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Abstract— An AC electric field to drive the IPS mode of a liquid‐crystal display (LCD) causes a reduction in the contrast after a long period of display operation. This phenomenon is refered to as the AC image‐sticking problem caused by long‐term driving. Thus far, there is no useful method of quantitatively evaluating AC image sticking. LCD panel products that use the IPS mode have been evaluated for a decade. In this paper, a new evaluation parameter (Δθ), which was recently proposed by Suzuki et al., is introduced. It was calculated from the slight difference in the deviation angle of LC molecules from the rubbing direction. Results from several conditions of test samples are presented in this paper as a phenomena that reflect the interaction between the surface of the PI alignment and the LC molecules. The results and discussions describe reasons for azimuthal gliding after long display operation for weak AC voltage driving. It is explained by suitably adopting the Kelvin‐Voigt model which is used to discuss the rheology of viscoelastic material. It is concluded that the surface rheology of PI alignment is one of the most important factors for the contrast reduction of the AC image‐sticking problem.

In-Plane Switching (IPS) mode, one of the widely used LC modes in the LCD-TV applications, has merits in the electro-optic characteristics such as viewing angle and moving picture quality. However, it has also been pointed out to have low contrast ratio and not good enough in the black image quality.In this paper, we elucidate what the main factors of the low contrast ratio of IPS mode are and how to improve this black issue. Firstly, we have studied the range of black luminance perceivable by human eyes, and defined the required black luminance under the various ambient light conditions. In addition, we have developed the specific IPS technologies in order to meet the required black reference in all viewing angle directions.

The identification of geriatric depression and anxiety is important because such conditions are the most common comorbid mood problems that occur in older adults. The goal of this study was to build a machine learning framework that identifies geriatric mood disorders of depression and anxiety using low-cost activity trackers and minimal geriatric assessment scales. We collected activity tracking data from 352 mild cognitive impairment patients, from 60 to 90 in age, by having them wear activity trackers on their wrist for more than a month. We then extracted the features of 24-h activity rhythms and sleep patterns from the time-series activity tracking data. To increase the accuracy, we designed a novel method to incorporate additional features from questionnaire-based assessments of the geriatric depression scale and geriatric anxiety inventory into the activity tracking features. In the multi-label classification, we applied the binary relevance method to develop two single-label classifiers for depression and anxiety. The best hyper-parameters of classification algorithms for each label were selected by comparing the classification performance. We finally selected the combination of classifiers for depression and anxiety with the lowest Hamming loss as a multi-label classifier. This study successfully demonstrated the possibility of identifying geriatric depression and anxiety using low-cost activity trackers and minimal geriatric assessment scales for use in the real fields.