• Convergence Security Journal
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• v.20 no.2
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• pp.91-99
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• 2020

The purpose of this study is to present the application of Information and Communication Technology(ICT) during the 4th Industrial Revolution for the efficient implementation of government emergency preparedness policies. Brainstorming by experts categorized the government's emergency preparedness policies into 4 types and 12 detailed tasks. Classification results were used by AHP(Analytic Hierarchy Process) to analyze relative importance and priorities. The AHP survey found that strengthening crisis management responsiveness was the most important detailed task. Artificial Intelligence(AI), Internet of Things(IoT), Unmanned Autonomy System, Virtual Reality(VR), and Augmented Reality(AR) were presented as major information and communication technology(ICT) for the efficient execution of detailed tasks.

• Journal of KIISE:Software and Applications
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• v.33 no.9
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• pp.802-809
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• 2006

J2ME mobile programs have been widely used as mobile devices like mobile phones become popular. Efficient use of resources in mobile programs is very important because mobile programs are executed in mobile environment with insufficient resources. Moreover, most J2ME programs are event-driven, so effective event handling is important for reliability and efficient use of resource. In this research, we develop a dynamic event analysis system, which can show event handling in real-time. In this system, users can trace only interesting events by selecting some options, and can get event profile after execution.

• Journal of KIISE:Computer Systems and Theory
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• v.27 no.7
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• pp.639-649
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• 2000

In the relational database systems, the join operation is one of the most time-consuming query operations. Many parallel join algorithms have been developed to reduce the execution time. Multiple hash join algorithm using allocation tree is one of most efficient ones. However, it may have some delay on the processing each node of allocation tree, which is occurred in tuple-probing phase by the difference between one page reading time of outer relation and the processing time of already read one. In this paper, to solve the performance degrading problem by the delay, we develop a join algorithm using the concept of 'synchronization of page execution time' for multiple hash joins. We reduce the processing time of each nodes in the allocation tree and improve the total system performance. In addition, we analyze the performance by building the analytical cost model and verify the validity of it by various performance comparison with previous method.

• The KIPS Transactions:PartA
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• v.13A no.7 s.104
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• pp.561-572
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• 2006

Performance of the mobile devices greatly depends on the efficient resource management because they are usually resource-restricted. In particular, the dynamic storage allocation algorithms very important part of the mobile device's operating system and OS-like software platform. The existing dynamic storage allocation algorithms did not consider application's execution style and the type, life-time, and characteristics of memory objects that the application uses. Those algorithms, as a result, could not manage memory efficiently Therefore, this Paper analyzes the mobile application's execution characteristics and proposes anew dynamic storage allocation algorithm which saves the memory space and improves mobile application's execution speed. The test result shows that the proposed algorithm works 6.5 times faster than the linked-list algorithm[11], 2.5 times faster better than the Doug. Lea's algorithm[12] and 10.5 times faster than the Brent algorithm[14].

• Journal of KIISE
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• v.42 no.2
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• pp.155-161
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• 2015

It is difficult and cumbersome to figure out whether a multithread program runs with concurrency bugs, such as data races and atomicity violations, because there are many possible executions of the program and a lot of the defects are hard to reproduce. Hence, monitoring techniques for collecting and analyzing the information from program execution, such as thread executions, memory accesses, and synchronization information, are important to locate data races for debugging multithread programs. This paper presents an efficient and practical monitoring tool, called VcTrace, that analyzes the partial ordering of concurrent threads and events during an execution of the program based on the vector clock system. Empirical results on C/C++ benchmarks using Pthreads show that VcTrace is a sound and practical tool for on-the-fly data race detection as well as for analyzing multithread programs.

• Nuclear Engineering and Technology
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• v.51 no.6
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• pp.1487-1503
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• 2019

The methods and performance of a pin-level nuclear reactor core thermal-hydraulics (T/H) code ESCOT employing the drift-flux model are presented. This code aims at providing an accurate yet fast core thermal-hydraulics solution capability to high-fidelity multiphysics core analysis systems targeting massively parallel computing platforms. The four equation drift-flux model is adopted for two-phase calculations, and numerical solutions are obtained by applying the Finite Volume Method (FVM) and the Semi-Implicit Method for Pressure-Linked Equation (SIMPLE)-like algorithm in a staggered grid system. Constitutive models involving turbulent mixing, pressure drop, and vapor generation are employed to simulate key phenomena in subchannel-scale analyses. ESCOT is parallelized by a domain decomposition scheme that involves both radial and axial decomposition to enable highly parallelized execution. The ESCOT solutions are validated through the applications to various experiments which include CNEN $4{\times}4$, Weiss et al. two assemblies, PNNL $2{\times}6$, RPI $2{\times}2$ air-water, and PSBT covering single/two-phase and unheated/heated conditions. The parameters of interest for validation include various flow characteristics such as turbulent mixing, spacer grid pressure drop, cross-flow, reverse flow, buoyancy effect, void drift, and bubble generation. For all the validation tests, ESCOT shows good agreements with measured data in the extent comparable to those of other subchannel-scale codes: COBRA-TF, MATRA and/or CUPID. The execution performance is examined with a mini-sized whole core consisting of 89 fuel assemblies and for an OPR1000 core. It turns out that it is about 1.5 times faster than a subchannel code based on the two-fluid three field model and the axial domain decomposition scheme works as well as the radial one yielding a steady-state solution for the OPR1000 core within 30 s with 104 processors.

• Proceedings of the Korea Association of Information Systems Conference
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• 2003.11a
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• pp.55-63
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• 2003

Workflow Management System (WFMS) is a software system to support an efficient execution, control and management of complex business processes. Since traditional commercial systems mainly focus on automating processes, they don't have methods for enhancing the task performer's efficiency. In this paper, we propose a new method of executing business processes more efficiently in that a whole process is scheduled considering the degree of the participants' workload. The method allows managing the largest constraints among constituent resources of the process. We utilize DBR scheduling techniques to develop the method. We first consider the differences between workflow process models and DBR application models, and then develop the modified drum, buffer and rope. This leads us to develop WF-DBR (WorkFlow-DBR) that can control the proper size of the task performers' work list and arrival rate of process instances. Use of WF-DBR improves the efficiency of the whole process as well as the participants' working condition. We then carry out a set of simulation experiments and compare the effectiveness of our approach with that of scheduling techniques used in existing systems.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.10 no.1
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• pp.117-135
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• 2016

Performance enhancement is one of the most important issues in high performance distributed computing systems. In such computing systems, online users submit their jobs anytime and anywhere to a set of dynamic resources. Jobs arrival and processes execution times are stochastic. The performance of a distributed computing system can be improved by using an effective load balancing strategy to redistribute the user tasks among computing resources for efficient utilization. This paper presents a multi-class load balancing strategy that balances different classes of user tasks on multiple heterogeneous computing nodes to minimize the per-class mean response time. For a wide range of system parameters, the performance of the proposed multi-class load balancing strategy is compared with that of the random distribution load balancing, and uniform distribution load balancing strategies using simulation. The results show that, the proposed strategy outperforms the other two studied strategies in terms of average task response time, and average computing nodes utilization.

• Journal of the Korea Institute of Building Construction
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• v.3 no.3
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• pp.99-106
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• 2003

The purpose of this study is to suggest the rational plans of Information Management in the interior finishing work. The results of this study are summarized as follows; I) Analytical survey of actual construction progress of interior finishing work 2) Review of computer-based development in construction planning & information management of interior finishing work 3) Structure of interior finishing work Planning and Management Support System And the expectancies of this paper are that it can be used as efficient data for improvement of system to systematize planning and execution of interior finishing work in korea.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.12 no.5
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• pp.521-528
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• 2019

Epilepsy is one of the most prevalent neurological diseases. Electroencephalogram (EEG) signals are widely used for monitoring and diagnosis tool for epileptic seizure. Typically, a huge amount of EEG signals is needed, where they are visually examined by experienced clinicians. In this study, we propose a simple automatic seizure detection framework using intracranial EEG signals. We suggest a sparse approximation based classification (SAC) scheme by solving overdetermined system. L1-norm minimization algorithms are utilized for efficient sparse signal recovery. For evaluation of the proposed scheme, the public EEG dataset obtained by five healthy subjects and five epileptic patients is utilized. The results show that the proposed fast L1-norm minimization based SAC methods achieve the 99.5% classification accuracy which is 1% improved result than the conventional L2 norm based method with negligibly increased execution time (42msec).