• Journal of Korean Institute of Industrial Engineers
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• v.23 no.1
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• pp.115-127
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• 1997

A GDHS(Geothermal District Heating System) is a heating system supplying a group of districts with heat extracted from geothermal sources. The advantages of GDHS include saving fuel consumption as well as reducing air pollution. This paper presents a case study for the economic feasibility model and analysis of a GDHS with which central/individual heating systems are replaced. Configuring to a simplified GDHS which consisits of subsurface systems, surface systems, and transmission/distribution systems, we find out the properties of the system and the model parameters affecting the initial investment/operating costs in order to develop a classical economic feasibility model given geothermal temperature. Based on our model parameter space, we analyzed the geothermal development project of the Jejoo Island probabilistically given prior information such as the expected geothermal power, the demand size and the length of transmission/distribution pipes.

• Journal of the Korea Safety Management & Science
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• v.7 no.4
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• pp.207-217
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• 2005

Line capacity calculation has been used to determine optimum efficiency and safe train service for train scheduling plan and investment priority order throughout detecting bottleneck section. Because of some problems of Yamagisi and UIC methods for line capacity calculation, developing of the method of line capacity caculation and evaluation for the Korea circumstance is important. This paper deals with the reliability improvement on the integrated system of TPS(Train Performance Simulator), PES(Parameter Evaluation Simulator), LCS(Line Capacity Simulator) and simulation and sensitivity analysis for line capacity.

• East Asian mathematical journal
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• v.38 no.3
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• pp.277-292
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• 2022

This study investigates the convergence of the optimal consumption and investment policies in a binomial-tree model to those in the continuous-time model of Merton (1969). We provide the convergence in explicit form and show that the convergence rate is of order ∆t, which is the length of time between consecutive time points. We also show by numerical solutions with realistic parameter values that the optimal policies in the binomial-tree model do not differ significantly from those in the continuous-time model for long-term portfolio management with a horizon over 30 years if rebalancing is done every 6 months.

• Journal of Labour Economics
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• v.25 no.3
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• pp.1-34
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• 2002

This paper investigates the effects of hours reduction on growth, investment, and consumption as well as employment. We adopt the basic framework of the indivisibility of labor developed by Hansen (1985) and Rogerson (1988) and extend it by allowing heterogeneity of workers in productive efficiency. On the basis of monthly panel data constructed from Economically Active Population Surveys and Household Income and Expenditure Surveys, we estimate the value of productive efficiency parameter of newly hired workers relative to existing workers by considering differences between the two groups in unobservable as well as observable worker characteristics. Numerical simulation of steady states demonstrates that reduction of statutory weekly hours from 44 to 40 leads to a rise in employees by 4.9 percent. However, GNP, investment, and consumption are all reduced by 2.03 percent, which is attributed to reduction in the amount of effective labor input, which in turn comes from reduction of actual average hours and productivity differences between exiting and newly hired workers.

• Journal of Electrical Engineering and Technology
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• v.13 no.1
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• pp.372-378
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• 2018

Life management technology is required as the failure risk of aged power transformers increases. Asset management technology is developed to evaluate the remaining life, establish the replacement strategies, and decide the optimal investment based on the reliability and economy of power transformers. The remaining life assessment uses data such as installation, operation, maintenance, refurbishment, and failure of power transformers. The optimal investment also uses data such as maintenance, outage, and social costs. To develop the asset management system for power transformers, determining the degradation parameters related to the aging of power transformers and evaluating the condition of power transformers using these parameters are important. In this study, since 1983, 110,000 Dissolved Gas Analysis (DGA) data have been analyzed to determine the degradation parameters related to the aging of power transformers. The alarm rates of combustible gases ($H_2$, $C_2H_2$, $C_2H_4$, $CH_4$, and $C_2H_6$), TCG, CO, and $CO_2$ were analyzed. The end of life and failure rate (bathtub curve) of power transformers were also calculated based on the failure data from 1981 to 2014. The DGA gases related to discharge, overheating, and insulation degradation were determined based on alarm and failure rates. $C_2H_2$, $C_2H_6$, and $CO_2$ were discharge, oxidation, and insulation degradation parameters related to the aging of power transformers.

• Journal of Korean Society of Forest Science
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• v.90 no.2
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• pp.176-183
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• 2001

Loblolly pine (Pinus taeda L.) is the most economically important timber producing species in the southern United States. Much attention has been given to predicting diameter distributions for the solution of multiple-product yield estimates. The three-parameter Weibull diameter distribution yield prediction systems were developed for loblolly pine plantations. A parameter recovery procedure for the Weibull distribution function based on four percentile equations was applied to develop diameter distribution yield prediction models. Four percentiles (0th, 25th, 50th, 95th) of the cumulative diameter distribution were predicted as a function of quadratic mean diameter. Individual tree height prediction equations were developed for the calculation of yields by diameter class. By using individual tree content prediction equations, expected yield by diameter class can be computed. To reduce rounding-off errors, the Weibull cumulative upper bound limit difference procedure applied in this study shows slightly better results compared with upper and lower bound procedure applied in the past studies. To evaluate this system, the predicted diameter distributions were tested against the observed diameter distributions using the Kolmogorov-Smirnov two sample test at the ${\alpha}$=0.05 level to check if any significant differences existed. Statistically, no significant differences were detected based on the data from 516 evaluation data sets. This diameter distribution yield prediction system will be useful in loblolly pine stand structure modeling, in updating forest inventories, and in evaluating investment opportunities.

• Journal of Information Technology Services
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• v.19 no.2
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• pp.109-124
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• 2020

Facing the 4^th Industrial Revolution era, researches on artificial intelligence have become active and attempts have been made to apply machine learning in various fields. In the field of finance, Robo Advisor service, which analyze the market, make investment decisions and allocate assets instead of people, are rapidly expanding. The stock price prediction using the machine learning that has been carried out to date is mainly based on the prediction of the market index such as KOSPI, and utilizes technical data that is fundamental index or price derivative index using financial statement. However, most researches have proceeded without any explicit verification of the prediction rate of the learning data. In this study, we conducted an experiment to determine the degree of market prediction ability of basic indicators, technical indicators, and system risk indicators (AR) used in stock price prediction. First, we set the core parameters for each financial indicator and define the objective function reflecting the return and volatility. Then, an experiment was performed to extract the sample from the distribution of each parameter by the Markov chain Monte Carlo (MCMC) method and to find the optimum value to maximize the objective function. Since Robo Advisor is a commodity that trades financial instruments such as stocks and funds, it can not be utilized only by forecasting the market index. The sample for this experiment is data of 17 years of 1,500 stocks that have been listed in Korea for more than 5 years after listing. As a result of the experiment, it was possible to establish a meaningful trading strategy that exceeds the market return. This study can be utilized as a basis for the development of Robo Advisor products in that it includes a large proportion of listed stocks in Korea, rather than an experiment on a single index, and verifies market predictability of various financial indicators.

• The KIPS Transactions:PartD
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• v.9D no.1
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• pp.75-80
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• 2002

Putnam develops SLIM (Software LIfecycle Management) model based upon the assumption that the manpower utilization during software project development is followed by a Rayleigh distribution. To obtain the manpower distribution, we have to be estimate the total development effort and difficulty ratio parameter. We need a way to accurately estimate these parameters early in the requirements and specification phase before investment decisions have to be made. Statistical tests show that system attributes are highly correlation (redundant) so that Putnam discards one and get a parameter estimator from the other attributes. But, different statistical method has different system attributes and presents different performance. To select the principle system attributes, this paper uses the principle component analysis (PCA) instead of Putnam's method. The PCA's results improve a 9.85 percent performance more than the Putnam's result. Also, this model seems to be simple and easily realize.

• Asia pacific journal of information systems
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• v.7 no.1
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• pp.67-83
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• 1997

The utility of learning techniques in investment analysis has been demonstrated in many areas, ranging from forecasting individual stocks to entire market indexes. To date, however, the application of artificial intelligence to financial forecasting has focused largely on short predictive horizons. Usually the forecast window is a single period ahead; if the input data involve daily observations, the forecast is for one day ahead; if monthly observations, then a month ahead; and so on. Thus far little work has been conducted on the efficacy of long-term prediction involving multiperiod forecasting. This paper examines the impact of alternative procedures for extended prediction using knowledge discovery techniques. One dimension in the study involves temporal granularity: a single jump from the present period to the end of the forecast window versus a web of short-term forecasts involving a sequence of single-period predictions. Another parameter relates to the numerosity of input variables: a technical approach involving only lagged observations of the target variable versus a fundamental approach involving multiple variables. The dual possibilities along each of the granularity and numerosity dimensions entail a total of 4 models. These models are first evaluated using neural networks, then compared against a multi-input jump model using case based reasoning. The computational models are examined in the context of forecasting the S&P 500 index.

• Journal of the Korean Society for Library and Information Science
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• v.38 no.3
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• pp.103-118
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• 2004

Much investment has been to improve quality of web service as internet service is in pursuit of equilibrium. Web site of electronic library which offers various information has to be set a new standard for improving users service satisfaction by developing objsctive evaluation system. In this research we acquired standard of evaluation parameter such as contents, design, navigation, feedback, reference, privacy by analyzing domestic web site of electronic library We evaluated constructed model of usability evaluation using AHP(Analytic Hierarchy Process). We develop 'digital library web site evaluation model' based systematic analysis through polling category selected as evaluation guide.