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The structure of the color CRT. 

The structure of the color CRT. 

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This paper presents a method and a system to identify the number of magnetic correction shunts and their positions for deflection yoke tuning to correct the misconvergence of colors of a cathode ray tube. The method proposed consists of two phases, namely, learning and optimization. In the learning phase, the radial basis function neural network is...

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... from examples. This paper presents an image analysis and neural networks based intelligent system for tuning magnetic ®eld of a de ̄ection yoke (DY) of a cathode ray tube (CRT). Several prototypes of the system have been made. The prototypes developed have been installed in the ``Vilniaus Vingis'' company, Lithuania, and are successfully used on production line. The company is one of the largest TV DY producers in Europe. The most widely used display device for television and computer monitors is the color CRT (Whitaker, 1994). The CRT produces visible light by bombarding a thin layer of phosphor material by an energetic beam of electrons. In the color CRT (Fig. 1), three electron guns producing three beams of electrons hitting three color phosphors are used. The red (R), green (G), and blue (B) phosphors are placed on the inner part of a TV screen as dots or strips. For a particular beam to hit a proper color phosphor a shadow mask in the CRT ...

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This paper presents a method and a system for deflection yoke tuning to correct the misconvergence of colours of a cathode ray tube. The misconvergence of colours is characterised by 18 distances measured between the primary colour beam traces of the same picture element. The vision system grabs the picture at nine control positions and the compute...

Citations

... 10.018 ingly used to achieve excellent performance in many fields of the manufacturing industry, e.g. process control, remote sensing, quality inspection and material analysis, etc. [2,4,23]. One of the recently emphasized strategies to realize the intelligent systems is an autonomous learning approach, which elicits the useful expert knowledge from the examples that are predetermined [13]. ...
... The final step in the quality control process is performed by sticking one or several magnetic shunts on the inner part of the DY. The neuro-fuzzy modeling or the radial basis function neural network based approaches have been proposed to automate the fine tuning step [3,21,23]. ...
... For the identification of the DY misconvergence types, 9, 17, or 25 points are examined [3,16,23]. The larger number of points decreases the probability of missing some screen points that could show a high degree of the color misconvergence. ...
Article
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.
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