• Journal of Korean Society of Industrial and Systems Engineering
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• v.23 no.55
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• pp.25-31
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• 2000

To make a better decision about when to shutdown a nuclear power plant, we build a decision model using influence diagrams. We proceed the analysis adopting a bayesian approach. Firstly, an accident arrival rate is assumed to be known and this assumption is relaxed later. We perform our analysis on the cases of exponential time to accidents, and gamma distribution for the arrival rate. An optimal shutdown time is obtained considering the trade-off between the costs incurred by an accident due to late shutdown and the possible loss of revenues due to the early shutdown. We also derive the upper bound of the failure rate where we may operate the plant.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.12 no.19
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• pp.67-72
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• 1989

This paper concerns with the decision maker has the job of forecasting capital investments and operating expenses to aid the decision making in choosing and evaluating present annual and future alternatives. The cost estimating function eventually analysis, evaluates and choose the alternatives. And also, the marginal analysis performed originally from a preliminary design of some sort, and eventually plans are made to investigate investment possibilities. This paper provide the discounted net cash flows and the present, annual and future worth methods. In despite of any choice for an analytical methods, there remains the problems of predicting and assessments certain future events. Therefore, these models dealing with the optimal plant sizing, equipments replacement, and lease or buy will be discussed.

• 제어로봇시스템학회:학술대회논문집
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• 2004.08a
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• pp.1859-1864
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• 2004

An effective methodology is reported for the optimal design of multisite batch production/transportation and storage networks under uncertain demand forecasting. We assume that any given storage unit can store one material type which can be purchased from suppliers, internally produced, internally consumed, transported to or from other plant sites and/or sold to customers. We further assume that a storage unit is connected to all processing and transportation stages that consume/produce or move the material to which that storage unit is dedicated. Each processing stage transforms a set of feedstock materials or intermediates into a set of products with constant conversion factors. A batch transportation process can transfer one material or multiple materials at once between plant sites. The objective for optimization is to minimize the probability averaged total cost composed of raw material procurement, processing setup, transportation setup and inventory holding costs as well as the capital costs of processing stages and storage units. A novel production and inventory analysis formulation, the PSW(Periodic Square Wave) model, provides useful expressions for the upper/lower bounds and average level of the storage inventory. The expressions for the Kuhn-Tucker conditions of the optimization problem can be reduced to two sub-problems. The first yields analytical solutions for determining lot sizes while the second is a separable concave minimization network flow subproblem whose solution yields the average material flow rates through the networks for the given demand forecast scenario. The result of this study will contribute to the optimal design and operation of large-scale supply chain system.

• Korean Journal of Remote Sensing
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• v.32 no.2
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• pp.155-169
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• 2016

In this study, we developed a multi-sensor blending short-term rainfall forecasting technique using radar and satellite data during extreme rainfall occurrences in Busan and Gyeongnam region in August 2014. The Tropical Z-R relationship ($Z=32R^{1.65}$) has applied as a optimal radar Z-R relation, which is confirmed that the accuracy is improved during 20mm/h heavy rainfall. In addition, the multi-sensor blending technique has applied using radar and COMS (Communication, Ocean and Meteorological Satellite) data for quantitative precipitation estimation. The very-short-term rainfall forecasting performance was improved in 60 mm/h or more of the strong heavy rainfall events by multi-sensor blending. AWS (Automatic Weather System) and MAPLE data were used for verification of rainfall prediction accuracy. The results have ensured about 50% or more in accuracy of heavy rainfall prediction for 1-hour before rainfall prediction, which are correlations of 10-minute lead time have 0.80 to 0.53, and root mean square errors have 3.99 mm/h to 6.43 mm/h. Through this study, utilizing of multi-sensor blending techniques using radar and satellite data are possible to provide that would be more reliable very-short-term rainfall forecasting data. Further we need ongoing case studies and prediction and estimation of quantitative precipitation by multi-sensor blending is required as well as improving the satellite rainfall estimation algorithm.

• Journal of Korean Society of Transportation
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• v.26 no.5
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• pp.101-115
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• 2008

This study reports UTIS(Urban Traffic Information System), which has been generalized in developed countries through brisk research and development and is being promoted for introduction by National Police Agency and Road Traffic Authority to reduce the astronomical amount of social expenses including traffic congestion expenses. Also this study investigates the proper charges for using by the preestimate of demand and contentment according to methods of payment after the service is introduced. The results of this study are as follows. First, demand forecast model is constructed by Binary Logit Model. Second, forecast models of using aspects of UTIS service according to methods of payment are established by Ordered Probit Model. Third, the proper charges for using of UTIS service according to methods of payment are presented to the supplier in the aspects of users. For this, preferences by using aspects and methods of payment are captured. And unit elasticity of coefficient of utilization is understood through responsiveness analysis according to methods of payment.

• Journal of Bio-Environment Control
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• v.31 no.3
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• pp.152-162
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• 2022

This study developed simulation model for predicting the greenhouse interior environment using artificial intelligence machine learning techniques. Various methods have been studied to predict the internal environment of the greenhouse system. But the traditional simulation analysis method has a problem of low precision due to extraneous variables. In order to solve this problem, we developed a model for predicting the temperature inside the greenhouse using machine learning. Machine learning models are developed through data collection, characteristic analysis, and learning, and the accuracy of the model varies greatly depending on parameters and learning methods. Therefore, an optimal model derivation method according to data characteristics is required. As a result of the model development, the model accuracy increased as the parameters of the hidden unit increased. Optimal model was derived from the GRU algorithm and hidden unit 6 (r² = 0.9848 and RMSE = 0.5857℃). Through this study, it was confirmed that it is possible to develop a predictive model for the temperature inside the greenhouse using data outside the greenhouse. In addition, it was confirmed that application and comparative analysis were necessary for various greenhouse data. It is necessary that research for development environmental control system by improving the developed model to the forecasting stage.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.30 no.5D
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• pp.477-483
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• 2010

The existing mass transport system, with its limited capacity and the saturated road networks, has given cause for a new means of transport to be developed, and strong demands for such new means of transport are observed more than ever. However, the traffic authority is seeking a new transport system that focuses more on LRT(Light Rail Transit), a downsized version of the existing urban railroad, rather than one that is appropriate to solve the traffic problems. Moreover, local governments are experiencing difficulties in planning their own mass transportation(bus or urban railroad) as they have no specified criteria for selecting a mass transport system. Accordingly, there has been an increasingly loud voice that calls for criteria to determine which mass transport system befits each transportation corridor. This paper develops a mass-transport demand forecasting model based on the GIS Buffering analysis of each transportation corridor in the city, sets up the capacity for each mass transport system and presents the criteria for selecting an optimal mass transport system for each transportation corridor. It also presents a methodology that identifies necessary and sufficient conditions for selection and evaluation, since it is most important to select the optimal mass-transport system that can meet the demand by each mass-transportation corridor.

• International Journal of Internet, Broadcasting and Communication
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• v.12 no.3
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• pp.125-130
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• 2020

At the end of 1997, the volatility of the exchange rate intensified as the nation's exchange rate system was converted into a free-floating exchange rate system. As a result, managing the exchange rate is becoming a very important task, and the need for forecasting the exchange rate is growing. The exchange rate prediction model using the existing exchange rate prediction method, statistical technique, cannot find a nonlinear pattern of the time series variable, and it is difficult to analyze the time series with the variability cluster phenomenon. And as the number of variables to be analyzed increases, the number of parameters to be estimated increases, and it is not easy to interpret the meaning of the estimated coefficients. Accordingly, the exchange rate prediction model using artificial neural network, rather than statistical technique, is presented. Using DNN, which is the basis of deep learning among artificial neural networks, and LSTM, a recurrent neural network model, the number of hidden layers, neurons, and activation function changes of each model found the optimal exchange rate prediction model. The study found that although there were model differences, LSTM models performed better than DNN models and performed best when the activation function was Tanh.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.43 no.4
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• pp.76-83
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• 2020

Postal logistics organizations are characterized as having high labor intensity and short response times. These characteristics, along with rapid change in mail volume, make load scheduling a fundamental concern. Load analysis of major postal infrastructures such as post offices, sorting centers, exchange centers, and delivery stations is required for optimal postal logistics operation. In particular, the performance of mail traffic forecasting is essential for optimizing the resource operation by accurate load analysis. This paper addresses a traffic forecast problem of postal parcel that arises at delivery stations of Korea Post. The main purpose of this paper is to describe a method for predicting short-term traffic of postal parcel based on self-similarity analysis and to introduce an application of the traffic prediction model to postal logistics system. The proposed scheme develops multiple regression models by the clusters resulted from feature engineering and individual models for delivery stations to reinforce prediction accuracy. The experiment with data supplied by main postal delivery stations shows the advantage in terms of prediction performance. Comparing with other technique, experimental results show that the proposed method improves the accuracy up to 45.8%.

• KIEAE Journal
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• v.16 no.6
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• pp.69-75
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• 2016

Climate change has resulted in the urban heat island (UHI) effect throughout the globe, contributing to heat-related illness and fatalities. In order to reduce such damage, it is necessary to improve the climate observation network for precise observation of the urban thermal environment and quick UHI forecasting system. Purpose: This study analyzed the effective range of the climate observation network and the distribution of the existing Automatic Weather Stations (AWS) in Seoul to propose optimal locations for additional installment of AWS. Method: First, we performed quality analysis to pinpoint missing values and outliers within the high-density temperature data measured. With the result from the analysis, a spatial autocorrelation structure in the temperature data was tested to draw the effective range and correlation distance for each major time period. Result: As a result, it turned out that the optimal effective range for the climate observation network in Seoul in July was a radius of 2.8 kilometers. Based on this result, population density, and temperature data, we selected the locations for additional installment of AWS. This study is expected to be used to generate urban temperature maps, select and move measurement locations since it is able to suggest valid, specific spatial ranges when the data measured in point is converted into surface data.