• International Journal of Contents
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• v.17 no.3
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• pp.67-73
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• 2021

Dark matter is barely known because it cannot be explained using the Standard Model. In addition, dark matter has not been detected yet. It is currently being explored through various ways. In this paper, we studied dark matter in an electron-positron collider using MadGraph5. The signal channel is e⁺e^- → 𝜇⁺𝜇^-A' where A' decays to dimuon. We studied the cross-section by increasing the center-of-mass energy. Central processing unit (CPU) time of simulation was compared with that using a local Linux machine and a KISTI-5 supercomputer (Knight Landing and Skylake). Furthermore, one or more cores were used for comparing CPU time among machines. Results of this study will enable the exploration of dark matter in electron-positron experiments. This study also serves as a reference for optimizing high-energy physics simulation toolkits.

• Journal of Information Processing Systems
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• v.17 no.3
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• pp.453-461
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• 2021

Many deep learning approaches have been studied for image classification in computer vision. However, there are not enough data to generate accurate models in medical fields, and many datasets are not annotated. This study presents a new method that can use both unlabeled and labeled data. The proposed method is applied to classify cervix images into normal versus cancerous, and we demonstrate the results. First, we use a patch self-supervised learning for training the global context of the image using an unlabeled image dataset. Second, we generate a classifier model by using the transferred knowledge from self-supervised learning. We also apply attention learning to capture the local features of the image. The combined method provides better performance than state-of-the-art approaches in accuracy and sensitivity.

• Journal of Platform Technology
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• v.8 no.2
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• pp.3-9
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• 2020

Big Data is a new concept in the global and local area. This field has gained tremendous momentum in the recent years and has attracted attention of several researchers. Big Data is a data analysis methodology enabled by recent advances in information and communications technology. However, big data analysis requires a huge amount of computing resources making adoption costs of big data technology. Therefore, it is not affordable for many small and medium enterprises. We survey the concepts and characteristics of Big Data along with a number of tools like HADOOP, HPCC for managing Big Data. It also presents an overview of big data like Characteristics of Big data, big data technology, big data management tools etc. We have also highlighted on some challenges and opportunities related to the fields of big data.

• International Journal of Internet, Broadcasting and Communication
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• v.14 no.2
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• pp.30-35
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• 2022

Rendering is widely used for visual effects in animations and movies. Although rendering is computing-intensive, we observe that it accompanies heavy I/O because of large input data. This becomes technical hurdles for multi-node rendering performed on public cloud nodes. To reduce the overhead of data transmission in multi-node rendering, this paper analyzes the characteristics of rendering workloads, and presents the cooperative caching scheme for multi-node rendering. Our caching scheme has the function of synchronization between original data in local storage and cached data in rendering nodes, and the cached data are shared between multiple rendering nodes. We perform measurement experiments in real system environments and show that the proposed cooperative caching scheme improves the conventional caching scheme used in the network file system by 27% on average.

• Journal of Korea Multimedia Society
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• v.25 no.8
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• pp.1007-1021
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• 2022

Autonomous driving of unmanned aerial vehicles may have to pay expensive cost to create and switch new routes if unexpected obstacles exist or local map updates occured by the control system due to incorrect route information. Integrating digital twins into the path-following process requires more computing resources to quickly switch the wrong path to an alternative path, but it can quickly update the path during flight. In this study, we design a DEVS-based simulation environment which can modify optimized paths through short-term simulation of multi-virtual UAVs for applying digital twin concepts to path follow. Through simulation, we confirmed the possibility of increasing the mission stability of UAV.

• Kyungpook Mathematical Journal
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• v.62 no.4
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• pp.699-713
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• 2022

This paper proposes a hybrid successive over-relaxation iterative method for the numerical solution of a nonlinear diffusion in a one-dimensional porous medium. The considered mathematical model is discretized using a computational complexity reduction scheme called half-sweep finite differences. The local truncation error and the analysis of the stability of the scheme are discussed. The proposed iterative method, which uses explicit group technique and modified successive over-relaxation, is formulated systematically. This method improves the efficiency of obtaining the solution in terms of total iterations and program elapsed time. The accuracy of the proposed method, which is measured using the magnitude of absolute errors, is promising. Numerical convergence tests of the proposed method are also provided. Some numerical experiments are delivered using initial-boundary value problems to show the superiority of the proposed method against some existing numerical methods.

• Journal of Korea Spatial Information System Society
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• v.4 no.2 s.8
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• pp.41-54
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• 2002

The main questions of this study are how to reuse GIS applications and what to do for interoperability of the applications in local governments. To answer the questions, related technologies and standards of GIS are investigated. International standard organizations, such as ISO/TC211 and OGC(OpenGIS Consortium), are working on GIS interoperability standards based on component technology and distributed computing environments. In this study, a standard model for interoperability of GIS applications in local governments is proposed based on the international standards. Standardization process of GIS interfaces in local governments is as followed: 1) modeling of GIS business and 2) establishment of GIS service architectures 3) defining GIS standard interfaces 4) GIS component. In conclusion, by developing interoperable GIS applications based on component technology the reusability in local governments can be realized.

• Journal of the Society of Naval Architects of Korea
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• v.30 no.2
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• pp.141-154
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• 1993

In this paper, a method to calculate the ultimate compressive strength of welded one-sided stiffened plates simply supported along all edges is proposed. At first initial imperfections such as distortions and residual stresses due to welding are predicted by using simplified methods. Then, the collapse modes of the stiffened plate are assumed and collapse loads for each mode are calculated. Among these loads, the lowest value is selected as the ultimate strength of the plate. Collapse modes are assumed as follows ; (1) Overall buckling of the stiffened plate$\rightarrow$Overall collapse due to stiffener bending (2) Local buckling of the plate part$\rightarrow$Local collapse of the plate part$\rightarrow$Overall collapse due to stiffener yielding (3) Local buckling of the plate part$\rightarrow$Overall collapse due to stiffener berthing (4) Local buckling of the plate part$\rightarrow$Local collapse of the plate part$\rightarrow$Overall collapse due to stiffener tripping. The elastic large deflection analysis based on the Rayleigh-Ritz method is carried out, and plastic analysis assuming hinge lines is also carried out. Collapse load is defined as the cross point of the two analysis curves. This method enables the utimate strength to be calculated with small computing time and a good accuracy. Using the present method, characteristics of the stiffener including torsional rigidity, bending and tripping can also be clarified.

• The KIPS Transactions:PartA
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• v.10A no.6
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• pp.635-642
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• 2003

The radical development of recent network technology provides the basic foundation which can establish a high speed and cheap cluster system. It is a general trend that conventional cluster systems are built as the system over a fixed level based on stabilized and high speed local networks. A multi-distributed web cluster group is a web cluster model which can obtain high performance, high efficiency and high availability through mutual cooperative works between effective job division and system nodes through parallel performance of a given work and shared memory of SC-Server with low price and low speed system nodes on networks. For this, multi-distributed web cluster group builds a sub-cluster group bound with single imaginary networks of multiple system nodes and uses the web distributed shared memory of system nodes for the effective data transmission within sub-cluster groups. Since the presented model uses a load balancing and parallel computing method of large-scale work required from users, it can maximize the processing efficiency.

• Journal of Computing Science and Engineering
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• v.5 no.2
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• pp.111-120
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• 2011

Data mining algorithms should exploit new hardware technologies to accelerate computations. Such goal is difficult to achieve in database management system (DBMS) due to its complex internal subsystems and because data mining numeric computations of large data sets are difficult to optimize. This paper explores taking advantage of existing multithreaded capabilities of multicore CPUs as well as caching in RAM memory to efficiently compute summaries of a large data set, a fundamental data mining problem. We introduce parallel algorithms working on multiple threads, which overcome the row aggregation processing bottleneck of accessing secondary storage, while maintaining linear time complexity with respect to data set size. Our proposal is based on a combination of table scans and parallel multithreaded processing among multiple cores in the CPU. We introduce several database-style and hardware-level optimizations: caching row blocks of the input table, managing available RAM memory, interleaving I/O and CPU processing, as well as tuning the number of working threads. We experimentally benchmark our algorithms with large data sets on a DBMS running on a computer with a multicore CPU. We show that our algorithms outperform existing DBMS mechanisms in computing aggregations of multidimensional data summaries, especially as dimensionality grows. Furthermore, we show that local memory allocation (RAM block size) does not have a significant impact when the thread management algorithm distributes the workload among a fixed number of threads. Our proposal is unique in the sense that we do not modify or require access to the DBMS source code, but instead, we extend the DBMS with analytic functionality by developing User-Defined Functions.