• Proceedings of the Korea Inteligent Information System Society Conference
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• 2000.04a
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• pp.235-241
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• 2000

This article suggests integrated neural network models for the interest rate forecasting using change point detection. The basic concept of proposed model is to obtain intervals divided by change point, to identify them as change-point groups, and to involve them in interest rate forecasting. the proposed models consist of three stages. The first stage is to detect successive change points in interest rate dataset. The second stage is to forecast change-point group with data mining classifiers. The final stage is to forecast the desired output with BPN. Based on this structure, we propose three integrated neural network models in terms of data mining classifier: (1) multivariate discriminant analysis (MDA)-supported neural network model, (2) case based reasoning (CBR)-supported neural network model and (3) backpropagation neural networks (BPN)-supported neural network model. Subsequently, we compare these models with a neural networks (BPN)-supported neural network model. Subsequently, we compare these models with a neural network model alone and, in addition, determine which of three classifiers (MDA, CBR and BPN) can perform better. This article is then to examine the predictability of integrated neural network models for interest rate forecasting using change-point detection.

• The Journal of Information Technology
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• v.5 no.4
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• pp.173-183
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• 2002

In this paper, we propose an efficient forecasting methodology of the mid and long-term frequency demand in Korea. The methodology consists of the following three steps: classification of basic service group, calculation of effective traffic, and frequency forecasting. Based on the previous studies, we classify the services into wide area mobile, short range radio, fixed wireless access and digital video broadcasting in the step of the classification of basic service group. For the calculation of effective traffic, we use the measures of erlang and bps. The step of the calculation of effective traffic classifies the user and basic application, and evaluates the effective traffic. Finally, in the step of frequency forecasting, different methodology will be proposed for each service group.

• Journal of the Korea Institute of Military Science and Technology
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• v.27 no.3
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• pp.406-415
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• 2024

As the equipment of the military has become more advanced and expensive, the cost of securing spare parts is also constantly increasing along with the increase in equipment assets. In particular, forecasting demand for spare parts one of the important management tasks in the military, and the accuracy of these predictions is directly related to military operations and cost management. However, because the demand for spare parts is intermittent and irregular, it is often difficult to make accurate predictions using traditional statistical methods or a single statistical or machine learning model. In this paper, we propose a model that can increase the accuracy of demand forecasting for irregular patterns of spare parts demanding by using a combination of statistical and machine learning algorithm, and through experiments on Cheonma spare parts demanding data.

• Journal of Electrical Engineering and Technology
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• v.10 no.3
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• pp.832-837
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• 2015

The group method of data handling (GMDH) algorithm has proven to be a powerful and effective way to extract rules or polynomials from an electric load pattern. However, because it is nonstationary, the load pattern needs to be decomposed using a discrete wavelet transform. In addition, if a load pattern has a complicated curve pattern, GMDH should use a higher polynomial, which requires complex computing and consumes a lot of time. This paper suggests a method for short-term electric load forecasting that uses a wavelet transform and a GMDH algorithm. Case studies with the proposed algorithm were carried out for one-day-ahead forecasting of hourly electric loads using data during the years 2008-2011. To prove the effectiveness of our proposed approach, the results were evaluated and compared with those obtained by Holt-Winters method and artificial neural network. Our suggested method resulted in better performance than either comparison group.

• Korean System Dynamics Review
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• v.7 no.1
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• pp.67-90
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• 2006

A series of system dynamics model was developed for forecasting demand and supply of human resource in the electricity industry. To forecast demand of human resource in the electric power industry, BLS (Bureau of Labor Statistics) methodology was used. To forecast supply of human resource in the electric power industry, forecasting on the population of our country and the number of students in the department of electrical engineering were performed. After performing computer simulation with developed system dynamics model, it is discovered that the shortage of human resource in the electric power industry will be 3,000 persons per year from 2006 to 2015, and more than a double of current budget is required to overcome this shortage of human resource.

• Journal of Information Technology Applications and Management
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• v.13 no.1
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• pp.77-94
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• 2006

Case-based Reasoning (CBR) is widely used in business and industry prediction. It is suitable to solve complex and unstructured business problems. Recently, the prediction accuracy of CBR has been enhanced by not only various machine learning algorithms such as genetic algorithms, relative weighting of Artificial Neural Network (ANN) input variable but also data mining technique such as feature selection, feature weighting, feature transformation, and instance selection As a result, CBR is even more widely used today in business area. In this study, we investigated the usefulness of the CBR method in forecasting success in implementing ERP systems. We used a CBR method based on the feature weighting technique to compare the performance of three different models : MDA (Multiple Discriminant Analysis), GECBR (GEneral CBR), FWCBR (CBR with Feature Weighting supported by Analytic Hierarchy Process). The study suggests that the FWCBR approach is a promising method for forecasting of successful ERP implementation in Small and Medium sized Enterprises.

• KEPCO Journal on Electric Power and Energy
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• v.8 no.2
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• pp.55-59
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• 2022

This paper introduce Renewable Energy forecasting technology, which is a part of renewable management system. Then, calculation method of dedicated transmission line's meteorological data to forecast renewable energy is suggested. As the case of dedicated transmission line, there is only power output data combined the number of renewable plants' output that acquired from circuit breakers. So it is need to calculate meteorological data for dedicated transmission line that matched combined power output data. this paper suggests two calculation method. First method is select the plant has the largest capacity, and use it's meteorological data as line meteorological data. Second method is average with weight that given according to plants' capacity. In case study, suggested methods are applied to real data. Then use calculated data to Renewable forecasting and analyze the forecasting results.

• Journal of the Korea Institute of Military Science and Technology
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• v.23 no.3
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• pp.286-293
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• 2020

Since Dr. Lipke announces earned schedule management(ESM) in 2002, it has been used in project management to make up for the insufficient schedule management function of earned value management technique. However, it is difficult to accurately forecast the schedule of delayed defense research and development(R&D) projects with the ESM technique. Therefore, this paper proposes a new schedule forecasting method considering the progress of delayed work in ESM technique. This concept can also be adopted to the traditional project progress management (PPM) technique. We verify the effectiveness of the proposed concept through several defense R&D projects and prove that it is possible to supplement the schedule forecasting of the ESM and PPM technique.

• Proceedings of the Korea Inteligent Information System Society Conference
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• 2000.11a
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• pp.417-426
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• 2000

This study suggests integrated neural network models for Interest rate forecasting using change-point detection, classifiers, and classification functions based on structural change. The proposed model is composed of three phases with tee-staged learning. The first phase is to detect successive and appropriate structural changes in interest rare dataset. The second phase is to forecast change-point group with classifiers (discriminant analysis, logistic regression, and backpropagation neural networks) and their. combined classification functions. The fecal phase is to forecast the interest rate with backpropagation neural networks. We propose some classification functions to overcome the problems of two-staged learning that cannot measure the performance of the first learning. Subsequently, we compare the structured models with a neural network model alone and, in addition, determine which of classifiers and classification functions can perform better. This article then examines the predictability of the proposed classification functions for interest rate forecasting using structural change.

• International Journal of Contents
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• v.17 no.3
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• pp.1-14
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• 2021

Since the outbreak of the coronavirus disease 2019 (COVID-19) pandemic, a lot of efforts have been made in the field of data science to help combat against this disease. Among them, forecasting the number of cases of infection is a crucial problem to predict the development of the pandemic. Many deep learning-based models can be applied to solve this type of time series problem. In this research, we would like to take a step forward to incorporate spatial data (geography) with time series data to forecast the cases of region-level infection simultaneously. Specifically, we model a single spatio-temporal graph, in which nodes represent the geographic regions, spatial edges represent the distance between each pair of regions, and temporal edges indicate the node features through time. We evaluate this approach in COVID-19 in a Korean dataset, and we show a decrease of approximately 10% in both RMSE and MAE, and a significant boost to the training speed compared to the baseline models. Moreover, the training efficiency allows this approach to be extended for a large-scale spatio-temporal dataset.