• 조명전기설비학회논문지
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• 제25권2호
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• pp.10-18
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• 2011

In order to develop highly-integrated RGBY(Red, Green, Blue, Yellow) LED light, a high thermal radiation ceramic package was manufactured, and the encapsulation process was applied with a vacuum printing encapsulation system(VPES). After the completion of vacuum printing, the shape of the encapsulation layer could be controlled by heat treatment during the curing process, and the optical power became highly increased as the encapsulation layer approached a dome shape. The optical characteristics involved in a Correlated Color Temperature(CCT), a Color Rendering Index (CRI), and the efficiency of RGBY LED light were able to be identified by the experimental designing method. Regarding the characteristics of the white light of RGBY LED light, which were measured on the basis of the aforementioned optical characteristics, CRI posted 88, CCT recorded 5,720[$^{\circ}K$], and efficiency exhibited 52[lm/W]. The chip temperature of RGBY LEDs was below 55[$^{\circ}C$] when the consumption power of LED chips was 0.1[W] for the red, 0.3[W] for the green, 0.08[W] for the blue, and 0.24[W] for the yellow. Also, the thermal resistance of the highly-integrated RGBY LED light measured by T3Ster was 2.3[K/W].

• Journal of the Optical Society of Korea
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• 제18권6호
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• pp.777-782
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• 2014

This paper proposes an RGB to RGBY color conversion algorithm for liquid crystal display (LCD) using RGW pixel structure with two-field (yellow and blue) sequential driving method. The proposed algorithm preserves the hue and saturation of the original color by maintaining the RGB ratio, and it increases the luminance. The performance of the proposed RGBY conversion algorithm is verified using the MATLAB simulation with 24 images of Kodak lossless true color image suite. The simulation results of average color difference CIEDE2000 (${\delta}E^*_{00}$) and scaling factor are 0.99 and 1.89, respectively. These results indicate that the average brightness is increased 1.89 times compared to LCD using conventional RGB pixel structure, without increasing the power consumption and degrading the image quality.

• 한국조명전기설비학회:학술대회논문집
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• 한국조명전기설비학회 2008년도 추계학술대회 논문집
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• pp.157-160
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• 2008

색온도 및 광색 가변형 100W RGBY LED 투광기를 개발하기 위하여, 투광기의 광학 및 방열기구를 설계하였다. 또한 100W LED 광원모듈을 구동하기 위한 구동회로와 색온도 및 광색 가변을 위한 제어회로를 개발하였다. 이와 같은 설계결과를 적용하여 100W RGBY 투광기의 시제품을 제작하였고, 시제품의 광학적 성능은 협각 $20^{\circ}$, 중각 $50^{\circ}$, 광각 $75^{\circ}$이상을 만족시켰다. 또한 색온도의 변화는 CIE 1931 색좌표에서 흑체궤적과 일치하여 $2,000K{\sim}10,000K$까지 변화하도록 R,G,B,Y LED의 Duty Ratio를 설정하였다. 또한 방열 특성은 100W 구동시 녹색 LED의 온도는 $37{\sim}41.3^{\circ}C$, heatsink의 온도는 $34{\sim}35^{\circ}C$에서 동작되도록 하였다.

• 한국조명전기설비학회:학술대회논문집
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• 한국조명전기설비학회 2009년도 추계학술대회 논문집
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• pp.201-203
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• 2009

RGBY LED PKG를 이용하여 무대 사용을 위한 100W 급 LED 조명기구를 설계하고 무대에 적용하였을 때의 조명 환경을 예측, 분석 하였다. 이를 위하여 색온도 및 광색가변에 적합한 4 in 1 LED Package를 선정하고, 이를 이용하여 광원모듈을 구성하였으며, 또한 무대조명에 적용하기 위한 렌즈를 제작하였다. 이러한 LED 조명기구를 또한 무대조명에 적용하여 조명상황을 시뮬레이션 하고 무대 또는 객석에서의 평균조도 및 조도균제도 등을 계산하였다.

• Current Optics and Photonics
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• 제4권3호
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• pp.247-252
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• 2020

Important determinants for selecting outdoor lighting are the color-rendering index (CRI) and scotopic-to-photopic (S/P) ratio of the lighting units. The higher the S/P ratio, the better energy savings and visual performance. In this study, CCT-tunable LED lighting units were optimized and fabricated by spectral combination of red, green, blue, and yellow LEDs. The measured results for RGB LEDs provided S/P ratios of 1.55~2.58 and those of RGBY LEDs gave 1.46~2.46 to the correlated color temperatures (CCTs) ranging from 2700 K to 6500 K, with CRI values of over 80 at the same time.

• Journal of Semiconductor Engineering
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• 제2권1호
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• pp.125-129
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• 2021

As the interest of deep learning, techniques to control the color of images in image processing field are evolving together. However, there is no clear standard for color, and it is not easy to find a way to represent only the color itself like the color-palette. In this paper, we propose a novel color palette extraction system by chroma fine-tuning with reinforcement learning. It helps to recognize the color combination to represent an input image. First, we use RGBY images to create feature maps by transferring the backbone network with well-trained model-weight which is verified at super resolution convolutional neural networks. Second, feature maps are trained to 3 fully connected layers for the color-palette generation with a generative adversarial network (GAN). Third, we use the reinforcement learning method which only changes chroma information of the GAN-output by slightly moving each Y component of YCbCr color gamut of pixel values up and down. The proposed method outperforms existing color palette extraction methods as given the accuracy of 0.9140.