• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.15 no.5
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• pp.69-74
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• 2015

In this paper the patterns of information system is reduced so that control rules can guarantee fast response of queries in database. Generally an information system includes many kinds of necessary and unnecessary attribute. In particular, inconsistent information system is less likely to acquire the accuracy of response. Hence we are interested in the simple and understandable rules that can represent useful patterns by means of rough entropy and Bayesian posterior probability. We propose an algorithm which can reduce control rules to a minimum without inadequate patterns such that the implication between condition attributes and decision attributes is measured through the framework of rough entropy. Subsequently the validation of the proposed algorithm is showed through test information system of new employees appointment.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.43 no.1
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• pp.88-95
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• 2015

This paper considers the effects of propellant mixture ratio and loading errors on the performance of a liquid rocket. Propellant residuals generated by error sources are analyzed for a launch vehicle model whose first stage consists of a cluster rocket of four 75-tonf class engines using a statistical Monte-Carlo approach and then the optimal fuel biases minimizing residuals are computed. The results are validated through comparison with analytic method using approximate formula, which have been applied for other space launch vehicles.

• Journal of the Korea Convergence Society
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• v.11 no.4
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• pp.11-16
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• 2020

As a plan for oil spill accidents, research to collect and analyze optimal equipment assignments is essential. However, studies that have diversified and analyzed the optimal equipment assignments for responding to oil spill accidents have not been preceded. In response to the need for analyzing optimal equipment assignments study, we devised a genetic algorithm for optimal equipment assignments. The designed genetic algorithm yielded 10,000 optimal equipment assignments. We clustered using the k-means algorithm. As a result, the two clusters of Yeosu, Daesan, and Ulsan, which are expected to be the largest spills, were clearly identified. We also projected 16-dimensional data in two dimensions via Sammon's mapping. The projected data were analyzed for distribution. We confirmed that results of the simulation were better than those of optimal equipment assignments included in the cluster.In the future, it will be possible to implement an approximate model with excellent performance based on this study.