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相关概念视频

Upsampling01:22

Upsampling

262
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...
262
Downsampling01:20

Downsampling

184
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...
184
Instantaneous Center of Zero Velocity01:20

Instantaneous Center of Zero Velocity

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General plane motion, often observed in a rolling wheel, refers to a type of movement where the wheel is simultaneously rotating and translating. This complex motion can be understood by breaking it down into individual components.
To analyze this, consider two points on the wheel: point A and point B. The absolute velocity of point B can be expressed as the vector sum of the absolute velocity of point A and the relative velocity of point B with respect to point A. To simplify this analysis,...
487
Sequence Networks of Rotating Machines01:24

Sequence Networks of Rotating Machines

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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...
123
Linear Approximation in Frequency Domain01:26

Linear Approximation in Frequency Domain

110
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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Choosing Between z and t Distribution01:25

Choosing Between z and t Distribution

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The z and the Student t distribution estimate the population mean using the sample mean and standard deviation. However, to decide which distribution to use for a calculation, one needs to determine the sample size, the nature of the distribution, and whether the population standard deviation is known. If the population standard deviation is known and the population is normally distributed, or if the sample size is greater than 30, the z distribution is preferred. The Student t distribution is...
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相关实验视频

Updated: Jul 18, 2025

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
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Published on: December 15, 2023

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长距离零射击生成深度网络定量化远程零射击.

Yan Luo1, Yangcheng Gao1, Zhao Zhang2

  • 1School of Computer Science and Information Engineering, Hefei University of Technology, Hefei, China.

Neural networks : the official journal of the International Neural Network Society
|August 21, 2023
PubMed
概括
此摘要是机器生成的。

长距离零射击生成深度网络定量化 (LRQ) 通过生成多样化的合成数据来提高模型性能. 这种新的方法克服了传统的零射击定量化的局限性,提高了深度网络的效率.

关键词:
对抗性利额外增加深度网络量化深度网络量化远程发电机是一个远程发电机.合成数据生成的合成数据生成.

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科学领域:

  • 深度学习 (Deep Learning) 是一种深度学习.
  • 量化模型的量化模型
  • 生成型模型 生成型模型

背景情况:

  • 量子化通过使用低位宽的数字来降低深度网络的计算成本.
  • 零射击量化合成数据以近似真实数据分布,而无需访问原始数据.
  • 现有的零射击方法由于数据多样性和功能丰富性有限而受到影响.

研究的目的:

  • 提出一种新的深度网络定量器,长距离零射击生成深度网络定量化 (LRQ).
  • 为了解决当前零射击量化技术的局限性.
  • 提高量子化深度网络的性能,而无需访问真实数据.

主要方法:

  • 引入了长距离生成器 (LRG),结合了长距离注意力和大内核卷积,以增强全球特征学习.
  • 开发了一个对抗性附加边际 (AMA) 模块,以扩大类内角分离.
  • 利用脱的知识蒸来从完全精确的网络转移知识.

主要成果:

  • 拟议的LRQ方法与现有的量子化技术相比,显示出更高的性能.
  • LRG有效地捕获远程信息,从而导致更高的合成数据多样性.
  • AMA模块成功地提高了特征的独特性和类内异质性.

结论:

  • 通过生成高质量的合成数据,LRQ在零射击量化方面取得了重大进展.
  • LRG和AMA模块的组合有效地克服了零射击量化中的性能差距.
  • 在资源有限的环境中,LRQ为高效的深度网络部署提供了可行的解决方案.