Press Characterization Recalibration by Principal Component Analysis and DEBP Model

Ting Chen; Wei Li; Dong Bai Zhao; Ting Zhang

doi:10.4028/www.scientific.net/AMM.200.670

Paper Titles

Preparation and Properties of Rare Earth Europium Fluorescent Falsification-Resistant Inks
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Study on the Effect of Slurry Formulation and Printing Process on HTCC Resistance
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A Wavelet-Based Digital Watermarking Algorithm for Packaging Security
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Influence of Filler on the Rheological Properties of UV Curable Offset Ink
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Press Characterization Recalibration by Principal Component Analysis and DEBP Model
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Influence of the Viscosity of Edible Ink to Piezoelectric Ink-Jet Printing Drop State
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Effect of Viscosity Reducer and Varnish on Ink-Transferring Ratio
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The Research on Ink Adhesion of Vacuum Aluminized Paper
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Research and Achievement on Cigarette Label Printing Defect Detection Algorithm
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HomeApplied Mechanics and MaterialsApplied Mechanics and Materials Vol. 200Press Characterization Recalibration by Principal...

Press Characterization Recalibration by Principal Component Analysis and DEBP Model

Abstract:

Artificial neural networks (ANN) combined with PCA are widely being used. This study addresses the problem of updating a CMYK printer characterization in response to systematic changes（printing material, press）in device characteristics with PCA-DEBP model. In this study, training samples of test chart, which were normalized through principal component analysis (PCA), were applied as inputs to a differential evolution back propagation (DEBP) neural network with one hidden layer. This DEBP model has been used to predict the present printing characterization using the last ICC profile by measurement with high convergence speed. Experiment results show that the predicted printing characterizations compare with that by the measurement has little color difference. So a PCA-DEBP model can be used to exactly recalibrate the ICC profile over time with low cost.