Advance technology such as various types of automatic data
acquisitions, management, and networking systems has created a
tremendous capability for managers to access valuable production
information to improve their operation quality and efficiency.
Signal processing and data mining techniques are more popular than
ever in many fields including intelligent manufacturing. As data
sets increase in size, their exploration, manipulation, and
analysis become more complicated and resource consuming. Timely
synthesized information such as functional data is needed for
product design, process trouble-shooting, quality/efficiency
improvement and resource allocation decisions. A major obstacle in
those intelligent manufacturing system is that tools for
processing a large volume of information coming from numerous
stages on manufacturing operations are not available. Thus, the
underlying theme of this thesis is to reduce the size of data in a
mathematical rigorous framework, and apply existing or new
procedures to the reduced-size data for various decision-making
purposes. This thesis, first, proposes {it Wavelet-based
Random-effect Model} which can generate multiple functional data
signals which have wide fluctuations(between-signal variations) in
the time domain. The random-effect wavelet atom position in the
model has {it locally focused impact} which can be distinguished
from other traditional random-effect models in biological field.
For the data-size reduction, in order to deal with heterogeneously
selected wavelet coefficients for different single curves, this
thesis introduces the newly-defined {it Wavelet Vertical Energy}
metric of multiple curves and utilizes it for the efficient data
reduction method. The newly proposed method in this thesis will
select important positions for the whole set of multiple curves by
comparison between every vertical energy metrics and a threshold
({it Vertical Energy Threshold; VET}) which will be optimally
decided based on an objective function. The objective function
balances the reconstruction error against a data reduction ratio.
Based on class membership information of each signal obtained,
this thesis proposes the {it Vertical Group-Wise Threshold}
method to increase the discriminative capability of the
reduced-size data so that the reduced data set retains salient
differences between classes as much as possible. A real-life
example (Tonnage data) shows our proposed method is promising.
| Identifer | oai:union.ndltd.org:GATECH/oai:smartech.gatech.edu:1853/5084 |
| Date | 12 July 2004 |
| Creators | Jung, Uk |
| Publisher | Georgia Institute of Technology |
| Source Sets | Georgia Tech Electronic Thesis and Dissertation Archive |
| Language | en_US |
| Detected Language | English |
| Type | Dissertation |
| Format | 1272209 bytes, application/pdf |
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