• Smart Media Journal
• /
• v.5 no.3
• /
• pp.88-92
• /
• 2016

This paper proposes a method of forecasting the computer technology development direction. Most computer technology researches in Korea are developed from hot research issues. This approach to take research topics mostly produces less valuable results. In order to choose more valuable research topics, researchers should company with the technology development trends. This paper proposes a way to forecast the computer technologies on the way to the future. It analyzes the development history of programming languages, and forecasts future directions as extensions of the history.

• Journal of Korean Society for Geospatial Information Science
• /
• v.20 no.2
• /
• pp.73-79
• /
• 2012

This study attempts to analyze changes in land use patterns in a compound urban and agricultural city Kimje-si, using LANDSAT TM imagery and to forecast future changes accordingly. As a new approach to supervised classification, HSB(Hue, Saturation, Brightness)-transformed images were used to select training zones, and in doing so classification accuracy increased by more than 5 percent. Land use changes were forecasted by using a cellular automaton algorithm developed by applying Markov Chain techniques, and by taking into account classification results and GIS data, such as population of the pertinent region by area, DEMs, road networks, water systems. Upon comparing the results of the forecast of the land use changes, it appears that geographical features had the greatest influence on the changes. Moreover, a forecast of post-2030 land use change patterns demonstrates that 21.67 percent of mountain lands in Kimje-si is likely to be farmland, and 13.11 percent is likely to become city areas. The major changes are likely to occur in small mountain lands located in the heart of the city. Based on the study result, it seems certain that forecasting future land use changes can help plan land use in a compound urban and agricultural city to procure food resources.

• The Journal of The Korea Institute of Intelligent Transport Systems
• /
• v.20 no.3
• /
• pp.59-73
• /
• 2021

There are many lives lost due traffic accidents, and which have not decreased despite advances in technology. In order to prevent traffic accidents, it is necessary to accurately forecast how they will change in the future. Until now, traffic accident-frequency forecasting has not been a major research field, but has been analyzed microscopically by traditional methods, mainly based on statistics over a previous period of time. Despite the recent introduction of AI to the traffic accident field, the focus is mainly on forecasting traffic flow. This study converts into time series data the records from 1,339,587 traffic accidents that occurred in Korea from 2014 to 2019, and uses the AI algorithm to forecast the frequency of traffic accidents based on driver's age and time of day. In addition, the forecast values and the actual values were compared and verified based on changes in the traffic environment due to COVID-19. In the future, these research results are expected to lead to improvements in policies that prevent traffic accidents.

• Journal of The Korean Society of Agricultural Engineers
• /
• v.57 no.4
• /
• pp.135-141
• /
• 2015

The objective of this study is to develop an early warning system for rainfall induced landslides. For this study, we suggested an analysis process using rainfall forecast data. 1) For a selected slope, safety factor with saturated depth was analyzed and safety factor threshold was established (warning FS threshold=1.3, alarm FS threshold=1.1). 2) If rainfall started, saturated depth and safety factor was calculated with rainfall forecast data, 3) And every hour after safety factor is compared with threshold, then warning or alarm can issued. In the future, we plan to make a early warning system combined with the in-situ inclinometer sensors.

• Korean Institute of Interior Design Journal
• /
• no.27
• /
• pp.168-175
• /
• 2001

This study focuses to enhance the general understanding on the new approach and to provide a clue for a new paradigm in man-made physical and residential environment of the 21st Century. Through case analysis, this study aims to examine the characteristics of digital culture itself and of its physical and residential environment and to ultimately forecast the changeability of physical and residential environment prompted by the new digital wave. Without having precedent study about digital culture related to environmental issues, this study aimed to analyze the effect of digital culture in the field of physical environment and residential environment, and to forecast the changability in those fields. On the other hand, this study also atoned to be applicable in construction of a new paradigm in designing physical & residential environment in the near future, so as to contribute to the development of a new environment which will eventually improve the quality of human life.

• Journal of Digital Convergence
• /
• v.16 no.10
• /
• pp.125-132
• /
• 2018

The purpose of this study is to analyze the effect of managerial predictive accuracy on managerial performance-compensation. In this study, we compared managerial performance with managerial performance, And to analyze the relationship between manager compensation and manager compensation using managerial profit prediction accuracy. As a result of this study, there is a significant positive relationship between profit prediction accuracy and manager compensation, which can be interpreted as a result of manager's ability to compensate manager's ability to predict the future well. In this paper, we propose a new methodology that can be used to analyze the effects of managerial compensation on managerial compensation. This is because there is a difference in that it is proved to be a factor. Therefore, it is important to note that the prediction of the future of the company also identifies the additional determinants that affect manager compensation contracts with the key managerial capabilities.

• International journal of advanced smart convergence
• /
• v.7 no.3
• /
• pp.73-78
• /
• 2018

This study focuses on presenting the IT program module provided by BKLS measure in order to solve the problem of capital cost due to information asymmetry of external investors and corporate executives. Barron at al(1998) set up a BKLS measure to guide the market by intermediate analysts. The BKLS measure was measured by using the changes in the analyst forecast dispersion and analyst mean forecast error squared. This study suggests a model of the algorithm that the BKLS measure can be provided to all investors immediately by IT program in order to deliver the meaningful value in the domestic capital market as measured. 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. Because BKLS measure is not carried out in a concrete method, it is practically very difficult to estimate the BKLS measure. It is expected that the BKLS measure of Barron at al(1998) introduced in this study and the model of IT module provided in real time will be the starting point for the follow-up study for the introduction and realization of IT technology in the future.

• Korean Journal of Construction Engineering and Management
• /
• v.8 no.4
• /
• pp.203-213
• /
• 2007

In the past, several construction projects have exceeded their schedule resulting in financial losses to the owners; at present there are very few methods available to accurately forecast the completion date of a project. These nay be because of unforeseen outcomes that cannot be accounted for earlier and because of deficiency of proper tools to forecast completion date of said project. To overcome these difficulties, project managers may need a tool to predict the completion date at the early stage of project development. Bayesian Inference introduced in this paper is one such tool that can be employed to forecast project progress at all construction stages. Using this inference, project managers can combine an initially planned project progress (growth curve) with reported information from ongoing projects during the development, and in addition, dynamically revise this initial plan and quantify the uncertainty of completion date. This study introduces a theoretical model and proposes a mathematically information-based framework to forecast a project completion date that corresponds with the actual progress data and to monitor the modified uncertainties using Bayesian Inference.

• IE interfaces
• /
• v.13 no.4
• /
• pp.619-626
• /
• 2000

In this paper, we place the focus on suggesting a method of forecasting demand for PCS resale service with survey data in Korea. It is important for the service provider to forecast the diffusion process when designing marketing strategies and analyzing the costs and benefits. For the reason, we conduct a survey of three groups composed of non-subscribers, cellular subscribers, and PCS subscribers in order to forecast the demand according to several possible scenarios and business strategies. We consider the survey item that is measured by multiple point scales in response to a question if he would subscribe to the mobile telephone service in the future. We propose a method to forecast the size of market potential by classifying each individual into the two extreme groups, that is, yes or no. Then, by integrating survey data and historical data, we forecast the demand for PCS resale service that varies according to scenarios and strategies. From the results, we can find several implications for the provider of PCS resale service.

• Proceedings of the Korean Operations and Management Science Society Conference
• /
• 1996.10a
• /
• pp.139-145
• /
• 1996

A forecast on the past output data sets of small bar steels is very important information to make a decision on the future production quantities. In many cases, however, it has been mainly determined by experience (or rule of thumb). In this paper, past basic data sets of each small bar steels are statistically analyzed by some graphical and statistical forecasting methods. This work is mainly done by SAS. Among various quantitative forecasting methods in SAS, STEPAR forecasting method was best performed to the above data sets. By the method, the future production quantities of each small bar steels are forecasted. As a result of this statistical analysis, 95% confidence intervals for future forecast quantities are very wide. To improve this problem, a suitable systematic database system, integrated management system of demand-production-inventory and integrated computer system should be required.