Search | Korea Science

Kwak, Ji Hoo;Shin, Dong Gap;Kim, Dae-Eun
- Transactions of the Society of Information Storage Systems
- /
- v.10 no.2
- /
- pp.39-44
- /
- 2014
LCD panels are used widely in all sorts of devices. Since glass is the main material used to make the panels, scratch resistance is an important issue in acquiring high quality LCD panels. In this work the wear behaviors of three types of commercially available LCD panel glasses were investigated. A pin-on-reciprocating tribotester was used to perform the wear tests using the glass specimens against a stainless steel ball. The hardness of the specimens was initially obtained. It was shown that the wear amount varied with respect to the applied load as well as the type of glass. The wear pattern of the glass specimen was also characterized using confocal microscopy. It is expected that the results of this work will aid in improving the tribological properties of LCD panel glass.
https://doi.org/10.9797/TSISS.2014.10.2.039 인용 PDF KSCI

이동욱;권오경
- Proceedings of the IEEK Conference
- /
- 2000.06b
- /
- pp.133-136
- /
- 2000
A new Op-Amp output buffer is presented for driving large-size LCD panels. The proposed Op-Amp is designed by combining a common source and a common drain amplifier to have a high slew rate and to minimize the quiescent current. The proposed circuits are simulated in a high-voltage 0.6${\mu}{\textrm}{m}$ CMOS process, dissipates only 20${\mu}{\textrm}{m}$ static current, and have 83dB open-loop DC gain and 60$^{\circ}$phase margin.
PDF

Ko, Jung-Hwan
- 전자공학회논문지 IE
- /
- v.48 no.2
- /
- pp.26-31
- /
- 2011
In this paper we show the LCD simulator for defect inspection using image processing algorithm and neural network. The defect inspection algorithm of the LCD consists of preprocessing, feature extraction and defect classification. Preprocess removes noise from LCD image, using morphology operator and neural network is used for the defect classification. Sample images with scratch, pinhole, and spot from real LCD color filter image are used. From some experiments results, the proposed algorithms show that defect detected and classified in the ratio of 92.3% and 94.5 respectively. Accordingly, in this paper, a possibility of practical implementation of the LCD defect inspection system is finally suggested.
PDF KSCI