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相关概念视频

Reducing Line Loss01:18

Reducing Line Loss

178
In a three-phase circuit, line loss is an indicator of energy dissipated as heat due to the resistance of transmission lines. To address this, incorporating transformers into the system—a step-up transformer at the source and a step-down transformer at the load—is a strategic solution. Two three-phase transformers are introduced to improve this.
With a step-up transformer at the source, the voltage is increased, thereby reducing the current in the transmission lines since power loss...
178
Propagation of Uncertainty from Random Error00:59

Propagation of Uncertainty from Random Error

743
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...
743
Residuals and Least-Squares Property01:11

Residuals and Least-Squares Property

7.4K
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...
7.4K
Sequence Networks of Rotating Machines01:24

Sequence Networks of Rotating Machines

126
A Y-connected synchronous generator, grounded through a neutral impedance, is designed to produce balanced internal phase voltages with only positive-sequence components. The generator's sequence networks include a source voltage that is exclusively in the positive-sequence network. The sequence components of line-to-ground voltages at the generator terminals illustrate this configuration.
Zero-sequence current induces a voltage drop across the generator's neutral impedance and other...
126
Linear Approximation in Frequency Domain01:26

Linear Approximation in Frequency Domain

119
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....
119
Random Variables01:09

Random Variables

12.4K
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.
Uppercase letters such as X or Y denote a random variable. Lowercase letters like x or y denote the value of a random variable. If X is a random variable, then X is written in words, and x is given as a number.
For example, let X = the...
12.4K

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Quantification of Information Encoded by Gene Expression Levels During Lifespan Modulation Under Broad-range Dietary Restriction in C. elegans
09:23

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稀有添加机具有电流引起的损失.

Peipei Yuan, Xinge You, Hong Chen

    IEEE transactions on neural networks and learning systems
    |June 8, 2023
    PubMed
    概括

    我们引入了一种强大的分类方法,Sparse Additive Machines with correntropy-induced loss (CSAM),以改进高维数据分析. CSAM有效地处理异常值,提高变量选择和分类准确性.

    科学领域:

    • 机器学习 机器学习
    • 统计学学习理论

    背景情况:

    • 稀疏添加机 (SAM) 为高维数据提供了灵活性和可解释性.
    • 现有的SAM方法由于无边界或非平滑损失函数而与异常值作斗争.

    研究的目的:

    • 为高维数据开发一种强大的分类方法,这种方法对异常值具有弹性.
    • 为了提高Sparse添加机在有噪音数据的情况下的性能.

    主要方法:

    • 将电流引起的损失 (C-损失) 整合到添加机器中.
    • 使用数据依赖的假设空间和加权的 -norm调整器.
    • 使用新型错误分解和度估计进行理论分析.

    主要成果:

    • 理论概括误差极限和趋同率是在特定的参数条件下建立的.
    • 理论上可以保证变量选择的一致性.
    • 实验结果表明,与现有方法相比,其效率和稳定性更高.

    结论:

    • 拟议的CSAM方法提供了一个强大的和有效的解决方案,用于分类和变量选择在高维,异常倾向的数据集.
    • CSAM在具有挑战性的现实场景中推进了增材机器的应用.

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