• Korean Journal of Remote Sensing
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• v.33 no.2
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• pp.125-134
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• 2017

Recent advance in the satellite-based remote sensing technology demands abilities to efficiently processthe massive satellite data. In thisstudy, we focused on developing GOCI Products Reprocessing System (GPRS) based on server virtualization and distributed processing in order to efficiently reprocess massive GOCI data. Experimental results revealed that GPRS allows raising the usage rates of memory and central processing unit (CPU) up to about 100% and 75%, respectively. This meansthat the proposed system enables us to save the hardware resources and increase the work process speed at the same time when we process massive satellite data.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2010.05a
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• pp.540-543
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• 2010

POWER PC series are common to the Central Processing Unit for Military Single Board Computer. Among them, G4 group, which contains the 74xx series supported by Freescale manufacturer is mainly used in the Military applications. We focus on the Interface between memory and controller. PCB stacking method, component routing, impedance matching and harsh environment for Military spec are the main constraints for implementation. Also, we developed memory as a module for the consideration of Military environments. The overall type of SBC should be designed by the form of 6U VME or 3U VME.

• Journal of computational fluids engineering
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• v.17 no.2
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• pp.35-41
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• 2012

This paper presents incompressible Navier-Stokes solution algorithm for 2D Free-surface flow problems on the Cartesian mesh, which was implemented to run on Graphics Processing Units(GPU). The INS solver utilizes the variable arrangement on the Cartesian mesh, Finite Volume discretization along Constrained Interpolation Profile-Conservative Semi-Lagrangian(CIP-CSL). Solution procedure of incompressible Navier-Stokes equations for free-surface flow takes considerable amount of computation time and memory space even in modern multi-core computing architecture based on Central Processing Units(CPUs). By the recent development of computer architecture technology, Graphics Processing Unit(GPU)'s scientific computing performance outperforms that of CPU's. This paper focus on the utilization of GPU's high performance computing capability, and presents an efficient solution algorithm for free surface flow simulation. The performance of the GPU implementations with double precision accuracy is compared to that of the CPU code using an representative free-surface flow problem, namely. dam-break problem.

• Journal of Electrical Engineering and Technology
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• v.6 no.4
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• pp.573-579
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• 2011

To meet the scalability and performance requirements of data analyses, which often involve voluminous data, efficient parallel or concurrent algorithms and frameworks are essential. We present a high-performance Korean morphological analyzer which employs the MapReduce framework on the graphics processing unit (GPU). MapReduce is a programming framework introduced by Google to aid the development of web search applications on a large number of central processing units (CPUs). GPUs are designed as a special-purpose co-processor. Their programming interfaces are typically formulated for graphics applications. Compared to CPUs, GPUs have greater computation power and memory bandwidth; however, GPUs are more difficult to program because of the design of their architectures. The performance of the Korean morphological analyzer using the MapReduce framework on the GPU is evaluated in comparison with the CPU-based model. The proposed Korean Morphological analyzer shows promising scalable performance on distributed computing with the GPU.

• Journal of Korea Water Resources Association
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• v.55 no.12
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• pp.991-998
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• 2022

The purpose of this study is to develop GPU (Graphics Processing Unit) acceleration technique for 2-dimensional model and to assess the effectiveness for high resolution flood simulation in wide area In this study, GPU acceleration technique was implemented in the G2D (Grid based 2-Dimensional land surface flood model) model, using implicit scheme and uniform square grid, by using CUDA. The technique was applied to flood simulation in Jinju-si. The spatial resolution of the simulation domain is 10 m × 10 m, and the number of cells to calculate is 5,090,611. Flood period by typhoon Mitag, December 2019, was simulated. Rainfall radar data was applied to source term and measured discharge of Namgang-Dam (Ilryu-moon) and measured stream flow of Jinju-si (Oksan-gyo) were applied to boundary conditions. From this study, 2-dimensional flood model could be implemented to reproduce the measured water level in Nam-gang (Riv.). The results of GPU acceleration technique showed more faster flood simulation than the serial and parallel simulation using CPU (Central Processing Unit). This study can contribute to the study of developing GPU acceleration technique for 2-dimensional flood model using implicit scheme and simulating land surface flood in wide area.

• Journal of the Optical Society of Korea
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• v.17 no.1
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• pp.68-72
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• 2013

We developed an SD-OCT (Spectral Domain-Optical Coherence Tomography) system which uses a GPU (Graphics Processing Unit) for processing. The image size from the SD-OCT system is $1024{\times}512$ and the speed is 110 frame/sec in real-time. K-domain linearization, FFT (Fast Fourier Transform), and log scaling were included in the GPU processing. The signal processing speed was about 62 ms using a CPU (Central Processing Unit) and 1.6 ms using a GPU, which is 39 times faster. We performed an in-vivo retinal scan, and reconstructed a 3D visualization based on C-scan images. As a result, there were minimal motion artifacts and we confirmed that tomograms of blood vessels, the optic nerve, and the optic disk are clearly identified. According to the results of this study, this SD-OCT can be applied to real-time 3D display technology, particularly auxiliary instruments for eye operations in ophthalmology.

• Journal of Power System Engineering
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• v.13 no.4
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• pp.56-61
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• 2009

In recent years, since the continuing increase in the capacity in personal computer such as the optimal performance, high quality and high resolution image, the computer system's components produce large amounts of heat during operation. This study analyzes and investigates the ability and efficiency of a cooling system inside a computer by means of central processing unit (CPU) and power supply cooling fan. This research was conducted to enhancement of efficiency of the cooling system inside the computer by making a structure which produces different air pressures in an air inflow tube. Consequently, when temperatures of the CPU and room inside computer were compared with a general personal computer, temperatures of the tested CPU, the room and the heat sink were as low as $5^{\circ}C$, $2.5^{\circ}C$ and $7^{\circ}C$ respectively. In addition to, revolution per minute (RPM) was shown as low as 250 after 1 hour operation. This research explored the possibility of enhancing the effective cooling of high-performance computer systems.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.37 no.6
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• pp.445-452
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• 2019

In this study, parallel processing codes of k-means clustering algorithm were developed and implemented in a PC-cluster for unsupervised classification of large satellite images. We implemented intra-node code using multicores of CPU (Central Processing Unit) based on OpenMP (Open Multi-Processing), inter-nodes code using a PC-cluster based on message passing interface, and hybrid code using both. The PC-cluster consists of one master node and eight slave nodes, and each node is equipped with eight multicores. Two operating systems, Microsoft Windows and Canonical Ubuntu, were installed in the PC-cluster in turn and tested to compare parallel processing performance. Two multispectral satellite images were tested, which are a medium-capacity LANDSAT 8 OLI (Operational Land Imager) image and a high-capacity Sentinel 2A image. To evaluate the performance of parallel processing, speedup and efficiency were measured. Overall, the speedup was over N / 2 and the efficiency was over 0.5. From the comparison of the two operating systems, the Ubuntu system showed two to three times faster performance. To confirm that the results of the sequential and parallel processing coincide with the other, the center value of each band and the number of classified pixels were compared, and result images were examined by pixel to pixel comparison. It was found that care should be taken to avoid false sharing of OpenMP in intra-node implementation. To process large satellite images in a PC-cluster, code and hardware should be designed to reduce performance degradation caused by file I / O. Also, it was found that performance can differ depending on the operating system installed in a PC-cluster.

• Proceedings of the Korea Information Processing Society Conference
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• 2021.05a
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• pp.457-458
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• 2021

딥러닝 알고리즘 중 DCNN(DeConvolutional Neural Network)은 이미지 업스케일링과 생성·복원 등 다양한 분야에서 뛰어난 성능을 보여주고 있다. DCNN은 많은 양의 데이터를 병렬로 처리할 수 있기 때문에 하드웨어로 설계하는 것이 유용하다. 최근 DCNN의 하드웨어 구조 연구에서는 overlapping sum 문제를 해결하기 위해 deconvolution 필터를 convolution 필터로 변환하는 TDC(Transforming the Deconvolutional layer into the Convolutional layer) 알고리즘이 제안되었다. 하지만 TDC를 CPU(Central Processing Unit)로 수행하기 때문에 연산의 최적화가 어려우며, 외부 메모리를 사용하기에 추가적인 전력이 소모된다. 이에 본 논문에서는 저전력으로 구동할 수 있는 FPGA 기반 TDC 하드웨어 구조를 제안한다. 제안하는 하드웨어 구조는 자원 사용량이 적어 저전력으로 구동 가능할 뿐만 아니라, 병렬 처리 구조로 설계되어 빠른 연산 처리 속도를 보인다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.10 no.6
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• pp.2648-2668
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• 2016

Modern mobile devices are equipped with various accelerated processing units to handle computationally intensive applications; therefore, Open Computing Language (OpenCL) has been proposed to fully take advantage of the computational power in heterogeneous systems. This article introduces a parallel software decoder of Low Density Parity Check (LDPC) codes on an embedded heterogeneous platform using an OpenCL framework. The LDPC code is one of the most popular and strongest error correcting codes for mobile communication systems. Each step of LDPC decoding has different parallelization characteristics. In the proposed LDPC decoder, steps suitable for task-level parallelization are executed on the multi-core central processing unit (CPU), and steps suitable for data-level parallelization are processed by the graphics processing unit (GPU). To improve the performance of OpenCL kernels for LDPC decoding operations, explicit thread scheduling, vectorization, and effective data transfer techniques are applied. The proposed LDPC decoder achieves high performance and high power efficiency by using heterogeneous multi-core processors on a unified computing framework.