• Journal of The Institute of Information and Telecommunication Facilities Engineering
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• v.8 no.4
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• pp.157-162
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• 2009

This study's purpose is to forecast the market demand of UVS (u-Transportation Vehicle Sensor) OBU (On-board Unit) of the ubiquitous Transportation. Bass model, Logistic model, and Gompertz model were used for the forecasting market demand. Firstly, this research focused on the market size for the u-T OBU. All three models were used for the market size prediction and the average values were used. The Bass model were calibrated and the market demand for the UVS OBU of the u-Transportation system were estimated using this model.

• Journal of information and communication convergence engineering
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• v.9 no.4
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• pp.369-374
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• 2011

Generally the neural network and the Fuzzy compensative algorithms are applied to forecast the time series for power demand with the characteristics of a nonlinear dynamic system, but, relatively, they have a few prediction errors. They also make long term forecasts difficult because of sensitivity to the initial conditions. In this paper, we evaluate the chaotic characteristic of electrical power demand with qualitative and quantitative analysis methods and perform a forecast simulation of electrical power demand in regular sequence, attractor reconstruction and a time series forecast for multi dimension using Lyapunov Exponent (L.E.) quantitatively. We compare simulated results with previous methods and verify that the present method is more practical and effective than the previous methods. We also obtain the hourly predictability of time series for power demand using the L.E. and evaluate its accuracy.

• Journal of Information Processing Systems
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• v.14 no.6
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• pp.1508-1520
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• 2018

In this paper, we propose an improved model to provide users with a better long-term prediction of waterworks operation data. The existing prediction models have been studied in various types of models such as multiple linear regression model while considering time, days and seasonal characteristics. But the existing model shows the rate of prediction for demand fluctuation and long-term prediction is insufficient. Particularly in the deep running model, the long-short-term memory (LSTM) model has been applied to predict data of water purification plant because its time series prediction is highly reliable. However, it is necessary to reflect the correlation among various related factors, and a supplementary model is needed to improve the long-term predictability. In this paper, convolutional neural network (CNN) model is introduced to select various input variables that have a necessary correlation and to improve long term prediction rate, thus increasing the prediction rate through the LSTM predictive value and the combined structure. In addition, a multiple linear regression model is applied to compile the predicted data of CNN and LSTM, which then confirms the data as the final predicted outcome.

• Journal of Korean Society for Quality Management
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• v.21 no.1
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• pp.11-21
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• 1993

In this paper, we survey the current status of measurement labor in Korean Industry. At the same time we try to predit the demand and supply of measurement labor to suggest policy measures for equilibrium in measurement labor market. We use a general production function for the prediction which include a set of general homethetic production function.

• The Journal of Bigdata
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• v.8 no.2
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• pp.165-175
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• 2023

LNG is a representative imported cargo at Incheon Port and has a relatively high contribution to the increase/decrease in overall cargo volume at Incheon Port. In addition, in the view point of nationwide, LNG is the one of the most important key resource to supply the gas and generate electricity. Thus, it is very essential to identify the factors that have impact on the demand fluctuation and build the appropriate forecasting model, which present the basic information to make balance between supply and demand of LNG and establish the plan for power generation. In this study, different to previous research based on macroscopic annual data, the weekly demand of LNG is converted from the cargo volume unloaded by LNG carriers. We have identified the periodicity and correlations among internal and external factors of demand variability. We have identified the input factors for predicting the LNG demand such as seasonality of weekly cargo volume, the peak power demand, and the reserved capacity of power supply. In addition, in order to predict LNG demand, considering the characteristics of the data, time series prediction with weekly LNG cargo volume as a dependent variable and prediction through an artificial neural network model were made, the suitability of the predictions was verified, and the optimal model was established through error comparison between performance and estimates.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.15 no.2
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• pp.38-48
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• 2001

Kalman Filter model of demand for residental water and consumption pattern wore tested for their ability to explain the hourly residental demand for water in metro-politan distribution system. The daily residental demand can be obtained from Kalman Filter model which is optimized by statistical analysis of input variables. The hourly residental demand for water is calculated from the daily residental demand and consumption pattern. The consumption pattern which has 24 time rates is characterized by data granulization in accordance with season kind, weather and holiday. The proposed approach is applied to water distribution system of metropolitan areas in Korea and its effectiveness is checked.

• Earthquakes and Structures
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• v.8 no.1
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• pp.185-204
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• 2015

This study investigates the impact of the earthquake incident angle on the structural demand and the influence of ground motion selection and scaling methods on seismic directionality effects. The structural demand produced by Non-Linear Time-History Analyses (NLTHA) varies with the seismic input incidence angle. The seismic directionality effects are evaluated by subjecting four three-dimensional reinforced concrete structures to different scaled and un-scaled records oriented along nine incidence angles, whose values range between 0 and 180 degrees, with an increment of 22.5 degrees. The results show that NLTHAs performed applying the ground motion records along the principal axes underestimate the structural demand prediction, especially when plan-irregular structures are analyzed. The ground motion records generate the highest demand when applied along the lowest strength structural direction and a high energy content of the records increases the structural demand corresponding to this direction. The seismic directionality impact on structural demand is particularly important for irregular buildings subjected to un-scaled accelerograms. However, the orientation effects are much lower if spectrum-compatible combinations of scaled records are used. In both cases, irregular structures should be analyzed first with pushover analyses in order to identify the weaker structural directions and then with NLTHAs for different incidence angles.

• Korean Journal of Agricultural Science
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• v.49 no.3
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• pp.679-690
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• 2022

As the supply and demand of pork has become a significant concern in Korea, controlling it has become a critical challenge for the industry. However, compared to the demand for pork, which has relatively stable consumption, it is not easy to maintain a stable supply. As the preparation of measures for a supply-demand crisis response and supply control in the pig industry has emerged as an important task, it has become necessary to establish a stable supply model and create an appropriate manual. In this study, a pork supply prediction model is constructed using reported data from the pig traceability system. Based on the derived results, a method for determining the supply-demand crisis stage using a statistical approach was proposed. From the results of the analysis, working days, African swine fever, heat wave, and Covid-19 were shown to affect the number of pigs graded in the market. A test of the performance of the model showed that both in-sample error rate and out-sample error rate were between 0.3 - 7.6%, indicating a high level of predictive power. Applying the forecast, the distribution of the confidence interval of the predicted value was established, and the supply crisis stage was identified, evaluating supply-demand conditions.

• Journal of Information Technology Services
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• v.21 no.5
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• pp.95-106
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• 2022

Recently, physical examinations have become an important strategy to reduce costs for individuals and society. Pre-physical counseling is important for an effective physical examination. However, incomplete counseling is being conducted because the demand for physical examinations is not predicted. Therefore, in this study, the demand for physical examination was predicted using text mining and stepwise regression. As a result of the analysis, the most recent text data showed a high explanatory power of the demand for physical examination. Also, large amounts of data have high explanatory power. In addition, it was found that the high frequency of the text "health food" reduces the number of health examination customers. And the higher the frequency of the text of the word "food", the lower the number of physical examination customers. However, when the word "wild ginseng" was exposed a lot on Twitter, the number of physical examination customers visiting hospitals increased. In other words, customers consume efficiently by comparing the health examination price with the price of consumer goods. The proposed research framework can help predict demand in other industries.

• Korea Journal of Hospital Management
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• v.25 no.2
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• pp.69-75
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• 2020

Purposes: This study aims to investigate the number of cataract surgeries and predict future trends using 13-year data. Methodology: Trends investigation and comparison of prediction methods was conducted to determine better prediction model using Major Surgery Statistics from Korean Statistical Information Service in 2006-2018. ARIMA(Auto Regressive Integrated Moving Average) was selected and prediction was conducted using R program. Findings: As a results, the number of surgeries will continue to increase. The trends was predicted to increase during January-April, and it declined over time and was the lowest in August. Pratical Implications: Therefore, it is necessary that management will be needed by continuously investigating and predicting the demand and trend for surgery to prepare an alternative to the increase.