• The Korean Journal of Applied Statistics
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• v.15 no.2
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• pp.213-222
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• 2002

The 3-hour-interval prediction of ground-level temperature up to +45 hours in Seoul area is performed using dynamic linear models(DLM). Numerical outputs and observations we used as input values of DLM. According to compare DLM forecasts to RDAPS forecasts using RMSE, DLM improve the accuracy of prediction and systematic error of numerical model outputs are eliminated by DLM.

• Journal of Astronomy and Space Sciences
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• v.19 no.2
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• pp.89-96
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• 2002

General along-track acceleration was estimated in the KOMPSAT-1 orbit determination process. Several sets of the atmospheric drag and solar radiation pressure coefficients were also derived with the different spacecraft area. State vectors in the orbit determination with the different spacecraft area were compared in the time frame. The orbit prediction using the estimated coefficients was performed and compared with the orbit determination results. The orbit prediction with the different general acceleration values was also carried out for the comparison

• Korean Journal of Artificial Intelligence
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• v.6 no.1
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• pp.16-20
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• 2018

The development of modern information technology has increased the amount of big data about patients' information and diseases. In this study, we developed a prediction model of diabetes using the health examination data provided by the public data portal in 2016. In addition, we graphically visualized diabetes incidence by sex, age, residence area, and income level. As a result, the incidence of diabetes was different in each residence area and income level, and the probability of accurately predicting male and female was about 65%. In addition, it can be confirmed that the influence of X on male and Y on female is highly to affect diabetes. This predictive model can be used to predict the high-risk patients and low-risk patients of diabetes and to alarm the serious patients, thereby dramatically improving the re-admission rate. Ultimately it will be possible to contribute to improve public health and reduce chronic disease management cost by continuous target selection and management.

• Journal of the Korean Society of Manufacturing Technology Engineers
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• v.22 no.3_1spc
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• pp.489-495
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• 2013

This study analyzed thevariation in the cutting force when the cutting area of a terrace volume is machined, which is generally left after the rough cutting of a sculptured surface. The numerically simulated results for the cutting forces are compared with cutting force measurements by considering the theoretical prediction of the cutting area formation and specific cutting volume. The variation in the cutting force is measured using a dynamometer installed on a machining center for 19 different kinds of test pieces, which are selected according to the variation in the terrace volume factor, tool diameter factor, and cutting depth factor. As a result, it is verified that the cutting forces evaluated by the numerical analysis coincide with the measured cutting forces, and it is proposed as a practical cutting force prediction model.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1996.10a
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• pp.253-256
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• 1996

Rainfall is one of the major and complicated elements of hydrologic system. Accurate prediction of rainfall is very important to mitigate storm damage. The neural network is a good model to be applied for the classification problem, large combinatorial optimization and nonlinear mapping. In this dissertation, rainfall predictions by the neural network theory were presented. A multi-layer neural network was constructed. The network learned continuous-valued input and output data. The network was used to predict rainfall. The online, multivariate, short term rainfall prediction is possible by means of the developed model. A multidimensional rainfall generation model is applied to Seoul metropolitan area in order to generate the 10-minute rainfall. Application of neural network to the generated rainfall shows good prediction. Also application of neural network to 1-hour real data in Seoul metropolitan area shows slightly good predictions.

• Proceedings of the KSRS Conference
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• 2000.04a
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• pp.9-9
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• 2000

For the planning of future land use for economic activities, an essential component is the identification of the vulnerable areas for natural hazard and environmental impacts from the activities. Also, exploration for mineral and energy resources is carried out by a step by step approach. At each step, a selection of the target area for the next exploration strategy is made based on all the data harnessed from the previous steps. The uncertainty of the selected target area containing undiscovered resources is a critical factor for estimating the exploration risk. We have developed not only spatial prediction models based on adapted artificial intelligence techniques to predict target and vulnerable areas but also validation techniques to estimate the uncertainties associated with the predictions. The prediction models will assist the scientists and decision-makers to make two critical decisions: (i) of the selections of the target or vulnerable areas, and (ii) of estimating the risks associated with the selections.

• Proceedings of the Earthquake Engineering Society of Korea Conference
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• 2006.03a
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• pp.366-373
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• 2006

In order to reduce the secondary earthquake disaster resulting from the damage of gas facilities it is indispensable to establish an early response system on the basis of damage prediction. In this study the procedure of damage prediction for gas facilities is proposed and applied to the gas supply model area. Model area is divided into several little blocks. The soil condition and the characteristics of facilities were investigated at each block. Using fragility curves of facilities the damage level was analyzed under various seismicities. It is confirmed that the exposure gas pipe line in several blocks is damaged seriously by the collapse of building structures.

• Nuclear Engineering and Technology
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• v.53 no.11
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• pp.3612-3624
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• 2021

At present, in the case of pump fast optimization, there is a problem of rapid, accurate and effective prediction of cavitation performance. In "A Cavitation Performance Prediction Method for Pumps PART1-Proposal and Feasibility" [1], a new cavitation performance prediction method is proposed, and the feasibility of this method is demonstrated in combination with experiments of a mixed flow pump. However, whether this method is applicable to vane pumps with different specific speeds and whether the prediction results of this method are accurate is still worthy of further study. Combined with the experimental results, the research evaluates the sensitivity and accuracy at different flow rates. For a certain operating condition, the method has better sensitivity to different flow rates. This is suitable for multi-parameter multi-objective optimization of pump impeller. For the test mixed flow pump, the method is more accurate when the area ratios are 13.718% and 13.826%. The cavitation vortex flow is obtained through high-speed camera, and the correlation between cavitation flow structure and cavitation performance is established to provide more scientific support for cavitation performance prediction. The method is not only suitable for cavitation performance prediction of the mixed flow pump, but also can be expanded to cavitation performance prediction of blade type hydraulic machinery, which will solve the problem of rapid prediction of hydraulic machinery cavitation performance.

• Proceedings of the Korea Inteligent Information System Society Conference
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• 2003.05a
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• pp.329-337
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• 2003

Stock market prediction is regarded as a challenging task of financial time-series prediction. There have been many studies using artificial neural networks in this area. Recently, support vector machines (SVMs) are regarded as promising methods for the prediction of financial time-series because they me a risk function consisting the empirical ewer and a regularized term which is derived from the structural risk minimization principle. In this study, I apply SVM to predicting the Korea Composite Stock Price Index (KOSPI). In addition, this study examines the feasibility of applying SVM in financial forecasting by comparing it with back-propagation neural networks and case-based reasoning. The experimental results show that SVM provides a promising alternative to stock market prediction.

• International Journal of Highway Engineering
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• v.18 no.6
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• pp.131-136
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• 2016

OBJECTIVES : The objective of this research is to determine the integrity of pavement structures for areas where voids exist. Furthermore, we conducted the study of voided-area analysis and remaining life prediction for pavement structures using finite element method. METHODS : To determine the remaining life of the existing voided areas under asphalt concrete pavements, field and falling weight deflectometer (FWD) tests were conducted. Comparison methods were used to have better accuracy in the finite element method (FEM) analysis compared to the measured surface displacements due to the loaded trucks. In addition, the modeled FEM used in this study was compared with well-known software programs. RESULTS : The results show that a good agreement on the analyzed and measured displacements can be obtained through comparisons of the surface displacement due to loaded trucks. Furthermore, the modeled FEM program was compared with the available pavement-structure software programs, resulting in the same values of tensile strains in terms of the thickness of asphalt concrete layers. CONCLUSIONS : The study, which is related to voided-area analysis and remaining life prediction using FEM for pavement structures, was successfully conducted based on the comparison between our methods and the sinkhole grade used in Japan.