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相关概念视频

Residuals and Least-Squares Property01:11

Residuals and Least-Squares Property

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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
If the observed data point lies above the line, the residual is positive, and the line underestimates the actual data value for y. If the observed data point lies below the line, the residual is negative, and the line overestimates the actual data value for y.
The process of fitting the best-fit...
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Linear Approximation in Frequency Domain01:26

Linear Approximation in Frequency Domain

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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.
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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Determination of Expected Frequency01:08

Determination of Expected Frequency

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Suppose one wants to test independence between the two variables of a contingency table. The values in the table constitute the observed frequencies of the dataset. But how does one determine the expected frequency of the dataset? One of the important assumptions is that the two variables are independent, which means the variables do not influence each other. For independent variables, the statistical probability of any event involving both variables is calculated by multiplying the individual...
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Multiple Regression01:25

Multiple Regression

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Multiple regression assesses a linear relationship between one response or dependent variable and two or more independent variables. It has many practical applications.
Farmers can use multiple regression to determine the crop yield based on more than one factor, such as water availability, fertilizer, soil properties, etc. Here, the crop yield is the response or dependent variable as it depends on the other independent variables. The analysis requires the construction of a scatter plot...
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Relative Frequency Distribution00:55

Relative Frequency Distribution

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A relative frequency distribution is the proportion or fraction of times a value occurs in a data set. To find the relative frequencies, one can divide each frequency by the total number of data points in the sample. It is very similar to a regular frequency distribution, except that instead of reporting how many data values fall in a class, a relative frequency distribution reports the fraction of data values that fall in a class. These fractions or proportions are called relative frequencies...
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Construction of Frequency Distribution01:15

Construction of Frequency Distribution

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A frequency distribution table can be constructed using the steps given below.
First, make a table with two columns—one with the title of the data that needs to be organized, and the other column for frequency. [Draw a third column for tally marks if needed]. Then, take a look at the items given in the data set and decide if an ungrouped frequency distribution table or a grouped frequency distribution table would be more suitable. If there are large sets of different values, then it is...
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相关实验视频

Updated: Jun 25, 2025

Transmission of Multiple Signals through an Optical Fiber Using Wavefront Shaping
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Transmission of Multiple Signals through an Optical Fiber Using Wavefront Shaping

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使用残余网络与随机森林回归的光学频率乘法.

Qi Zhang1, Xu Han1, Xinyu Fang1

  • 1College of Electrical and Electronics Engineering, Changchun University of Technology, 2055 Yanan Street, Changchun, 130012, China.

Heliyon
|May 30, 2024
PubMed
概括
此摘要是机器生成的。

本研究介绍了一种混合深度学习方法,使用ResNet和RFR进行光学频率乘法. 这种方法成功地产生了高阶毫米波信号,具有出色的抑制比率.

关键词:
深度学习算法 深度学习算法频率乘法调制频率乘法调制随机森林回归随机森林回归剩余网络的残余网络

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Rapid Repetition Rate Fluctuation Measurement of Soliton Crystals in a Microresonator
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科学领域:

  • 光子学 是一个光子学.
  • 机器学习 机器学习
  • 信号处理 信号处理

背景情况:

  • 光学频率乘法对于产生高频信号至关重要.
  • 传统方法在精度和参数优化方面面临挑战.
  • 深度学习为复杂的信号生成任务提供了一种新的方法.

研究的目的:

  • 开发和验证用于光学频率乘法的混合深度学习方法.
  • 为了演示生成8,12和16个结合的毫米波信号.
  • 分析拟议方法的性能和稳定性.

主要方法:

  • 剩余网络 (ResNet) 和随机森林回归 (RFR) 的整合用于参数预测.
  • 使用三种不同的频率乘法调制方案.
  • 数字模拟用于生成和评估毫米波信号.

主要成果:

  • 在所有乘法顺序中实现了高光学侧带抑制比 (OSSR) 约30dB.
  • 证明了出色的无线电频率虚假抑制比率 (RFSSR):42.29dB (8组),36.21dB (12组) 和34.52dB (16组).
  • 研究了振幅波动和偏差电压漂移对信号质量的影响.

结论:

  • 混合深度学习方法有效地实现了精确的光学频率乘法.
  • 该方法为生成高质量的毫米波信号提供了一个强大的框架.
  • 进一步的研究可以探索在不同条件下减少信号降解的优化.