• Journal of the Ergonomics Society of Korea
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• v.26 no.1
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• pp.79-85
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• 2007

Demand forecasting methods for a consumer product such as TV or refrigerator are widely known. However, sales forecast for a brand new product cannot be estimated using conventional forecasting methods. This study proposes a five-step procedure in forecasting a newly developed product. Step one defines functions in a High Touch product in order to estimate relative attraction of the product to consumer group. In step two, for a comparison purpose, a compatible product that is successfully penetrated into market is selected. Step three breaks a target population into many segments based on demography. Step four calculates relative attraction between the High Touch product and the compatible product. Finally, market penetration rate of the High Touch product is estimated using a bell-shaped diffusion curve of the compatible product. The process offers a method to estimate potential demand and growth pattern of the new High Touch product.

• Journal of The Korean Association For Science Education
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• v.14 no.1
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• pp.34-44
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• 1994

The major objective of this study was to develop effective teaching methods for middle school science. To achieve the objective, general characteristics of science teaching methods were discussed and the 6th national science curriculum was analyzed in terms of epistemological backgrounds, cognitive demand and organizational characteristics. It was analyzed that epistemological background of the curriculum was based on the traditional philosophy of science. It was also indicated that modern Philosophy of science was only partially reflected the objectives of the past curricula. The cognitive demand of the curricula has also been higher than students' actual level of cognitive development. Based upon these results of the analysis, several exemplary instructional methods were developed.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.43 no.2
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• pp.1-13
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• 2020

There are several methods of peak-shaving, which reduces grid power demand, electricity bought from electricity utility, through lowering "demand spike" during On-Peak period. An optimization method using linear programming is proposed, which can be used to perform peak-shaving of grid power demand for grid-connected PV+ system. Proposed peak shaving method is based on the forecast data for electricity load and photovoltaic power generation. Results from proposed method are compared with those from On-Off and Real Time methods which do not need forecast data. The results also compared to those from ideal case, an optimization method which use measured data for forecast data, that is, error-free forecast data. To see the effects of forecast error 36 error scenarios are developed, which consider error types of forecast, nMAE (normalizes Mean Absolute Error) for photovoltaic power forecast and MAPE (Mean Absolute Percentage Error) for load demand forecast. And the effects of forecast error are investigated including critical error scenarios which provide worse results compared to those of other scenarios. It is shown that proposed peak shaving method are much better than On-Off and Real Time methods under almost all the scenario of forecast error. And it is also shown that the results from our method are not so bad compared to the ideal case using error-free forecast.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2005.10a
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• pp.440-443
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• 2005

The wide spread of internet business recently necessitates recommendation systems which can recommend the most suitable product fur customer demands. Currently the recommendation systems use content-based filtering and/or collaborative filtering methods, which are unable both to explain the reason for the recommendation and to reflect constantly changing requirements of the users. These methods guarantee good efficiency only if there is a lot of information about users. This paper proposes an algorithm called 'demand articulate & integration' which can perceive user's continuously varying intents and recommend proper contents. A method of knowledge classification which can be applicable to this algorithm is also developed in order to disassemble knowledge into basic units and articulate indices. The algorithm provides recommendation outputs that are close to expert's opinion through the tracing of articulate index. As a case study, a knowledge base for heritage information is constructed with the expert guide's knowledge. An intelligent recommendation system that can guide heritage tour as good as the expert guider is developed.

• The Korean Journal of Applied Statistics
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• v.29 no.1
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• pp.193-203
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• 2016

This paper forecasts electricity demand as a critical element of a demand management system in Smart Grid environment. We present a prediction method of using a combination of predictive values by time series clustering. Periodogram-based normalized clustering, predictive analysis clustering and dynamic time warping (DTW) clustering are proposed for time series clustering methods. Double Seasonal Holt-Winters (DSHW), Trigonometric, Box-Cox transform, ARMA errors, Trend and Seasonal components (TBATS), Fractional ARIMA (FARIMA) are used for demand forecasting based on clustering. Results show that the time series clustering method provides a better performances than the method using total amount of electricity demand in terms of the Mean Absolute Percentage Error (MAPE).

• Earthquakes and Structures
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• v.10 no.5
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• pp.1181-1212
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• 2016

Fragility and loss functions are developed to predict damage and economic losses due to earthquake loading in Reinforced Concrete (RC) structural components with smooth rebars. The attention is focused on external/internal beam-column joints and ductile/brittle weak columns, designed for gravity loads only, using low-strength concrete and plain steel reinforcing bars. First, a number of damage states are proposed and linked deterministically with commonly employed methods of repair and related activities. Results from previous experimental studies are used to develop empirical relationships between damage states and engineering demand parameters, such as interstory and column drift ratios. Probability distributions are fit to the empirical data and the associated statistical parameters are evaluated using statistical methods. Repair costs for damaged RC components are then estimated based on detailed quantity survey of a number of pre-70 RC buildings, using Italian costing manuals. Finally, loss functions are derived to predict the level of monetary losses to individual RC components as a function of the experienced response demand.

• Membrane and Water Treatment
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• v.9 no.6
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• pp.455-462
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• 2018

The measurement and monitoring of the biochemical oxygen demand (BOD) play an important role in the planning and operation of wastewater treatment plants. The most basic method for determining biochemical oxygen demand is direct measurement. However, this method is both expensive and takes a long time. A five-day period is required to determine the biochemical oxygen demand. This study has been carried out in a wastewater treatment plant in Turkey (Hurma WWTP) in order to estimate the biochemical oxygen demand a shorter time and with a lower cost. Estimation was performed using artificial neural network (ANN) method. There are three different methods in the training of artificial neural networks, respectively, multi-layered (ML-ANN), teaching learning based algorithm (TLBO-ANN) and artificial bee colony algorithm (ABC-ANN). The input flow (Q), wastewater temperature (t), pH, chemical oxygen demand (COD), suspended sediment (SS), total phosphorus (tP), total nitrogen (tN), and electrical conductivity of wastewater (EC) are used as the input parameters to estimate the BOD. The root mean squared error (RMSE) and the mean absolute error (MAE) values were used in evaluating performance criteria for each model. As a result of the general evaluation, the ML-ANN method provided the best estimation results both training and test series with 0.8924 and 0.8442 determination coefficient, respectively.

• International Journal of Highway Engineering
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• v.15 no.2
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• pp.169-181
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• 2013

PURPOSES : This study is to suggest the Influence of road capital to industry and productivity growth in South Korea. METHODS : Based on the literature review, The relevant policy questions addressed in this report are : cost reduction and Scale elasticities of road, effect of road capital stock on demand for labor, capital and materials, marginal effect of road, industry TFP growth decomposition. RESULTS : The marginal benefits of the road capital at the industry level were calculated using the estimated cost elasticities. Demand for the road capital services varies across industries as do the marginal effects. The marginal benefits are positive for the principal industries. This suggests that for these industries the existing stock of road capital may be under supplied. The contribution of road capital to TFP growth is positive in principal industries. The main contribution of road capital is in the manufacturing industries ; the magnitudes of contribution varies among industries. These results indicate that growth in exogenous demand is most important contributor to TFP growth. CONCLUSIONS : The road capital have a significant effect on employment, private capital and demand for materials inputs in all industries. At a given level of output, an increase in road capital lead to variety to demand for all inputs in all industries.

• Plant Journal
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• v.15 no.1
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• pp.31-37
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• 2019

In this study, to improve the accuracy of forecast of heat demand in the district heating system, this study applied heat demand performance among the main factors of district heating demand forecast in Pankyo area as the heat sales information of the user facility instead of existing heat source facility heat supply information, and compared the existing method with the accuracy based on the actual value. As a result of comparing the difference of the forecasts values of the existing and changed methods based on the performance values over the one week (2018.01.08 ~ 01.14) during the hot water peak, the relative error decreased from 7% to 3% The relative error between the existing and revised forecasts was 9% and 4%, respectively, for the five-month cumulative heat demand from February to February 2018, Also, in case of the weekend where the demand of heat is differentiated, the relative error of the forecasts value is consistently reduced from 10% to 5%.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.12 no.11
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• pp.5178-5187
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• 2011

National Competency Standards(NCS) development related researches on the semiconductor industry were carried out partially, but the field training demand survey and analysis using that NCS were not done. The past demand survey for the job skill training had focused on personnel shortage and oversupply so it has the problems called skill mismatch. This study has the purpose to provide an alternative analysis of qualitative evaluation using the relative importance and gap of the job skill elements in the semiconductor industry. As research methods, we carried out related literature and report review, and a job skill demand survey on the semiconductor industry. We analyzed about the industry related jobs and job tasks, the qualitative demand for each job skill elements, and procurement methods for each job skills and manpower. We illustrated some related training courses to find out a relevant way for supplying the training programs.