• Journal of KIISE:Software and Applications
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• v.35 no.12
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• pp.800-807
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• 2008

We introduce some well-known approaches to formalization and automatization of the meta-theory of a programming language with binders. They represent the trends in POPLmark Challenge. We demonstrate some characteristics of each approach by showing how to formalize some basic notations and concepts of Lambda-calculus using the proof assistant Coq.

• The Korean Journal of Applied Statistics
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• v.29 no.5
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• pp.949-960
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• 2016

This paper uses all sections of highway data (VDS) for two years (Jan. 2014-Dec. 2015), with 15 minute units. The first purpose of this study is to find clusters with similar patterns that appear repeatedly with time variables of month, week and hour. The cluster analysis results indicate a variety of patterns of average traffic speeds by time variables depending on the clusters; subsequently, these can be utilized to model for the forecast of the speed at a specific time. The second purpose is to do cluster analysis for grouping sections by effect nets that are closely related to each other. For the similarity measure we use cross-correlation functions calculated after pre-whitening the speed of each section. The cluster analysis gets 19 clusters, and sections within a cluster are geographically close. These results are expected to help to forecast a real-time speed.

• Proceedings of the Korean Institute Of Construction Engineering and Management
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• 2004.11a
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• pp.472-475
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• 2004

The objective of this study is to develope reasonably accurate prediction models to assess hoisting times of tower cranes in the high-rise building construction. The efficient use of the tower crane is critical to achieving the Planned floor cycle time. This research describes the derivation of mathematical models to predict the hoisting times in using a tower crane. 28 factors such as nature of load, characteristics of tower cranes, hoisting movements, operation of cranes, weather conditions and so on is considered to influence hoisting times. In order to develop the predicting hoisting times Correctly, it is divided hoisting upward and downward. Then multiple regression models for predicting supply and return hoisting times have been built up separately.