• Computational Structural Engineering : An International Journal
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• v.3 no.1
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• pp.1-7
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• 2003

For system identification of large structures, it is not practical to identify the entire structure due to the prohibitive computational time and difficulty in numerical convergence. This paper explores the possibility of performing system identification at substructure level, taking advantage of reduction in both the number of unknowns and the number of degrees of freedom involved. Another advantage is that different portions (substructures) of a structural system can be identified independently and even concurrently with parallel computing. Two substructural identification methods are formulated on the basis whether substructural approach is used to obtain first-order or second-order model. For substructural first-order model, identification at the substructure level will be performed by means of the Observer/Kalman filter Identification (OKID) and the Eigensystem Realization Algorithm (ERA) whereas identification at the global level will be performed to obtain second-order model in order to evaluate the system's stiffness and mass parameters. In the case of substructural second-order model, identification will be performed at the substructure level throughout the identification process. The efficiency of the proposed technique is shown by numerical examples for multi-storey shear buildings subjected to random forces, taking into consideration the effects of noisy measurement data. The results indicate that both the proposed methods are effective and efficient for damage identification of large structures.

• KIISE Transactions on Computing Practices
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• v.23 no.10
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• pp.579-587
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• 2017

Cyber Mission Impact Assessment is one of the essential tasks which many militaries and industrial major companies should perform to effectively achieve their mission. The unexpected damage to an organization's assets results in damage to the whole system's performance of the organizations. In order to minimize the damage, it is necessary to quantify the available capacity of the mission, which can be achieved only with the remaining assets, and to immediately prepare a new second best plan in a moment. We therefore need to estimate the exact cyber attack's impact to the mission when the unwanted damage occurs by modeling the relationship between the assets and the missions. In this paper, we propose a new model which deals with the dependencies between assets and missions for obtaining the exact impact of a cyber attack. The proposed model distinguishes task management from asset management for an efficient process, and it is implemented to be optimized using a vectorized operation for parallel processing and using a buffer to reduce the computation time.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.33B no.1
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• pp.144-152
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• 1996

In this paper, a pulse-mode digital multilayer neural network with a massively parallel yet compact and flexible network architecture is presented. Algebraicneural operations are replaced by stochastic processes using pseudo-random pulse sequences and simple logic gates are used as basic computing elements. The distributions of the results from the stochastic processes are approximated using the hypergeometric distribution. A statistical model of the noise(error) is developed to estimate the relative accuracy associated with stochastic computing in terms of mean and variance. Numerical character recognition problems are applied to the network to evaluate the network performance and to justify the validity of analytic results based on the developed statistical model. The network architectures are modeled in VHDL using the mixed descriptions of gate-level and register transfer level (RTL). Experiments show that the statistical model successfully predicts the accuracy of the operations performed in the network and that the character classification rate of the network is competitive to that of ordinary Back-Propagation networks.

• The KIPS Transactions:PartC
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• v.8C no.6
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• pp.731-740
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• 2001

Multi-distributed web cluster model expanding conventional cluster system is the cluster system which processes large-scaled work demanded from users with parallel computing method by building a number of system nodes on open network into a single imaginary network. Multi-distributed web cluster model on the structured characteristics exposes internal system nodes by an illegal third party and has a potential that normal job performance is impossible by the intentional prevention and attack in cooperative work among system nodes. This paper presents the mutual authentication protocol of system nodes through key division method for the authentication of system nodes concerned in the registration, requirement and cooperation of service code block of system nodes and collecting the results and then designs SNKDC which controls and divides symmetrical keys of the whole system nodes safely and effectively. SNKDC divides symmetrical keys required for performing the work of system nodes and the system nodes transmit encoded packet based on the key provided. Encryption packet given and taken between system nodes is decoded by a third party or can prevent the outflow of information through false message.

• Proceedings of the KSR Conference
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• 2008.06a
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• pp.1809-1815
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• 2008

This experiment simulated the fire driven flow of an underground station through parallel processing method. Fire analysis program FDS(Fire Dynamics Simulation), using LES(Large Eddy Simulation), has been used and a 6-node parallel cluster, each node with 3.0Ghz_2set installed, has been used for parallel computation. Simulation model was based on the Kwangju-geumnan subway station. Underground station, and the total time for simulation was set at 600s. First, the whole underground passage was divided to 1-Mesh and 8-Mesh in order to compare the parallel computation of a single CPU and Multi-CPU. With matrix numbers($15{\times}10^6$) more than what a single CPU can handle, fire driven flow from the center of the platform and the subway itself was analyzed. As a result, there seemed to be almost no difference between the single CPU's result and the Multi-CPU's ones. $3{\times}10^6$ grid point one employed to test the computing time with 2CPU and 7CPU computation were computable two times and fire times faster than 1CPU respectively. In this study it was confirmed that CPU could be overcome by using parallel computation.

• The KIPS Transactions:PartA
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• v.10A no.1
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• pp.15-24
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• 2003

In this paper, an optimization model for the clustered node allocation systems in the distributed computing environment is presented. In the presented model with a distributed file system framework, the dynamics of system behavior over times is carefully thought over the nodes and hence the functionality of the cluster monitor node to check the feasibility of the current set of clustered node allocation is given. The cluster monitor node of the node allocation system capable of distributing the parallel modules to clustered nodes provides a good allocation solution using Genetic Algorithms (GA). As a part of the experimental studies, the solution quality and computation time effects of varying GA experimental parameters, such as the encoding scheme, the genetic operators (crossover, mutations), the population size, and the number of node modules, and the comparative findings are presented.

• Geophysics and Geophysical Exploration
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• v.7 no.4
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• pp.234-244
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• 2004

In order to calculate 3-dimensional wavefield using finite-element method in frequency domain, we must factor so huge sparse impedance matrix. Because of difficulties of handling of this huge impedance matrix, 3-dimensional wave equation modeling is conducted mainly in time domain. In this study, we simulate the 3-D wavefield using finite-element method in Laplace domain by combining high-performance parallel finite-element solver and SWEET (Suppressed Wave Equation Estimation of Traveltime) algorithm which can calculate the traveltime and the amplitude. To verify this combination, we applied it to the SEG/EAGE 3D salt model in serial and parallel computing environments.

• Atmosphere
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• v.23 no.2
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• pp.231-235
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• 2013

We parallelized WRF major physics routines for Nvidia GP-GPUs with CUDA Fortran. GP-GPUs are originally designed for graphic processing, but show high performance with low electricity for calculating numerical models. In the CUDA environment, a data domain is allocated into thread blocks and threads in each thread block are computing in parallel. We parallelized the WRF program to use of thread blocks efficiently. We validated the GP-GPU program with the original CPU program, and the WRF model using GP-GPUs shows efficient speedup.

• Journal of electromagnetic engineering and science
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• v.10 no.3
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• pp.166-170
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• 2010

To numerically calculate electromagnetic scattering from the electrical-large three-dimensional(3D) objects, the high-frequency approaches have been usually applied, but the accuracy and feasibility of these geometrical and physical optics(GO-PO) approaches, to some extent, are remained to be improved. In this paper, a new framework is developed for calculation of the near-field scattering field of an electrical-large 3D target by using a multilevel fast multipole algorithm(MLFMA) and generation of radar images by using a fast back-projection(FBP) algorithm. The MPI(Message Passing Interface) parallel computing is carried out to multiply the calculation efficiency greatly. Finally, a simple example of perfectly electrical conducting(PEC) patch and a canonical case of Fighting Falcon F-16 are presented.

• 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.