• Journal of the Korean Data and Information Science Society
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• v.25 no.2
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• pp.271-280
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• 2014

Foot-and-mouth Disease (FMD) is a highly infectious and fatal viral livestock disease that affects cloven-hoofed animals domestic and wild and the FMD outbreak in Korea in 2010/2011 was a disastrous incident for the country and the economy. Thus, efforts at the national level are put to prevent foot-and-mouth disease and to reduce the damage in the case of outbreak. As one of these efforts, it is useful to study the spread of the disease by using probabilistic model. In fact, after the FMD epidemic in the UK occurred in 2001, many studies have been carried on the spread of the disease using a variety of stochastic models as an effort to prepare future outbreak of FMD. However, for the FMD outbreak in Korea occurred in 2010/2011, there are few study by utilizing probabilistic model. This paper assumes a stochastic spatial-temporal susceptible-infectious-removed (SIR) epidemic model for the 2010/2011 FMD outbreak to understand spread of the disease. Since data on infections of FMD disease during 2010/2011 outbreak of Aniaml and Plant Quarantine Agency and on the livestock farms from the nationwide census in 2011 of Statistics Korea do not have detail informations on address or missing values, we generate detail information on address by randomly allocating farms within corresponding Si/Gun area. The kernel function is estimated using the infection data and by using simulations, the susceptibility and transmission of the spatial-temporal stochastic SIR models are determined.

• Journal of Korean Society for Geospatial Information Science
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• v.18 no.1
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• pp.11-20
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• 2010

The distributed rainfall-runoff model which is developed in the country requires a lot of time and effort to generate input data. Also, it takes a lot of time to calculate discharge by numerical analysis based on kinematic wave theory in runoff process. Therefore, most river basins using the distributed model are of limited scale, such as small river basins. However, recently, the necessity of integrated watershed management has been increasing due to change of watershed management concept and discharge calculation of whole river basin, including upstream and downstream of dam. Thus, in this study, the feasibility of the GIS based physical distributed rainfall-runoff model, K-DRUM(K-water hydrologic & hydraulic Distributed RUnoff Model) which has been developed by own technology was reviewed in the flood discharge process for the Geum River basin, including Yongdam and Daecheong Dam Watersheds. GIS hydrological parameters were extracted from basic GIS data such as DEM, land cover and soil map, and used as input data of the model. Problems in running time and inaccuracy setting using the existing trial and error method were solved by applying an auto calibration method in setting initial soil moisture conditions. The accuracy of discharge analysis for application of the method was evaluated using VER, QER and Total Error in case of the typhoon 'Ewiniar' event. and the calculation results shows a good agreement with observed data.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.38 no.2
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• pp.227-235
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• 2018

In this study, we developed a relational formula for observing high - resolution rainfall using vehicle rain sensor. The vehicle rain sensor consists of eight channels. Each channel generates a sensor signal by detecting the amount of rainfall on the windshield of the vehicle when rainfall occurs. The higher the rainfall, the lower the sensor signal is. Using these characteristics of the sensor signal generated by the rain sensor, we developed a relational expression. In order to generate specific rainfall, an artificial rainfall generator was constructed and the change of the sensor signal according to the variation of the rainfall amount in the artificial rainfall generator was analyzed. Among them, the optimal sensor channel which reflects various rainfall amounts through the sensitivity analysis was selected. The sensor signal was generated in 5 minutes using the selected channel and the representative values of the generated 5 - minute sensor signals were set as the average, 25th, 50th, and 75th quartiles. The calculated rainfall values were applied to the actual rainfall data using the constructed relational equation and the calculated rainfall amount was compared with the rainfall values observed at the rainfall station. Although the reliability of the relational expression was somewhat lower than that of the data of the verification result data, it was judged that the experimental data of the residual range was insufficient. The rainfall value was calculated by applying the developed relation to the actual rainfall, and compared with the rainfall value generated by the ground rainfall observation instrument observed at the same time to verify the reliability. As a result, the rain sensor showed a fine rainfall of less than 0.5 mm And the average observation error was 0.36mm.

• Journal of the Korean Data and Information Science Society
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• v.25 no.3
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• pp.601-609
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• 2014

The most widely used data mining technique for big data analysis is to generate meaningful association rules. This method has been used to find the relationship between set of items based on the association criteria such as support, confidence, lift, etc. Among them, confidence is the most frequently used, but it has the drawback that we can not know the direction of association by it. The attributably pure confidence was developed to compensate for this drawback, but the value was changed by the position of two item sets. In this paper, we propose four symmetrically and attributably pure confidence measures to compensate the shortcomings of confidence and the attributably pure confidence. And then we prove three conditions of interestingness measure by Piatetsky-Shapiro, and comparative studies with confidence, attributably pure confidence, and four symmetrically and attributably pure confidence measures are shown by numerical examples. The results show that the symmetrically and attributably pure confidence measures are better than confidence and the attributably pure confidence. Also the measure NSAPis found to be the best among these four symmetrically and attributably pure confidence measures.

• Economic and Environmental Geology
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• v.35 no.3
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• pp.273-284
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• 2002

The Gravity-Geologic Method (GGM) was implemented for bathymetric determinations in the Drake Passage, Antarctica, using global marine Free-air Gravity Anomalies (FAGA) data sets by Sandwell and Smith (1997) and local echo sounding measurements. Of the 6548 bathymetric sounding measurements, two thirds of these points were used as control depths, while the remaining values were used as checkpoints. A density contrast of 9.0 gm/㎤ was selected based on the checkpoints predictions with changes in the density contrast assumed between the seawater and ocean bottom topographic mass. Control depths from the echo soundings were used to determine regional gravity components that were removed from FAGA to estimate the gravity effects of the bathymetry. These gravity effects were converted to bathymetry by inversion. In particular, a selective merging technique was developed to effectively combine the echo sounding depths with the GGM bathymetiy to enhance high frequency components along the shipborne sounding tracklines. For the rugged bathymetry of the research area, the GGM bathymetry shows correlation coefficients (CC) of 0.91, 0.92, and 0.85 with local shipborne sounding by KORDI, GEODAS, and a global ETOPO5 model, respectively. The enhanced GGM by selective merging shows imploved CCs of 0.948 and 0.954 with GEODAS and Smith & Sandwell (1997)'s predictions with RMS differences of 449.8 and 441.3 meters. The global marine FAGA data sets and other bathymetric models ensure that the GGM can be used in conjunction with shipborne bathymetry from echo sounding to extend the coverage into the unmapped regions, which should generate better results than simply gridding the sparse data or relying upon lower resolution global data sets such as ETOPO5.

• Journal of Intelligence and Information Systems
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• v.14 no.1
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• pp.1-17
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• 2008

Data mining is defined as the process of discovering meaningful and useful pattern in large volumes of data. In particular, finding associations rules between items in a database of customer transactions has become an important thing. Some data structures and algorithms had been proposed for storing meaningful information compressed from an original database to find frequent itemsets since Apriori algorithm. Though existing method find all association rules, we must have a lot of process to analyze association rules because there are too many rules. In this paper, we propose a new data structure, called a Frequent Pattern Network (FPN), which represents items as vertices and 2-itemsets as edges of the network. In order to utilize FPN, We constitute FPN using item's frequency. And then we use a clustering method to group the vertices on the network into clusters so that the intracluster similarity is maximized and the intercluster similarity is minimized. We generate association rules based on clusters. Our experiments showed accuracy of clustering items on the network using confidence, correlation and edge weight similarity methods. And We generated association rules using clusters and compare traditional and our method. From the results, the confidence similarity had a strong influence than others on the frequent pattern network. And FPN had a flexibility to minimum support value.

• Journal of KIISE:Databases
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• v.35 no.1
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• pp.54-66
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• 2008

A spatio-temporal join is an expensive operation that is commonly used in spatio-temporal database systems. In order to generate an efficient query plan for the queries involving spatio-temporal join operations, it is crucial to estimate accurate selectivity for the join operations. Given two dataset $S_1,\;S_2$ of discrete data and a timestamp $t_q$, a spatio-temporal join retrieves all pairs of objects that are intersected each other at $t_q$. The selectivity of the join operation equals the number of retrieved pairs divided by the cardinality of the Cartesian product $S_1{\times}S_2$. In this paper, we propose aspatio-temporal histogram to estimate selectivity of spatio-temporal join by extending existing geometric histogram. By using a wide spectrum of both uniform dataset and skewed dataset, it is shown that our proposed method, called Spatio-Temporal Histogram, can accurately estimate the selectivity of spatio-temporal join. Our contributions can be summarized as follows: First, the selectivity estimation of spatio-temporal join for discrete data has been first attempted. Second, we propose an efficient maintenance method that reconstructs histograms using compression of spatial statistical information during the lifespan of discrete data.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.12 no.11
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• pp.5164-5171
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• 2011

In fuzzy modeling for nonlinear process, typically using the given data, the fuzzy rules are formed by the input variables and the space division by selecting the input variable and dividing the input space for each input variables. The premise part of the fuzzy rule is identified by selection of the input variables, the number of space division and membership functions and the consequent part of the fuzzy rule is identified by polynomial functions in the form of simplified and linear inference. In general, formation of fuzzy rules for nonlinear processes using the given data have the problem that the number of fuzzy rules exponentially increases. To solve this problem complex nonlinear process can be modeled by separately forming the fuzzy rules by means of fuzzy division of each input space. Therefore, this paper utilizes individual input space to generate fuzzy rules. The premise parameters of the fuzzy rules are identified by Min-Max method using the minimum and maximum values of input data set and membership functions are used as a series of triangular, gaussian-like, trapezoid-type membership functions. And lastly, using the data which is widely used in nonlinear process we evaluate the performance and the system characteristics.

• Journal of the Korea Institute of Building Construction
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• v.20 no.6
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• pp.505-514
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• 2020

This research introduces the method of developing Korea BIM standard (KBIMS) architectural and structural element library and the methodology of converting KBIMS IFC property names with special characters. Diverse BIM tools are utilizing in project, however BIM library researches lack diversity on BIM tool selection. This research described the method to generate twelve categories and seven hundred and ninety-three elements library containing geometrical and numerical data in CATIA V6. KBIMS has its special property data naming systems which was the challenge inputting to ENOVIA IFC database. Three mapping methods for special naming characters had been developed and the ASCII code method was applied. In addition, the convertor prototype had been developed for searching and replacing the ASCII codes into the original KBIMS IFC property names. The methodology was verified by exporting 2,443 entities without data loss in the sample model conversion test. This research would provide a wider choice of BIM tool selection for applying KBIMS. Furthermore, the research would help on the reduction of data interoperability issues in projects. The developed library would be open to the public, however the continuous update and maintenance would be necessary.

• Journal of Environmental Health Sciences
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• v.47 no.3
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• pp.267-278
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• 2021

Objectives: Using atmospheric dispersion representative models (AERMOD and CALPUFF), the emissions characteristics of each model were compared and analyzed in ready-mixed concrete manufacturing facilities that generate a large amount of particulate matter (PM-10, PM-2.5). Methods: The target facilities were the ready-mixed concrete manufacturing facilities (Siheung RMC, Goyang RMC, Ganggin RMC) and modeling for each facility was performed by dividing it into construction and operation times. The predicted points for each target facility were selected as 8-12ea (Siheung RMC 10, Goyang RMC 8, and Gangjin RMC 12ea) based on an area within a two-kilometer radius of each project district. The terrain input data was SRTM-3 (January-December 2019). The meteorological input data was divided into surface weather and upper layer weather data, and weather data near the same facility as the target facility was used. The predicted results were presented as a 24-hour average concentration and an annual average concentration. Results: First, overall, CALPUFF showed a tendency to predict higher concentrations than AERMOD. Second, there was almost no difference in the concentration between the two models in non-complex terrain such as in mountainous areas, but in complex terrain, CALPUFF predicted higher concentrations than AERMOD. This is believed to be because CALPUFF better reflected topographic characteristics. Third, both CALPUFF and AERMOD predicted lower concentrations during operation (85.2-99.7%) than during construction, and annual average concentrations (76.4-99.9%) lower than those at 24 hours. Fourth, in the ready-mixed concrete manufacturing facility, PM-10 concentration (about 40 ㎍/m³) was predicted to be higher than PM-2.5 (about 24 ㎍/m³). Conclusions: In complex terrain such as mountainous areas, CALPUFF predicted higher concentrations than AERMOD, which is thought to be because CALPUFF better reflected topographic characteristics. In the future, it is recommended that CALPUFF be used in complex terrain and AERMOD be used in other areas to save modeling time. In a ready-mixed concrete facility, PM-10, which has a relatively large particle size, is generated more than PM-2.5 due to the raw materials used and manufacturing characteristics.