• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2004.05b
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• pp.116-122
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• 2004

The HF band communication of roast radio station had operated with principal axis of maritime communication until early in the 1990. But, that is changed with development of data communication and satellite technique, which have operated a few by rapidly decrement of user with accomplishment of the GMDSS. Recently, the radio system of 11u band is expanded to globe maritime communication, which is support to maritime safety information and data traffic service by low charge with the SSB high speed modem. This study have proposed the digitalize method for maritime communication system using HF band in domestic.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.22 no.5
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• pp.185-191
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• 2022

In this study, we propose a novel super-resolution neural network based on U-Net, residual neural network, and sub-pixel convolution. To prevent the loss of detailed information due to the max pooling of U-Net, we propose down-sampling and connection using sub-pixel convolution. This uses all pixels in the filter, unlike the max pooling that creates a new feature map with only the max value in the filter. As a 2×2 size filter passes, it creates a feature map consisting only of pixels in the upper left, upper right, lower left, and lower right. This makes it half the size and quadruple the number of feature maps. And we propose two methods to reduce the computation. The first uses sub-pixel convolution, which has no computation, and has better performance, instead of up-convolution. The second uses a layer that adds two feature maps instead of the connection layer of the U-Net. Experiments with a banchmark dataset show better PSNR values on all scale and benchmark datasets except for set5 data on scale 2, and well represent local area patterns.

• Proceedings of the Korea Water Resources Association Conference
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• 2008.05a
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• pp.1779-1783
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• 2008

영상해석을 통한 흐름해석의 방법인 Large-Scale Particle Image Velocimetry (LSPIV)는 실험실내의 소규모 흐름해석에 이용하던 Particle Image Velocimetry (PIV)를 자연하천이나 실험실에서 넓은 영역($4m^2{\sim}45,000m^2$)에 적용할 수 있도록 확장시킨 것으로 지난 10여년전부터 세계적으로 널리 이에 대한 연구가 진행되고 있다. PIV는 seeding, illumination, recording 그리고 image processing으로 구성된다. LSPIV(Large Scale PIV)는 PIV의 기본원리를 근거로 하여 기존의 PIV에 비하여 실험실 내에서의 수리모형실험이나 일반 하천에서의 유속측정과 같은 큰 규모의 흐름해석을 할 수 있도록 seeding, illumination에 대한 조정이 필요 하고, 촬영된 image에 대한 왜곡을 없애는 작업이 필요하다. LSPIV는 PIV의 네 가지 단계를 포함하여 seeding, illumination, recording, image transformation, image processing 및 post-processing의 여섯 단계로 구성되어진다 (Li, 2002). LSPIV의 적용시 각 단계마다 유속계산시 오차를 발생시키는 27가지의 요인들이 존재하고 있는바 (Kim, 2006), 본 연구에서는 이들 중 실내의 실험실에서 파악이 가능한 인자들에 대해 그들 각각의 인자들이 유속 측정에 미치는 오차의 정도를 파악하고자 하였다. 본 연구에서는 LSPIV의 적용시 이용되는 이미지의 개수와 이미지 촬영시 적용된 이미지의 해상도에 따른 오차의 발생 정도를 조사하였다. 이미지 촬영에 있어서 비디오카메라를 이용할 경우 촬영시간에 따라 많은 수의 이미지를 취득할 수 있은바 이미지의 수에 따른 유속계산오차를 파악하고자 하였다. 또한 디지털 카메라를 이용할 경우 여러 가지 이미지 해상도를 이용할 수 있으므로 적용한 이미지 해상도에 따른 유속계산에 미치는 오차의 크기를 파악하고자 하였다. 이미지의 갯수가 유속계산시 미치는 오차의 영향의 정도를 조사하기 위해서 초당 30 frame을 촬영할 수 있는 비디오카메라를 이용하여 91초 동안 촬영된 이미지로부터 매 5번째의 이미지를 추출하여 455개의 이미지를 준비하였고 이로부터 이미지수를 10, 50, 100, 200, 300, 400의 순서로 증가시키면서 이미지 개수로부터 나타나는 유속계산 오차를 조사한 결과 이미지의 개수가 50매 이상인 경우는 이로 인한 오차가 1% 이하로 감소함을 파악하였다. 촬영된 이미지의 해상도가 유속계산시 미치는 영향을 조사하기 위해 디지털카메라를 적용하여 세가지 이미지 해상도(640*480, 1280*960, 2048*1536 pixel)로 변화시키면서 유속측정 오차를 분석한 결과 저해상도의 이미지를 이용한 경우 고해상도 이미지를 이용한 경우와 비교하여 3% 가량의 차이를 나타내었다.

• Journal of Broadcast Engineering
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• v.24 no.1
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• pp.132-141
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• 2019

The necessity of transmitting video data over a narrow-bandwidth exists steadily despite that video service over broadband is common. In this paper, we propose a scalable video coding framework for low-resolution video transmission over a very narrow-bandwidth network by super-resolution of decoded frames of a base layer using a convolutional neural network based super resolution technique to improve the coding efficiency by using it as a prediction for the enhancement layer. In contrast to the conventional scalable high efficiency video coding (SHVC) standard, in which upscaling is performed with a fixed filter, we propose a scalable video coding framework that replaces the existing fixed up-scaling filter by using the trained convolutional neural network for super-resolution. For this, we proposed a neural network structure with skip connection and residual learning technique and trained it according to the application scenario of the video coding framework. For the application scenario where a video whose resolution is $352{\times}288$ and frame rate is 8fps is encoded at 110kbps, the quality of the proposed scalable video coding framework is higher than that of the SHVC framework.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.19 no.5
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• pp.1189-1198
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• 2015

Recently, with rapid development of digital broadcasting technology, high-definition video service increased interest and demand. supplied mobile and image device support that improve 4~16 time existing Full HD. Such as high-definition contents supply, proposed compression for high-efficiency video codec (HEVC). Therefore, watermarking technology is necessary applying HEVC for protecting ownership and intellectual property. In this paper, analysis of prediction mode in intra frame and study feasibility of watermarking in re-encoding based HEVC. Proposed detect un-changed blocks in intra frame, using the result of analysis prediction mode.

• The Journal of the Acoustical Society of Korea
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• v.42 no.5
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• pp.422-428
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• 2023

Ultrafast ultrasound imaging has been applied to various imaging approaches, including shear wave elastography, ultrafast Doppler, and super-resolution imaging. However, these methods are still challenging in real-time implementation for three Dimension (3D) or portable applications because of their massive data rate required. In this paper, we proposed an adaptive quantization method that effectively reduces the data rate of large Radio Frequency (RF) data. In soft tissue, ultrasound backscatter signals require a high dynamic range, and thus typical quantization used in the current systems uses the quantization level of 10 bits to 14 bits. To alleviate the quantization level to expand the application of ultrafast ultrasound imaging, this study proposed a depth-sectional quantization approach that reduces the quantization errors. For quantitative evaluation, Field II simulations, phantom experiments, and in vivo imaging were conducted and CNR, spatial resolution, and SSIM values were compared with the proposed method and fixed quantization method. We demonstrated that our proposed method is capable of effectively reducing the quantization level down to 3-bit while minimizing the image quality degradation.

• Journal of the Korea Convergence Society
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• v.12 no.10
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• pp.63-70
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• 2021

Generating a super-resolution meteological data by using a high-resolution deep neural network can provide precise research and useful real-life services. We propose a new technique of generating improved training data for super-resolution deep neural networks. To generate high-resolution meteorological data with domain specific knowledge, Lambert conformal conic projection and objective analysis were applied based on observation data and ERA5 reanalysis field data of specialized institutions. As a result, temperature and humidity analysis data based on domain specific knowledge showed improved RMSE by up to 42% and 46%, respectively. Next, a super-resolution generative adversarial network (SRGAN) which is one of the aritifial intelligence techniques was used to automate the manual data generation technique using damain specific techniques as described above. Experiments were conducted to generate high-resolution data with 1 km resolution from global model data with 10 km resolution. Finally, the results generated with SRGAN have a higher resoltuion than the global model input data, and showed a similar analysis pattern to the manually generated high-resolution analysis data, but also showed a smooth boundary.

• Journal of Broadcast Engineering
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• v.25 no.1
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• pp.105-108
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• 2020

This paper presents a real-time low-resolution face recognition method that uses a super-resolution technique. Conventional face recognition methods are limited by low accuracy resulting from the distance between the camera and objects. Although super-resolution methods have been developed to resolve this issue, they are not suitable for integrated face recognition systems. The proposed method recognizes faces with low resolution using key frame selection, super resolution, face detection, and recognition on real-time processing. Experiments involving several databases indicated that the proposed algorithm is superior to conventional methods in terms of face recognition accuracy.

• Journal of Broadcast Engineering
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• v.25 no.6
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• pp.965-972
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• 2020

Depth maps have distance information of objects. They play an important role in organizing 3D information. Color and depth images are often simultaneously obtained. However, depth images have lower resolution than color images due to limitation in hardware technology. Therefore, it is useful to upsample depth maps to have the same resolution as color images. In this paper, we propose a novel method to upsample depth map by shifting the pixel position instead of compensating pixel value. This approach moves the position of the pixel around the edge to the center of the edge, and this process is carried out in several steps to restore blurred depth map. The experimental results show that the proposed method improves both quantitative and visual quality compared to the existing methods.

• Defense and Technology
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• no.3 s.181
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• pp.56-63
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• 1994

급속한 TSC 체계의 성장은 운용자에게 때로는 혼란을 주고 있다. 새롭게 개선된 체계에 대한 문서화와 운용자 교육훈련이 완료되기도 전에 더 새로운 체계가 배치되고 있는 새로운 문제를 야기시키고 있다. 이 때문에 미해군은 문서화 보다 개선된 TSC의 설치와 동시에 현장에서의 교육훈련을 수행하고 있다.