• Journal of KIISE:Software and Applications
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• v.28 no.9
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• pp.662-668
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• 2001

Since the problem of disproving a tautology is as hard as the problem of proving it, no polynomial time algorithm for falsification(or testing invalidity) is feasible. Previous algorithms are mostly based on either divide-and-conquer or graph representation. Most of them demonstrated satisfactory results on a variety of input under certain constraints. However, they have experienced difficulties dealing with big input. We propose a new falsification algorithm using a Merge Rule to produce a counterexample by constructing a minterm which is not satisfied by an input expression in DNF(Disjunctive Normal Form). We also show that the algorithm is consistent and sound. The algorithm is based on a greedy method which would seek to maximize the number or terms falsified by the assignment made at each step of the falsification process. Empirical results show practical performance on big input to falsify randomized nontautological problem instances, consuming O(nm$^2$) time, where n is the number of variables and m is number of terms.

• Journal of Digital Contents Society
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• v.17 no.5
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• pp.409-416
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• 2016

Recently, Big Data issue has become a buzzword and universities, industries and research institutes have been efforts to collect, analyze various data enabled. These things includes accumulated data from the past, even if it is not possible to analysis at this present immediately a which has the potential means. And we are obtained a valuable result from the collected a large amount of data via the semantic analysis. The demand for high-performance storage system that can handle large amounts of data required is increasing around the world. In addition, it must provide a distributed parallel file system that stability to multiple users too perform a variety of analyzes at the same time by connecting a large amount of the accumulated data In this study, we identify the I/O bandwidth of the storage system to be considered, and performance of the metadata in order to provide a file system in stability and propose a method for configuring the optimal environment.

• The Journal of the Korea Contents Association
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• v.18 no.9
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• pp.149-155
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• 2018

Due to the rapid development of medical information, vast amounts of medical data are accumulating, and such medical data is highly likely to be used as an important data for solving the aging population and the rapid rise in medical cost. Especially in Korea, there are resident registration numbers and computerized usage data for all citizens, so it can be superior to other countries in terms of medical infrastructure that can utilize big data. The purpose of this study was to analyze the factors affecting the mortality and death rate of Gangwon using the Big Data and the National Statistical Office data centered on Kangwon province. As a result of analysis, major variables related to the mortality rate of Gangwon were hospital infrastructure utilization rate, income level, aging population and population density. Therefore, inequalities due to income disparities and insufficient local medical infrastructures were affecting the local mortality rate, and policy support was needed to improve the local hospital infrastructure and income level. The results of this study were meaningful in that medical big data were used to analyze the deaths of people in Gangwon, and the causes of the deaths were analyzed through various social indicators and correlation analysis.

• The Korean Journal of Applied Statistics
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• v.27 no.2
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• pp.249-261
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• 2014

Ever increasing "Big data" can only be effectively processed by parallel computing. Parallel computing refers to a high performance computational method that achieves effectiveness by dividing a big query into smaller subtasks and aggregating results from subtasks to provide an output. However, it is well-known that parallel computing does not achieve scalability which means that performance is improved linearly by adding more computers because it requires a very careful assignment of tasks to each node and collecting results in a timely manner. Hadoop is one of the most successful platforms to attain scalability. In this paper, we propose a measurement for Hadoop optimization by utilizing a Lorenz curve which is a proxy for the inequality of hardware resources. Our proposed index takes into account the intrinsic overhead of Hadoop systems such as CPU, disk I/O and network. Therefore, it also indicates that a given Hadoop can be improved explicitly and in what capacity. Our proposed method is illustrated with experimental data and substantiated by Monte Carlo simulations.

• The Bulletin of The Korean Astronomical Society
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• v.37 no.2
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• pp.111.2-111.2
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• 2012

We observed an Ellerman bomb(EB) and its associated surge using the Fast Imaging Solar Spectrograph(FISS) and the broadband TiO filter of the 1.6 meter New Solar Telescope at Big Bear Solar Observatory. As is well-known, the EB appears as a feature that is very bright at the far wings of the H alpha line. The lambdameter method applied to these wings indicates that the EB is blue-shifted up to 6km/s in velocity. In the photospheric level below the EB, we see rapidly growing "granule-like" feature. The transverse velocity of the dark lane at the edge of the "granule" increased with time as reached a peak of 6km/s, at the time of the EB's occurrence. The surge was seen in absorption and varied rapidly both in the H alpha and the Ca II 8542 line. It originated from the Ellerman bomb, and was impulsively accelerated to 20km/s toward us(blueshift). Then the velocity of the surge gradually changed from blueshift of 20km/s to redshift of 40km/s. By adopting the cloud model, we estimated the temperature of the surge material at about 27000K and the non-thermal velocity at about 10km/s. Our results shed light on the conventional idea that an EB results from the magnetic reconnection of an emerging flux tube and pre-existing field line.

• The Journal of Bigdata
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• v.5 no.2
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• pp.85-95
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• 2020

In this study, a mobility analysis method is suggested to estimate an O/D trip demand estimation using Mobile Phone Signaling Data. Using mobile data based on mobile base station location information, a trip chain database was established for each person and daily traffic patterns were analyzed. In addition, a new algorithm was developed to determine the traffic characteristics of their mobilities. To correct the ping pong handover problem of communication data itself, the methodology was developed and the criteria for stay time was set to distinguish pass by between stay within the influence area. The big-data based method is applied to analyze the mobility pattern in inter-regional trip and intra-regional trip in both of an urban area and a rural city. When comparing it with the results with traditional methods, it seems that the new methodology has a possibility to be applied to the national survey projects in the future.

• Communications for Statistical Applications and Methods
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• v.30 no.2
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• pp.119-133
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• 2023

With the rapid growth of the economy and fossil fuel consumption, the concentration of air pollutants has increased significantly and the air pollution problem is no longer limited to small areas. We conduct statistical analysis with the actual data related to air quality that covers the entire of South Korea using R and Python. Some factors such as SO₂, CO, O₃, NO₂, PM₁₀, precipitation, wind speed, wind direction, vapor pressure, local pressure, sea level pressure, temperature, humidity, and others are used as covariates. The main goal of this paper is to predict air quality index (AQI) spatio-temporal data. The observations of spatio-temporal big datasets like AQI data are correlated both spatially and temporally, and computation of the prediction or forecasting with dependence structure is often infeasible. As such, the likelihood function based on the spatio-temporal model may be complicated and some special modelings are useful for statistically reliable predictions. In this paper, we propose several methods for this big spatio-temporal AQI data. First, random effects with spatio-temporal basis functions model, a classical statistical analysis, is proposed. Next, neural networks model, a deep learning method based on artificial neural networks, is applied. Finally, random forest model, a machine learning method that is closer to computational science, will be introduced. Then we compare the forecasting performance of each other in terms of predictive diagnostics. As a result of the analysis, all three methods predicted the normal level of PM_2.5 well, but the performance seems to be poor at the extreme value.

• International conference on construction engineering and project management
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• 2022.06a
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• pp.823-830
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• 2022

The risk of project execution increases due to the enlargement and complexity of Engineering, Procurement, and Construction (EPC) plant projects. In the fourth industrial revolution era, there is an increasing need to utilize a large amount of data generated during project execution. The design is a key element for the success of the EPC plant project. Although the design cost is about 5% of the total EPC project cost, it is a critical process that affects the entire subsequent process, such as construction, installation, and operation & maintenance (O&M). This study aims to develop a system using machine-learning (ML) techniques to predict risks and support decision-making based on big data generated in an EPC project's design and construction stages. As a result, three main modules were developed: (M1) the design cost estimation module, (M2) the design error check module, and (M3) the change order forecasting module. M1 estimated design cost based on project data such as contract amount, construction period, total design cost, and man-hour (M/H). M2 and M3 are applications for predicting the severity of schedule delay and cost over-run due to design errors and change orders through unstructured text data extracted from engineering documents. A validation test was performed through a case study to verify the model applied to each module. It is expected to improve the risk response capability of EPC contractors in the design and construction stage through this study.

• Journal of the Korean Society for Heat Treatment
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• v.14 no.2
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• pp.89-95
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• 2001

The 7175Al alloy is particularly interesting for its high strength and sufficient ductility, fracture toughness and corrosion resistance. Currently vigorous efforts have been made to develop large rockets usable for various purposes in the space. This has created the demand of big size of 7175Al billet which would be applied to heavy forgings. The aim of this study is to investigate the quality level of big billet and the effect of billet size on the mechanical properties of large 7175Al ring roll forgings. The billets range from 370 mm to 720 mm in diameter were homogenized and forged after direct chill casting. The size and volume fraction of second phase particles In ${\Phi}720$ mm billet are larger than those of ${\Phi}370$ mm billet, and its ductility is lower for the condition of homogenization and T6 treatment. The Cu-rich phases formed in interdendritic sites are considered to provide the preferential crack path during cold upsetting. The fracture toughness of ring roll forgings which are made by ${\Phi}370$ mm billet is higher than those of ${\Phi}720$ mm billet.

• Proceedings of the KSR Conference
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• 2010.06a
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• pp.2309-2318
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• 2010

It is a plan the government increases a railroad section SOC investment, and to activate railroad construction while a railroad wins the spotlight with green transportation. But an error of the demand forecast that is a base of a railroad investment evaluation follows in occurring big, there is it with an operation with an obstacle of a railroad investment. Case of the Incheon International Airport Railroad which went into operation recently, While a present transportation demand showed about 10% than a demand forecasted in a past conference, it was magnified in a social problem. A lot of research was gone on in road project about traffic demand forecast and error, a study to find out the error cause is an insufficient situation although errors of a railroad occurs big. So, this study looked for errors and causes about trip generation model and modes sharing model of railroad demand forecast but it was defined causes so that it can occur similar problems in the future. Especially it investigated causes after comparing rate of development plan for the realization and O/D size in trip generation model and after comparing rate of modes sharing of past and current and conducting a survey for airport users. In conclusion, it suggested method to reduce errors of railroad demand forecasting in the future.