• Journal of Astronomy and Space Sciences
• /
• v.38 no.1
• /
• pp.55-63
• /
• 2021

The universe is well known to be consists of dark energy, dark matter and the standard model (SM) particles. The dark matter dominates the density of matter in the universe. The dark matter is thought to be linked with dark photon which are hypothetical hidden sector particles similar to photons in electromagnetism but potentially proposed as force carriers. Due to the extremely small cross-section of dark matter, a large amount of data is needed to be processed. Therefore, we need to optimize the central processing unit (CPU) time. In this work, using MadGraph5 as a simulation tool kit, we examined the CPU time, and cross-section of dark matter at the electron-positron collider considering three parameters including the center of mass energy, dark photon mass, and coupling constant. The signal process pertained to a dark photon, which couples only to heavy leptons. We only dealt with the case of dark photon decaying into two muons. We used the simplified model which covers dark matter particles and dark photon particles as well as the SM particles. To compare the CPU time of simulation, one or more cores of the KISTI-5 supercomputer of Nurion Knights Landing and Skylake and a local Linux machine were used. Our results can help optimize high-energy physics software through high-performance computing and enable the users to incorporate parallel processing.

• Journal of Electrical Engineering and Technology
• /
• v.10 no.3
• /
• pp.812-819
• /
• 2015

This paper presents a new approach for simulating the internal faults of synchronous machines using distributed computing and Large Change Sensitivity (LCS) analysis. LCS analysis caters for a parallel solution of 3-phase model of a faulted machine within the symmetrical component-based model of interconnected network. The proposed method considers dynamic behavior of the faulty machine and connected system and tries to accurately solve the synchronous machine’s internal fault conditions in the system. The proposed method is implemented in stand-alone FORTRAN-based phasor software and the results have been compared with available recordings from real networks and precisely simulated faults by use of the ATP/EMTP as a time domain software package. An encouraging correlation between the simulation results using proposed method, ATP simulation and measurements was observed and reported. The simplified approach also enables engineers to quickly investigate their particular cases with a reasonable precision.

• Journal of Korea Water Resources Association
• /
• v.52 no.11
• /
• pp.887-894
• /
• 2019

We proposed a GPU (Grapic Processing Unit) accelerated kinematic wave model for rainfall runoff simulation and tested the accuracy and speed up performance of the proposed model. The governing equations are the kinematic wave equation for surface flow and the Green-Ampt model for infiltration. The kinematic wave equations were discretized using a finite volume method and CUDA fortran was used to implement the rainfall runoff model. Several numerical tests were conducted. The computed results of the GPU accelerated kinematic wave model were compared with several measured and other numerical results and reasonable agreements were observed from the comparisons. The speed up performance of the GPU accelerated model increased as the number of grids increased, achieving a maximum speed up of approximately 450 times compared to a CPU (Central Processing Unit) version, at least for the tested computing resources.

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

• 제어로봇시스템학회:학술대회논문집
• /
• 1996.10b
• /
• pp.920-923
• /
• 1996

This paper presents a new algorithm for the self-localization of a mobile robot using perspective invariant(Cross Ratio). Most of conventional model-based self-localization methods have some problems that data structure building, map updating and matching processes are very complex. Use of the simple cross ratio can be effective to the above problems. The algorithm is based on two basic assumptions that the ground plane is flat and two parallel walls are available. Also it is assumed that an environmental map is available for matching between the scene and the model. To extract an accurate steering angle for a mobile robot, we take advantage of geometric features such as vanishing points(V.P). Point features for computing cross ratios are extracted robustly using a vanishing point and the intersection points between floor and the vertical lines of door frames. The robustness and feasibility of our algorithms have been demonstrated through experiments in indoor environments using an indoor mobile robot, KASIRI-II(KAist SImple Roving Intelligence).

• International Journal of Aeronautical and Space Sciences
• /
• v.14 no.4
• /
• pp.387-397
• /
• 2013

An assessment of two-equation turbulence models, the low Reynolds k-${\varepsilon}$ and k-${\omega}$ SST models, with the compressibility corrections proposed by Sarkar and Wilcox, has been performed. The compressibility models are evaluated by investigating transonic or supersonic flows, including the arc-bump, transonic diffuser, supersonic jet impingement, and unsteady supersonic diffuser. A unified implicit finite volume scheme, consisting of mass, momentum, and energy conservation equations, is used, and the results are compared with experimental data. The model accuracy is found to depend strongly on the flow separation behavior. An MPI (Message Passing Interface) parallel computing scheme is implemented.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
• /
• 2010.05a
• /
• pp.636-638
• /
• 2010

Task scheduling is an integral part of parallel and distributed computing. Extensive research has been conducted in this area leading to significant theoretical and practical results. Stochastic reward nets (SRN) is an extension of stochastic Petri nets and provides compact modeling facilities for system analysis. In this paper, we address task scheduling model using extended timed marked graph, which is a special case of SRNs. And we analyze this model by giving reward measures in SRN.

• Journal of the Computational Structural Engineering Institute of Korea
• /
• v.30 no.2
• /
• pp.127-135
• /
• 2017

The computing environment has changed rapidly to enable large-scale finite element models to be analyzed at the PC or workstation level, such as multi-core CPU, optimal math kernel library implementing BLAS and LAPACK, and popularization of direct sparse solvers. In this paper, the design considerations on a parallel finite element code for shared memory based multi-core CPU system are proposed; (1) the use of optimized numerical libraries, (2) the use of latest direct sparse solvers, (3) parallelism using OpenMP for computing element stiffness matrices, and (4) assembly techniques using triplets, which is a type of sparse matrix storage. In addition, the parallelization effect is examined on the time-consuming works through a large scale finite element model.

• The Korean Journal of Applied Statistics
• /
• v.29 no.6
• /
• pp.1077-1094
• /
• 2016

Apache Spark is a fast and general-purpose cluster computing package. It provides a new abstraction named resilient distributed dataset, which is capable of support for fault tolerance while keeping data in memory. This type of abstraction results in a significant speedup compared to legacy large-scale data framework, MapReduce. In particular, Spark framework is suitable for iterative machine learning applications such as logistic regression and K-means clustering, and interactive data querying. Spark also supports high level libraries for various applications such as machine learning, streaming data processing, database querying and graph data mining thanks to its versatility. In this work, we introduce the concept and programming model of Spark as well as show some implementations of simple statistical computing applications. We also review the machine learning package MLlib, and the R language interface SparkR.

• IEIE Transactions on Smart Processing and Computing
• /
• v.4 no.5
• /
• pp.297-304
• /
• 2015

A multilayer gaseous detector has been developed for fast dose-verification measurements of raster-scan-mode therapeutic beams in particle therapy. The detector, which was constructed with eight thin parallel-plate ionization chambers (PPICs) and polymethyl methacrylate (PMMA) absorber plates, is closely tissue-equivalent in a beam's eye view. The gas-electron signals, collected on the strips and pad arrays of each PPIC, were amplified and processed with a continuous charge.integration mode. The detector was tested with 190-MeV raster-scan-mode beams that were provided by the Proton Therapy Facility at Samsung Medical Center, Seoul, South Korea. The detector responses of the PPICs for a 190-MeV raster-scan-mode proton beam agreed well with the dose data, measured using a 2D ionization chamber array (Octavius model, PTW). Furthermore, in this study it was confirmed that the detector simultaneously tracked the doses induced at the PPICs by the fast-oscillating beam, with a scanning speed of 2 m s-1. Thus, it is anticipated that the present detector, composed of thin PPICs and operating in charge.integration mode, will allow medical scientists to perform reliable fast dose-verification measurements for typical dynamic mode therapeutic beams.