• Proceedings of the Korean Institute of Electrical and Electronic Material Engineers Conference
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• 2005.07a
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• pp.388-389
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• 2005

In this paper, the electric properties of mercury Iodide multi-layer samples has been investigated. We measured and analyzed their performance parameters such as the X-ray sensitivity and dark-current for a mercury Iodide multi-layer X-ray detector with a dielectric layer. The digital X-ray image detector can be constructed by integrating photoconduction multi-layer that dielectric layer has characteristics of low dark-current, high X-ray sensitivity. However this process has found to have complexity on the performance of the sample. We have investigate dielectric layer that it substitute dielectric layer for HgO(Mercury Oxide). We have employed two approaches for producing the mercury Iodide sample : 1) Physical Vapor Deposition(PVD) and 2) Particle-In-Binder(PIB). In this paper fabricated by PIB Method with thicknesses ranging from approximately 180um to 240um and we could produce high-quality samples for each technique particular application. As results, the dielectric materials such as HgO between the dielectric layer and the top electrode may reduce the dark-current of the samples. Mercury Iodide multi-layer having HgO has characteristics of low dark-current, high X-ray sensitivity and simple processing. So we can acquired a enhanced signal to noise ratio. In this paper offer the method can reduce the dark-current in the X-ray detector.

• Journal of Korea Multimedia Society
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• v.21 no.5
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• pp.571-582
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• 2018

Recently, development of deep learning has shown better image classification result than human. According to recent research, a hidden layer of deep learning is deeper, and a preservation of extracted features shows good results. However, in the case of general images, the extracted features are clear and easy to sort. This study aims to classify plant leaf images. This plant leaf image has high similarity in each image. Since plant leaf images have high similarity not only between images of different species but also within the same species, classification accuracy is not increased by simply extending the hidden layer or connecting the layers. Therefore, in this paper, we tried to improve the hidden layer of the algorithm called DenseNet which shows the recent excellent classification results, and compare the results of several different modified layers. The proposed method makes it possible to classify plant leaf images collected in a natural environment more easily and accurately than conventional methods. This results in good classification of plant leaf image data including unnecessary noise obtained in a natural environment.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.43 no.1
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• pp.16-25
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• 2020

Vegetation segmentation in a field color image is a process of distinguishing vegetation objects of interests like crops and weeds from a background of soil and/or other residues. The performance of the process is crucial in automatic precision agriculture which includes weed control and crop status monitoring. To facilitate the segmentation, color indices have predominantly been used to transform the color image into its gray-scale image. A thresholding technique like the Otsu method is then applied to distinguish vegetation parts from the background. An obvious demerit of the thresholding based segmentation will be that classification of each pixel into vegetation or background is carried out solely by using the color feature of the pixel itself without taking into account color features of its neighboring pixels. This paper presents a new pixel-based segmentation method which employs a multi-layer perceptron neural network to classify the gray-scale image into vegetation and nonvegetation pixels. The input data of the neural network for each pixel are 2-dimensional gray-level values surrounding the pixel. To generate a gray-scale image from a raw RGB color image, a well-known color index called Excess Green minus Excess Red Index was used. Experimental results using 80 field images of 4 vegetation species demonstrate the superiority of the neural network to existing threshold-based segmentation methods in terms of accuracy, precision, recall, and harmonic mean.

• 한국정보디스플레이학회:학술대회논문집
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• 2003.07a
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• pp.760-763
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• 2003

We have investigated the electro-optical characteristics of image sticking in AC PDP. Although Image sticking is one of major factors to determine display quality in AC PDP, so far, it has not being reported why it is occurred and how we can prevent it. In this experiment, we have analyzed the effect of MgO protective layer and phosphor on the image sticking and we have measured the difference of firing voltage, brightness and discharge current between sticking image and normal image in AC PDP. As a result, Phosphor degradation is a more major factor than MgO protective layer and the firing voltage of gas discharge in sticking image is higher than that of normal discharge.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.10 no.7
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• pp.3397-3413
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• 2016

Considering that the layered cellular automata (LCA) are naturally fit for representing image data in various applications, a novel reverse iterative image encryption scheme based on LCA is proposed. Specifically, the plain image is set as the final configuration of an 8-layer CA, and some sequences derived from a random sequence are set as the pre-final configuration, which ensure that the same plain image will never be encrypted in the same way when encrypted many times. Then, this LCA is backward evolved by following some reversible two order rules, which are generated with the aid of a newly defined T-shaped neighborhood. The cipher image is obtained from the recovered initial configuration. Several analyses and experimental results show that the proposed scheme possesses a high security level and executive performance.

• Journal of Korea Multimedia Society
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• v.25 no.5
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• pp.676-684
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• 2022

As realistic media are widespread in various image processing areas, image or video compression is one of the key technologies to enable real-time applications with limited network bandwidth. Generally, image or video compression cause the unnecessary compression artifacts, such as blocking artifacts and ringing effects. In this study, we propose a Deep Residual Channel-attention Network, so called DRCAN, which consists of an input layer, a feature extractor and an output layer. Experimental results showed that the proposed DRCAN can reduced the total memory size and the inference time by as low as 47% and 59%, respectively. In addition, DRCAN can achieve a better peak signal-to-noise ratio and structural similarity index measure for compressed images compared to the previous methods.

• 한국정보디스플레이학회:학술대회논문집
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• 2005.07b
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• pp.1599-1602
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• 2005

In a three-dimensional/two-dimensional convertible parallax barrier display, an additional layer compensating the color dispersion for three-dimensional display can distort displayed image in the two-dimensional mode. We analyze the effect of the color dispersion compensating layer on two-dimensional image by computer simulations.

• Current Optics and Photonics
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• v.5 no.6
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• pp.672-679
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• 2021

We propose a high-speed layer-based algorithm for synthesizing computer-generated holograms (CGHs), featuring sparsity-based image segmentation and computational parallelism. The sparsity-based image segmentation of layer-based three-dimensional scenes leads to considerable improvement in the efficiency of CGH computation. The efficiency enhancement of the proposed algorithm is ascribed to the field localization of the fast Fourier transform (FFT), and the consequent reduction of FFT computational complexity.

• Proceedings of the KSME Conference
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• 2001.06d
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• pp.765-770
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• 2001

The objective of the present study is to investigate the effects of the various inlet boundary layer thickness on convective heat transfer distribution in a turbine cascade endwall and blade suction surface. In addition, the proper height of the boundary layer fences for various inlet boundary layer thickness were applied to turbine cascade endwall in order to reduce the secondary flow, and to verify its influence on the heat transfer process within the turbine cascade. Convective heat transfer distributions on the experimental regions were measured by the image processing system. The results show that heat transfer coefficients on the blade suction surface were increased with an augmentation of inlet boundary layer thickness. However, in a turbine cascade endwall, magnitude of heat transfer coefficients did not change with variation of inlet boundary layer thickness. The results also present that the boundary layer fence is effective in reducing heat transfer on the suction surface. On the other hand, in the endwall region, boundary layer fence brought about the subsidiary heat transfer increment.

• Proceedings of the KSME Conference
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• 2001.11b
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• pp.306-311
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• 2001

Effect of boundary layer thickness on the flow characteristics around a rectangular prism has been investigated by using a PIV(Particle Image Velocimetry) technique. Three different boundary layers(thick, medium and thin)were generated in the Atmospheric Boundary Layer Wind Tunnel at Pusan National University. The thick boundary layer having 670mm thickness was generated by using spires and roughness elements. The medium thickness of boundary layer$(\delta=270mm)$ was the natural turbulent boundary layer at the test section with fully long developing length(18m). The thin boundary layer with 36.5mm thickness was generated by on a smooth panel elevated 70cm from the wind tunnel floor. The Reynolds number based on the free stream velocity and the height of the model was $7.9{\times}10^3$. The mean velocity vector fields and turbulent kinetic energy distribution were measured and compared. The effect of boundary layer thickness is clearly observed not only in the length of separation bubble but also in the reattachment points. The thinner boundary layer thickness, the higher turbulent kinetic energy peak around the model roof. It is strongly recommended that the height ratio between model and approaching boundary layer thickness should be a major parameter.