering, Zhejiang University, Hangzhou, 310027, China.

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

这项研究通过将匹配的场处理重新制定为稀疏感知优化问题来增强水下被动定位,减少侧叶和提高声源定位的准确性.

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科学领域:

  • 声学 声学 在声学上.
  • 信号处理 信号处理
  • 海洋学 海洋学 海洋学

背景情况:

  • 在水下被动定位中匹配的现场处理面临着重大不匹配挑战.
  • 数据衍生复制品,特别是基于交叉相关性的复制品,提供了更好的稳定性,但遭受了高侧边波并需要广泛的频率样本.
  • 现有的文献突出了这些问题,但缺乏理论分析.

研究的目的:

  • 从理论上分析和解决基于交叉相关性的匹配场处理中的高侧叶和格子叶挑战.
  • 为增强水下被动本地化开发一种新的优化方法.
  • 扩展分布式传感器网络的方法.

主要方法:

  • 重视传统的基于相关性的匹配,通过将模两可的表面定制为未确定线性系统的最小规范解决方案.
  • 将本地化问题重新定义为一个精度意识的优化问题,以解决侧面问题和测量要求.
  • 使用模拟波导数据,空气中的麦克风数据和SWellEx-96数据验证方法.

主要成果:

  • 提议的优化视角提供了对侧边的理论理解.
  • 稀疏度意识的方法有效地减轻了高侧叶,并减少了对广泛频率样本的需求.
  • 在各种数据集中展示了卓越的本地化性能,并扩展了对分布式传感器网络的适用性.

结论:

  • 新型稀疏感知优化框架为被动本地化匹配场处理的长期挑战提供了强大的解决方案.
  • 这种方法在各种声环境中显著提高了定位精度和效率.
  • 扩展到分布式传感器网络突出了其在先进的水下监视和监控方面的潜力.