• Journal of Advanced Navigation Technology
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• v.15 no.5
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• pp.879-886
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• 2011

Warning notification system base on mobile communication providers, is a warning information provider which senses the disaster and warning condition, provides the warning message to the entrepreneur and its mobile terminal. In this paper, for protecting our lives and properties, we study on disaster warning system ISO TC233's WG3's Public Warning model, Korea Disaster Forecasting and Warning System, disaster Forecasting and Warning System's role, and Colour-coded Alert etc.

• Journal of Navigation and Port Research
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• v.44 no.5
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• pp.407-413
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• 2020

In this paper we examined a novel extension of the convergence empirics for the maritime business cycle which considers structure breaks and/or changes. To provide theoretical justification, the convergence hypothesis uses the relaxed assumption to technology shocks. Based on the recent empirical results provided by Kim and Chang (2020), we consider nonlinear dynamics that capture the properties on structural changes in the equilibrium adjustment process. This approach bridges the gap between the theoretical framework and empirical specifications. In particular, we applied the convergence hypothesis to the multiple structure change model for the maritime business cycle. Our application to the maritime data showed support of the convergence hypothesis allowing multiple structure changes during the high volatile period and offers additional insight into the forecasting maritime business cycles.

• Proceedings of the Korean Society of Agricultural Engineers Conference
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• 2003.10a
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• pp.507-510
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• 2003

The development of a basin-wide runoff analysis model is to analysis monthly and daily hydrologic runoff components including surface runoff, subsurface runoff, return flow, etc. at key operation station in the targeted basin. A short-term water demand forecasting technology will be developed taking into account the patterns of municipal, industrial and agricultural water uses. For the development and utilization of runoff analysis model, relevant basin information including historical precipitation and river water stage data, geophysical basin characteristics, and water intake and consumptions needs to be collected and stored into the hydrologic database of Integrated Real-time Water Information System. The well-known SSARR model was selected for the basis of continuous daily runoff model for forecasting short and long-term natural flows.

• Proceedings of the KIEE Conference
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• summer
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• pp.722-724
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• 2004

Demand estimates in electric power systems have traditionally consisted of time-series analyses over long time periods. The resulting database consisted of huge amounts of data that were then analyzed to create the various coefficients used to forecast power demand. In this research, we take advantage of universally used analysis techniques analysis, but we also use easily available data-mining techniques to analyze patterns of days and special days(holidays, etc.). We then present a new method for estimating and forecasting power flow using decision tree analysis. And because analyzing the relationship between the estimate and power system ceiling Trices currently set by the Korea Power Exchange. We included power system ceiling prices in our estimate coefficients and estimate method.

• Journal of Advanced Marine Engineering and Technology
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• v.27 no.7
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• pp.817-825
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• 2003

The amount of damage suffered from the red tide occurring at the near shore is increasing rapidly. The Windows visual system discussed in this paper is developed in order to help minimize the damage. The system is focused on the monitoring the coastal environment. and forecasting the red tide occurrence. Although several similar systems are now existing. most of them are based on the web application. which cause the large response time. limited presentation ability of data. and inability of data storing at client side. The Windows visual system described in this paper operates on the Internet to get the ubiquitous access. One of three components of the Windows visual system. client system is developed as a Windows application in order to overcome the weak points of the previous systems. The gathering. analysis, and monitoring of data can be done at real time using the Windows visual system.

• International journal of advanced smart convergence
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• v.7 no.2
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• pp.86-94
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• 2018

This study focuses on presenting the IT program module provided by real - time forecasting and database of the voluntary disclosure quality measure in order to solve the problem of capital cost due to information asymmetry of external investors and corporate executives. This study suggests a model of the algorithm that the quality of real - time voluntary disclosure can be provided to all investors immediately by IT program in order to deliver the meaningful value in the domestic capital market. This is a method of generating and analyzing real-time or non-real-time prediction models by transferring the predicted estimates delivered to the Big Data Log Analysis System through the statistical DB to the statistical forecasting engine.

• Proceedings of the KSRS Conference
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• 2007.10a
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• pp.591-593
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• 2007

This paper is to cluster the AMR (Automatic Meter Reading) data. The load survey system has been applied to record the power consumption of sampling the contract assortment in KEPRI AMR. The effect of the contract assortment change to the customer power consumption is determined by executing the clustering on the load survey results. We can supply the power to customer according to usage to the analysis cluster. The Korea a class of the electricity supply type is less than other country. Because of the Korea electricity markets exists one electricity provider. Need to further divide of electricity supply type for more efficient supply. We are found pattern that is different from supplied type to customer. Out experiment use the Clementine which data mining tools.

• Journal of Korean Institute of Industrial Engineers
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• v.13 no.1
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• pp.25-29
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• 1987

When the historical data are available, the diffusion model, which describes the time pattern of the adoption process of a new product or technology or service, has been used as a reasonable predictor in the telecommunication demand forecasting area. This paper shows that the diffusion model is applicable when the historical data are not available. The model used is in the form of a "logistic" function. The parameters of the function are estimated using the questionnaire and the historical data of reference products. From the questionnaire, an initial and an upper limit long run value of the market share are estimated, and the diffusion time to the upper limit value is determined by the relation between the investment and the utility.

• IE interfaces
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• v.1 no.2
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• pp.53-65
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• 1988

This study was carried out to investigate TF(Technological Forecasting) of ISDN (Integrated Services Digital Network) by Delphi method. Since the technology of science has been developed rapidly, the necessity of TF, which can predict the future direction and intensities of technological progress, has been gradually recognized in various fields. Therefore the purpose of this study was to explore the normative statistical data so that our business or nation may have better decision-making with the TF of ISDN which will be come into in the 21st century, presenting the appropriate methodology to the application of methods.

• Industrial Engineering and Management Systems
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• v.7 no.1
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• pp.9-22
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• 2008

As an extension of a previous work by the authors (Song and Esogbue, 2006), a new algorithm for automated modeling of nonstationary seasonal time series is presented in this paper. Issues relative to the methodology for building automatically seasonal time series models and periodic time series models are addressed. This is achieved by inspecting the trend, estimating the seasonality, determining the orders of the model, and estimating the parameters. As in our previous work, the major instruments used in the model identification process are correlograms of the modeling errors while the least square method is used for parameter estimation. We provide numerical illustrations of the performance of the new algorithms with respect to building both seasonal time series and periodic time series models. Additionally, we consider forecasting and exercise the models on some sample time series problems found in the literature as well as real life problems drawn from the retail industry. In each instance, the models are built automatically avoiding the necessity of any human intervention.