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相关概念视频

Understanding Deception01:14

Understanding Deception

200
Deception is a pervasive aspect of human communication. Empirical studies have shown that most individuals engage in some form of deceit on a daily basis, with approximately 20% of social exchanges involving deceptive elements. Lying follows a developmental trajectory, peaking during adolescence and declining with age, possibly due to the maturation of cognitive control and social accountability.Cognitive and Social Factors in Deception DetectionDespite its prevalence, accurately detecting...
200
Aliasing01:18

Aliasing

717
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...
717
Propagation of Uncertainty from Systematic Error01:10

Propagation of Uncertainty from Systematic Error

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The atomic mass of an element varies due to the relative ratio of its isotopes. A sample's relative proportion of oxygen isotopes influences its average atomic mass. For instance, if we were to measure the atomic mass of oxygen from a sample, the mass would be a weighted average of the isotopic masses of oxygen in that sample. Since a single sample is not likely to perfectly reflect the true atomic mass of oxygen for all the molecules of oxygen on Earth, the mass we obtain from this...
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相关实验视频

Updated: Feb 26, 2026

Real-Time Proxy-Control of Re-Parameterized Peripheral Signals using a Close-Loop Interface
11:54

Real-Time Proxy-Control of Re-Parameterized Peripheral Signals using a Close-Loop Interface

Published on: May 8, 2021

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基于采样数据的安全同步控制延迟合模糊惯性神经网络在欺骗攻击下.

Ziye Zhang, Shuwen Lv, Chong Lin

    IEEE transactions on cybernetics
    |February 24, 2026
    PubMed
    概括
    此摘要是机器生成的。

    本研究引入了一个模糊采样数据安全控制器,以解决对延迟合模糊惯性神经网络 (FINNs) 的欺骗攻击. 该方法确保了这些网络的指数级同步,尽管存在敌对干扰.

    相关实验视频

    Last Updated: Feb 26, 2026

    Real-Time Proxy-Control of Re-Parameterized Peripheral Signals using a Close-Loop Interface
    11:54

    Real-Time Proxy-Control of Re-Parameterized Peripheral Signals using a Close-Loop Interface

    Published on: May 8, 2021

    5.2K

    科学领域:

    • 控制理论 控制理论
    • 人工智能的人工智能
    • 网络安全 网络安全

    背景情况:

    • 延迟合模糊惯性神经网络 (FINNs) 容易受到欺骗攻击.
    • 在逆境条件下确保网络安全和同步是至关重要的.

    研究的目的:

    • 设计FINNs针对欺骗攻击的强有力的安全控制策略.
    • 在欺骗性干扰下分析系统的行为,并保证指数级同步.

    主要方法:

    • 设计了一个模糊采样数据安全控制器.
    • 使用了利亚普诺夫函数 (LKF) 和不等式技术.
    • 线性矩阵不等式 (LMIs) 用于建立同步标准.

    主要成果:

    • 为分析受欺骗的闭环系统行为制定了一个理论框架.
    • 成功建立了实现指数级同步的标准.
    • 数字模拟证实了拟议的安全控制方法的有效性.

    结论:

    • 开发的安全控制方法有效地减轻了欺骗攻击.
    • 延迟联的FINN的指数级同步甚至可以在逆境条件下实现.
    • 该方法证明了在保护智能网络的实际应用性.