• Computers and Concrete
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• v.15 no.4
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• pp.485-501
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• 2015

Cooling by the flow of water through an embedded cooling pipe has become a common and effective artificial thermal control measure for massive concrete structures. However, an extreme thermal gradient induces significant thermal stress, resulting in thermal cracking. Using a mesoscopic finite-element (FE) mesh, three-phase composites of concrete namely aggregate, mortar matrix and interfacial transition zone (ITZ) are modeled. An equivalent probabilistic model is presented for failure study of concrete by assuming that the material properties conform to the Weibull distribution law. Meanwhile, the correlation coefficient introduced by the statistical method is incorporated into the Weibull distribution formula. Subsequently, a series of numerical analyses are used for investigating the influence of the correlation coefficient on tensile strength and the failure process of concrete based on the equivalent probabilistic model. Finally, as an engineering application, damage and failure behavior of concrete cracks induced by a water-cooling pipe are analyzed in-depth by the presented model. Results show that the random distribution of concrete mechanical parameters and the temperature gradient near water-cooling pipe have a significant influence on the pattern and failure progress of temperature-induced micro-cracking in concrete.

• Journal of Korean Society of Coastal and Ocean Engineers
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• v.8 no.1
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• pp.87-94
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• 1996

Random wave breaking is one of the most important phenomena in coastal engineering. For two and half decades, various models have been proposed to predict wave height variations in the surf zone. However, some models are applicable to plane beaches only, some requires clumsy computation for a joint probability density of wave heights and periods, and some others need calibration with individual wave data. The present study aims at formulating a model simple enough but reasonably accurate. The merits of the present model are as follows: It is applicable to any shapes of bottom profiles; It requires the input data of incident wave heights and periods only without necessity of coefficient calibration with field data; and its computation time is minimal because it deals with representative waves directly.

• Magazine of the Korean Society of Agricultural Engineers
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• v.18 no.4
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• pp.4232-4241
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• 1976

This study was carried out to clarify the stochastic characteristics of monthly rainfalls and to select a proper model for generating the sequential monthly rainfall amounts. The results abtained are as follows: 1. Log-Normal distribution function is the best fit theoretical distribution function to the empirical distribution of monthly rainfall amounts. 2. Seasonal and random components are found to exist in the time series of monthly rainfall amounts and non-stationarity is shown from the correlograms. 3. The Monte Carlo model shows a tendency to underestimate the mean values and standard deviations of monthly rainfall amounts. 4. The 1st order Markov model reproduces means, standard deviations, and coefficient of skewness with an error of ten percent or less. 5. A correlogram derived from the data generated by 1st order Markov model shows the charaterstics of historical data exactly. 6. It is concluded that the 1st order Markov model is superior to the Monte Carlo model in their reproducing ability of stochastic properties of monthly rainfall amounts.

• Environmental and Resource Economics Review
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• v.11 no.3
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• pp.377-396
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• 2002

This paper investigates whether an inverted U relationship between pollution and economic development could be found in the Seoul metropolitan region using a panel data for the period of 1985~1999. We uses a model with a more flexible random coefficients specification which allows for a greater degree of regional heterogeneity. The emissions of sulfur dioxidetotal($SO_2$), suspended particulates(TSP), nitrogen dioxide($NO_2$), and carbon monoxide(CO) were selected as four major pollutants. We found that the emissions of these pollutants per capita except sulfur dioxidetotal exhibited inverted U shapes with per capita gross regional domestic product (GRDP). We also noted that the turning points for Seoul metropolitan region occured at a range of incomes, from $3,000 to $5,000 per capita.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2002.05a
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• pp.1071-1076
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• 2002

The purpose of this paper is to examine sound absorption characteristics of sintered Al(aluminum) plate. Comparison between experiment and theoretical analysts by using empirical formula are made. Based on comparison. it is found that Voronina model gives more reasonable explanation for sound absorption characteristics of sintered Al plates. Effect of air gap with varying the thickness of plates are also investigated, which concludes that the air gap generally increase absorption but for too thick thickness of Al plates. Al plates with air gap shows 0.85∼0.9 of NRC(Noise Reduction Coefficient) measured in reverberation room. which is comparable to glass wool. Comparison between normal and random Incident absorption shows that random incident absorption is higher than normal incident absorption.

• Proceedings of the Korean Association of Geographic Inforamtion Studies Conference
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• 2004.03a
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• pp.523-528
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• 2004

This study proposed an anisotropic diffusion restoration fer image classification. The anisotropic diffusion restoration uses a probabilistic model based on Markov random field, which represents geographical connectedness existing in many remotely sensed images, and restores them through an iterative diffusion processing. In every iteration, the bonding-strength coefficient associated with the spatial connectedness is adaptively estimated as a function of brightness gradient. This study made experiments on the satellite images remotely sensed on the Korean peninsula. The experimental results show that the proposed approach is also very effective on image classification in remote sensing.

• ETRI Journal
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• v.25 no.4
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• pp.258-265
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• 2003

In our study, we extracted the market, finance, and government factors determining R&D investment of individual firms in the IT industry in Korea. We collected the financial data of 515 individual firms belonging to IT and non-IT industries between 1980 and 1999 from the Korea Investors Service's database and investigated the empirical relationship between the factors using an ordinary regression model, a fixed effects model, and a random effects model. The main findings of our study are as follows: i) The Herfindahl Index variable representing the degree of market concentration is statistically insignificant in explaining R&D expenditures in the IT manufacturing industry. ii) Assets, which is used as a proxy variable for firm size, have a positive and statistically significant coefficient. These two results suggest that the Schumpeterian Hypothesis may be only partially applied to the IT manufacturing industry in Korea. iii) The dividend variable has a negative value and is statistically significant, indicating that a tendency of high dividends can restrict the internal cash flow for R&D investment. iv) The sales variable representing growth potential shows a positive coefficient. v) The subsidy as a proxy variable for governmental R&D promotion policies is positively correlated with R&D expenditure. This suggests that government policy has played a significant role in promoting R&D activities of IT firms in Korea since 1980. vi) Using a dummy variable, we verified that firms reduced their R&D investments to secure sufficient liquidity under the restructuring pressure during Korea's 1998 and 1999 economic crisis.

• Journal of Korean Association for Spatial Structures
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• v.21 no.2
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• pp.99-110
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• 2021

This study presents the estimation of crack depth by analyzing temperatures extracted from thermal images and environmental parameters such as air temperature, air humidity, illumination. The statistics of all acquired features and the correlation coefficient among thermal images and environmental parameters are presented. The concrete crack depths were predicted by four different machine learning models: Multi-Layer Perceptron (MLP), Random Forest (RF), Gradient Boosting (GB), and AdaBoost (AB). The machine learning algorithms are validated by the coefficient of determination, accuracy, and Mean Absolute Percentage Error (MAPE). The AB model had a great performance among the four models due to the non-linearity of features and weak learner aggregation with weights on misclassified data. The maximum depth 11 of the base estimator in the AB model is efficient with high performance with 97.6% of accuracy and 0.07% of MAPE. Feature importances, permutation importance, and partial dependence are analyzed in the AB model. The results show that the marginal effect of air humidity, crack depth, and crack temperature in order is higher than that of the others.

• The Journal of Engineering Geology
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• v.21 no.1
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• pp.1-13
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• 2011

Joint roughness is one of the most important parameters in analysis of rock slope stability. Especially in probabilistic analysis, the random properties of joint roughness influence the probability of slope failure. Therefore, a large dataset on joint roughness is required for the probabilistic analysis but the traditional direct measurement of roughness in the field has some limitations. Terrestrial LiDAR has advantagess over traditional direct measurement in terms of cost and time. JRC (Joint Roughness Coefficient) was calculated from statistical parameters which are known from quantitative methods of converting the roughness of the material surface into JRC. The mean, standard deviation and distribution function of JRC were obtained, and we found that LiDAR is useful in obtaining large dataset for random variables.

• Journal of Digital Convergence
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• v.10 no.9
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• pp.357-362
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• 2012

We have compared and predicted for non-linear time series data which are real data having different variences using GRCA(1) model and neural network method. In particular, using Korea Composite Stock Price Index rate, mean square errors of prediction are obtained in genaralized random coefficient autoregressive model and neural network method. Neural network method prove to be better in short-term forecasting, however GRCA(1) model perform well in long-term forecasting.