• Journal of Information Technology Services
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• v.2 no.1
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• pp.75-83
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• 2003

Among EC areas. the B2B EC market is being spotlighted as an important interesting research area considering its size and the potential impact on companeies and the whole society. In comparison with private consumers in B2C EC. business buyers in B2B EC have to precisely keep track of the purchase records. and integrate them with the buyer's e-procurement system, which might have been implemented as a part of integrated ERP (Enterprise Resource Planning) systems. There are three approaches for such integration between ERP and e-marketplace in B2B EC; Two previous approaches are Inside-Out approach and Outside-In approach. And a newly, one is b-cart approach. In this paper, we try to survey these three approaches and make a comparison analysis. From this research. we identify that the b-cart approach is the most efficient framework in integrating ERP with e-marketplace in B2B EC.

• Proceedings of the KIEE Conference
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• 1998.07b
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• pp.578-580
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• 1998

The paper presents an approach to the structure identification based on the CART (Classification And Regression Tree) algorithm and to the parameter identification by hybrid learning method in neuro-fuzzy system. By using the CART algorithm, the proposed method can roughly estimate the numbers of membership function and fuzzy rule using the centers of decision regions. Then the parameter identification is carried out by the hybrid learning scheme using BP (Back-propagation) and RLSE (Recursive Least Square Estimation) from the numerical data. Finally, we will show it's usefulness for fuzzy modeling to truck backer upper control.

• Journal of Korean Society for Quality Management
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• v.34 no.2
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• pp.129-135
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• 2006

In the advance of computer technology, it is possible to keep all the related informations for monitoring equipments in control and huge amount of real time manufacturing data in a data base. Thus, the statistical analysis of large data sets with hundreds of thousands observations and hundred of independent variables whose some of values are missing at many observations is needed even though it is a formidable computational task. A tree structured approach to classification is capable of screening important independent variables and their interactions. In a Six Sigma project handling large amount of manufacturing data, one of the goals is to screen vital few variables among trivial many variables. In this paper we have reviewed and summarized CART, C4.5 and CHAID algorithms and proposed a simple method of screening vital few variables by selecting common variables screened by all the three algorithms. Also how to develop a logistics regression model on a large data set is discussed and illustrated through a large finance data set collected by a credit bureau for th purpose of predicting the bankruptcy of the company.