A classifier learning system using a coevolution method for deflection yoke misconvergence pattern classification problem

Abstract

Deflection yoke (DY) is one of the core components of a cathode ray tube (CRT) in a computer monitor or a television that determines the image quality. Once a DY anomaly is found from beam patterns on a display in the production line of CRTs, the remedy process should be performed through three steps: identifying misconvergence types from the anomalous display pattern, adjusting manufacturing process parameters, and fine tuning. This study focuses on discovering a classifier for the identification of DY misconvergence patterns by applying a coevolutionary classification method. The DY misconvergence classification problems may be decomposed into two subproblems, which are feature selection and classifier adaptation. A coevolutionary classification method is designed by coordinating the two subproblems, whose performances are affected by each other. The proposed method establishes a group of partial sub-regions, defined by regional feature set, and then fits a finite number of classifiers to the data pattern by using a genetic algorithm in every sub-region. A cycle of the cooperation loop is completed by evolving the sub-regions based on the evaluation results of the fitted classifiers located in the corresponding sub-regions. The classifier system has been tested with real-field data acquired from the production line of a computer monitor manufacturer in Korea, showing superior performance to other methods such as k-nearest neighbors, decision trees, and neural networks.