• Journal of the Korean Data and Information Science Society
• /
• v.10 no.2
• /
• pp.473-483
• /
• 1999

We introduce a way to construct a supercomputing capable system with some networked PCs, running the Linux operating system and computing power comparable with expensive commercial workstations, and with the Parallel Virtual Machine (PVM) software which enables one to control the total CPUs and memories of the networked PCs. By benchmarking the system using a PVM parallel program, we find that the system's parallel efficiency is close to 90 %.

• 한국전산유체공학회:학술대회논문집
• /
• 2006.05a
• /
• pp.361-363
• /
• 2006

• Journal of the Korea Institute of Information Security & Cryptology
• /
• v.29 no.2
• /
• pp.321-330
• /
• 2019

Prevention of corporate confidentiality leakage by insiders in enterprises is an essential task for the survival of enterprises. In order to prevent information leakage by insiders, companies have adopted security solutions, but there is a limit to effectively detect abnormal behavior of insiders with access privileges. In this study, we use the Unsupervised Learning algorithm of the machine learning technique to effectively and efficiently cluster the normal and abnormal access logs of the worker's work screen in the manufacturing information system, which includes the company's product manufacturing history and quality information. We propose an optimal feature selection model for anomaly detection by studying clustering methods.

• The Bulletin of The Korean Astronomical Society
• /
• v.44 no.2
• /
• pp.63.3-63.3
• /
• 2019

Nearby young moving groups (NYMGs hereafter) are gravitationally unbound loose young stellar associations located within 100 pc of the Sun. Since NYMGs are crucial laboratories for studying low-mass stars and planets, intensive searches for NYMG members have been performed. For identification of NYMG members, various strategies and methods have been applied. As a result, the reliability of the members in terms of membership is not uniform, which means that a careful membership re-assessment is required. In this study, I developed a NYMG membership probability calculation tool based on Bayesian inference (Bayesian Assessment of Moving Groups: BAMG). For the development of the BAMG tool, I constructed ellipsoidal models for nine NYMGs via iterative and self-consistent processes. Using BAMG, memberships of claimed members in the literature (N~2000) were evaluated, and 35 per cent of members were confirmed as bona fide members of NYMGs. Based on the deficiency of low-mass members appeared in mass function using these bona fide members, low mass members from Gaia DR2 are identified. About 2000 new M dwarf and brown dwarf candidate members were identified. Memberships of ~70 members with RV from Gaia were confirmed, and the additional ~20 members were confirmed via spectroscopic observation. Not relying on previous knowledge about the existence of nine NYMGs, unsupervised machine learning analyses were applied to NYMG members. K-means and Agglomerative Clustering algorithms result in similar trends of grouping. As a result, six previously known groups (TWA, beta-Pic, Carina, Argus, AB Doradus, and Volans-Carina) were rediscovered. Three the other known groups are recognized as well; however, they are combined into two new separate groups (ThOr+Columba and TucHor+Columba).

• Proceedings of the KSR Conference
• /
• 2008.06a
• /
• pp.1773-1780
• /
• 2008

When the fire occur in the deeply underground subway station, the difficulties of passenger evacuation are expected because of many stairs to the exit. In this study, SOONGSIL-University station (7 line, 47m depth) is the one of the deepest subway stations of the each line in the Seoul metro. The numerical computational-simulation was performed for the fire driven flow in the subway station. Hot and smoke flow was analyzed from the simulation results. The proper plan of evacuation against fire was considered through the results. The fire driven flow was simulated using FDS code in which LES method was applied. The Heat Release Rate was 10MW and the ultrafast model was applied for the growing model of the fire source. The proper mesh size was determined from the characteristic length of fire size. The parallel computational method was employed to compute the flow and heat eqn's in the meshes, which are about 10,000,000, with 6cpu of the linux clustering machine.