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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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When we take repeated measurements on the same or replicated samples, we will observe inconsistencies in the magnitude. These inconsistencies are called errors. To categorize and characterize these results and their errors, the researcher can use statistical analysis to determine the quality of the measurements and/or suitability of the methods.
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Reliability and validity are two important considerations that must be made with any type of data collection. Reliability refers to the ability to consistently produce a given result. In the context of psychological research, this would mean that any instruments or tools used to collect data do so in consistent, reproducible ways.
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In analytical chemistry, we often perform repetitive measurements to detect and minimize inaccuracies caused by both determinate and indeterminate errors. Despite the cares we take, the presence of random errors means that repeated measurements almost never have exactly the same magnitude. The collective difference between these measurements - observed values - and the estimated or expected value is called uncertainty. Uncertainty is conventionally written after the estimated or expected value.
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When one or more data points appear far from the rest of the data, there is a need to determine whether they are outliers and whether they should be eliminated from the data set to ensure an accurate representation of the measured value. In many cases, outliers arise from gross errors (or human errors) and do not accurately reflect the underlying phenomenon. In some cases, however, these apparent outliers reflect true phenomenological differences. In these cases, we can use statistical methods...
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Scientists typically make repeated measurements of a quantity to ensure the quality of their findings and to evaluate both the precision and the accuracy of their results. Measurements are said to be precise if they yield very similar results when repeated in the same manner. A measurement is considered accurate if it yields a result that is very close to the true or the accepted value. Precise values agree with each other; accurate values agree with a true value. 
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量子可靠性 量子可靠性

L X Cui1, Y-M Du2, C P Sun1,2,3

  • 1Beijing Computational Science Research Center, Beijing 100193, China.

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PubMed
概括
此摘要是机器生成的。

这项研究引入了量子可靠性的新指标,超越了状态忠实性来分析量子轨迹. 该框架为评估古典和量子设备的可靠性提供了一种通用方法.

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科学领域:

  • 量子物理学 量子物理学 是一种量子物理学.
  • 可靠性工程可靠性工程
  • 量子信息科学 量子信息科学

背景情况:

  • 量子技术的进步需要对量子设备进行强有力的可靠性评估.
  • 现有的经典可靠性理论缺乏适合量子系统的指标.
  • 量子忠实性不能完全捕捉由量子过程影响的可靠性损失.

研究的目的:

  • 开发一个量子可靠性及其损失的系统度量.
  • 建立适用于古典和量子设备的可靠性理论的通用框架.
  • 通过将设备性能与量子过程联系起来,为量子工程提供一个新的视角.

主要方法:

  • 将焦点从量子状态区分转移到量子轨迹区分用于可靠性评估.
  • 在量子概率振幅或波函数中接地量子可靠性,而不是经典的二进制变量.
  • 开发可靠性理论的通用框架.

主要成果:

  • 已经建立了量子可靠性的新型指标.
  • 拟议的指标侧重于量子轨迹的独特性.
  • 介绍了一个包含经典和量子可靠性的通用框架.

结论:

  • 新的量子可靠性指标提供了比单独的量子忠实性更准确的评估.
  • 这项研究为理解和设计可靠的量子设备提供了基础框架.
  • 了解真实量子过程的影响对于设备性能至关重要.