• Journal of Biosystems Engineering
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• 제29권2호
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• pp.175-186
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• 2004

The study was designed to forecast and derive future core technologies using Delphi method in Korea agriculture. The technologies will make agriculture for core and strategic industry that has high value-added in 21 century. Questions were given to specialists by each technology in order to survey importance, realization time, level of R＆D in Korea and foremost country, leading group of R＆D, effective policy, etc. for each technology. The target of the survey for Delphi is confined specialists in the area of Bioystems Engineering. 55 core technologies were derived and 31 specialists answered the survey.

• Journal of the Korean Operations Research and Management Science Society
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• 제27권1호
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• pp.1-17
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• 2002

The objective of this paper is to develop a new priority setting algorithm that considers the cross-impact of the future technology alternatives and that satisfies the final goal of the technology management through multi-hierarchy evaluation criteria. By combining the Analytic Hierarchy Process (AHP) model, which is a well-known priority setting model, and Cross Impact Analysis (CIA) model, which is a technological forecasting method that considers cross-impact among R&D Items, we developed an Integrated Cross-Impact Hierarchical (CIH) model, which sets the priority by considering technological forecasting and technology dependency simultaneously. A step-by-step numerical example of the model developed here is presented as backup of its practicality.

• Journal of the Korean Operations Research and Management Science Society
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• 제16권1호
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• pp.113-119
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• 1991

The author is currently assistant professor of Management Science at Korea Advanced Institute of Science and Technology, following a few years as assistant professor of Industrial Engineering at Kyung Hee University, Korea. He received his doctorate from the department of Industrial Engineering and Operations Research, University of California, Berkeley. His research interests are time series and forecasting modelling, Bayesian forecasting and the related software development. He is now teaching time series analysis and econometrics at the graduate level.

• Journal of Electrical Engineering and Technology
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• 제11권5호
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• pp.1093-1099
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• 2016

• Proceedings of the Korean Operations and Management Science Society Conference
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• 한국경영과학회/대한산업공학회 1999년도 춘계공동학술대회:정보화시대의 지식경영
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• pp.268-268
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• 1999

• The Magazine of the IEIE
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• 제40권2호
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• pp.57-63
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• 2013

• The Journal of the Institute of Internet, Broadcasting and Communication
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• 제15권1호
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• pp.223-228
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• 2015

Korean climate becoming increasingly subtropical by climate warming makes local heavy rainfall frequently. To avoid damages from the local heavy rainfall, we need a forecasting service for a great number of clients. However, there is not the framework for the service based on wireless data broadcasting yet. In this paper, we design and implement a service framework for local heavy rainfall forecasting using wireless data broadcast. The developed service framework has scalability that can adopt various data scheduling and indexing schemes. We show the efficiency of the proposed framework to forecast local heavy rainfall through a simulation study.

• Journal of the Korean Society for Railway
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• 제17권4호
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• pp.307-312
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• 2014

This study suggested the ARIMA model taking into consideration the seasonal characteristic factor as a method for efficiently forecasting passenger transport demand of the Joongang Line. The forecasting model was built including the demand for the central inland region tourist train (O-train, V-train), which was opened to traffic in April-, 2013 and run in order to reflect the recent demand for the tourism industry. By using the monthly time series data (103) from January-, 2005 to July-, 2013, the optimum model was selected. The forecasting results of passenger transport demand of the Joongang Line showed continuous increase. The developed model forecasts the short-term demand of the Joongang Line.

• Nutrition Research and Practice
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• 제12권3호
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• pp.251-257
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• 2018

BACKGROUND/OBJECTIVES: There are few studies that forecast the future prevalence of obesity based on the predicted prevalence model including contributing factors. The present study aimed to identify factors associated with obesity and construct forecasting models including significant contributing factors to estimate the 2020 and 2030 prevalence of obesity and abdominal obesity. SUBJECTS/METHODS: Panel data from the Korea National Health and Nutrition Examination Survey and national statistics from the Korean Statistical Information Service were used for the analysis. The study subjects were 17,685 male and 24,899 female adults aged 19 years or older. The outcome variables were the prevalence of obesity (body mass index ${\geq}25kg/m^2$) and abdominal obesity (waist circumference ${\geq}90cm$ for men and ${\geq}85cm$ for women). Stepwise logistic regression analysis was used to select significant variables from potential exposures. RESULTS: The survey year, age, marital status, job status, income status, smoking, alcohol consumption, sleep duration, psychological factors, dietary intake, and fertility rate were found to contribute to the prevalence of obesity and abdominal obesity. Based on the forecasting models including these variables, the 2020 and 2030 estimates for obesity prevalence were 47% and 62% for men and 32% and 37% for women, respectively. CONCLUSIONS: The present study suggested an increased prevalence of obesity and abdominal obesity in 2020 and 2030. Lifestyle factors were found to be significantly associated with the increasing trend in obesity prevalence and, therefore, they may require modification to prevent the rising trend.

• ETRI Journal
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• 제18권2호
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• pp.87-106
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• 1996

This paper proposes a new model as a framework for forecasting demand and technological substitution, which can accommodate different patterns of technological change. This model, which we named, "Adaptive Diffusion Model", is formalized from a conceptual framework that incorporates several underlying factors determining the market demand for technological products. The formulation of this model is given in terms of a period analysis to improve its explanatory power for dynamic processes in the real world, and is described as a continuous form which approximates a discrete derivation of the model. In order to illustrate the applicability and generality of this model, time-series data of the diffusion rates for some typical products in electronics and telecommunications market have been empirically tested. The results show that the model has higher explanatory power than any other existing model for all the products tested in our study. It has been found that this model can provide a framework which is sufficiently robust in forecasting demand and innovation diffusion for various technological products.