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相关概念视频

Linear Approximation in Frequency Domain01:26

Linear Approximation in Frequency Domain

91
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....
91
Aliasing01:18

Aliasing

136
Accurate signal sampling and reconstruction are crucial in various signal-processing applications. A time-domain signal's spectrum can be revealed using its Fourier transform. When this signal is sampled at a specific frequency, it results in multiple scaled replicas of the original spectrum in the frequency domain. The spacing of these replicas is determined by the sampling frequency.
If the sampling frequency is below the Nyquist rate, these replicas overlap, preventing the original...
136
Time and frequency -Domain Interpretation of Phase-lag Control01:21

Time and frequency -Domain Interpretation of Phase-lag Control

95
Phase-lag controllers are widely used in control systems to improve stability and reduce steady-state errors. A dimmer switch controlling the brightness of a light bulb serves as a practical example of phase-lag control, gradually adjusting the bulb's brightness. Mathematically, phase-lag control or low-pass filtering is represented when the factor 'a' is less than 1.
Phase-lag controllers do not place a pole at zero, but instead influence the steady-state error by amplifying any...
95
Frequency-Domain Interpretation of PD Control01:24

Frequency-Domain Interpretation of PD Control

111
Proportional-Derivative (PD) controllers are widely used in fan control systems to improve stability and performance. A fan control system can be effectively represented using a Bode plot to illustrate the impact of a PD controller through its transfer function. The Bode plot visually conveys how PD control modifies the fan's response across various frequencies, providing a frequency domain interpretation of the controller's behavior.
The proportional control gain, combined with the...
111
Properties of Fourier Transform II01:24

Properties of Fourier Transform II

215
The Fourier Transform (FT) is an essential mathematical tool in signal processing, transforming a time-domain signal into its frequency-domain representation. This transformation elucidates the relationship between time and frequency domains through several properties, each revealing unique aspects of signal behavior.
The Frequency Shifting property of Fourier Transforms highlights that a shift in the frequency domain corresponds to a phase shift in the time domain. Mathematically, if x(t) has...
215
Associative Learning01:27

Associative Learning

362
Associative learning is a fundamental concept in behavioral psychology, wherein a connection is established between two stimuli or events, leading to a learned response. This process is critical in understanding how behaviors are acquired and modified. Conditioning, the mechanism through which associations are formed, can be divided into two main types: classical conditioning and operant conditioning, each elucidating different aspects of associative learning.
Classical conditioning, also known...
362

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相关实验视频

Updated: Jul 2, 2025

Measuring Statistical Learning Across Modalities and Domains in School-Aged Children Via an Online Platform and Neuroimaging Techniques
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基于频域注入的联合学习后门攻击

Jiawang Liu1, Changgen Peng1, Weijie Tan1,2

  • 1State Key Laboratory of Public Big Data, College of Compute Science and Technology, Guizhou University, Guiyang 550025, China.

Entropy (Basel, Switzerland)
|February 23, 2024
PubMed
概括

这项研究引入了一种新的频域后门攻击,用于联合学习 (FL). 新方法比现有攻击更隐蔽,更有效,保护分布式机器学习中的全球模型.

关键词:
富里叶变换是什么意思 富里叶变换后门攻击后门攻击联合学习的联合学习频率域频率域是一个频率域.

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Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
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科学领域:

  • 计算机科学 计算机科学
  • 人工智能的人工智能
  • 机器学习 机器学习

背景情况:

  • 联合学习 (FL) 允许在没有数据共享的情况下进行协作模式培训.
  • FL系统容易受到恶意客户端的后门攻击.
  • 现有的攻击通常使用可见触发器并破坏语义信息.

研究的目的:

  • 为联合学习提出一种新的,更隐蔽的后门攻击.
  • 克服现有的空间域攻击的局限性.
  • 为了提高后门攻击在FL的有效性.

主要方法:

  • 开发了一种基于频域注入的后门攻击.
  • 利用里埃转换来混合频域中的触发器和清洁图像.
  • 在保留语义内容的同时注入低频触发信息.

主要成果:

  • 拟议的攻击比现有方法更隐蔽.
  • 攻击在FL场景中显示出更高的有效性.
  • 在多个图像分类数据集上进行了实验.

结论:

  • 频域攻击提供了一个更强大的方法来插入后门在FL.
  • 这种方法保留了语义信息,使得攻击更难被检测.
  • 这些发现突出了分布式机器学习的新安全挑战.