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相关概念视频

Propagation of Uncertainty from Systematic Error01:10

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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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Stereotype Content Model02:16

Stereotype Content Model

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The Stereotype Content Model (SCM) was first proposed by Susan Fiske and her colleagues (Fiske, Cuddy, Glick & Xu, 2002; see also Fiske, 2012 and Fiske, 2017). The SCM specifies that when someone encounters a new group, they will stereotype them based on two metrics: warmth—or that group’s perceived intent, and how likely they are to provide help or inflict harm—and competence—or their ability to carry out that objective. Depending on the warmth-competence...
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Systematic Error: Methodological and Sampling Errors01:15

Systematic Error: Methodological and Sampling Errors

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In the case of systematic errors, the sources can be identified, and the errors can be subsequently minimized by addressing these sources. According to the source, systematic errors can be divided into sampling, instrumental, methodological, and personal errors.
Sampling errors originate from improper sampling methods or the wrong sample population. These errors can be minimized by refining the sampling strategy. Defective instruments or faulty calibrations are the sources of instrumental...
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Propagation of Uncertainty from Random Error00:59

Propagation of Uncertainty from Random Error

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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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Random and Systematic Errors01:20

Random and Systematic Errors

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Scientists always try their best to record measurements with the utmost accuracy and precision. However, sometimes errors do occur. These errors can be random or systematic. Random errors are observed due to the inconsistency or fluctuation in the measurement process, or variations in the quantity itself that is being measured. Such errors fluctuate from being greater than or less than the true value in repeated measurements. Consider a scientist measuring the length of an earthworm using a...
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State Space Representation01:27

State Space Representation

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The frequency-domain technique, commonly used in analyzing and designing feedback control systems, is effective for linear, time-invariant systems. However, it falls short when dealing with nonlinear, time-varying, and multiple-input multiple-output systems. The time-domain or state-space approach addresses these limitations by utilizing state variables to construct simultaneous, first-order differential equations, known as state equations, for an nth-order system.
Consider an RLC circuit, a...
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Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit
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对于有传感器故障和假数据注入攻击下的令牌桶协议的非线性网络物理系统的状态估计.

Yu-Ang Wang, Zidong Wang, Lei Zou

    IEEE transactions on cybernetics
    |October 1, 2025
    PubMed
    概括

    本研究介绍了一种针对非线性网络物理系统 (CPSs) 的递归状态估计方法,该系统在令牌桶协议 (TBPs) 下面临传感器故障和虚假数据注入 (FDI) 攻击. 开发的算法确保了尽管网络限制和恶意入侵,强大的估计.

    科学领域:

    • 控制系统工程 控制系统工程
    • 网络物理系统安全 网络物理系统安全
    • 网络化系统 网络化系统

    背景情况:

    • 网络物理系统 (CPS) 越来越容易受到传感器故障和虚假数据注入 (FDI) 攻击的影响.
    • 代币桶协议 (TBPs) 引入了网络诱导的CPS数据传输的限制.
    • 现有的状态估计方法经常难以解决同时存在的TBP约束,传感器故障和FDI攻击.

    研究的目的:

    • 为非线性CPS开发一个强大的递归状态估计算法.
    • 为了同时应对TBPs,传感器故障和FDI攻击所带来的挑战.
    • 为了最大限度地降低估计误差协变率的上限.

    主要方法:

    • 使用密集的随机技术和感应方法来推导估计误差共变率的上限.
    • 递归计算估计器收益以最大限度地降低导出的上限.
    • 根据伯努利分布,采用随机FDI攻击的随机模型.

    主要成果:

    • 提出了一个有效的递归状态估计算法.
    • 该算法展示了同时处理TBP约束,传感器故障和FDI攻击的能力.
    • 估计误差共变率的上限已成功被最小化.

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    结论:

    • 拟议的状态估计方案有效地提高了非线性CPS的安全性和可靠性.
    • 该方法为在具有挑战性的网络条件和对抗性攻击下进行状态估计提供了强大的解决方案.
    • 本示例验证了开发的估计算法的实际适用性和性能.