This report describes a quantification method for luminance non-uniformity of a large LED backlight. In experiments described herein, participants subjectively evaluated artificial indistinct Mura images that simulated luminance non-uniformity of an LED backlight. We measured the luminance distribution of the Mura images. Then, the measured luminance distribution was converted into S-CIELAB, in which anisotropic properties of the spatial frequency response of human vision were considered. Subsequently, some indexes for the quantification model were extracted. We conducted multiple regression analyses using the subjective evaluation value and the index values obtained from measured luminance of Mura image. We proposed a quantification model consisting of four indexes: high and low luminance area, number of Mura edges, sum of Mura edge areas, and maximum luminance difference.

In the present paper, we propose a novel cyber-attack detection model based on two multivariate-analysis methods to the audit data observed on a host machine. The statistical techniques used here are the well-known Hayashi's quantification method IV and cluster analysis method. We quantify the observed qualitative audit event sequence via the quantification method IV, and collect similar audit event sequence in the same groups based on the cluster analysis. It is shown in simulation experiments that our model can improve the cyber-attack detection accuracy in some realistic cases where both normal and attack activities are intermingled.