Diagnostic Feature Extraction From Stamping Tonnage Signals Based on Design of Experiments

1999 ◽  
Vol 122 (2) ◽  
pp. 360-369 ◽  
Author(s):  
Jionghua Jin ◽  
Jianjun Shi
Keyword(s):  
Feature Extraction ◽  
Design Of Experiments ◽  
Principal Component ◽  
Fractional Factorial ◽  
Diagnostic Feature ◽  
Root Cause ◽  
Variation Patterns ◽  
Factorial Design Of Experiments ◽  
New Feature ◽  
Diagnostic Research

Diagnostic feature extraction with consideration of interactions between variables is very important, but has been neglected in most diagnostic research. In this paper, a new feature extraction methodology is developed to consider variable interactions by using a fractional factorial design of experiments (DOE). In this methodology, features are extracted by using principal component analysis (PCA) to represent variation patterns of tonnage signals. Regression analyses are performed to model the relationship between features and process variables. Hierarchical classifiers and the cross-validation method are used for root-cause determination and diagnostic performance evaluation. A real-world example is used to illustrate the new methodology. [S1087-1357(00)00302-6]

Understanding the effects of electrochemical parameters on the areal capacitance of electroactive polymers

2014 ◽  
Vol 2 (20) ◽  
pp. 7509-7516 ◽  
Author(s):  
D. Eric Shen ◽  
Leandro A. Estrada ◽  
Anna M. Österholm ◽  
Danielle H. Salazar ◽  
Aubrey L. Dyer ◽  
...  
Keyword(s):  
Design Of Experiments ◽  
Factorial Design ◽  
Fractional Factorial Design ◽  
Electroactive Polymers ◽  
Fractional Factorial ◽  
Areal Capacitance ◽  
Electrochemical Parameters ◽  
Factorial Design Of Experiments

A fractional factorial design of experiments allowed us to optimize the areal capacitance of electropolymerized films over 7 variables using a dramatically reduced set of experiments.

Nuclear norm based two-dimensional sparse principal component analysis

2018 ◽  
Vol 16 (02) ◽  
pp. 1840002 ◽  
Author(s):  
Yudong Chen ◽  
Zhihui Lai ◽  
Jiajun Wen ◽  
Can Gao
Keyword(s):  
Principal Component Analysis ◽  
Feature Extraction ◽  
Principal Component ◽  
Component Analysis ◽  
Extraction Methods ◽  
Reconstruction Error ◽  
Nuclear Norm ◽  
Two Dimensional ◽  
Sparse Principal Component Analysis ◽  
New Feature

Two-Dimensional Principal Component Analysis (2D-PCA) is one of the most simple and effective feature extraction methods in the field of pattern recognition. However, the traditional 2D-PCA lacks robustness and the function of sparse feature extraction. In this paper, we propose a new feature extraction approach based on the traditional 2D-PCA, which is called Nuclear Norm Based Two-Dimensional Sparse Principal Component Analysis (N-2D-SPCA). To improve the robustness of 2D-PCA, we utilize nuclear norm to measure the reconstruction error of loss function. At the same time, we obtain sparse feature extraction by adding [Formula: see text]-norm and [Formula: see text]-norm regularization terms to the model. By designing an alternatively iterative algorithm, we can solve the optimization problem and learn a projection matrix for use with feature extraction. Besides, we present a bilateral projections model (BN-2D-SPCA) to further compress the dimensions of the feature matrix. We verify the effectiveness of our method on four benchmark face databases including AR, ORL, FERET and Yale databases. Experimental results show that the proposed method is more robust than some state-of-the-art methods and the traditional 2D-PCA.

Robustness testing of live attenuated rubella vaccine potency assay using fractional factorial design of experiments

Vaccine ◽  
2010 ◽  
Vol 28 (33) ◽  
pp. 5497-5502 ◽  
Author(s):  
Leonida Kutle ◽  
Nediljko Pavlović ◽  
Marko Dorotić ◽  
Ivana Zadro ◽  
Marijana Kapustić ◽  
...  
Keyword(s):  
Design Of Experiments ◽  
Factorial Design ◽  
Fractional Factorial Design ◽  
Fractional Factorial ◽  
Rubella Vaccine ◽  
Potency Assay ◽  
Robustness Testing ◽  
Vaccine Potency ◽  
Factorial Design Of Experiments

Pattern Recognition for Classification and Matching of Car Tires

2005 ◽  
Vol 33 (1) ◽  
pp. 2-17 ◽  
Author(s):  
D. Colbry ◽  
D. Cherba ◽  
J. Luchini
Keyword(s):  
Computer Vision ◽  
Feature Extraction ◽  
Pattern Matching ◽  
Fourier Transforms ◽  
Two Dimensions ◽  
Multiple Sources ◽  
Matching Process ◽  
Visual Searches ◽  
New Feature ◽  
2D Fft

Abstract Commercial databases containing images of tire tread patterns are currently used by product designers, forensic specialists and product application personnel to identify whether a given tread pattern matches an existing tire. Currently, this pattern matching process is almost entirely manual, requiring visual searches of extensive libraries of tire tread patterns. Our work explores a first step toward automating this pattern matching process by building on feature analysis techniques from computer vision and image processing to develop a new method for extracting and classifying features from tire tread patterns and automatically locating candidate matches from a database of existing tread pattern images. Our method begins with a selection of tire tread images obtained from multiple sources (including manufacturers' literature, Web site images, and Tire Guides, Inc.), which are preprocessed and normalized using Two-Dimensional Fast Fourier Transforms (2D-FFT). The results of this preprocessing are feature-rich images that are further analyzed using feature extraction algorithms drawn from research in computer vision. A new, feature extraction algorithm is developed based on the geometry of the 2D-FFT images of the tire. The resulting FFT-based analysis allows independent classification of the tire images along two dimensions, specifically by separating “rib” and “lug” features of the tread pattern. Dimensionality of (0,0) indicates a smooth treaded tire with no pattern; dimensionality of (1,0) and (0,1) are purely rib and lug tires; and dimensionality of (1,1) is an all-season pattern. This analysis technique allows a candidate tire to be classified according to the features of its tread pattern, and other tires with similar features and tread pattern classifications can be automatically retrieved from the database.

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