• Atmosphere
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• v.22 no.1
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• pp.109-115
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• 2012

We parallelized the CFD_NIMR model, which is a numerical meteorological model, for best performance on both of distributed and shared memory parallel computers. This hybrid parallelization uses MPI (Message Passing Interface) to apply horizontal 2-dimensional sub-domain out of the 3-dimensional computing domain for distributed memory system, as well as uses OpenMP (Open Multi-Processing) to apply vertical 1-dimensional sub-domain for utilizing advantage of shared memory structure. We validated the parallel model with the original sequential model, and the parallel CFD_NIMR model shows efficient speedup on the distributed and shared memory system.

• Proceedings of the KIEE Conference
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• 1998.07c
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• pp.997-999
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• 1998

An operationally secure power system is one low probability of blackout or equipment damage. The power system is needed to maintain a designated security level at minimum operating cost. The inclusions of security make power system problem complex. But, because security and optimality are normally conflicting requirement, the separate treatments of both are inappropriate. So, a unified hierarchical formulation is needed. In this paper, the overview of security constrained optimal power flow (SCOPF) is presented and an introduction of parallel distributed formulation to SCOPF is also presented.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.19 no.3
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• pp.529-536
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• 2015

As personal information protection act was recently enforced, a mechanism which saves encrypted personal information has been used to Information Security systems. To use the mechanism, a millions of personal information which are already saved on the system first have to be encrypted. At the moment, it may cause a resource scarcity on server, and also take a lot of time. Thus, this paper suggests a way to encrypt millions of personal information by using multi-server with low specifications and measures its performance on test environment. And, I was compared with the performance of high- specification server. As a compared result, the mechanism with three devices by parallel and distributed processing improved its performance by 128%, and the mechanism with five devices by the same processing improved its performance by 158%.

• The Transactions of the Korea Information Processing Society
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• v.3 no.7
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• pp.1680-1686
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• 1996

The deterministic update method of intensional predicates in a parallel deductive database that deductive database is distributed in a parallel computer architecture in needed. Using updated data from the deterministic update method, a strategy for parallel evaluation of intensional predicates is required. The paper is concerned with an approach to updating parallel deductive database in which very insertion or deletion can be performed in a deterministic way, and an extended parallel semi-naive evaluation algorithm in a parallel computer architecture. After presenting an approach to updating intensional predicates and strategy for parallel evaluation, its implementation is discussed. A parallel deductive database consists of the set of facts being the extensional database and the set of rules being the intensional database. We assume that these sets are distributed in each processor, research how to update intensional predicates and evaluate using the update method. The parallel architecture for the deductive database consists of a set of processors and a message passing network to interconnect these processors.

• IEMEK Journal of Embedded Systems and Applications
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• v.18 no.4
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• pp.165-172
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• 2023

Heterogeneous computing is a technology that utilizes different types of processors to perform parallel processing. It maximizes task processing and energy efficiency by leveraging various computing resources such as CPUs, GPUs, and FPGAs. On the other hand, edge computing has developed with IoT and 5G technologies. It is a distributed computing that utilizes computing resources close to clients, thereby offloading the central server. It has evolved to intelligent edge computing combined with artificial intelligence. Intelligent edge computing enables total data processing, such as context awareness, prediction, control, and simple processing for the data collected on the edge. If heterogeneous computing can be successfully applied in the edge, it is expected to maximize job processing efficiency while minimizing dependence on the central server. In this paper, experiments were conducted to verify the feasibility of various parallel programming models on high-end and low-end edge devices by using benchmark applications. We analyzed the performance of five parallel programming models on the Raspberry Pi 4 and Jetson Orin Nano as low-end and high-end devices, respectively. In the experiment, OpenACC showed the best performance on the low-end edge device and OpenSYCL on the high-end device due to the stability and optimization of system libraries.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.17 no.5
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• pp.1168-1173
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• 2013

This paper proposes a new visualization method based on cloud technique which uses internal relationship of user correlation and external relation of social network to visualize user relationship hierarchy. The visualization method of this paper can well represent user-focused relationship hierarchy on social networks by a correlation matrix. The importance of a access node reflects into user relationship hierarchy by exploiting external relation of social network. Users of the method can well understand user relationships on account of representing user relationship hierarchy from social networks. In addition, the method use hadoop and hive for distribution storing and parallel processing which the result of calculation visualizes hierarchy graph using D3.

• The Journal of the Korea Contents Association
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• v.12 no.10
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• pp.1-10
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• 2012

Many statistical methods have been adapted for terminology recognition to improve its accuracy. However, since previous studies have been carried out in a single core or a single machine, they have difficulties in real-time analysing explosively increasing documents. In this study, the task where bottlenecks occur in the process of terminology recognition is classified into linguistic processing in the process of 'candidate terminology extraction' and collection of statistical information in the process of 'terminology weight assignment'. A terminology recognition system is implemented and experimented to address each task by means of the distributed parallel processing-based MapReduce. The experiments were performed in two ways; the first experiment result revealed that distributed parallel processing by means of 12 nodes improves processing speed by 11.27 times as compared to the case of using a single machine and the second experiment was carried out on 1) default environment, 2) multiple reducers, 3) combiner, and 4) the combination of 2)and 3), and the use of 3) showed the best performance. Our terminology recognition system contributes to speed up knowledge extraction of large scale science and technology documents.

• 제어로봇시스템학회:학술대회논문집
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• 2001.10a
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• pp.88.1-88
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• 2001

The paper describes a new approach to the organization of an artificial brain for mobile multi-robot systems, where individual robots are not considered as independent entities, but rather forming together a universal parallel and distributed machine capable of processing both information and physical matter in distributed worlds. This spatial machine, operating without any central control, is driven on top by distributed mission scenarios in WAVE-WP language. The scenarios can be written on a variety of levels, and any mixture of them, supporting the needed system flexibility and freedom ...

• Proceedings of the Korean Environmental Sciences Society Conference
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• 2003.11a
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• pp.73-78
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• 2003

In this research, we implement Realtime Air Diffusion Prediction System which is a parallel Fortran model running on distributed-memory parallel computers. The system is designed for air diffusion simulations with four-dimensional data assimilation. For regional air quality forecasting a series of dynamic downscaling technique is adopted using the NCAR/Penn. State MM5 model which is an atmospheric model. The realtime initial data have been provided daily from the KMA (Korean Meteorological Administration) global spectral model output. It takes huge resources of computation to get 24 hour air quality forecast with this four step dynamic downscaling (27km, 9km, 3km, and lkm). Parallel implementation of the realtime system is imperative to achieve increased throughput since the realtime system have to be performed which correct timing behavior and the sequential code requires a large amount of CPU time for typical simulations. The parallel system uses MPI (Message Passing Interface), a standard library to support high-level routines for message passing. We validate the parallel model by comparing it with the sequential model. For realtime running, we implement a cluster computer which is a distributed-memory parallel computer that links high-performance PCs with high-speed interconnection networks. We use 32 2-CPU nodes and a Myrinet network for the cluster. Since cluster computers more cost effective than conventional distributed parallel computers, we can build a dedicated realtime computer. The system also includes web based Gill (Graphic User Interface) for convenient system management and performance monitoring so that end-users can restart the system easily when the system faults. Performance of the parallel model is analyzed by comparing its execution time with the sequential model, and by calculating communication overhead and load imbalance, which are common problems in parallel processing. Performance analysis is carried out on our cluster which has 32 2-CPU nodes.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.18 no.9
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• pp.2155-2160
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• 2014

In this paper, we proposes a new document summarization method using the extracted semantic feature which the semantic feature is extracted by distributed parallel processing based Hadoop. The proposed method can well represent the inherent structure of documents using the semantic feature by the non-negative matrix factorization (NMF). In addition, it can summarize the big data document using Hadoop. The experimental results demonstrate that the proposed method can summarize the big data document which a single computer can not summarize those.