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A vertical-energy-thresholding procedure for data reduction with multiple complex curves.

Uk Jung1, Myong K Jeong, Jye-Chyi Lu

  • 1College of Business Administration, Dongguk University, Seoul 100-715, Korea.

IEEE Transactions on Systems, Man, and Cybernetics. Part B, Cybernetics : a Publication of the IEEE Systems, Man, and Cybernetics Society
|October 14, 2006
PubMed
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This study introduces a vertical-energy-thresholding (VET) procedure to reduce large functional datasets for manufacturing. VET effectively captures key patterns, improving decision-making in fault detection and quality control.

Area of Science:

  • Engineering
  • Data Science
  • Manufacturing Technology

Background:

  • Modern manufacturing generates vast amounts of process data, posing challenges for timely decision-making.
  • Existing methods struggle with high-dimensional functional data for fault detection and quality improvement.

Purpose of the Study:

  • To develop an efficient data preprocessing procedure for large, complex functional datasets.
  • To reduce data size while preserving essential patterns for enhanced decision analysis.

Main Methods:

  • Developed a novel vertical-energy-thresholding (VET) procedure.
  • VET balances data reduction efficiency with reconstruction error.
  • Utilized selected wavelet coefficients as reduced-size data for analysis.

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Main Results:

  • The VET procedure effectively reduces data size from multiple functional signals.
  • It captures key patterns crucial for subsequent analyses.
  • Demonstrated superior performance compared to ad hoc techniques in real-life examples.

Conclusions:

  • The VET procedure enhances statistical and machine-learning capabilities for high-dimensional functional data.
  • It supports timely decision-making in manufacturing applications like fault detection and quality improvement.