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相关概念视频

Classification of Signals01:30

Classification of Signals

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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.
A continuous-time signal holds a value at every instant in time, representing information seamlessly. In contrast, a discrete-time signal holds values only at specific moments, often denoted as x(n), where...
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Linear Approximation in Time Domain01:21

Linear Approximation in Time Domain

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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,...
62
Linear Approximation in Frequency Domain01:26

Linear Approximation in Frequency Domain

85
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.
In contrast, nonlinear systems do not inherently possess these properties. However, for small deviations around an operating point, a nonlinear system can often be approximated as linear....
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Rectangular and Triangular Pulse Function01:19

Rectangular and Triangular Pulse Function

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The unit rectangular pulse function is mathematically represented by a rectangular function centered at the origin with a height of one unit. This function is defined by two parameters: T, which specifies the center location of the pulse along the time axis, and τ, which determines the pulse duration.
For example, consider a rectangular pulse with a 5V amplitude, a 3-second duration, and centered at t=2 seconds. This pulse can be expressed using the rectangular function, written as,
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Reconstruction of Signal using Interpolation01:10

Reconstruction of Signal using Interpolation

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Signal processing techniques are essential for accurately converting continuous signals to digital formats and vice versa. When a continuous signal is sampled with a period T, the resulting sampled signal exhibits replicas of the original spectrum in the frequency domain, spaced at intervals equal to the sampling frequency. To handle this sampled signal, a zero-order hold method can be applied, which creates a piecewise constant signal by retaining each sample's value until the next...
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IR Spectrum Peak Splitting: Symmetric vs Asymmetric Vibrations01:08

IR Spectrum Peak Splitting: Symmetric vs Asymmetric Vibrations

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Identical bonds within a polyatomic group can stretch symmetrically (in-phase) or asymmetrically (out-of-phase). Similar to hydrogen bonding, these vibrations also influence the shape of the IR peak. Generally, asymmetric stretching frequencies are higher than symmetric stretching frequencies. For example, primary amines exhibit two distinct IR peaks between 3300–3500 cm−1 corresponding to the symmetric and asymmetric N-H stretching, while secondary amines exhibit a single...
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一个优化的SVR算法用于脉冲堆叠校正在脉冲形状歧视.

Xianghe Liu1,2, Bingqi Liu2,3, Mingzhe Liu1,2

  • 1The College Nuclear Technology and Automation Engineering, Chengdu University of Technology, Chengdu 610059, China.

Sensors (Basel, Switzerland)
|December 17, 2024
PubMed
概括

这项研究引入了一个优化的支持向量回归 (SVR) 算法来纠正辐射检测中的脉冲堆积. 该方法在具有挑战性的环境中提高了中子-马脉冲形状区分的准确性.

关键词:
堆积起来的脉冲脉冲.脉冲形状的区别是脉冲形状的区别.支持向量的回归.

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科学领域:

  • 核物理和辐射检测仪器仪表.

背景情况:

  • 脉冲堆积在核辐射测量中显著扭曲信号,阻碍了准确的中子-马脉冲形状歧视.
  • 现有的方法与脉冲扭曲作斗争,导致混合辐射场的识别精度降低.

研究的目的:

  • 开发和验证一个优化的支向量回归 (SVR) 算法,以有效地纠正脉冲堆积.
  • 为了提高中子-马脉冲形状歧视在高干扰环境中的准确性和可靠性.

主要方法:

  • 开发了一个优化的支持向量回归 (SVR) 算法,集成了虫优化器 (DBO) 和鱼优化算法 (WOA).
  • 使用电荷比较方法 (CCM) 评估性能,以进行脉冲形状歧视,并分析模拟的脉冲堆积特征.
  • 实验是在混合中子-马辐射场中使用塑料闪器进行的.

主要成果:

  • 拟议的DBO-WOA优化SVR算法有效地纠正了脉冲堆积,显著改善了中子-马区分.
  • 该方法在识别中子和玛辐射方面表现出高精度,即使有重叠脉冲.
  • 实验结果证实了脉冲形状歧视的增强忠实性和整体系统可靠性.

结论:

  • 优化的SVR算法为核辐射检测中的脉冲堆积校正提供了强大的解决方案.
  • 这一进步提高了在复杂,高干扰环境中运行的辐射检测系统的可靠性.
  • 该研究强调了核仪器仪表中先进优化算法的潜力,以提高测量准确度.