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相关概念视频

Propagation of Uncertainty from Random Error00:59

Propagation of Uncertainty from Random Error

681
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...
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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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Estimating Population Mean with Unknown Standard Deviation01:22

Estimating Population Mean with Unknown Standard Deviation

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In practice, we rarely know the population standard deviation. In the past, when the sample size was large, this did not present a problem to statisticians. They used the sample standard deviation s as an estimate for σ and proceeded as before to calculate a confidence interval with close enough results. However, statisticians ran into problems when the sample size was small. A small sample size caused inaccuracies in the confidence interval.
William S. Gosset (1876–1937) of the...
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Norton's Theorem01:14

Norton's Theorem

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Norton's theorem is a fundamental principle stating that a linear two-terminal circuit can be substituted with an equivalent circuit, which comprises a current source (ⅠN) in parallel with a resistor (RN). Here, ⅠN represents the short-circuit current flowing through the terminals, and RN stands for the input or equivalent resistance at the terminals when all independent sources are deactivated. This implies that the circuit illustrated in Figure (a) can be exchanged with the...
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One-Compartment Open Model: Wagner-Nelson and Loo Riegelman Method for ka Estimation01:24

One-Compartment Open Model: Wagner-Nelson and Loo Riegelman Method for ka Estimation

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This lesson introduces two critical methods in pharmacokinetics, the Wagner-Nelson and Loo-Riegelman methods, used for estimating the absorption rate constant (ka) for drugs administered via non-intravenous routes. The Wagner-Nelson method relates ka to the plasma concentration derived from the slope of a semilog percent unabsorbed time plot. However, it is limited to drugs with one-compartment kinetics and can be impacted by factors like gastrointestinal motility or enzymatic degradation.
On...
482
Sampling Theorem01:15

Sampling Theorem

329
In signal processing, the analysis of continuous-time signals, denoted as x(t), often involves sampling techniques to convert these signals into discrete-time signals. This process is essential for digital representation and manipulation. A critical component in sampling is the train of impulses, characterized by the sampling interval and the sampling frequency. The relationship between these parameters and the original signal's properties dictates the success of the sampling process.
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相关实验视频

Updated: Jun 28, 2025

Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks
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对于有未知但有边界噪声的人工神经网络的安全状态估计:一个同态加密方案.

Kaiqun Zhu, Zidong Wang, Derui Ding

    IEEE transactions on neural networks and learning systems
    |April 24, 2024
    PubMed
    概括

    本研究介绍了使用同型加密 (HES) 的人工神经网络 (ANN) 的安全状态估计方法. 它可以在没有数据解密的有限带宽网络上进行安全估计,确保数据完整性.

    科学领域:

    • 控制系统工程 控制系统工程
    • 网络安全 网络安全
    • 人工智能的人工智能

    背景情况:

    • 对于有噪音数据和有限通信的系统,安全状态估计至关重要.
    • 人工神经网络 (ANN) 越来越多地使用,但需要安全的数据处理.
    • 开放的,带宽有限的网络在传输敏感测量数据方面带来了挑战.

    研究的目的:

    • 开发一个安全的状态估计算法,用于在未知但有界噪声下ANN.
    • 通过使用新型加密技术在带宽有限的网络上传输数据时确保数据安全.
    • 能够直接从加密数据中进行状态估计,而无需进行解密.

    主要方法:

    • 一个新的同型加密方案 (HES),结合编码解码机制 (EDM) 和Paillier加密.
    • 在加密数据上运行的安全集合成员状态估计算法的开发.
    • 使用优化和拉格朗奇乘法推导安全状态估计器收益.

    主要成果:

    • 在噪声和HES约束下确定了圆形集存在的足够条件.
    • 拟议的安全状态估计算法有效地从加密数据计算估计.
    • 该方法确保了整个估计过程中的数据安全性.

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    结论:

    • 开发的安全状态估计方法对杂的,带宽有限的环境中的ANN有效.
    • 同型加密方案在传输和估计过程中提供了强大的数据保护.
    • 这项工作促进了网络控制系统中安全可靠的状态估计.