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윤재환
- Magazine of the Korea Concrete Institute
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- v.4 no.2
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- pp.75-81
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- 1992
염분을 함유한 철근콘크리트중의 철근부식에 관한 2년 촉진시험으로부터 콘크리트표면에서 측정한 자연전위값과 실제의 철근 부식상황과를 비교한 결과 철근의 수식상황을 자연전위법을 이용하여 비파괴적으로 진단하는 방법이 유효함을 알았다. 포화칼로멜전극을 사용했을 경우 -300mV이하이면 부식이 발생하였으며 -200mV이상이면 부식이 발생하지 않았다. 또한 부시공시체에 대한 휨강도시험도 행하였으며 중성화에 대한 검토로 행하였다.
https://doi.org/10.22636/MKCI.1992.4.2.75 인용 PDF

Ki, Ho-Yeon;Shin, Kyung-shik
- The Journal of Bigdata
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- v.5 no.1
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- pp.197-208
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- 2020
Lexical ambiguity means that a word can be interpreted as two or more meanings, such as homonym and polysemy, and there are many cases of word sense ambiguation in words expressing emotions. In terms of projecting human psychology, these words convey specific and rich contexts, resulting in lexical ambiguity. In this study, we propose an emotional classification model that disambiguate word sense using bidirectional LSTM. It is based on the assumption that if the information of the surrounding context is fully reflected, the problem of lexical ambiguity can be solved and the emotions that the sentence wants to express can be expressed as one. Bidirectional LSTM is an algorithm that is frequently used in the field of natural language processing research requiring contextual information and is also intended to be used in this study to learn context. GloVe embedding is used as the embedding layer of this research model, and the performance of this model was verified compared to the model applied with LSTM and RNN algorithms. Such a framework could contribute to various fields, including marketing, which could connect the emotions of SNS users to their desire for consumption.
https://doi.org/10.36498/kbigdt.2020.5.1.197 인용 PDF KSCI