• Magazine of the Korean Society of Agricultural Engineers
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• v.32 no.4
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• pp.61-70
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• 1990

In rural settlement planning, its spatial development framework should be constructed on the basis of the hierachial settlement system. However, up to now, there does not exist widely - accepted model on rural settlement hierachy. In this study the basic planning principles and directions on the modelling of the settlement hireachy in rural areas have been consolidated through theoretical study and situational analysis on planning environments. And also, a new yardstick on the grading of the middle - level centers between villages and rural towns has been tried to find out. The research resulted that the existance of periodical rural market might be used as a simple and innovative yardstick on the hierachial ordering of rural settlement system. Based on the above results, an alternative 4 - step model of rural settlement hierachy was proposed ; Rural Town(county hall sitted) -Market Center(having standard periodical market) - Village Center(having no market) - Village. Finally, because the conclusion has been made by very limited case studies and several previous works, there should need continuous studies and checks in future for full reasoning of the proposed model.

• The Korean Journal of Applied Statistics
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• v.35 no.3
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• pp.407-419
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• 2022

The semiconductor fabrication process is complex and time-consuming. There are sometimes errors in the process, which results in defective die on the wafer bin map (WBM). We can detect the faulty WBM by finding some patterns caused by dies. When one manually seeks the failure on WBM, it takes a long time due to the enormous number of WBMs. We suggest a two-step approach to discover the probable pattern on the WBMs in this paper. The first step is to separate the normal WBMs from the defective WBMs. We adapt a hierarchical clustering for de-noising, which nicely performs this work by wisely tuning the number of minimum points and the cutting height. Once declared as a faulty WBM, then it moves to the next step. In the second step, we classify the patterns among the defective WBMs. For this purpose, we extract features from the WBM. Then machine learning algorithm classifies the pattern. We use a real WBM data set (WM-811K) released by Taiwan semiconductor manufacturing company.