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相关概念视频

Classification of Signals01:30

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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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Probability is the likelihood of an event occurring. The term event is defined as a collection of results of a procedure. An event is a simple event when an outcome cannot be divided into simpler parts.
An example of a simple event is a coin toss. The result of a coin toss is either a head or a tail. Here, head and tail are two simple events. These two simple events make up the sample space. Further, the probability of an event occurring falls within the range of 0 to 1. The probability of an...
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A random variable is a single numerical value that indicates the outcome of a procedure. The concept of random variables is fundamental to the probability theory and was introduced by a Russian mathematician, Pafnuty Chebyshev, in the mid-nineteenth century.
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An experiment often consists of more than a single step. In this case, measurements at each step give rise to uncertainty. Because the measurements occur in successive steps, the uncertainty in one step necessarily contributes to that in the subsequent step. As we perform statistical analysis on these types of experiments, we must learn to account for the propagation of uncertainty from one step to the next. The propagation of uncertainty depends on the type of arithmetic operation performed on...
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强大的向量BOTDA信号处理与概率机器学习.

Abhishek Venketeswaran1, Nageswara Lalam1,2, Ping Lu1,2

  • 1National Energy Technology Laboratory, 626 Cochrans Mill Road, Pittsburgh, PA 15236, USA.

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|July 14, 2023
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概括
此摘要是机器生成的。

一个新的概率机器学习 (PML) 框架准确地估计了矢量Brillouin光学时域分析 (VBOTDA) 系统中的Brillouin频率偏移 (BFS). 该方法还量化了预测不确定性,提高了VBOTDA的性能.

关键词:
数据分析数据分析.深度神经网络是一个神经网络.分布式光纤传感器分布式光纤传感器光纤传感器的光学纤维传感器传感器数据 传感器数据

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科学领域:

  • 光电学是指光电子产品.
  • 机器学习 机器学习
  • 光纤传感传感器是指光纤传感器.

背景情况:

  • 矢量Brillouin光学时域分析 (VBOTDA) 对于光纤传感至关重要.
  • 准确估计Brillouin频率转移 (BFS) 对VBOTDA的性能至关重要.
  • 目前用于BFS估计的方法可能缺乏准确性和不确定性评估.

研究的目的:

  • 在VBOTDA中引入一种新的概率机器学习 (PML) 框架,用于BFS估计.
  • 评估与基于PML的BFS估计相关的预测不确定性.
  • 将PML框架的性能与传统方法进行比较.

主要方法:

  • 开发一个概率机器学习 (PML) 框架.
  • 应用PML框架来估计BFS从Brillouin增益和阶段光谱.
  • 与传统的曲线拟合方法进行比较.
  • 使用两个BOTDA系统 (10公里和25公里光纤) 验证.

主要成果:

  • 该PML框架准确地预测了沿传感纤维的BFS.
  • 该框架提供了对BFS预测不确定性的可靠评估.
  • 在10公里和25公里VBOTDA系统中证明了有效性.
  • 与传统方法相比,可能减少数据处理时间.

结论:

  • 拟议的PML框架为VBOTDA中的BFS估计提供了一个强大的方法.
  • 这种方法通过提供准确的BFS预测和不确定性量化来提高VBOTDA系统性能.
  • 对于推进光纤传感技术来说,PML是一个有前途的方向.