• Proceedings of the KOR-KST Conference
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• 1999.10a
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• pp.47-52
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• 1999

The methods currently in use to evaluate traffic impacts on the transportation network involve some fundamental shortcomings. First, the methodss do not properly take into account regional and local traffic impacts on the transportation network simultaneously. Second, temporal distribution of traffic, a major contributor to transportation problems, is not accurately accounted for. Third, traffic impact studies require costly and labor-intensive efforts to collect necessary data and to establish to collect necessary data and to establish traffic impact models. In this research, a new method called AMIS is developed for congestion management, access control, and impact simulation to overcome the shortcomings involved in the current methods. The new method is designed for a variety of scenarios such as access management strategies, land use policies, traffic impacts, and other congestion management strategies. This method can effectively be used, with little modification, anywhere in the United States. It is an improvement over the current traffic impact simulation methods that produces more reliable and accurate traffic impact estimates. The case studies conducted in this research have offered evidence that the new method, AMIS, is a credible congestion management tool. Most importantly, a case study presented in this paper illustrates how the new method can be used not only to estimate regional and local impacts of alternate supply management policies in the course of a day, but virtually on an hour-by-hour basis.

• Management & Information Systems Review
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• v.3
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• pp.15-45
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• 1999

Processing techniques of remote sensed image data using computer have been recognized very necessary techniques to all social fields, such as, environmental observation, land cultivation, resource investigation, military trend grasp and agricultural product estimation, etc. Especially, accurate classification and analysis to remote sensed image da are important elements that can determine reliability of remote sensed image data processing systems, and many researches have been processed to improve these accuracy of classification and analysis. Traditionally, remote sensed image data processing systems have been processed 2 or 3 selected bands in multiple bands, in this time, their selection criterions are statistical separability or wavelength properties. But, it have be bring up the necessity of bands selection method by data distribution characteristics than traditional bands selection by wavelength properties or statistical separability. Because data sensing environments change from multispectral environments to hyperspectral environments. In this paper for efficient data classification in multispectral bands environment, a band feature extraction method using the Rough sets theory is proposed. First, we make a look up table from training data, and analyze the properties of experimental multispectral image data, then select the efficient band using indiscernibility relation of Rough set theory from analysis results. Proposed method is applied to LANDSAT TM data on 2 June 1992. From this, we show clustering trends that similar to traditional band selection results by wavelength properties, from this, we verify that can use the proposed method that centered on data properties to select the efficient bands, though data sensing environment change to hyperspectral band environments.

• Journal of the Korean Operations Research and Management Science Society
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• v.42 no.1
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• pp.53-63
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• 2017

Managing the quantitative measurement of the data control activities in enterprise wide is important to secure management of data governance. However, research on data governance is limited to concept definitions and components, and data governance research on evaluation models is lacking. In this study, we developed a model of quantitative assessment for data governance including the assessment area, evaluation index and evaluation matrix. We also, proposed a method of developing the model of quantitative assessment for data governance. For this purpose, we used previous studies and expert opinion analysis such as the Delphi technique, KJ method in this paper. This study contributes to literature by developing a quantitative evaluation model for data governance at the early stage of the study. This paper can be used for the base line data in objective evidence of performance in the companies and agencies of operating data governance.

• Journal of the Korean Operations Research and Management Science Society
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• v.42 no.3
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• pp.25-34
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• 2017

K-means is a popular and efficient data clustering method that only uses intra-cluster distance to establish a valid index with a previously fixed number of clusters. K-means is useless without a suitable number of clusters for unsupervised data. This paper aimsto propose the Group Search Optimization (GSO) using Silhouette to find the optimal data clustering solution with a number of clusters for unsupervised data. Silhouette can be used as valid index to decide the number of clusters and optimal solution by simultaneously considering intra- and inter-cluster distances. The performance of GSO using Silhouette is validated through several experiment and analysis of data sets.

• Proceedings of the Korea Information Processing Society Conference
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• 2024.05a
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• pp.27-30
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• 2024

A digital platform government without boundaries between the public and private sectors and between government ministries is impossible without national data management. Logical verification was carried out in this study following the definition of the national data management system's purpose, elements, and mode of implementation. Specifically, it was broken down into three dimensions in an effort to review different aspects: the management subject, the management method, and the designation target of national data. Finally, a description of the national master data management system and organization was given. The direction for the implementation of the digital platform government will be presented by this study.

• Journal of the Korea Safety Management & Science
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• v.16 no.1
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• pp.213-220
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• 2014

This study suggests new approach to identify core technologies through patent analysis. Specially, the approach applied data mining technique and multi-criteria decision making method to the co-classification information of registered patents. First, technological interrelationship matrices of intensity, relatedness, and cross-impact perspectives are constructed with support, lift and confidence values calculated by conducting an association rule mining on the co-classification information of patent data. Second, the analytic network process is applied to the constructed technological interrelationship matrices in order to produce the importance values of technologies from each perspective. Finally, data envelopment analysis is employed to the derived importance values in order to identify priorities of technologies, putting three perspectives together. It is expected that suggested approach could help technology planners to formulate strategy and policy for technological innovation.

• IE interfaces
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• v.12 no.1
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• pp.79-93
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• 1999

As most of information systems developed are data-centric rather than process-centric, it is difficult for users to understand and manage the system from the viewpoint of work processes. To resolve the problem of the data-centric design, we propose a new method to design WFMSs(Workflow Management Systems), which are focused on processes and modified from current information engineering methods. In this research, the drawing approval and engineering change approval process of a K manufacturing company has been analyzed as a sample process. This method takes two steps, i.e., process analysis and system design. In the prosess analysis, data and processes are analyzed, and functions and tasks are derived from the processes. In the system design, a data model for the operation of WFMS is designed, and based on this data model, build-time and run-time functions of WFMS are designed.

• Journal of Navigation and Port Research
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• v.46 no.2
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• pp.92-98
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• 2022

Maritime accidents result in enormous economic loss and loss of life; thus, such accidents must be prevented, and risks must be managed to prevent these occurrences Risk management must be based on statistical evidence such as variables. Because calculating when variables increase statistically can be difficult, compressing the designated variables is necessary to use the maritime accident data in Korea. Thus, in this study, variables of marine accident data are compressed using statistical methods. The date, ship type, and marine accident type included in all maritime accident data were extracted, the number of optimal variables was confirmed using the hierarchical clustering analysis method, and the data were compressed. For the compressed variables, the validity of the data use was statistically confirmed using analysis of variance, and the data of the variables identified using the variable compression method were designated. Consequently, among the monthly and yearly data, statistical significance was confirmed in yearly data, and compression was possible. The significance of the data was confirmed in six and eight types of ships and accidents, respectively, and these were compressed. These results can be directly used for prevention or prediction based on past maritime accident data. Additionally, the data range extracted from past maritime accidents and the number of applicable data will be studied in the future.

• Proceedings of the Korean Society of Agricultural Engineers Conference
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• 1999.10c
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• pp.618-624
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• 1999

For many soft ground embankment projects , instrumentation programs for stability and settlement management is being essential . This usually leads to generate large volume of data, which can be used for further research. Database technique is most effective method for data management . Data produced by soft ground embankment instrumentation can not be used by itself but must be reproduced using geotechnical analysis technique. In this study, a intelligent database system for softground called IDSIM was developed to examine applicability intellgent database. . The IDSIM analysis instrumentation data automatically and present results by Web/DB interface successfully.

• Journal of the Korean Operations Research and Management Science Society
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• v.17 no.2
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• pp.25-33
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• 1992

Manufacturing cell formation is one of the most important problems faced in designing cellular manufacturing systems. The purpose of this study is to design effective manufacturing cell systems by developing a method which forms machines/parts into optimal machine cells/part families. The 0-1 data matrix structure is used to form a basis for manufacturing cell formation. In this paper, we propose a CE method to reorder the 0-1 data matrix for manufacturing cell formation. The resulting solutions are shown to demonstrate the effectiveness of the CE method.