• The Transactions of the Korea Information Processing Society
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• v.4 no.7
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• pp.1749-1758
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• 1997

This paper suggests the development of dynamic forecasting model for short-term power demand based on Radial Basis Function Network and Pal's GLVQ algorithm. Radial Basis Function methods are often compared with the backpropagation training, feed-forward network, which is the most widely used neural network paradigm. The Radial Basis Function Network is a single hidden layer feed-forward neural network. Each node of the hidden layer has a parameter vector called center. This center is determined by clustering algorithm. Theatments of classical approached to clustering methods include theories by Hartigan(K-means algorithm), Kohonen(Self Organized Feature Maps %3A SOFM and Learning Vector Quantization %3A LVQ model), Carpenter and Grossberg(ART-2 model). In this model, the first approach organizes the load pattern into two clusters by Pal's GLVQ clustering algorithm. The reason of using GLVQ algorithm in this model is that GLVQ algorithm can classify the patterns better than other algorithms. And the second approach forecasts hourly load patterns by radial basis function network which has been constructed two hidden nodes. These nodes are determined from the cluster centers of the GLVQ in first step. This model was applied to forecast the hourly loads on Mar. $4^{th},\;Jun.\;4^{th},\;Jul.\;4^{th},\;Sep.\;4^{th},\;Nov.\;4^{th},$ 1995, after having trained the data for the days from Mar. $1^{th}\;to\;3^{th},\;from\;Jun.\;1^{th}\;to\;3^{th},\;from\;Jul.\;1^{th}\;to\;3^{th},\;from\;Sep.\;1^{th}\;to\;3^{th},\;and\;from\;Nov.\;1^{th}\;to\;3^{th},$ 1995, respectively. In the experiments, the average absolute errors of one-hour ahead forecasts on utility actual data are shown to be 1.3795%.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.21 no.5
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• pp.458-465
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• 2020

The purpose of this study is to analyze the effect of policy in Jeju, using a macro-economic forecasting model of Jeju. First, the model's reality explanatory power improved by updating its statistics to 2017 and expanding new policy variables and modules. Also, the industrial structure of the model was further subdivided and extended to be considered simultaneously in the demand side of Keynesian theory. Second, it was determined that the predictive power for the model of this study was better than that of the existing model. However, with some endogenous variables, it was possible to identify implications that should be developed and considered when the model is improved with additional data in the future. Third, when the second airport construction was considered, it was observed that its effect was an increase of 1.25 times for GRDP, 1.2 times for employment, 1.48 times for private consumption, and 2.06 times for investment. Also, the economic growth rate was estimated to be 1.6% point higher than when the second airport was not constructed. Finally, the results of this study are expected to be used for policy decision making of the Jeju Government.

• Journal of Navigation and Port Research
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• v.48 no.1
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• pp.7-16
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• 2024

Although the number of ocean-going merchant ships is increasing, the number of Korean marine officers is decreasing. This manpower shortage problem is becoming more serious. This study objectively measured factors determining the demand and supply of ocean-going merchant ship officers and forecasted the exact manpower demand and supply. Demand was predicted by applying the number of ship officers required for each ship size to the number of ships forecasted. The supply was predicted by segmenting by position and age using the Markov model, reflecting increase/decrease factors such as promotion, turnover, retirement, and new entry by year. The demand for ocean-going merchant ship officers will increase from 11,638 in 2023 to 13,879 in 2030 while the supply will decrease from7,006 in 2023 to 6,426 in 2030, with the shortage expected to exceed 10,000 in 2040. This study can be used as a reference to solve the problem of manpower shortage for ocean-going merchant ship officers by improving the accuracy of predictions through objective data, scientific analysis methods, and logical reasoning.

• Journal of Korean Institute of Industrial Engineers
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• v.5 no.1
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• pp.1-6
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• 1979

This paper discusses an application of discrete variable Servo Theory to the analysis of the effectiveness of production-inventory control system which uses exponential smoothing as a specific forecasting technique by establishing a new model which consists of such three departments as production planning, production, and inventory. The objective of the new production-inventory model is to keep the production to the optimal level of minimum production cost in production planning problem for obtaining, the stability of inventory subject to demand variation. On this basis, the dynamic characteristic of the system with the change of the parameters is clarified by the numerical analysis. The results of the numerical analysis show the effect that is obtained by the simultaneous stability of production and inventory as soon as possible.

• Journal of Korean Society for Quality Management
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• v.19 no.1
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• pp.141-150
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• 1991

This paper is aimed to construct a CIM(Computer Intergrated Manufacturing) model through optimal flexible manufacturing systems for factory automation in order to provide higher productivity. This paper provides a ease through an analytical method to construct flexible manufacturing systems for factory automation. The contents of this paper include forecasting of demands which analyze variation of demand using Winter's model, line balancing utilizing Lanked Positional Weight Method, and a case analysis through simulation by Monte Carlor Method. The result shows the manpower and net present value of investment have decreased 42% and 19.6%, respectively, and yearly net profit has increased 30%.

• Journal of Korean Society for Geospatial Information Science
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• v.8 no.1 s.15
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• pp.65-72
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• 2000

Travel demand forecasting is the important process of transportation policy and planning, especially trip assignment is also important because it finds deficiency of network GIS can be applied to transportation due to its various merits. Recently Program development environment is changed to component-based and transportation-component is necessary. This study evolves in implementing trip assignment model with GIS and tries to apply the system to the Cheongju City.

• ETRI Journal
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• v.46 no.3
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• pp.379-391
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• 2024

To meet increasing traffic requirements in mobile networks, small base stations (SBSs) are densely deployed, overlapping existing network architecture and increasing system capacity. However, densely deployed SBSs increase energy consumption and interference. Although these problems already exist because of densely deployed SBSs, even more SBSs are needed to meet increasing traffic demands. Hence, base station (BS) switching operations have been used to minimize energy consumption while guaranteeing quality-of-service (QoS) for users. In this study, to optimize energy efficiency, we propose the use of deep reinforcement learning (DRL) to create a BS switching operation strategy with a traffic prediction model. First, a federated long short-term memory (LSTM) model is introduced to predict user traffic demands from user trajectory information. Next, the DRL-based BS switching operation scheme determines the switching operations for the SBSs using the predicted traffic demand. Experimental results confirm that the proposed scheme outperforms existing approaches in terms of energy efficiency, signal-to-interference noise ratio, handover metrics, and prediction performance.

• Journal of Intelligence and Information Systems
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• v.20 no.4
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• pp.1-23
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• 2014

Market forecasting aims to estimate the sales volume of a product or service that is sold to consumers for a specific selling period. From the perspective of the enterprise, accurate market forecasting assists in determining the timing of new product introduction, product design, and establishing production plans and marketing strategies that enable a more efficient decision-making process. Moreover, accurate market forecasting enables governments to efficiently establish a national budget organization. This study aims to generate a market growth curve for ICT (information and communication technology) goods using past time series data; categorize products showing similar growth patterns; understand markets in the industry; and forecast the future outlook of such products. This study suggests the useful and meaningful process (or methodology) to identify the market growth pattern with quantitative growth model and data mining algorithm. The study employs the following methodology. At the first stage, past time series data are collected based on the target products or services of categorized industry. The data, such as the volume of sales and domestic consumption for a specific product or service, are collected from the relevant government ministry, the National Statistical Office, and other relevant government organizations. For collected data that may not be analyzed due to the lack of past data and the alteration of code names, data pre-processing work should be performed. At the second stage of this process, an optimal model for market forecasting should be selected. This model can be varied on the basis of the characteristics of each categorized industry. As this study is focused on the ICT industry, which has more frequent new technology appearances resulting in changes of the market structure, Logistic model, Gompertz model, and Bass model are selected. A hybrid model that combines different models can also be considered. The hybrid model considered for use in this study analyzes the size of the market potential through the Logistic and Gompertz models, and then the figures are used for the Bass model. The third stage of this process is to evaluate which model most accurately explains the data. In order to do this, the parameter should be estimated on the basis of the collected past time series data to generate the models' predictive value and calculate the root-mean squared error (RMSE). The model that shows the lowest average RMSE value for every product type is considered as the best model. At the fourth stage of this process, based on the estimated parameter value generated by the best model, a market growth pattern map is constructed with self-organizing map algorithm. A self-organizing map is learning with market pattern parameters for all products or services as input data, and the products or services are organized into an $N{\times}N$ map. The number of clusters increase from 2 to M, depending on the characteristics of the nodes on the map. The clusters are divided into zones, and the clusters with the ability to provide the most meaningful explanation are selected. Based on the final selection of clusters, the boundaries between the nodes are selected and, ultimately, the market growth pattern map is completed. The last step is to determine the final characteristics of the clusters as well as the market growth curve. The average of the market growth pattern parameters in the clusters is taken to be a representative figure. Using this figure, a growth curve is drawn for each cluster, and their characteristics are analyzed. Also, taking into consideration the product types in each cluster, their characteristics can be qualitatively generated. We expect that the process and system that this paper suggests can be used as a tool for forecasting demand in the ICT and other industries.

• Korean Journal of Agricultural Science
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• v.50 no.4
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• pp.629-643
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• 2023

The purpose of this study was to examine the effectiveness and efficiency of a policy by comparing and analyzing the impact of the rice market isolation system and production adjustment system (strategic crops direct payment system that induces the cultivation of other crops instead of rice) on rice supply, rice price, and government's financial expenditure. To achieve this purpose, a rice supply and demand forecasting and policy simulation model was developed in this study using a partial equilibrium model limited to a single item (rice), a dynamic equation model system, and a structural equation system that reflects the casual relationship between variables with economic theory. The rice policy analysis model used a recursive model and not a simultaneous equation model. The policy is distinct from that of previous studies, in which changes in government's policy affected the price of rice during harvest and the lean season before the next harvest, and price changes affected the supply and demand of rice according to the modeling, that is, a more specific policy effect analysis. The analysis showed that the market isolation system increased government's financial expenditure compared to the production adjustment system, suggesting low policy financial efficiency, low policy effectiveness on target, and increased harvest price. In particular, the market isolation system temporarily increased the price during harvest season but decreased the price during the lean season due to an increase in ending stock caused by increased production and government stock. Therefore, a decrease in price during the lean season may decrease annual farm-gate prices, and the reverse seasonal amplitude is expected to intensify.

• Journal of the Korean Society of Costume
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• v.50 no.2
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• pp.29-45
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• 2000

The object of this study is to predict the import demands for korean textile, textile products and clothing products. The analyzing method performs through demand prediction method is by using Exponential Smoothing Model and STATGRAPHICS. The result from the practice of study is as follows ; Textile import ratio is expected to be increased constantly and the portion of textile import in our national total import is precited to reach to 3.92% in 2003. The import of the textile product to textile will be increased to 33.12% in 2003. The import ratio of clothing product ratio is also estimated to increase annually, Import ratio of clothing-product in textile-product import reaching to total 6.42% (83.89% in 2000, 90.31% in 2003), the growth rate of clothing import will be much higher than that of clothing export. From 2000 to 2003 , textile import is precited to be 5.23%. The import of the textile product will be increased by 8.04%. The import of clothing product will reaches 11.21%, which would be the highest rate among the products under review. Also , it predicts the constant increase as a result of prediction in the nation's total amount of import including the import amount of textile, textile-product, and clothing product.