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Related Concept Videos

Confidence Coefficient01:24

Confidence Coefficient

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The confidence coefficient is also known as the confidence level or degree of confidence. It is the percent expression for the probability, 1-α, that the confidence interval contains the true population parameter assuming that the confidence interval is obtained after sufficient unbiased sampling; for example, if the CL = 90%, then in 90 out of 100 samples the interval estimate will enclose the true population parameter. Here α is the area under the curve, distributed equally under...
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Confidence Intervals01:21

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An unbiased point estimate is often insufficient to predict a population estimate, such as population mean or population proportion. In this scenario, a confidence interval is used. A confidence interval is an estimate similar to a  sample proportion. However, unlike the point estimate which is a single value, the confidence interval  contains a range of values. These values have lower and upper limits, known as confidence limits, and can be designated as L1 and L2, respectively.
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Uncertainty: Confidence Intervals00:54

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The confidence interval is the range of values around the mean that contains the true mean. It is expressed as a probability percentage. The interpretation of a 95% confidence interval, for instance, is that the statistician is 95% confident that the true mean falls within the interval. The upper and lower limits of this range are known as confidence limits. The confidence limits for the true mean are estimated from the sample's mean, the standard deviation, and the statistical factor...
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Interpretation of Confidence Intervals01:19

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A confidence interval is a better estimate of the population than a point estimate, as it uses a range of values from a sample instead of a single value.
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Distribution Reliability and Automation01:25

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Distribution reliability in electrical power systems is critical for ensuring an uninterrupted power supply to consumers at minimal cost. According to IEEE Standard Terms, reliability is the probability that a device will function without failure over a specified time period or amount of usage. For electric power distribution, this translates to maintaining continuous power supply and addressing customer concerns over power outages. Several indices, as defined by IEEE Standard 1366-2012, are...
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Guidelines and Strategies for Safe Computer Charting01:18

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The guidelines and strategies provided by the American Nurses Association (ANA) and the Canadian Nurses Association (CNA) offer essential principles for ensuring safe and secure computer charting systems in healthcare settings. Let's break down each recommendation:
Maintain Confidentiality and Security:
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An Industrial Internet Security Assessment Model Based on a Selectable Confidence Rule Base.

Qingqing Yang1, Shiming Li1, Yuhe Wang1

  • 1College of Computer Science and Information Engineering, Harbin Normal University, Harbin 150025, China.

Sensors (Basel, Switzerland)
|December 17, 2024
PubMed
Summary

This study introduces a novel confidence rule-based security assessment model for the industrial internet, enhancing network security. The model uses selective modeling and an optimization algorithm to improve accuracy with limited data.

Keywords:
choosing covariance matrix adaptive evolution strategyexpert systemindustrial internetrule based belief systemselective modeling criteria

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Area of Science:

  • Computer Science
  • Cybersecurity
  • Industrial Internet

Background:

  • Network security is critical for industrial internet production environments.
  • Existing models face challenges with insufficient or poor-quality data.

Purpose of the Study:

  • To propose a confidence rule-based security assessment model for the industrial internet.
  • To enhance network security and mitigate impacts on production environments.

Main Methods:

  • Developed a selective modeling definition tailored for the industrial internet.
  • Introduced the Selectable Belief Rule Base (BRB-s) model assessment process.
  • Designed a parameter optimization method using the Selection covariance matrix adaptive evolution strategy (S-CMA-ES) algorithm.

Main Results:

  • The BRB-s model with S-CMA-ES optimization achieved improved evaluation results.
  • Effectively addressed reduced modeling accuracy due to data limitations.
  • Experimental comparisons validated the model's accuracy and the algorithm's effectiveness.

Conclusions:

  • The proposed industrial internet network security assessment model demonstrates high accuracy.
  • The S-CMA-ES optimization algorithm is feasible and effective for parameter tuning.
  • The approach enhances industrial internet security and data-driven decision-making.