• Journal of Korean Society of Industrial and Systems Engineering
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• v.40 no.2
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• pp.129-136
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• 2017

Among the information technology and automation that have rapidly developed in the manufacturing industries recently, tens of thousands of quality variables are estimated and categorized in database every day. The former existing statistical methods, or variable selection and interpretation by experts, place limits on proper judgment. Accordingly, various data mining methods, including decision tree analysis, have been developed in recent years. Cart and C5.0 are representative algorithms for decision tree analysis, but these algorithms have limits in defining the tolerance of continuous explanatory variables. Also, target variables are restricted by the information that indicates only the quality of the products like the rate of defective products. Therefore it is essential to develop an algorithm that improves upon Cart and C5.0 and allows access to new quality information such as loss cost. In this study, a new algorithm was developed not only to find the major variables which minimize the target variable, loss cost, but also to overcome the limits of Cart and C5.0. The new algorithm is one that defines tolerance of variables systematically by adopting 3 categories of the continuous explanatory variables. The characteristics of larger-the-better was presumed in the environment of programming R to compare the performance among the new algorithm and existing ones, and 10 simulations were performed with 1,000 data sets for each variable. The performance of the new algorithm was verified through a mean test of loss cost. As a result of the verification show, the new algorithm found that the tolerance of continuous explanatory variables lowered loss cost more than existing ones in the larger is better characteristics. In a conclusion, the new algorithm could be used to find the tolerance of continuous explanatory variables to minimize the loss in the process taking into account the loss cost of the products.

• Journal of agriculture & life science
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• v.50 no.3
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• pp.69-80
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• 2016

The present study was to survey the site environment, vegetation structure and soil characteristics in the wild habitats of Acer tegmentosum Maxim. and offers basic information for habitats conservation and restoration. Most of the wild habitats were located at altitudes between 605~1,413m with inclinations ranged as 8~30°. The bare rock rate were 8~50%. The vegetation structure by the PC-ORD based on the Two Way Cluster Analysis were divided into three groups Community I(Acer tegmentosum - Quercus mongolica), Community II(Acer tegmentosum - Carpinus cordata), Community III(Quercus mongolica - Tilia amurensis). The species diversity(H') was highest in Community II as 1.474, Community I was 1.471, Community III was 1.219. The soil textures were Clay loam, The average soil pH was 4.8, Soil organic matter was 15.15% and available phosphorus was 2.33ppm. Ordination analysis result by soil characteristics, community, characteristic species showed that the greatest effect factors were slope, altitude, tree and shrub's cover rate, organic matter, total-nitrogen, calcium, magnesium. Correlation analysis between environment factor result showed that O.M. - (T.N., K+, Mg2+, CEC, EC), T.N. - (K+, Mg2+, CEC, EC) were positive correlations.