• Proceedings of the Korea Association of Information Systems Conference
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• 2002.11a
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• pp.289-312
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• 2002

This study suggests the Link weight analysis approach to choose input variables and an integrated model to make more accurate bankruptcy prediction model. the Link weight analysis approach is a method to choose input variables to analyze each input node's link weight which is the absolute value of link weight between an input nodes and a hidden layer. There are the weak-linked neurons elimination method, the strong-linked neurons selection method in the link weight analysis approach. The Integrated Model is a combined type adapting Bagging method that uses the average value of the four models, the optimal weak-linked-neurons elimination method, optimal strong-linked neurons selection method, decision-making tree model, and MDA. As a result, the methods suggested in this study - the optimal strong-linked neurons selection method, the optimal weak-linked neurons elimination method, and the integrated model - show much higher accuracy than MDA and decision making tree model. Especially the integrated model shows much higher accuracy than MDA and decision making tree model and shows slightly higher accuracy than the optimal weak-linked neurons elimination method and the optimal strong-linked neurons selection method.

• Journal of Physiology & Pathology in Korean Medicine
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• v.23 no.6
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• pp.1460-1464
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• 2009

In spite of many studies on statistical model for pattern identifications (PIs), little attention has been paid to the complexity of pattern diagnosis processed by oriental physicians. The aim of this study is to develop a statistical diagnostic model which discriminates four PIs using multiple indicators in stroke. Clinical data were collected from 981 stroke patients and 516 data of which PIs were agreed by two independent physicians were included. Discriminant analysis was carried out using clinical indicators such as symptoms and signs which referred to pattern diagnosis, and applied to validation samples which contained all symptoms and signs manifested. Four Fischer's linear discriminant models were derived and their accuracy and prediction rates were 93.2% and 80.43%, respectively. It is important to consider the pattern diagnosis processed by oriental physicians in developing statistical model for PIs. The discriminant model developed in this study using multiple indicators is valid, and can be used in the clinical fields.

• Journal of Applied Reliability
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• v.17 no.4
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• pp.296-304
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• 2017

Purpose: Recent weapon systems in defense have increased the complexity and importance of software when developing multifunctional equipment. In this study, we analyze the accuracy of the proposed software reliability model when applied to weapon systems. Methods: Determine the similarity between software reliability analysis results (prediction/estimation) utilizing data from developing weapon systems and system failures data during operation of weapon systems. Results: In case of a software reliability prediction model, the predicted failure rate was higher than the actual failure rate, and the estimation model was consistent with actual failure history data. Conclusion: The software prediction model needs to adjust the variables that are appropriate for the domestic weapon system environment. As the reliability of software is increasingly important in the defense industry, continuous efforts are needed to ensure accurate reliability analysis in the development of weapon systems.

• Journal of Korean Society for Quality Management
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• v.35 no.3
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• pp.75-87
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• 2007

This paper deals with an integration model of three major quality management systems. The well known global standards such as ISO 9001 quality system, Malcolm Baldrige model, and EFQM model are integrated as a prototype quality system. The proposed model is then applied to help a power generation company to select improvement tasks for enhancing quality management. Here, we discuss three quality management systems, an integration process of the three systems, the integrated model, and a real world application case.

• Proceedings of the IEEK Conference
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• 2000.07b
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• pp.1119-1122
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• 2000

This paper describes on RF circuit simulation technique, especially on a RF modeling and a model extraction of a LDMOS(Lateral Diffused MOS) that has gate-width (Wg) dependence. Small-signal model parameters of the LDMOSs with various gate-widths extracted from S-parameter data are applied to make the relation between the RF performances and gate-width. It is proved that a source inductance (Ls) was not applicable to scaling rules. These extracted small-signal model parameters are also utilized to remove extrinsic elements in an extraction of a large-signal model (using HP Root MOSFET Model). Therefore, we can omit an additional measurement to extract extrinsic elements. When the large-signal model with Ls having the above gate-width dependence is applied to a high-power LDMOS module, the simulated performances (Output power, etc.) are in a good agreement with experimental results. It is proved that our extracted model and RF circuit simulation have a good accuracy.

• Journal of Electrical Engineering and Technology
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• v.10 no.6
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• pp.2228-2239
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• 2015

A novel integrated optimization method based on the Generalized Bender’s Decomposition (GBD) is proposed to combine both generation and transmission expansion problems. Most of existing researches on the integrated expansion planning based on the GBD theory incorporate DC power flow model to guarantee the convergence and improve the computation time. Inherently the GBD algorithm based on DC power flow model cannot consider variables and constraints related bus voltages and reactive power. In this paper, an integrated optimization method using the GBD algorithm based on a linearized AC power flow model is proposed to resolve aforementioned drawback. The proposed method has been successfully applied to Garver’s six-bus system and the IEEE 30-bus system which are frequently used power systems for transmission expansion planning studies.

• Journal of Construction Engineering and Project Management
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• v.7 no.3
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• pp.1-14
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• 2017

Delay claim should actually be supported by a set of proper information so that the contractors could prove their validity. The so-called information should be able to clarify the relationship between delay events and how they impact on the whole project. Therefore, exploiting an integrated system by people who are involved in construction business would certainly prove helpful. In the present study, delay analysis methods have been investigated along with selecting a relatively comprehensive method which has been modified, and eventually, a novel model and its required modules have been proposed for evaluating delay claims. The suggested integrated model is formed to identify delayed events, to classify delays, to measure the impacts of delays on the project scheduling, and finally to estimate the damages which were caused by those so-called delays. A decision support system (DSS) model which is related to the integrated system is actually extracted from Iran's general contract conditions, that is, 4311 magazine (equivalent to red FIDIC book). It is then programmed and coded by C# program. This DSS model can be used as an input of Easy Plan program. In addition, at the end of this research, the coded DSS has been used along with the so-called program so that a modified and developed model could be generated.

• The Transactions of the Korean Institute of Power Electronics
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• v.20 no.1
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• pp.45-50
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• 2015

The simulation model of All-Electric-Ship consists of electrical and mechanical systems. Running the total simulation requires considerable time and causes a lack of computer memory, because the two systems have different dynamic characteristics. Therefore, integrated simulation is practically impossible. This paper proposes the simplified model of electrical system to reduce simulation time significantly, compared to the detailed model. The validity of the proposed simplified model is verified by comparing detailed and simplified simulation results. Thus, the simplified models are applied to the integrated system. As a result, total system simulation can be implemented.

• Journal of the Korean Society for Precision Engineering
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• v.15 no.3
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• pp.82-87
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• 1998

In order to provide a manufacturing system with efficiency and flexibility to cope with the changes in shop floor status, the integration of process planning and operations scheduling is required. In this paper, an integrated system of process planning and operations scheduling based on the concept of process net model and genetic algorithm is suggested. The process net model includes the alternative process plans. The integrated system is applied for prismatic parts.

• 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.