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相关概念视频

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...
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Random Variables01:09

Random Variables

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A random variable is a single numerical value that indicates the outcome of a procedure. The concept of random variables is fundamental to the probability theory and was introduced by a Russian mathematician, Pafnuty Chebyshev, in the mid-nineteenth century.
Uppercase letters such as X or Y denote a random variable. Lowercase letters like x or y denote the value of a random variable. If X is a random variable, then X is written in words, and x is given as a number.
For example, let X = the...
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Randomized Experiments01:13

Randomized Experiments

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The randomization process involves assigning study participants randomly to experimental or control groups based on their probability of being equally assigned. Randomization is meant to eliminate selection bias and balance known and unknown confounding factors so that the control group is similar to the treatment group as much as possible. A computer program and a random number generator can be used to assign participants to groups in a way that minimizes bias.
Simple randomization
Simple...
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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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Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

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Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...
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Entropy Change in Reversible Processes01:10

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In the Carnot engine, which achieves the maximum efficiency between two reservoirs of fixed temperatures, the total change in entropy is zero. The observation can be generalized by considering any reversible cyclic process consisting of many Carnot cycles. Thus, it can be stated that the total entropy change of any ideal reversible cycle is zero.
The statement can be further generalized to prove that entropy is a state function. Take a cyclic process between any two points on a p-V diagram.
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基于随机自编码神经网络和合混乱映射的联合加密模型.

Anqi Hu1,2, Xiaoxue Gong1,2, Lei Guo1,2

  • 1School of Communication and Information Engineering, Chongqing University of Posts and Telecommunications, No. 2, Chongwen Road, Nanan District, Chongqing 400065, China.

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概括
此摘要是机器生成的。

这项研究引入了一种新的联合加密模型,使用随机选择性自编码神经网络 (AENN) 和混乱映射. 该模型增强了一维的混乱,以实现安全,资源高效的一次性加密,抵御常见攻击.

关键词:
在 AENN 随机化.结合的混沌映射绘制数据的加密数据的加密.联合加密 联合加密一次性片

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科学领域:

  • 密码学和网络安全
  • 人工智能和机器学习
  • 混沌理论和动态系统

背景情况:

  • 一维混乱在时间和复杂性方面具有局限性,阻碍其在安全通信中的应用.
  • 现有的关键同步方法用于一次性密码可能是资源密集型的.
  • 需要先进的加密模型,将混乱的动态与神经网络相结合,以提高安全性和效率.

研究的目的:

  • 提出一种随机选择性自编码神经网络 (AENN) 和结合的混乱映射,以克服一维混乱的局限性.
  • 开发一种改进的密钥同步方法,用于一次性Pad加密,从而节省通道资源.
  • 引入一个联合加密模型,整合随机AENN和混乱映射,用于高安全性数据传输.

主要方法:

  • 深入分析一个维的混乱.
  • 开发一个随机选择性自编码神经网络 (AENN).
  • 将AENN与用于联合加密模型的新型混乱合映射集成.
  • 实施了改进的密钥同步技术,以实现高效的道资源利用.

主要成果:

  • 拟议的加密模型展示了庞大的密钥空间和高灵敏度,实现了一次性块加密的效果.
  • 实验验证证证实了该模型能够抵御常见的加密分析攻击,包括详尽的,选择性的纯文本和统计攻击.
  • 与传统方法相比,联合加密模型显著减少了安全通道资源的使用.

结论:

  • 开发的联合加密模型通过利用增强的混乱动态和AENN.提供了强大的安全通信解决方案.
  • 该模型提供了高水平的安全性,可与一次性加密进行比较,同时优化了资源利用.
  • 这种方法为开发下一代安全通信系统提供了一个有希望的方向,该系统可以抵抗复杂的攻击.