• Korean Journal of Construction Engineering and Management
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• v.2 no.2 s.6
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• pp.68-80
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• 2001

While opportunities for international construction firms have been growing with globalization, the risk of international construction projects is significantly increasing in severity and complexity. However, the traditional risk management approach in the construction industry has maintained a profit focus. In addition, this approach has not considered the overall risk at the corporate level, but rather has focused only on the risk of individuals at the project level. Corporate risk management should be implemented from the initial stages of new project selection. This paper suggests the Multi-criteria Integrated Systematic Analysis as a strategic decision-making tool for international construction contractors. The model integrates the multi-criteria of risk, return, and efficiency to choose the optimal set of new portfolios at the corporate level. This model also introduces the Value at Risk (VaR) concept to the international construction industry to present the total risk at the corporate level. To validate this model, this paper tested an experimental case study using the historical data of a global general contractor.

• Proceedings of the Korea Inteligent Information System Society Conference
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• 1999.03a
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• pp.175-186
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• 1999

Detecting the features of significant patterns from their own historical data is so much crucial to good performance specially in time-series forecasting. Recently, a new data filtering method (or multi-scale decomposition) such as wavelet analysis is considered more useful for handling the time-series that contain strong quasi-cyclical components than other methods. The reason is that wavelet analysis theoretically makes much better local information according to different time intervals from the filtered data. Wavelets can process information effectively at different scales. This implies inherent support for multiresolution analysis, which correlates with time series that exhibit self-similar behavior across different time scales. The specific local properties of wavelets can for example be particularly useful to describe signals with sharp spiky, discontinuous or fractal structure in financial markets based on chaos theory and also allows the removal of noise-dependent high frequencies, while conserving the signal bearing high frequency terms of the signal. To data, the existing studies related to wavelet analysis are increasingly being applied to many different fields. In this study, we focus on several wavelet thresholding criteria or techniques to support multi-signal decomposition methods for financial time series forecasting and apply to forecast Korean Won / U.S. Dollar currency market as a case study. One of the most important problems that has to be solved with the application of the filtering is the correct choice of the filter types and the filter parameters. If the threshold is too small or too large then the wavelet shrinkage estimator will tend to overfit or underfit the data. It is often selected arbitrarily or by adopting a certain theoretical or statistical criteria. Recently, new and versatile techniques have been introduced related to that problem. Our study is to analyze thresholding or filtering methods based on wavelet analysis that use multi-signal decomposition algorithms within the neural network architectures specially in complex financial markets. Secondly, through the comparison with different filtering techniques results we introduce the present different filtering criteria of wavelet analysis to support the neural network learning optimization and analyze the critical issues related to the optimal filter design problems in wavelet analysis. That is, those issues include finding the optimal filter parameter to extract significant input features for the forecasting model. Finally, from existing theory or experimental viewpoint concerning the criteria of wavelets thresholding parameters we propose the design of the optimal wavelet for representing a given signal useful in forecasting models, specially a well known neural network models.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.31 no.6B
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• pp.551-563
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• 2011

Water resource projects need an enormous national budget. Therefore, a reasonable and reliable decision making is required for the planning of water resource projects, but decision making has been mostly performed by economic analysis. The objective of this study is to develop a Multi-criteria Decision Making(MCDM) model which can assess the project in various aspects for the selection of preferable alternative and determination of investment priority in water resource projects. In this study, the criteria involves economic feasibility, policies, vulnerability, and sub-items which have weights obtained from the expert survey for the consistent evaluation. We also derived the utility function considering risk trend of each item based on the expert survey. Then, the total score was estimated by weights of each item and utility score of each attribute. The results show that vulnerability is a major contributor for the criteria. This study will contribute to the selection of proper water resource projects considering efficiency of project and fairness for vulnerable area.

• Proceedings of the Korean Institute Of Construction Engineering and Management
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• 2004.11a
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• pp.476-479
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• 2004

In the process of industrialization of our country in 1970's, Korea's housing strategy has focused on housing supply. Rapid completion and supply of apartment house buildings have caused a problem of their early deterioration due to their poor quality. In 1987 Housing Construction Promotion Act, therefore, included new regulations on housing re-construction. In July 2001, other new regulations on remodeling apartment buildings were also included in the Act. Reconstruction and remodeling have been considered to deal with such problems. This study suggests an appraisal criteria if they can select reconstruction or remodeling method in conducting their apartment building project. At first, evaluation items should be selected by the criteria of function31ity and economical efficiency. Second, the selected item is screened by AHP and multi-criteria decision-making methods. The result of study shows that the owners of apartment units will be able to select reasonable alternatives through the suggested appraisal method

• KSCE Journal of Civil and Environmental Engineering Research
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• v.11 no.3
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• pp.59-65
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• 1991

Failure criteria for asphalt concrete pavements are developed combining the AASHTO design equation and the multi-layered elastic theory. Thickness range including typical layer thicknesses of four-layer Korea highway structures are employed for pavement structure models. Total of 2430 pavement models with different layer thicknesses and moduli are analyzed. Models with crushed stone and asphalt stabilized base courses are equally included in the analysis. Number of load repetition and the maximum tensile strain at the bottom of asphalt layer are computed from the AASHTO design equation with terminal PSI=2.5 and multi-layered elastic computer program, SINELA, respectively. Failure criteria are developed through the regression analysis. From the analysis, failure criteria for the asphalt concrete pavements with 50% and 95% reliability levels are developed. It is found that the failure criterion of 95% reliability level gives similar results with existing fatigue failure criteria whose terminal performance condition is crack development when compared in a graphical form an equation to estimate failure criterion for a specific reliability level is also proposed.

• Proceedings of the Society of Korea Industrial and System Engineering Conference
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• 2002.05a
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• pp.177-187
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• 2002

In this paper, we consider a multi-factor, multi-cause decision making problem of supply chain. And we show how to measure the operational efficiency of the components in supply chain and also how to improve the efficiency of each component and whole supply chain. As a methodology, the data envelopment analysis (DEA) is adopted to measure the efficiency by considering weight factors such as flexibility, information sharing, logistics level, etc. The proposed algorithm allows whole supply chain to have the improved efficiency rate.

• Korean Management Science Review
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• v.13 no.3
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• pp.173-186
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• 1996

This paper presents an approach for classifying scheduling problems and selecting a heuristic rule to yield best solution in terms of certain performance measure(s). Classification parameters are employed from previous studies on job shop scheduling and project scheduling. Neural network is used for learning and estimating the performance of heuristic rules. In addition, multi-criteria decision making techniques are employed to combine the preferences for each performance measure and heuristic rule for the problems with multi-objectives.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.22 no.3
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• pp.423-431
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• 1997

A new decision tree generator MEC is proposed in this paper, which uses the difference of multi-base entropy as a consistent criterion for discretization and selection of attributes. To evaluate the performance of the proposed generator, it is compared to other generators which use criteria based on entropy and adopt different discretization styles. As an experimental result, it is shown that the proposed generator produces the most efficient classifiers, which have the least number of leaves at the same error rate, regardless of whether attribute values constituting the training set are discrete or continuous.

• Journal of Korean Institute of Industrial Engineers
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• v.3 no.2
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• pp.63-72
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• 1977

The trend of computerization is significant in Korea even at its beginning stage, especially for governmental administration. However, full-fledged success of computerization in an organization is reported to be rare while the cost of computerization has been high and increasing. This paper is concerned with two features for the successful implementation of a computerized system in an organization selection criteria for the computerization among the possible candidate projects and project selection models. Due to the dearth of literature regarding successful implementation of computerization, other sources of literature with respect to R & D management, method engineering, etc. were reviewed to develop a set of factors influencing successful computerization. Thus, project selection criteria for computerization of governmental administration are developed and organized as follows: cost of computerization project including both system development and operating cost, quanitative and qualitative benefits of computerization project, probability of technical and implementation success of computerization and other organizational and political factors to be considered. These criteria are broken down into detailed sets of subcriteria to be measured. To select a project after thorough consideration of the selection criteria, a project selection model which takes into account all criteria together has to be developed. In the study three project selection models are suggested and developed. They are the checklist model, multi-stage cut-off model, and composite criteria model. A detailed procedure for each of the three models is illustrated. Although the project selection criteria and models are developed here primarily for the computerization of governmental administration, they are easily applicable to other settings of computerization. Finally, some caveats for the use of selection criteria and models are discussed.

• International Journal of Contents
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• v.13 no.3
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• pp.25-31
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• 2017

With tremendous advancement of information and communication technologies, mobile learning systems have been widely adopted in language learning contexts, and several frameworks have been developed for identifying and categorizing different factors of mobile-assisted language learning (MALL). However, pre-existing frameworks have limitations when evaluating the importance level of criteria. The purpose of this study is to develop a comprehensive hierarchical framework for identifying and categorizing success factors of MALL and prioritizing them according to the importance level. To do that, AHP method is used to quantitatively estimate weight values of MALL criteria. Results reveal that the priority of MALL criteria is ordered as follows: content, system, learner, language learning. Local weights of each criterion are also analyzed; for example, usefulness, accuracy, and authenticity are critical factors for improving MALL contents. Ease of use and mobility of MALL systems are also considered more critical than other systematic factors. In addition, availability of immediate feedback and self-directness has the highest weight values of importance. The findings of the study are discussed regarding hierarchical orders of MALL criteria and conclude that successful MALL implementation may be achieved if related elements are diversely measured and evaluated. Pedagogical implications and suggestions for further research are also presented.