• KIPS Transactions on Computer and Communication Systems
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• v.4 no.9
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• pp.297-306
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• 2015

Cloud computing has been increasingly used in various fields due to its flexibility, scalability, cost effectiveness, etc. Recently, many scientific communities have been attempting to use cloud computing as a way to deal with difficulties in constructing and operating a research infrastructure. Especially, since they need various collaborations based on networking, such as sharing experimental data, redistributing experimental results, and so forth, cloud computing environment that supports high performance networking is required for scientific communities. To address these issues, we propose RealLab, a high performance cloud platform for collaborative research that provides virtual experimental research environment and data sharing infrastructure over KREONET/GLORIAD. Additionally, we describe some RealLab use cases for showing the swift creation of experimental environment and explain how massive experimental data can be transferred and shared among the community members.

• Proceedings of the Korean Society of Computer Information Conference
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• 2014.01a
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• pp.55-57
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• 2014

본 논문에서는 자동차 번호판 인식을 위해 직선검출법, 모폴로지에 의한 검출법을 사용하지 않고, Blob 레이블링 기법을 이용한 번호판 인식 기법을 제안한다. 고성능 컴퓨팅 시스템의 성능 향상을 위한 효율적인 동적 작업부하 균등화 정책을 제안한다. ITS분야에서 가장 중요한 요소라 할 수 있는 자동차 번호판 인식은 자동화된 차량 관리 시스템 구성에 필수적인 요소로 요구된다. 또한, 자동차와 관련된 정보는 직, 간접적으로 높은 중요도를 가지고 있으며, 자동차와 관련된 정보가 이용되는 영역은 교통관리, 교통량분석, 자동 요금 징수 시스템, 자동차 위법 단속 등 응용범위가 나날이 넓어지고 있다. 본 논문에서는 자동차 번호판 인식을 위해 Blob 레이블링 기법을 이용하였으며, 번호판 인식을 위한 영상 샘플은 오츠알고리즘을 이용하여 이진화된 영상을 사용하였다.

• KIISE Transactions on Computing Practices
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• v.23 no.3
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• pp.171-176
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• 2017

Recently, several information-technology research projects such as those for high-performance computing, the cloud service, and the DevOps methodology have been advanced to develop the efficiency of satellite data-processing systems. In March 2019, the Geostationary Ocean Color Imager II (GOCI-II) will be launched for its predictive capability regarding marine disasters and the management of the fishery environment; moreover, the GOCI-II Ground Segment (G2GS) system for data acquisition/processing/storing/distribution is being designed at the Korea Ocean Satellite Center (KOSC). The G2GS is composed of the following six functional subsystems: data-acquisition subsystem (DAS), data-correction subsystem (DCS), precision-correction subsystem (PCS), ocean data-processing subsystem (ODPS), data-management subsystem (DMS), and operation and quality management subsystem (OQMS). The G2GS will enable the real-time support of the GOCI-II ocean-color data for government-related organizations and public users.

• KIISE Transactions on Computing Practices
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• v.22 no.2
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• pp.95-100
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• 2016

In the current era of big data and cloud computing, the amount of data utilized is increasing, and various systems to process this big data rapidly are being developed. A distributed file system is often used to store the data, and glusterFS is one of popular distributed file systems. As computer technology has advanced, NAND flash SSDs (Solid State Drives), which are high performance storage devices, have become cheaper. For this reason, datacenter operators attempt to use SSDs in their systems. They also try to install glusterFS on SSDs. However, since the glusterFS is designed to use HDDs (Hard Disk Drives), when SSDs are used instead of HDDs, the performance is degraded due to structural problems. The problems include the use of I/O-cache, Read-ahead, and Write-behind Translators. By removing these features that do not fit SSDs which are advantageous for random I/O, we have achieved performance improvements, by up to 255% in the case of 4KB random reads, and by up to 50% in the case of 64KB random reads.

• Journal of KIISE:Computing Practices and Letters
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• v.14 no.7
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• pp.636-645
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• 2008

OneNAND flash is a high-performance hybrid flash memory that combines the advantages of both NAND flash and NOR flash. OneNAND flash has not only all virtues of NAND flash but also greatly enhanced read performance which is considered as a downside of NAND flash. As a result, it is widely used in mobile applications such as mobile phones, digital cameras, PMP, and portable game players. However, most of the general purpose operating systems, such as Linux, can not exploit the read performance of OneNAND flash because of the restrictions imposed by their virtual memory system and block I/O architecture. In order to solve that problem, we suggest a new approach called virtual I/O segment. By using virtual I/O segment, the superior read performance of OneNAND flash can be exploited without modifying the existing block I/O architecture and MTD subsystem. Experiments by implementations show that this approach can reduce read latency of OneNAND flash as much as 54%.

• Proceedings of the Korea Contents Association Conference
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• 2007.11a
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• pp.565-568
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• 2007

e-Science is for increasing the research productivity in epochal way using research resources like high performance computers, large capacity data storage, experimental instrument and researchers. For the research activities without being limited by time and space, the collaboration system is prerequisite condition. This paper shows the comparisons and analysis of features of popular collaboration systems in the point of e-Science. We considered that the number of supporting video streams, scalability and cost are main issues for the e-Science collaboration system. By these, we conclude that Access-Grid(AG) is best suitable for the e-Science collaborative research system. This paper shows the current AG status and the future way of AG.

• The KIPS Transactions:PartA
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• v.12A no.2 s.92
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• pp.161-170
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• 2005

Grid makes a virtual high-performance computing resource by connecting geographically distributed heterogeneous resources. Building access control system is an important factor In the grid environment In this paper, we design and implement a grid access control system based on Globus Toolkit, which is one of the popular grid middleware. Especially, to guarantee the site autonomy for resource provider, we use several environment configuration files. Moreover, we design and implement PGAM to produce more detail and diverse information to ease the development of value added services.

• KIPS Transactions on Software and Data Engineering
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• v.2 no.2
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• pp.97-106
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• 2013

Since human genome project finished, the cost for human genome analysis has decreased very rapidly. This results in the sharp increase of human genome data to be analyzed. As the need for fast analysis of very large bio data such as human genome increases, non IT researchers such as biologists should be able to execute fast and effectively many kinds of bio applications, which have a variety of characteristics, under HPC environment. To accomplish this purpose, a biologist need to define a sequence of bio applications as workflow easily because generally bio applications should be combined and executed in some order. This bio workflow should be executed in the form of distributed and parallel computing by allocating computing resources efficiently under HPC cluster system. Through this kind of job, we can expect better performance and fast response time of very large bio data analysis. This paper proposes a workflow-based data analysis system specialized for bio applications. Using this system, non-IT scientists and researchers can analyze very large bio data easily under HPC environment.

• Proceedings of the Korean Society for Agricultural Machinery Conference
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• 2017.04a
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• pp.171-171
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• 2017

전산유체역학(CFD: Computational Fluid Dynamics)를 이용한 스마트팜 환경 내부의 정밀 제어 연구가 진행 중이다. 시계열 데이터의 난해한 동적 해석을 극복하기위해, 비선형 모델링 기법의 일종인 인공신경망을 이용하는 방안을 고려하였다. 선행 연구를 통하여 환경 데이터의 비선형 모델링을 위한 Tensorflow활용 방법이 하드웨어 가속 기능을 바탕으로 월등한 성능을 보임을 확인하였다. 그럼에도 오프라인 일괄(Offline batch)처리 방식의 한계가 있는 인공신경망 모델링 기법과 현장 보급이 불가능한 고성능 하드웨어 연산 장치에 대한 대안 마련이 필요하다고 판단되었다. CFD 해석을 위한 Solver로 SU2(http://su2.stanford.edu)를 이용하였다. 운영 체제 및 컴파일러는 1) Mac OS X Sierra 10.12.2 Apple LLVM version 8.0.0 (clang-800.0.38), 2) Windows 10 x64: Intel C++ Compiler version 16.0, update 2, 3) Linux (Ubuntu 16.04 x64): g++ 5.4.0, 4) Clustered Linux (Ubuntu 16.04 x32): MPICC 3.3.a2를 선정하였다. 4번째 개발환경인 병렬 시스템의 경우 하드웨어 가속는 OpenCL(https://www.khronos.org/opencl/) 엔진을 이용하고 저전력 ARM 프로세서의 일종인 옥타코어 Samsung Exynos5422 칩을 장착한 ODROID-XU4(Hardkernel, AnYang, Korea) SBC(Single Board Computer)를 32식 병렬 구성하였다. 분산 컴퓨팅을 위한 환경은 Gbit 로컬 네트워크 기반 NFS(Network File System)과 MPICH(http://www.mpich.org/)로 구성하였다. 공간 분해능을 계측 주기보다 작게 분할할 경우 발생하는 미지의 바운더리 정보를 정의하기 위하여 3차원 Kriging Spatial Interpolation Method를 실험적으로 적용하였다. 한편 병렬 시스템 구성이 불가능한 1,2,3번 환경의 경우 내부적으로 이미 존재하는 멀티코어를 활용하고자 OpenMP(http://www.openmp.org/) 라이브러리를 활용하였다. 64비트 병렬 8코어로 동작하는 1,2,3번 운영환경의 경우 32비트 병렬 128코어로 동작하는 환경에 비하여 근소하게 2배 내외로 연산 속도가 빨랐다. 실시간 CFD 수행을 위한 분산 컴퓨팅 기술이 프로세서의 속도 및 운영체제의 정보 분배 능력에 따라 결정된다고 판단할 수 있었다. 이를 검증하기 위하여 4번 개발환경에서 운영체제를 64비트로 개선하여 5번째 환경을 구성하여 검증하였다. 상반되는 결과로 64비트 72코어로 동작하는 분산 컴퓨팅 환경에서 단일 프로세서 기반 멀티 코어(1,2,3번) 환경보다 보다 2.5배 내외 연산속도 향상이 있었다. ARM 프로세서용 64비트 운영체제의 완성도가 낮은 시점에서 추후 성공적인 실시간 CFD 모델링을 위한 지속적인 검토가 필요하다.

• Journal of KIISE:Computer Systems and Theory
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• v.35 no.7
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• pp.305-317
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• 2008

As a mobile computing environment is rapidly changing, increasing user demand for multimedia-over-wireless capabilities on embedded processors places constraints on performance, power, and sire. In this regard, this paper proposes color media instructions (CMI) for single instruction, multiple data (SIMD) parallel processors to meet the computational requirements and cost goals. While existing multimedia extensions store and process 48-bit pixels in a 32-bit register, CMI, which considers that color components are perceptually less significant, supports parallel operations on two-packed compressed 16-bit YCbCr (6 bit Y and 5 bits Cb, Cr) data in a 32-bit datapath processor. This provides greater concurrency and efficiency for YCbCr data processing. Moreover, the ability to reduce data format size reduces system cost. The reduction in data bandwidth also simplifies system design. Experimental results on a representative SIMD parallel processor architecture show that CMI achieves an average speedup of 6.3x over the baseline SIMD parallel processor performance. This is in contrast to MMX (a representative Intel's multimedia extensions), which achieves an average speedup of only 3.7x over the same baseline SIMD architecture. CMI also outperforms MMX in both area efficiency (a 52% increase versus a 13% increase) and energy efficiency (a 50% increase versus an 11% increase). CMI improves the performance and efficiency with a mere 3% increase in the system area and a 5% increase in the system power, while MMX requires a 14% increase in the system area and a 16% increase in the system power.