• 한국전산유체공학회:학술대회논문집
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• 2008.03b
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• pp.634-637
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• 2008

A large eddy simulation with an explicit filter on unstructured mesh is presented. The flow filed is semi-implicitly marched by a fractional step method. Spatial discretization of the solver is designed to guarantee the second order accuracy. An isotropic explicit filter is adopted for measuring the level of subgrid scale velocity fluctuation. The filter is linearity-preserving and has second order commutation error. The developed subgrid scale model is basically eddy viscosity model which depends on the explicitly filtered fields and needs no additional ad hoc wall treatment, such as van Driest damping function. For the validation, the flows in a channel and a pipe are calculated and compared to experimental data and numerical results in the literature.

• Journal of Korean Society of Coastal and Ocean Engineers
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• v.36 no.1
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• pp.20-36
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• 2024

In this study, probabilistic models for the wave- and lifting forces were derived directly from long-term in-situ wave data embedding the Korean marine environment characteristics varying from sea to sea based on the Three-Parameter Weibull distribution. Korean marine environment characteristics varying from sea to sea carved out their presence on the probability coefficients of probabilistic models for wave- and lifting forces. Energetic wave conditions along the southern coast of Korea distinguish themselves from the others with a relatively large scale coefficient, small location coefficient, and shape coefficient around 1.3. On the other hand, mild marine environment along the western coast has a small variability, leading to small scale-coefficient, large location coefficient and shape coefficient around 2.0. In the sea off Mokpo, near the boundary between the South- and West Seas, marine environment was characterized by small scale-coefficient, large location coefficient, and shape coefficient around 1.2, implying that marine environments characteristics of the South-and West Sea coexist in the sea off Mokpo.

• International Journal of Knowledge Content Development & Technology
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• v.3 no.1
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• pp.65-80
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• 2013

Today, citation index information is used as an outcome scale of spreading technology and encouraging research. Article citation information is an important factor to determine the authority of the relevant author. Google Scholar uses the article citation information to organize academic article search results with a rank algorithm. For an accurate analysis of such important citation index information, large amounts of bibliographic data are required. Therefore, this study aims to build a fast and efficient system for large amounts of bibliographic data, and to design and develop a system for quickly analyzing cited information for that data. This study also aims to use and analyze citation data to be a basic element for providing various advanced services to the academic article search system.

• Proceedings of the Korean Society for Language and Information Conference
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• 1998.02a
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• pp.295-302
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• 1998

• Transactions of the Korean Society of Mechanical Engineers A
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• v.36 no.12
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• pp.1535-1542
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• 2012

A Heat Recovery Steam Generator(HRSG) is the main component of a Combined Cycle Power Plant(CCPP). It is a very large structure that is made from relatively thin metal sheets. Therefore, the structural integrity of an HRSG is very important to ensure safe operation during plant lifetime. In particular, thermal deformation and thermal fatigue have been revealed as the main causes of the mechanical degradation of an HRSG. In order to prevent unexpected damage, safety evaluation based on a large-scale analysis is necessary. Therefore, this study aims to improve the safety of HRSG by using Finite Element Analysis(FEA) results derived from large-scale analysis. Furthermore, the modified design is verified by comparing it with the original one. This result will be used as basic data for improving the safety of a vertical-type HRSG.

• Convergence Security Journal
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• v.19 no.2
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• pp.11-19
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• 2019

The blockchain is a technique for managing transaction data in distributed computing manner without the involvement of central trust authority. The blockchain has been used in various area such as manufacturing, culture, and public as well as finance because of its advantage of the security, efficiency and applicability. In the blockchain, it was considered safe against 51% attack because the adversary could not have more than 50% hash power. However, there have been cases caused by large-scale computing attacks such as 51% and selfish mining attack, and the frequency of these attacks is increasing. In addition, since the development of quantum computers can hold exponentially more information than their classical computer, it faces a new type of threat using quantum algorithms. In this paper, we perform the security analysis of blockchain attacks composing the large computing capabilities including quantum computing attacks. Finally, we suggest the technologies and future direction of the blockchain development in order to be safe against large-scale computing attacks.

• Communications for Statistical Applications and Methods
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• v.25 no.6
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• pp.619-631
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• 2018

Functional data analysis continues to attract interest because advances in technology across many fields have increasingly permitted measurements to be made from continuous processes on a discretized scale. Particulate matter is among the most harmful air pollutants affecting public health and the environment, and levels of PM10 (particles less than 10 micrometers in diameter) for regions of California remain among the highest in the United States. The relatively high frequency of particulate matter sampling enables us to regard the data as functional data. In this work, we investigate the dominant modes of variation of PM10 using functional data analysis methodologies. Our analysis provides insight into the underlying data structure of PM10, and it captures the size and temporal variation of this underlying data structure. In addition, our study shows that certain aspects of size and temporal variation of the underlying PM10 structure are associated with changes in large-scale climate indices that quantify variations of sea surface temperature and atmospheric circulation patterns.

• Journal of KIISE
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• v.44 no.4
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• pp.383-391
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• 2017

As we enter a new era of Big Data, the amount of semantic data has rapidly increased. In order to derive meaningful information from this large semantic data, studies that utilize the SWRL(Semantic Web Rule Language) are being actively conducted. SWRL rules are based on data extracted from a user's empirical knowledge. However, conventional reasoning systems developed on single machines cannot process large scale data. Similarly, multi-node based reasoning systems have performance degradation problems due to network shuffling. Therefore, this paper overcomes the limitations of existing systems and proposes more efficient distributed inference methods. It also introduces data partitioning strategies to minimize network shuffling. In addition, it describes a method for optimizing the incremental reasoning process through data selection and determining the rule order. In order to evaluate the proposed methods, the experiments were conducted using WiseKB consisting of 200 million triples with 83 user defined rules and the overall reasoning task was completed in 32.7 minutes. Also, the experiment results using LUBM bench datasets showed that our approach could perform reasoning twice as fast as MapReduce based reasoning systems.

• KIPS Transactions on Software and Data Engineering
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• v.3 no.8
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• pp.293-298
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• 2014

Detecting and understanding the changes between RDF data is crucial in the evolutionary process, synchronization system, and versioning system on the web of data. However, current researches on detecting changes still remain unsatisfactory in that they did neither consider the large scale of RDF data nor accurately produce the RDF deltas. In this paper, we propose a scalable and effective change detection using a MapReduce framework which has been used in many fields to process and analyze large volumes of data. In particular, we focus on the structure-based change detection that adopts a strategy for the comparison of blank nodes in RDF data. To achieve this, we employ a method which is composed of two MapReduce jobs. First job partitions the triples with blank nodes by grouping each triple with the same blank node ID and then computes the incoming path to the blank node. Second job partitions the triples with the same path and matchs blank nodes with the Hungarian method. In experiments, we show that our approach is more accurate and effective than the previous approach.

• Journal of The Korean Astronomical Society
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• v.37 no.5
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• pp.329-335
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• 2004

Observations of magnetic fields on scales up to several Mpc are important for understanding cluster and large-scale structure evolution. Our current census of such structures is heavily biased - towards fields of several $\mu$G, towards fields in deep potential wells, and towards high inferred field strengths m cooling flow and other clusters from improper analysis of rotation measure data. After reviewing these biases, I show some recent results on two relics that are powered in very different ways. I describe new investigations that are now uncovering weak diffuse fields in the outskirts of clusters and other low density environments, and the good prospects for further progress.