• Proceedings of the IEEK Conference
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• 2003.11a
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• pp.347-350
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• 2003

In this paper, a new automated defects detection method for a TFT-LCD panel is presented. An input image is preprocessed to lessen small abnormal noises and non-uniformity of the image. The adaptive multi-thresholds are used to detect Muras, which are the major defects occurred on TFT-LCD panels. Those are determined adaptively depending on the brightness and the brightness distribution of a local block. For the synthetic images and real Mura images, the proposed algorithm can effectively detect Muras in a reasonable time.

• Nuclear Engineering and Technology
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• v.50 no.8
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• pp.1199-1209
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• 2018

After the Fukushima-Daiichi accident in 2011, the multi-unit risk, i.e., the risk due to several nuclear power plants (NPPs) in a site has become an important issue in several countries such as Korea, Canada, and China. However, the multi-unit risk has been discussed for a long time in the nuclear community before the Fukushima-Daiichi nuclear accident occurred. The regulatory authorities around the world and the international organizations had proposed requirements or guidelines to reduce the multi-unit risk. The concerns regarding the multi-unit risk can be summarized in the following three questions: How much the accident of an NPP in a site affects the safety of other NPPs in the same site? What is the total risk of a site with many NPPs? Will the risk of the simultaneous accidents at several NPPs in a site such as the Fukushima Daiichi accident be low enough? The multi-unit risk assessment (MURA) in an integrated framework is a practical approach to obtain the answers for the above questions. Even though there were few studies to assess the multi-unit risk before the Fukushima-Daiichi nuclear accident, there are still several issues to be resolved to perform the complete MURA. This article aims to provide an overview of the multi-unit risk issues and its assessment. We discuss the several critical issues in the current MURA to get useful insights regarding the multi-unit risk with the current state art of probabilistic safety assessment (PSA) technologies. Also, the qualitative answers for the above questions are addressed.

• Proceedings of the Korea Fashion and Costume Design Association Conference
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• autumn
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• pp.16-18
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• 2004

• Asian Journal of Atmospheric Environment
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• v.4 no.1
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• pp.20-25
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• 2010

The gaseous $^{222}Rn$ concentration at the level of clouds was estimated by using the wet scavenging model of its decay products with the observed data of environmental radiation at the ground. And the origin of the $^{222}Rn$ was also discussed. The estimation was done for a precipitation event on Dec. 26-27, 2003, when a large increase of the radiation was observed in Tokai-mura in Ibaraki, Japan. From a backward trajectory analysis, the origin of $^{222}Rn$ atoms for that event was traced back to the northeastern part of China, and it was expected that the large amount of $^{222}Rn$ emitted in the northeastern part of China was transported to Tokai-mura by the Eurasian continental air mass.

• 한국정보디스플레이학회:학술대회논문집
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• 2006.08a
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• pp.780-783
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• 2006

In this paper, we confirmed the yellowish gravity-mura phenomenon in Fringe-Field Switching (FFS) mode using 2-D simulation. As the cell gap increases, while the LC efficiency of blue wavelength remains almost same, that of red and green wavelength increases continuously. As a result, yellowish phenomenon occurs.

• Journal of Korean Society of Rural Planning
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• v.10 no.3 s.24
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• pp.19-26
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• 2004

In this study, a new model for improving the settlement environment and revitalizing hilly and mountainous areas was presented. A case study was conducted in two rural villages of Nakatsu-Mura in Wakayama-Prefecture, Japan. The settlement environment was evaluated from the point of view of the local residents, and the expectation of the local residents about the regional revitalization of their village in the future was assessed. From the analysis, each factor used far the assessment displayed characteristics of the two rural villages well. The type of improvement in rural villages was classified by a combination of the degree of satisfaction over the current settlement environment and expectation about the regional revitalization of the villages in the future. A method for improving and revitalizing settlement environments was clarified.

• 한국정보디스플레이학회:학술대회논문집
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• 2009.10a
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• pp.850-853
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• 2009

Many well-known pixel compensation circuits have been applied to control TFT $V_{th}$ variations on small size AMOLED panels. For large (>30-inch) AMOLEDs, luminance and color uniformity are affected by TFT variations, but also by ELVDD IR drop and cavity non-uniformity which are not easily compensated by in-pixel circuits. AMOLED panels may also suffer from manufacturing-induced mura. An external compensation method based on optical measurements is proposed and applied to large AMOLED panels. It improves luminance uniformity by up to 95% at 200nits and color uniformity by up to 99% (${\Delta}$u'v' <0.004) on large AMOLED panels, and provides-increased margin against processinduced mura.

• Journal of computational fluids engineering
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• v.16 no.3
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• pp.47-51
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• 2011

Dark Mura phenomena which can happen at the region with high temperature gradient in a Notebook LCD Monitor using LED light source has numerically been studied. The calculation was conducted under the nearly realistic conditions by considering the anisotropic thermal properties of materials and the real dimensions of each component. The two performance indices of LED monitor, i.e., the maximum temperature and the spacial gradient of temperature were examined for the various shapes, lengths and thickness of heat sink plate. Calculated results give more reasonable temperature distribution comparing with experimental results.

• Journal of Institute of Control, Robotics and Systems
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• v.14 no.5
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• pp.415-419
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• 2008

Detecting and correcting defects on LCD glasses early in the manufacturing process becomes important for panel makers to reduce the manufacturing costs and to improve productivity. Many attempts have been made and were successfully applied to detect and identify simple defects such as scratches, dents, and foreign objects on glasses. However, it is still difficult to robustly detect low-contrast defect region, called Mura or blemish area on glasses. Typically, these defect areas are roughly defined as relatively large, several millimeters of diameter, and relatively dark and/or bright region of low Signal-to-Noise Ratio (SNR) against background of low-frequency signal. The aim of this article is to present a robust algorithm to segment these blemish defects. Early 90's, a highly robust estimator, known as the Model-Fitting (MF) estimator was developed by X. Zhuang et. al. and have been successfully used in many computer vision application. Compared to the conventional Least-Square (LS) estimator the MF estimator can successfully estimate model parameters from a dataset of contaminated Gaussian mixture. Such a noise model is defined as a regular white Gaussian noise model with probability $1-\varepsilon$ plus an outlier process with probability $varepsilon$. In the sense of robust estimation, the blemish defect in images can be considered as being a group of outliers in the process of estimating image background model parameters. The algorithm developed in this paper uses a modified MF estimator to robustly estimate the background model and as a by-product to segment the blemish defects, the outliers.

• Journal of KIISE:Software and Applications
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• v.34 no.11
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• pp.953-960
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• 2007

To correctly detect muras, visual defects in TFT-LCD panels, image distortion occurring on the profess of capturing panels should be corrected. In general vision systems, there are several known methods to restore the observed image. However, the vignetting effect particularly shown only in panel images cannot be easily restored through traditional methods because it is combined with background non-uniformity due to the unique characteristic of panel. To increase the reliability of image restoration, the vignetting effect should be properly corrected after being separated from image background. Therefore, in this paper we present a new method to analyze and correct the vignetting effect of panel images using principal component analysis. Experimental results for a total of 175 test images showed that the average contrast error of the muras in the distorted images was reduced from 37% to 11% and the mura misidentification rate was decreased from 14.8% to 2.2% by image restoration.