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相关概念视频

Detection of Gross Error: The Q Test01:00

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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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Error is the deviation of the obtained result from the true, expected value or the estimated central value. Errors are expressed in absolute or relative terms.
Absolute error in a measurement is the numerical difference from the true or central value. Relative error is the ratio between absolute error and the true or central value, expressed as a percentage.
Errors can be classified by source, magnitude, and sign. There are three types of errors: systematic, random, and gross.
Systematic or...
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Sometimes, a data set can have a recorded numerical observation that greatly  deviates from the rest of the data. Assuming that the data is normally distributed, a statistical method called the Grubbs test can be used to determine whether the observation is truly an outlier.  To perform a two-tailed Grubbs test, first, calculate the absolute difference between the outlier and the mean. Then, calculate the ratio between this difference and the standard deviation of the sample. This...
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The limit of detection (LOD) is the smallest amount of analyte that can be distinguished from the background noise. The LOD value corresponds to the concentration at which the analyte signal is three times larger than the standard deviation of the blank signal. Below this value, the analyte signal cannot be differentiated from the background noise. It is calculated by dividing the calibration slope by 3 times the standard deviation of the blank signals.
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Subsurface Defect Localization by Structured Heating Using Laser Projected Photothermal Thermography
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从异常检测到缺陷分类

Jaromír Klarák1, Robert Andok1, Peter Malík1

  • 1Institute of Informatics, Slovak Academy of Sciences, 845 07 Bratislava, Slovakia.

Sensors (Basel, Switzerland)
|January 23, 2024
PubMed
概括
此摘要是机器生成的。

本研究引入了一种使用无监督和监督机器学习的新型缺陷检测系统,以精确定位轮图像中的确切受损区域. 该方法有效地识别和分类缺陷,为现有的检测方法提供了替代方案.

关键词:
检测异常检测异常检测自动编码器自动编码器自动化自动化自动化自动化聚类集群是指聚类的聚类.深度学习是一种深度学习.发现缺陷检测检测缺陷检测视觉检查 视觉检查 视觉检查 视觉检查

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科学领域:

  • 计算机视觉 计算机视觉
  • 机器学习 机器学习
  • 工业检查 工业检查 工业检查

背景情况:

  • 自动缺陷检测对于制造中的质量控制至关重要.
  • 现有的方法往往难以准确地定位各种缺陷模式.

研究的目的:

  • 开发一个缺陷检测系统,准确地识别和突出显示视觉数据中的确切受损区域.
  • 提出一种新的混合方法,将无监督和监督学习结合起来,以加强缺陷定位.

主要方法:

  • 使用自动编码器通过比较原始和重建图像来检测异常.
  • 应用了DBSCAN集群,将异常分组为感兴趣的区域.
  • 采用预先训练的Xception网络对检测到的缺陷进行监督分类.
  • 将这些结合成一个无监督-无监督-监督 (U2S-CNN) 方法.

主要成果:

  • 该系统成功识别了177个地区,其中108个正确确定了205个发生的受损区域.
  • 实现了缺陷的准确定位,证明了系统专注于确切的受损区域.
  • 展示了检测广泛缺陷模式的能力.

结论:

  • 拟议的U2S-CNN系统为精确的缺陷区域检测提供了可行的概念验证.
  • 这种方法为工业检查提供了对YOLO,自动编码器和变压器等既定方法的潜在替代方案.
  • 突出结合不同机器学习范式用于复杂的视觉任务的有效性.