• Proceedings of the Korean Society of Precision Engineering Conference
• /
• 2006.10a
• /
• pp.485-486
• /
• 2006

• Journal of the Korean Data and Information Science Society
• /
• v.19 no.4
• /
• pp.1007-1018
• /
• 2008

The purpose of this case study is to demonstrate database-marketing management. First, we explore original variables for insurance customer's data, modify them if necessary, and go through variable selection process before analysis. Then, we develop churn prediction models using logistic regression, neural network and SVM analysis. We also compare these three data mining models in terms of misclassification rate.

• Journal of Intelligence and Information Systems
• /
• v.28 no.4
• /
• pp.191-209
• /
• 2022

This paper is a review of studies that focus on the prediction of a won/dollar exchange rate before and after the covid 19 pandemic. The Korea economy has an unprecedent situation starting from 2021 up till 2022 where the won/dollar exchange rate has exceeded 1,400 KRW, a first time since the global financial crisis in 2008. The US Federal Reserve has raised the interest rate up to 2.5% (2022.7) called a 'Big Step' and the Korea central bank has also raised the interested rate up to 2.5% (2022.8) accordingly. In the unpredictable economic situation, the prediction of the won/dollar exchange rate has become more important than ever. The authors separated the period from 2015.Jan to 2022.Aug into three periods and built a best fitted ARIMA/ARDL prediction model using the period 1. Finally using the best the fitted prediction model, we predicted the won/dollar exchange rate for each period. The conclusions of the study were that during Period 3, when the usual relationship between exchange rates and economic factors appears, the ARDL model reflecting the variable relationship is a better predictive model, and in Period 2 of the transitional period, which deviates from the typical pattern of exchange rate and economic factors, the SARIMA model, which reflects only historical exchange rate trends, was validated as a model with a better predictive performance.

• Journal of Korean Society of Industrial and Systems Engineering
• /
• v.43 no.3
• /
• pp.1-9
• /
• 2020

Interest rate spreads indicate the conditions of the economy and serve as an indicator of the recession. The purpose of this study is to predict Korea's interest rate spreads using US data with long-term continuity. To this end, 27 US economic data were used, and the entire data was reduced to 5 dimensions through principal component analysis to build a dataset necessary for prediction. In the prediction model of this study, three RNN models (BasicRNN, LSTM, and GRU) predict the US interest rate spread and use the predicted results in the SVR ensemble model to predict the Korean interest rate spread. The SVR ensemble model predicted Korea's interest rate spread as RMSE 0.0658, which showed more accurate predictive power than the general ensemble model predicted as RMSE 0.0905, and showed excellent performance in terms of tendency to respond to fluctuations. In addition, improved prediction performance was confirmed through period division according to policy changes. This study presented a new way to predict interest rates and yielded better results. We predict that if you use refined data that represents the global economic situation through follow-up studies, you will be able to show higher interest rate predictions and predict economic conditions in Korea as well as other countries.

• Journal of Information Processing Systems
• /
• v.10 no.2
• /
• pp.314-333
• /
• 2014

When analyzing default predictions in real estate companies, the number of non-defaulted cases always greatly exceeds the defaulted ones, which creates the two-class imbalance problem. This lowers the ability of prediction models to distinguish the default sample. In order to avoid this sample selection bias and to improve the prediction model, this paper applies a minority sample generation approach to create new minority samples. The logistic regression, support vector machine (SVM) classification, and neural network (NN) classification use an imbalanced dataset. They were used as benchmarks with a single prediction model that used a balanced dataset corrected by the minority samples generation approach. Instead of using prediction-oriented tests and the overall accuracy, the true positive rate (TPR), the true negative rate (TNR), G-mean, and F-score are used to measure the performance of default prediction models for imbalanced dataset. In this paper, we describe an empirical experiment that used a sampling of 14 default and 315 non-default listed real estate companies in China and report that most results using single prediction models with a balanced dataset generated better results than an imbalanced dataset.

• Journal of Korean Society for Quality Management
• /
• v.25 no.3
• /
• pp.96-107
• /
• 1997

The recent stream to reliability prediction is that it is totally inclusive in depth to consider even the operating and environmental condition at the level of finished goods as well as component itselves. In this study, firstly we present the reliability prediction methods by entire failure rate model which failure rate at the system level is added to the failure rate model at the component level. Secondly we build up the improved bases of reliability demonstration through a, pp.ication of Kaplan-Meier, Cumulative hazard, Johnson's methods as non-parametric and Maximum Likelihood Estimator under exponential & Weibull distribution as parametric. And also present the methods of curve fitting to piecewise failure rate under Weibull distribution, PRST (Probability Ratio Sequential Test), curve fitting to S-shaped reliability growth curve, computer programs of each methods. Lastly we show the practical for determination of optimal burn-in time as a method of reliability enhancement, and also verify the practical usefulness of the above study through the a, pp.ication of failure and test data during 1 year.

• Proceedings of the Korean Geotechical Society Conference
• /
• 2008.03a
• /
• pp.926-931
• /
• 2008

This paper show two different standardized test methods(Japanese Geotechnical Society; JGS 2003). One test is a "Test Method for Frost Heave Prediction Test, JGS 0171-2003", and the other test is a "Test Method for Frost Susceptibility, JGS 0172-2003". The purpose of this test is to obtain the freezing rate(freezing speed), frost heave ratio(heave to sample height), frost heave rate(heaving speed), and other parameters to be used for frost heave prediction and determine the frost susceptibility by freezing test with water intake. This method shall be used to predict the frost heave in frozen ground and evaluate the frost susceptibility of natural and replacement materials.

• Proceedings of the Korean Operations and Management Science Society Conference
• /
• 1996.04a
• /
• pp.741-744
• /
• 1996

Artifical Neural Network(ANN) models were used for forecasting interest rate as a new methodology, which has proven itself successful in financial domain. This research intended to construct ANN models which can maximize the performance of prediction, regarding Corporate Bond Yield (CBY) as interest rate. Synergistic Market Analysis (SMA) was applied to the construction of models [Freedman et al.]. In this aspect, while the models which consist of only time series data for corporate bond yield were devloped, the other models generated through conjunction and reorganization of fundamental variables and market variables were developed. Every model was constructed to predict 1,6, and 12 months after and we obtained 9 ANN models for interest rate forecasting. Multi-layer perceptron networks using backpropagation algorithm showed good performance in the prediction for 1 and 6 months after.

• Journal of Environmental Science International
• /
• v.11 no.4
• /
• pp.383-389
• /
• 2002

Odor sources of a chemical plant in Ulsan were surveyed and temperatures, humidities and flow rates of each exhaust gas were measured. The air samples collected from each source were transferred to the laboratory for sensory test and their odor concentrations were investigated. The odor emission rate of each source was estimated from the recorded results and assigned the sources expected to be needed for the odor prevention policy using the simple prediction equation of the affection by malodor to the nearest residential area. From the total odor emission rate of the examined plant and the relation table for expectable affection area it was concluded that total odor emission of this plant might be decreased for the prevention of residential complaint.

• Transactions of the Korean Society of Automotive Engineers
• /
• v.7 no.7
• /
• pp.94-103
• /
• 1999

A model for the prediction of combustion and exhaust emissions of DI diesel engine has been formulated and developed . This model is a quasi-dimensional phenomenological one and is based on multi-zone combustion modelling concept. It takes into consideration, on a zonal basis ,detailed of fuel spray formation, droplet evaporation, air-fuel mixing, spray wall interaction, swirl , heat transfer, self ignition and burning rate . The emission model is considered with chemical equipment , as well as the kinetics of fuel. NO and soot reactions in order to calculate the pollutant concentrations within each zone and the whole of cylinder . The accuracy of prediction versus experimental data and the capability of the model in predicting engine heat release, cylinder pressure and all the major exhaust emissions on zonal and cumulative basis., is demonstrated. Detailed prediction results showing the sensitivity of the model bv various injection rates are presented and discussed.