• Journal of the Korean Society of Hazard Mitigation
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• v.3 no.4 s.11
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• pp.119-129
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• 2003

Concrete and steel are essential structural materials in the construction. But, concrete, different from steel, consists of many materials and is affected by many factors such as properties of materials, site environmental situations, and skill of constructors. Concrete have two kinds of properties, immediately knowing properties such as slump, air contents and time dependent one like strength. Therefore, concrete mixes depend on experiences of experts. However, at point of time using High Performance Concrete, new method is wanted because of more ingredients like mineral and chemical admixtures and lack of data. Artificial Neural Networks(ANN) are a mimic models of human brain to solve a complex nonlinear problem. They are powerful pattern recognizers and classifiers, also their computing abilities have been proven in the fields of prediction, estimation and pattern recognition. Here, among them, the back propagation network and radial basis function network ate used. Compositions of high-performance concrete mixes are eight components(water, cement, fine aggregate, coarse aggregate, fly ash, silica fume, superplasticizer and air-entrainer). Compressive strength, slump, and air contents are measured. The results show that neural networks are proper tools to minimize the uncertainties of the design of concrete mixtures.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.19 no.1
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• pp.27-37
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• 2001

Soil erosion by outflow of water or rainfall has caused many environmental problems as declining agricultural productivity, damaging pasture and preventing flow of water. As the interest in environment is increasing lately, soil erosion is considered as a serious problem, whereas the systematic regulation and analysis for that have not established yet. This research shows the method of extracting factor entered model which expects soil erosion by GSIS. There are several erosion model such as ANSWER, WEPP, RUSLE. The research used RUSLE erosion model which could expect general soil erosion connected easily with GSIS data. RUSLE's input factors are composed of rainfall runoff factor(R). soil erodibility factor(K), slope length factor(L), slope steepness factor(S), cover management factor(C) and support practice factor(P). The general equation used to extract L, S factor on the RUSLE to be oriented for agricultural area has some limitation to apply whole watershed. So, on this study we used a revised empirical equation applicable to the watershed by grid on the GSIS. Also, we analyzed RUSLE factors by watershed being analyzed with watershed extraction algorithm. Then we could calculate the minimum, maximum. mean and standard deviation of RUSLE factors by watershed.

• Journal of the Korean Data and Information Science Society
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• v.25 no.2
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• pp.373-384
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• 2014

Data mining is to clarify pattern or correlation of mass data of complicated structure and to predict the diverse outcomes. This technique is used in the fields of finance, telecommunication, circulation, medicine and so on. In this paper, we selected risk factors of respiratory diseases in the field of medicine. The data we used was divided into respiratory diseases group and health group from the Gyeongsangbuk-do database of Community Health Survey conducted in 2012. In order to select major risk factors, we applied data mining techniques such as neural network, logistic regression, Bayesian network, C5.0 and CART. We divided total data into training and testing data, and applied model which was designed by training data to testing data. By the comparison of prediction accuracy, CART was identified as best model. Depression, smoking and stress were proved as the major risk factors of respiratory disease.

• Journal of Digital Convergence
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• v.14 no.2
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• pp.197-208
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• 2016

In this paper, data mining regression analysis and decision tree analysis techniques were used to analyze factors affecting the life satisfaction of middle school students. For this purpose, we analyzed Korean Children and Youth Panel Survey(KCYPS) data. As results, the common influencing factors to the life satisfaction were derived from regression analysis. Those factors are self-esteem, depression, total grade satisfaction, regional community awareness, career identity, annual delinquency damage experience, siblings' factors, trust, behavioral control, and concentration. Based on the result described by decision tree analysis, the factors that indicate a significant impact on the life satisfaction of middle school students were self-esteem, depression, career identity and attention factor.

• Proceedings of the Korean Geotechical Society Conference
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• 1991.10a
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• pp.332-347
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• 1991

Accurate prediction of future settlement is essential for the settlement control of soft soil by pre-loading method. To predict future settlement in clayey soft soils, several methods like Asaoka method, Hyperbolic Method and Hoshino method are currently being used. These methods predict the future sett1ement by mathmatical treatment of the measured settlement data on the basis of consolidtion theory and empiricism. But the correlation coefficient between the measured and the predicted settlement was relatively low (0.8~0.9). Also, the prediction of future settlemet for the design load is very difficult. In this article, the measured field settlement data was treated as the the field consolidation test. Hence, condolidation coefficient(Cv) and compression index(Cc) was evaluated from the field settlement data. Cv and Cc values from field data was used to calculate the degree of consolidation and settlement at desired time. By this method, the correlation coefficent between the measured and the predicted settlement was significantly increased(0.97~0.99). Also the settlement by the design load after the improvement of soft soil could be predicted reasonably. This method is quite rational and sound but it requires thousands of calculation steps. Today, by the aid of low priced personal computers above mentioned technique could be used much acre economically and effectively than conventional methods. This article presented the mechanisms and capacities of this method and demonstrated the enhanced correlation coefficient when applied to actual field settlement data.

• Spatial Information Research
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• v.17 no.3
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• pp.301-307
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• 2009

Recently, the various studies has been focused on evaluating the damage and stability of long span bridge through measuring and monitoring to ensure the stability and usability. But, even if various studies are performed, it is hard to predict and evaluate the real motion of structure. The aim of this study is check the application of GPS to the motion monitoring of long span bridge by comparing data acquired form RTK-GPS and laser displacement meter.

• Journal of the Korean Society of Child Welfare
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• no.54
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• pp.79-111
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• 2016

The purpose of this study was to examine the structural models in which early cumulative risk factors affect children's language(indicated by expressive vocabularies) and social development(indicated by peer competence) at age 3 thorough their effects on the home learning environment. To examine the hypothesized models, the data of 1,725 families from the second and the fourth waves of the Panel Study of Korean Children was used. Correlation analysis and structural equation modeling were conducted to test the models. First, the cumulative risk factors at age 1 and 3 were highly correlated, implying the stability of the risk factors over time. The more cumulative risk factors at age 1 predicted the lower level of the home learning environment at age 3, which, in turn, was significantly related to both language and social development at age 3. However, the early cumulative risk factors did not directly influence later developmental outcomes. Moreover, the cumulative risk factors at age 3 were directly related to the child's language development, but neither social development northe home learning environment. In addition, the mediational role of the home learning environment (i.e., cumulative risk factors at age 1${\rightarrow}$home learning environment${\rightarrow}$language and social development) was statistically supported. In conclusion, the early cumulative risk factors in infancy indirectly predicted children's development at age 3 through the home learning environment. The practical implications for the early intervention and support for the families with infants who are experiencing multiple risk factors were discussed.

• The Journal of the Korea Contents Association
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• v.14 no.11
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• pp.686-694
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• 2014

Criminal profiling, also known as offender profiling is designed to predict the characteristics of unknown criminal perpetrator through an analysis of the crime scene. Until now, there has been conflict about the effectiveness of criminal profiling among academics. In this study, 113 police investigators', working in serious crime divisions, were interviewed about their experiences with criminal profiling, and their belief about its effectiveness. 63.7% of the respondents agreed that criminal profiling is a valuable investigative tool and 62.8% agreed that profilers are valuable to criminal investigations. A total of 31.8% agreed that profilers help the police identify offenders and 15.0% agreed that there is no risk of profiler misdirecting and investigation. 61.5% of the respondents who had reported using a profile agreed that profiling is helpful and 71.4% told that they would use profiling again in the future.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.14 no.2
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• pp.307-316
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• 1994

The rainfall forecasting model of the short term is improved at the point where meterological data is not gaged. In this study, the adopted model is based on the assumptions for simulation model of rainfall process, meteorological homogeneousness, prediction and estimation of meteorological data. A Kalman Filter technique is used for rainfall forecasting. In the existing models, the equation of the model is non-linear type with regard to rainfall rate, because hydrometer size distribution (HSD) depends on rainfall intensity. The equation is linearized about rainfall rate as HSD is formulated by the function of the water storage in the cloud. And meteorological input variables are predicted by emprical model. It is applied to the storm events over Taech'ong Dam area. The results show that root mean square error between the forecasted and the observed rainfall intensity is varing from 0.3 to 1.01 mm/hr. It is suggested that the assumptions of this study be reasonable and our model is useful for the short term rainfall forecasting at the ungaged point of the meteorological data.

• Journal of the Korean Geotechnical Society
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• v.37 no.9
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• pp.13-24
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• 2021

Recently, attempts have been made to detect fouling patterns in the ground using Ground Penetrating Radar (GPR) during the maintenance of gravel ballast railway tracks. However, dealing with GPR signal data obtained with a large amount of noise in a site where complex ground conditions are mixed, often depends on the experience of experts, and there are many difficulties in precise analysis. Therefore, in this study, a numerical modeling technique that can quantitatively simulate the GPR signal characteristics according to the degree of fouling of the gravel ballast material was proposed using python-based open-source code gprMax and RSA (Random sequential Absorption) algorithm. To confirm the accuracy of the simulation model, model tests were manufactured and the results were compared to each other. In addition, the identification of the fouling layer in the model test and analysis by various test conditions was evaluated and the results were analyzed.