• The Journal of the Korea Contents Association
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• v.9 no.1
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• pp.147-158
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• 2009

Recently, the data model in stream data environment has massive, continuous, and infinity properties. However the stream data processing like query process or data analysis is conducted using a limited capacity of disk or memory. In these environment, the traditional frequent pattern discovery on transaction database can be performed because it is difficult to manage the information continuously whether a continuous stream data is the frequent item or not. In this paper, we propose the method which we are able to predict the frequent items using the regression model on continuous stream data environment. We can use as a prediction model on indefinite items by constructing the regression model on stream data. We will show that the proposed method is able to be efficiently used on stream data environment through a variety of experiments.

• Journal of the Korean Society of Safety
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• v.33 no.6
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• pp.1-7
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• 2018

The Continuous Ship Unloader (CSU) is an equipment that unloads freight from the ship docked in the port to the land. And the design target life time is designed to be 30 to 50 years, and it is classified as a semi-permanent large facility. However, cracks may occur due to structural defects, abnormal loads, and corrosion, and fatigue failure may occur before the design life is reached. In this study, we predicted the remaining life time of the main component of the CSU considering crack. And also proposed inspection cycle for maintenance of CSU based on the results of the remaining life time prediction. For this purpose, the structure, operational stresses of the CSU were analyzed and main members were selected. And tensile tests and fatigue crack propagation tests were performed with SM490YA and SM570TMC, which are used as main materials for CSU.

• Transactions of Materials Processing
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• v.6 no.6
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• pp.482-488
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• 1997

New carbon equivalent equation for the better prediction for the better prediction of roll force in a continuous hot strip mill has been formulated by applying a neural network method. In predicting roll force of steel strip, carbon equivalent equation which normalize the effects of various alloying elements by a carbon equivalent content is very critical for the accurate prediction of roll force. To overcome the complex relationships between alloying elements and operational variables such as temperature, strain, strain rate and so forth, a neural network method which is effective for multi-variable analysis was adopted in the present work as a tool to determine a proper carbon equivalent equation. The application of newly formulated carbon equivalent equation has increased prediction accuracy of roll force significantly and the effectiveness of neural network method is well confirmed in this study.

• Journal of the Earthquake Engineering Society of Korea
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• v.20 no.1
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• pp.45-54
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• 2016

This study suggests a prediction model of ground motion spectral shape considering characteristics of earthquake records in Korea. Based on the Graizer and Kalkan's prediction procedure, a spectral shape model is defined as a continuous function of period in order to improve the complex problems of the conventional models. The approximate spectral shape function is then developed with parameters such as moment magnitude, fault distance, and average shear velocity of independent variables. This paper finally determines estimator coefficients of subfunctions which explain the corelation among the independent variables using the nonlinear optimization. As a result of generating the prediction model of ground motion spectral shape, the ground motion spectral shape well estimates the response spectrum of earthquake recordings in Korea.

• Proceedings of the Korean Geotechical Society Conference
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• 2009.09a
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• pp.233-242
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• 2009

Based on the data obtained from field investigation and soil testing to slope hazards occurrence section and non-occurrence section in crystalline rocks like gneiss, granite, and so on, a prediction model was developed by the use of a decision tree model. The classification standard of the selected prediction model is composed of the slope angle, the coefficient of permeability and the void ratio in the order. The computer program, SHAPP ver. 1.0 for prediction of slope hazards around an important national facilities using GIS technique and the developed model. To prove the developed prediction model and the computer program, the field data surveyed from Jumunjin, Gangneung city were compared with the prediction result in the same site. As the result of comparison, the real occurrence location of slope hazards was similar to the predicted section. Through the continuous study, the accuracy about prediction result of slope hazards will be upgraded and the computer program will be commonly used in practical.

• Korean Journal of Artificial Intelligence
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• v.10 no.2
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• pp.7-11
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• 2022

OLED displays cannot be used permanently due to burn-in or generation of dark spots due to degradation. Therefore, the time when the display can operate normally is very important. It is close to impossible to physically measure the time when the display operates normally. Therefore, the time that works normally should be predicted in a way other than a physical way. Therefore, if you do computer simulations based on artificial intelligence, you can increase the accuracy of prediction by saving time and continuous learning. Therefore, if we do computer simulations based on artificial intelligence, we can increase the accuracy of prediction by saving time and continuous learning. In this paper, a dataset in the form of development from generation to diffusion of dark spots, which is one of the causes related to the life of OLED, was generated by applying the finite element method. The dark spots were generated in nine conditions, such as 0.1 to 2.0 ㎛ with the size of pinholes, the number was 10 to 100, and 50% with water content. The learning data created in this way may be a criterion for generating an artificial intelligence-based dataset.

• Proceedings of the Korean Institute of Building Construction Conference
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• 2022.11a
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• pp.99-100
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• 2022

As the danger of exposure to the asbestos has been revealed, the importance of demolition asbestos in existing buildings has been raised. Extensive body of study has been conducted to evaluate the risk of demolition asbestos, but there were confined types of variables caused by not reflecting categorical information and limitations in collecting quantitative information. Thus, this study aims to derive a model that predicts the risk in workplace of demolition asbestos by collecting categorical and continuous variables. For this purpose, categorical and continuous variables were collected from asbestos demolition reports, and the risk assessment score was set as the dependent variable. In this study, the influence of each variable was identified using logistic regression, and the risk prediction model methodologies were compared through decision tree regression and artificial neural network. As a result, a conditional risk prediction model was derived to evaluate the risk of demolition asbestos, and this model is expected to be used to ensure the safety of asbestos demolition workers.

• The Korean Journal of Physiology and Pharmacology
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• v.28 no.2
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• pp.121-127
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• 2024

Vancomycin is a frequently used antibiotic in intensive care units, and the patient's renal clearance affects the pharmacokinetic characteristics of vancomycin. Several advantages have been reported for vancomycin continuous intravenous infusion, but studies on continuous dosing regimens based on patients' renal clearance are insufficient. The aim of this study was to develop a vancomycin serum concentration prediction model by factoring in a patient's renal clearance. Children admitted to our institution between July 1, 2021, and July 31, 2022 with records of continuous infusion of vancomycin were included in the study. Sex, age, height, weight, vancomycin dose by weight, interval from the start of vancomycin administration to the time of therapeutic drug monitoring sampling, and vancomycin serum concentrations were analyzed with the linear regression analysis of the mixed effect model. Univariable regression analysis was performed using the vancomycin serum concentration as a dependent variable. It showed that vancomycin dose (p < 0.001) and serum creatinine (p = 0.007) were factors that had the most impact on vancomycin serum concentration. Vancomycin serum concentration was affected by vancomycin dose (p < 0.001) and serum creatinine (p = 0.001) with statistical significance, and a multivariable regression model was obtained as follows: Vancomycin serum concentration (mg/l) = -1.296 + 0.281 × vancomycin dose (mg/kg) + 20.458 × serum creatinine (mg/dl) (adjusted coefficient of determination, R² = 0.66). This prediction model is expected to contribute to establishing an optimal continuous infusion regimen for vancomycin.

• Communications of the Korean Mathematical Society
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• v.26 no.4
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• pp.709-720
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• 2011

We obtain special type of differential equations which their solution are random variable with known continuous density function. Stochastic differential equations (SDE) of continuous distributions are determined by the Fokker-Planck theorem. We approximate solution of differential equation with numerical methods such as: the Euler-Maruyama and ten stages explicit Runge-Kutta method, and analysis error prediction statistically. Numerical results, show the performance of the Rung-Kutta method with respect to the Euler-Maruyama. The exponential two parameters, exponential, normal, uniform, beta, gamma and Parreto distributions are considered in this paper.

• Journal of the Korean earth science society
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• v.26 no.2
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• pp.114-128
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• 2005

Fuzzy information representation of multi-source spatial data is applied to landslide hazard mapping. Information representation based on frequency ratio and non-parametric density estimation is used to construct fuzzy membership functions. Of particular interest is the representation of continuous data for preventing loss of information. The non-parametric density estimation method applied here is a Parzen window estimation that can directly use continuous data without any categorization procedure. The effect of the new continuous data representation method on the final integrated result is evaluated by a validation procedure. To illustrate the proposed scheme, a case study from Jangheung, Korea for landslide hazard mapping is presented. Analysis of the results indicates that the proposed methodology considerably improves prediction capabilities, as compared with the case in traditional continuous data representation.