• Proceedings of the Korean Society of Soil and Groundwater Environment Conference
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• 2003.04a
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• pp.397-400
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• 2003

In this study, humic and fulvic acids in soils at the vicinity of domestic atomic power plants(NPPs), located in Yungkwang(YK), Uljin(UJ), Kori(KR), Koseong(KS), Wolseong(WS) area, and in volcanic ash soils of the Cheju island(Mt. Hanla(HL), Manjanggul(MJ)) were isolated, and characterized using chemical(elemental analysis, proton exchange capacity, molecular size distribution) and spectroscopic(UV/Vis., IR, FL, $^{13}$ C NMR spectra) methods. The results were compared with one another and compiled for their DB establishments. The humic substances distribution (humic acid, fulvic acid, Humin) in the soils were also determined by IHSS standard method. Main purpose of this study was to provide a basic data needed to evaluate the effect of humic substances on the migrational behaviour of radioactive elements in contaminated surface soil.

• Journal of Labour Economics
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• v.28 no.3
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• pp.39-76
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• 2005

This study examines the job changes of the youth university graduates in Korea. The pattern of job changes get observed in the Unemployment Insurance Database. This data enables us tracing all job changes in the labor market. The average number of job youth graduates have for four years from graduate is 1.68. The more jobs youth have, the longer total tenure youth have. Youth are much more likely to change careers to different occupation or industry. They move usually into upper occupations but small establishments. They enhance the level of wage through job changes. The science and engineering graduates are more likely to change jobs than the humanity and social science graduates. The higher the level of wage they get is, the lower the probability of their job change is. Those who have good scores in Scholastic Aptitude Test are less likely to change job.

• Journal of the Korea Convergence Society
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• v.12 no.4
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• pp.23-30
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• 2021

An Automated Industry and Occupation Coding System assigns statistical classification code to the enormous amount of natural language data collected from people who write about their industry and occupation. Unlike previous studies that applied information retrieval, we propose a system that does not need an index database and gives proper code regardless of the level of classification. Also, we show our model, which utilized KoBERT that achieves high performance in natural language downstream tasks with deep learning, outperforms baseline. Our method achieves 95.65%, 91.51%, and 97.66% in Occupation/Industry Code Classification of Population and Housing Census, and Industry Code Classification of Census on Basic Characteristics of Establishments. Moreover, we also demonstrate future improvements through error analysis in the respect of data and modeling.