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相关概念视频

Upsampling01:22

Upsampling

238
Managing signal sampling rates is essential in digital signal processing to maintain signal integrity. A decimated signal, characterized by a reduced frequency range due to its lower sampling rate, can be upsampled by inserting zeros between each sample. This upsampling process expands the original spectrum and introduces repeated spectral replicas at intervals dictated by the new Nyquist frequency. To refine this zero-inserted sequence, it is passed through a lowpass filter with a cutoff...
238
Deconvolution01:20

Deconvolution

163
Deconvolution, also known as inverse filtering, is the process of extracting the impulse response from known input and output signals. This technique is vital in scenarios where the system's characteristics are unknown, and they must be inferred from the observable signals.
Deconvolution involves several mathematical techniques to derive the impulse response. One common approach is polynomial division. In this method, the input and output sequences are treated as coefficients of...
163
Scaling01:26

Scaling

248
In designing and analyzing filters, resonant circuits, or circuit analysis at large, working with standard element values like 1 ohm, 1 henry, or 1 farad can be convenient before scaling these values to more realistic figures. This approach is widely utilized by not employing realistic element values in numerous examples and problems; it simplifies mastering circuit analysis through convenient component values. The complexity of calculations is thereby reduced, with the understanding that...
248
Downsampling01:20

Downsampling

162
When considering a sampled sequence with zero values between sampling instants, one can replace it by taking every N-th value of the sequence. At these integer multiples of N, the original and sampled sequences coincide. This process, known as decimation, involves extracting every N-th sample from a sequence, thereby creating a more efficient sequence.
The Fourier transform of the decimated sequence reveals a combination of scaled and shifted versions of the original spectrum. This...
162
Multiple Regression01:25

Multiple Regression

3.0K
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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Calibration Curves: Linear Least Squares01:20

Calibration Curves: Linear Least Squares

1.3K
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.
For data that follow a straight line, the standard method for fitting is the linear...
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Author Spotlight: Advancing Alzheimer's Research &#8211; Exploring Early Detection and Multi-Omics Approaches
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嵌套AE:可解释的嵌套自编码器用于多尺度材料表征.

Nikhil Thota1, Maitreyee Sharma Priyadarshini2,1, Rigoberto Hernandez2,1,3

  • 1Chemical and Biomolecular Engineering Department, Johns Hopkins University, Baltimore, MD, USA.

Materials horizons
|November 22, 2023
PubMed
概括
此摘要是机器生成的。

我们开发了NestedAE,这是一个用于多尺度材料的可解释机器学习模型. 与标准的自动编码器相比,这种架构显示出更好的噪声强度和更低的重建错误,将材料特性与设备性能联系起来.

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科学领域:

  • 材料科学 材料科学 材料科学
  • 机器学习 机器学习
  • 数据科学数据科学数据科学

背景情况:

  • 多尺度材料在数据分析中存在复杂的挑战,因为它们的属性尺度不同.
  • 可解释的机器学习模型对于理解结构-属性-性能关系至关重要.

研究的目的:

  • 介绍NestedAE,这是一个新的可解释机器学习架构,用于分析多尺度材料.
  • 在性能和稳定性方面,与传统的自动编码器 (AE) 相比,NestedAE的基准.
  • 使用现实数据调查晶体尺度属性与设备性能之间的关系.

主要方法:

  • 开发了NestedAE,这是一个带有嵌套结构的监督自动编码架构.
  • 在已知维度的合成数据集上验证了NestedAE.
  • 应用NestedAE到一个多尺度的MHP数据集,结合原子/离子特性和设备J-V特性.

主要成果:

  • 嵌套AE表现出优越的噪音稳定性和较低的重建损失,而不是香草AE.
  • 该模型成功地确定了晶体结构特性与设备性能之间的联系.
  • 结果与多尺度材料的现有实验观测结果一致.

结论:

  • 嵌套AE提供了一个强大的和可解释的方法,用于多尺度材料分析.
  • 该架构有效地弥合了基本材料特性和宏观设备行为之间的差距.
  • 这项工作有助于在先进材料开发中更深入地理解和预测.