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Related Concept Videos

Median01:08

Median

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Besides mean, the median is a widely used measure of central tendency. Typically, median is defined as the central or middle value of a data set, measured by arranging the data elements in an increasing or decreasing order. Since this middle value is not affected by the precise numerical values of the outliers or fluctuations, it is insensitive to them. Hence, in cases where a data set may have outliers or the extreme values are not known, the median is a better measure of the central tendency...
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Measures of Central Tendency02:16

Measures of Central Tendency

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The "center" of a data set is also a way of describing location. The two most widely used measures of the "center" of the data are the mean (average) and the median. The words "mean" and "average" are often used interchangeably. The substitution of one word for the other is common practice. The technical term is "arithmetic mean" and "average" is technically a center location. However, in practice among non-statisticians,...
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Midrange01:07

Midrange

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A somewhat easy to compute quantitative estimate of a data set’s central tendency is its midrange, which is defined as the mean of the minimum and maximum values of an ordered data set.
Simply put, the midrange is half of the data set’s range. Similar to the mean, the midrange is sensitive to the extreme values and hence the prospective outliers. However, unlike the mean, the midrange is not sensitive to all the values of the data set that lie in the middle. Thus, it is prone to...
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Sign Test for Median of Single Population01:20

Sign Test for Median of Single Population

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In general, the sign test serves as a nonparametric method to test hypotheses about the median of a single population when the data does not follow a known distribution. This simplicity makes it particularly useful for small sample sizes or when the assumptions of parametric tests cannot be met. The process begins with identifying a null hypothesis, typically stating that the population median equals a specific value. The alternative hypothesis could be that the median is either not equal to,...
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Central Tendency: Analysis01:10

Central Tendency: Analysis

149
Measures of central tendency are tools used in biostatistics to identify the average or center of a dataset. They offer a single representative value for understanding and summarizing data distribution.
The mean is one such measure, calculated by totaling all values in a dataset and dividing by the number of values. For instance, the mean blood pressure reading (120, 130, 140, 150) would be 135. However, the mean can be affected by extreme values or outliers.
The median, another measure,...
149
Cluster Sampling Method01:20

Cluster Sampling Method

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Appropriate sampling methods ensure that samples are drawn without bias and accurately represent the population. Because measuring the entire population in a study is not practical, researchers use samples to represent the population of interest.
To choose a cluster sample, divide the population into clusters (groups) and then randomly select some of the clusters. All the members from these clusters are in the cluster sample. For example, if you randomly sample four departments from your...
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Data Clustering Utilization Technologies Using Medians of Current Values for Improving Arc Sensing in Unstructured

Hee-Jun Kim1,2, Jeong-Ho Kim2,3, Shin-Nyeong Heo1

  • 1Samsung Heavy Industries Co., Ltd., Geoje-si 53261, Republic of Korea.

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Summary

This study introduces a novel median-based spatial clustering (MBSC) algorithm to improve robotic welding seam tracking. The MBSC algorithm enhances accuracy and responsiveness in challenging shipbuilding environments with curved workpieces.

Keywords:
DBSCANarc sensingdata mininginverter welderseam tracking

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Area of Science:

  • Robotics and Automation
  • Materials Science and Engineering
  • Manufacturing Processes

Background:

  • Welding automation in shipbuilding frequently uses arc-sensing due to space constraints.
  • Short-circuit transitions in arc sensing reduce feedback current reliability with curved or gapped workpieces, causing seam tracking failures and damage.

Purpose of the Study:

  • To develop a robust seam tracking algorithm for robotic welding in challenging shipbuilding environments.
  • To enhance the accuracy and responsiveness of welding robots dealing with workpiece curvature and gaps.

Main Methods:

  • Proposed a novel algorithm: median-based spatial clustering (MBSC).
  • MBSC is based on the DBSCAN (density-based spatial clustering of applications with noise) clustering algorithm.
  • The algorithm clusters data based on median values within weaving areas and analyzes feedback current data characteristics, using outliers to improve tracking.

Main Results:

  • The MBSC algorithm demonstrated enhanced seam tracking accuracy.
  • Improved responsiveness in unstructured and challenging welding scenarios was observed.
  • Effectiveness was validated through real-world welding experiments in a shipyard.

Conclusions:

  • The MBSC algorithm offers a significant improvement over traditional methods for robotic welding seam tracking.
  • The technique is particularly effective in overcoming challenges posed by workpiece curvature and gaps.
  • This advancement contributes to more reliable and damage-free automated welding in shipbuilding.