ABSTRACT
Luminance is an essential quality of a TFT-LCD display. Manufacturers have attempted to improve the soft-to-hard lamination stage to enhance the luminance of their TFT-LCD displays. In addition, many customers have complained about the insufficient luminance of the TFT-LCD displays of the case company. While product engineers have kept tuning the control factors in the soft-to-hard lamination stage through the trial and error method, the improvement of the luminance was not good enough. This study aims to assist the product engineers to fine-tune the settings of the control factors using a new method composed of the Taguchi method, a neural network, and a genetic algorithm. The confirmation experiments showed that the proposed method had increased the average luminance of the TFT-LCD displays from 17.03 to 25.15, which was higher than the required luminance value of 25. As a result, the number of complaints on the TFT-LCD displays had been significantly reduced.
ABSTRACT
For microarray data analysis, most of them focus on selecting relevant genes and calculating the classification accuracy by the selected relevant genes. This paper wants to detect the relation between the gene expression levels and the classes of a cancer (or a disease) to assist researchers for initial diagnosis. The proposed method is called a Two Stages Weighted Sampling strategy (TSWS strategy). According to the results, the performance of TSWS strategy is better than other existing methods in terms of the classification accuracy and the number of selected relevant genes. Furthermore, TSWS strategy also can use to understand and detect the relation between the gene expression levels and the classes of a cancer (or a disease).
