• Proceedings of the Korea Air Pollution Research Association Conference
• /
• 2005.11a
• /
• pp.419-420
• /
• 2005

• Proceedings of the Korea Air Pollution Research Association Conference
• /
• 2006.04a
• /
• pp.154-155
• /
• 2006

• International Journal of Highway Engineering
• /
• v.10 no.4
• /
• pp.139-150
• /
• 2008

In this study, It is focused on development of the forecasting model about trumpet InterChange(IC) ramp accident because of the frequency of accident in ramp more than highway basic section and trend the increasing accident in ramp. The independent variables was selected through statistical analysis(correlation analysis, multi-collinearity etc) by ramp types(direct, semi-direct and loop). The independent variables and accident rate is non-linear relationship. So it made new variables by transformation of the independent variables. The forecasting models according to exit-ramp type (direct, semi-direct and loop) are built with statistical multi-variable regression using all possible regression method. And the forecasts of the models showed high accuracy statistically. It is expected that the developed models could be employed to design trumpet IC ramp more cost-efficiently and safely and to analyze the causes of traffic accidents happened on the IC ramp.

• Journal of the Korean Data and Information Science Society
• /
• v.28 no.2
• /
• pp.237-250
• /
• 2017

A new field of e-sports gains the great popularity in Korea as well as abroad. AOS (aeon of strife) genre games are quickly gaining popularity with gamers from all over the world and the game companies hold game competitions. The e-sports broadcasting teams and webzines use a variety of statistical indicators. In this paper, as an AOS genre game, League of Legends game data is used for statistical analysis using the indicators to predict the outcome. We develop new indicators with the factor analysis to improve existing indicators. Also we consider discriminant function, neural network model, and SVM (support vector machine) for make winning forecasting models. As a result, the new position indicators reflect the nature of the role in the game and winning forecasting models show more than 95 percent accuracy.

• Journal of the Korea Computer Industry Society
• /
• v.6 no.2
• /
• pp.241-248
• /
• 2005

Conventional estimating methods forecast the future that it usually using the past statistical numerical value. In order to forecast the farming price, it must need many effort and accuracy knowledge. Therefore, to solve the these problems, this paper to improve forecasting farming price using fuzzy rules and neural network as a preprocessing. Also, we developed an intelligent farming expert system for real time forecasting as a postprocessing about unexpectable conditions. Computer simulation results proved reducing pricing error which proposed farming price expecting system better than conventional demand forecasting system does not using fuzzy rules.

• Korean Journal of Hydrosciences
• /
• v.4
• /
• pp.1-9
• /
• 1993

It is a major objective for the management and operation of water resources system to forecast streamflows. The applicability of artificial neural network model to hydrologic system is analyzed and the performance is compared by statistical method with observed. Multi-layered perception was used to model rainfall-runoff process at Pyung Chang River Basin in Korea. The neural network model has the function of learning the process which can be trained with the error backpropagation (EBP) algorithm in two phases; (1) learning phase permits to find the best parameters(weight matrix) between input and output. (2) adaptive phase use the EBP algorithm in order to learn from the provided data. The generalization results have been obtained on forecasting the daily and hourly streamflows by assuming them with the structure of ARMA model. The results show validities in applying to hydrologic forecasting system.

• The Korean Journal of Applied Statistics
• /
• v.24 no.1
• /
• pp.93-102
• /
• 2011

In this paper, we compared the performance of the several time series models for tourism demand forecasting. We showed that seasonal effects in the data(Japan, China, USA, and Philippines) exist in the tourism data and the forecasting accuracies are compared by the RMSE criterion.

• Proceedings of the Korean Society for Emotion and Sensibility Conference
• /
• 2000.04a
• /
• pp.318-327
• /
• 2000

Managerial intuition is a well-recognized cognitive ability but still poorly understood for the purpose of developing effective decision support systems. this research investigates whether the differences in accuracy of "time series forecasting" are related to the differences in one's cognitive style, using statistical test The hypotheses established in the research model did not have positive correlation The lack of correlation between "cognitive style and physiological measures" and accuracy in forecasting may be caused by uncontrolled external variable. Thus, further analyses on physiological characteristics and brainwaves are needed. The approaches such as neural network and data mining are proposed.

• Ocean and Polar Research
• /
• v.36 no.4
• /
• pp.505-514
• /
• 2014

The development of deep seabed manganese nodules has been carried out with the aim of commercial development in 2023. It is important to forecast the price of the four metals (copper, nickel, cobalt, and manganese) extracted from manganese nodules because price change is a criterion for investment decision. The main purpose of the study is to forecast the price of four metals using the ARIMA model and VAR model, and calculate the MAPE to compare a goodness-of-fit between the two models. The estimated results of the two models reveal statistical significance and are in keeping with economic theory. The results of MAPE for goodness-of-fit show that the VAR model is between 0.1 and 0.2, and the ARIMA model is between 0.4 and 0.6. That is, the VAR model is better than the ARIMA model in forecasting changes in the price of metals.

• Proceedings of the Korean Operations and Management Science Society Conference
• /
• 1995.09a
• /
• pp.334-343
• /
• 1995

In recent years, neural networks have been developed as an alternative to traditional statistical techniques. In this study, a neural network model was compared to traditional forecasting models in terms of their capabilities to forecast passenger traffic for flights between U.S. and Korea. The results show that the forecasting ability of the neural networks was superior to the traditional models. In terms of accuracy, the performance of the neural networks was quite encouraging. Using mean absolute deviation, the neural network performed best. The new technique is easy to learn and apply with commercial neural network software. Therefore, airline decision makers should benefit from using neural networks in forecasting passenger loads.