• Korean Journal of Hydrosciences
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• v.3
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• pp.31-44
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• 1992

Precise run-off forecasting depends on the ability to predict quantitative rainfall intensity. The purpose of this study is to develop a stochastic model for the shori-term rainfall prediction. It is required for the model to predict rainfall intensities at all the telemetered rain-gauge locations simultaneously. All the model parameters, which are used in this work ; velocity and direction of storm movement, radial spectrum, and dimensionless time distribution of rainfall, are the results of the previous study. We formulated the model and operated it, so that in this study was analyzed particulary the influence of 4 dimensionless time distributions on the prediction and the influence of the model on run-off.

• Korean Journal of Computational Design and Engineering
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• v.1 no.2
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• pp.150-157
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• 1996

The hob is considered as an effective gear cutting tool for achieving the various gears such as spur gear, helical gear, worm gear and so on. To enhance the productivity and precision of hobs and the competitive ability of domestic CNC hob relieving lathes, a CAM system for CNC hob relieving lathe needs to be realized. In this study, the CAM system is developed based on the personal computer and C language. Besides the automatic generation of CNC data, the developed CAM system has the various capabilities related to the generation of tool path, the cutting simulation for verifying the generated CNC data and forecasting the cutting time, the DNC operation for communicating the CNC data with CNC controller by RS232C port, and the estimation of undercut length for verifying the hob cutting conditions.

• Journal of the Ergonomics Society of Korea
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• v.21 no.1
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• pp.1-14
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• 2002

The main purpose of this paper is to present selection criteria for ROK Airforce pilot training candidates in order to save costs involved in sequential pilot training. We use classification models such Decision Tree, Logistic Regression and Neural Network based on aptitude test results of 288 ROK Air Force applicants in 1994-1996. Different models are compared in terms of classification accuracy, ROC and Lift-value. Neural network is evaluated as the best model for each sequential flight test result while Logistic regression model outperforms the rest of them for discriminating the last flight test result. Therefore we suggest a pilot selection criterion based on this logistic regression. Overall. we find that the factors such as Attention Sharing, Speed Tracking, Machine Comprehension and Instrument Reading Ability having significant effects on the flight results. We expect that the use of our criteria can increase the effectiveness of flight resources.

• Water for future
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• v.23 no.3
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• pp.341-350
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• 1990

Precise run-off forecasting depends on the ability to predict quantitative rainfall intensity. This study suggests a stochastic model for 1 hour order rainfall prediction. The model simultaneously predicts rainfall intensity at all telemetered rain-gauge locations. All model parameters, velocity and direction of storm movement, radial spectrum, dimensionless time distribution of rainfall, are estimated from telemetered and historical data for the basin being predicted. Also the estimated parameters are based on the previous study. The results are the influence of dimensionless time distributions on the prediction and the model on run-off.

• Advances in Computational Design
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• v.7 no.1
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• pp.37-56
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• 2022

Phase-type distributions are the distributions of the time to absorption in finite and absorbing Markov chains. They generalize, while at the same time, retain the tractability of the exponential distributions and their family. They are widely used as stochastic models from queuing theory, reliability, dependability, and forecasting, to computer networks, security, and computational design. The ability to fit phase-type distributions to intractable or empirical distributions is, therefore, highly desirable for many practical purposes. Many methods and tools currently exist for this fitting problem. In this paper, we present the results of our investigation on using orthogonal-distance fitting as a method for fitting phase-type distributions, together with a comparison to the currently existing fitting methods and tools.

• Korea Trade Review
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• v.46 no.5
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• pp.83-98
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• 2021

This study aims to examine the effects of international trade in intermediate input on the implications of international business cycle properties in Korea. To do this, I have extended standard one goods New Keynesian international business cycle model to incorporate the role of intermediate inputs. After constructing the DSGE model, I have analysed the impulse response function and varian decomposition results. The results show that the model could introduce a new channel, that is, "cost channel" like Eyquem and Kamber (2014). In other words, the model has changed the dynamics of aggregate inflation by the cost channel. When the trade in intermediate goods increase, which is measured by openness of foreign input, the volatility of output, consumption and inflation increase two or three times. However, the model itself fails to explain the full account of cycle behavior of historical data, but the results imply that the trade in intermediate input assumption can help to improve the forecasting ability of international business cycle models.

• Korean Journal of Poultry Science
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• v.39 no.3
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• pp.233-239
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• 2012

Public forecasts for broiler's prices such as Korea Rural Economic Institute (KREI) outlook information are important for producers and agribusiness decision makers in enhancing economic decision making. However, the KREI forecasts have not been fully evaluated so far. In this study agricultural outlook price forecasts for broiler are evaluated under accuracy-based measures and classification-based measures which test the ability to categorize price movements directionally or within a forecasted range. Agricultural outlook price forecasts for broiler are efficient but biased. In the aspect of the monthly direction of price change 59% of its forecasts over the sample period are correct, and actual prices fall within the forecasted range 32% of the time. Results suggest that it is necessary and meaningful for the agricultural outlook center to evaluate the current forecasting method and try to find an alternative method for improving the forecasting technique.

• Journal of Korea Port Economic Association
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• v.32 no.2
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• pp.157-178
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• 2016

The purpose of this study is to forecast the ship-management market size and to propose a management improvement scheme to support Korean ship management companies in the stagnating world shipping market. Recently, global shipping companies have begun outsourcing all ship management activities. However, the Korean ship-management market represents just 3.75% of ocean shipping companies' sales, making it necessary to enlarge this market. This study performs a business analysis of ship management companies in Korea. The findings show that these companies' profitability and financial structures are not very good, mainly because of insufficient management ability and small firm sizes. Therefore, we propose that the Korean government supports crew training programs and shipping financial systems.

• International Area Studies Review
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• v.14 no.3
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• pp.62-84
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• 2010

Accurate forecasting of volatility is of considerable interest in financial volatility research, particularly in regard to portfolio allocation, option pricing and risk management because volatility is equal to market risk. So, we attempted to delineate a model with good ability to forecast and identified stylized features of volatility, with a focus on volatility persistence or long memory in the Australian futures market. In this context, we assessed the long-memory property in the volatility of index futures contracts using three conditional volatility models, namely the GARCH, IGARCH and FIGARCH models. We found that the FIGARCH model better captures the long-memory property than do the GARCH and IGARCH models. Additionally, we found that the FIGARCH model provides superior performance in one-day-ahead volatility forecasts. As discussed in this paper, the FIGARCH model should prove a useful technique in forecasting the long-memory volatility in the Australian index futures market.

• The Bulletin of The Korean Astronomical Society
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• v.46 no.1
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• pp.42.1-42.1
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• 2021

In this study, we present a visual explanation of a deep learning solar flare forecast model and its relationship to physical parameters of solar active regions (ARs). For this, we use full-disk magnetograms at 00:00 UT from the Solar and Heliospheric Observatory/Michelson Doppler Imager and the Solar Dynamics Observatory/Helioseismic and Magnetic Imager, physical parameters from the Space-weather HMI Active Region Patch (SHARP), and Geostationary Operational Environmental Satellite X-ray flare data. Our deep learning flare forecast model based on the Convolutional Neural Network (CNN) predicts "Yes" or "No" for the daily occurrence of C-, M-, and X-class flares. We interpret the model using two CNN attribution methods (guided backpropagation and Gradient-weighted Class Activation Mapping [Grad-CAM]) that provide quantitative information on explaining the model. We find that our deep learning flare forecasting model is intimately related to AR physical properties that have also been distinguished in previous studies as holding significant predictive ability. Major results of this study are as follows. First, we successfully apply our deep learning models to the forecast of daily solar flare occurrence with TSS = 0.65, without any preprocessing to extract features from data. Second, using the attribution methods, we find that the polarity inversion line is an important feature for the deep learning flare forecasting model. Third, the ARs with high Grad-CAM values produce more flares than those with low Grad-CAM values. Fourth, nine SHARP parameters such as total unsigned vertical current, total unsigned current helicity, total unsigned flux, and total photospheric magnetic free energy density are well correlated with Grad-CAM values.