• Magazine of the Korea Concrete Institute
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• v.5 no.1
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• pp.139-144
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• 1993

콘크리트부재의 함수량거동을 조사하기 위해 기존연구로부터 함수측정법으로 전기저항에 의한 전극법을 선정하였으며, 전구법으로 철근콘크리트 구조물의 함수량을 측정한 결과, 상온하에서의 함수측정에는 실용성이 있음을 확인하였다. 이같은 전극법은 175$^{\circ}C$ 고온히에서도 사용가능하도록 교정곡선의 작성을 위한 밀도법, 측정한 함수량의 정밀도등을 실험으로 검사한 결과, 고온을 받는 매스콘크리트 부재의 함수량거동이 전극법으로 측정될수 있음을 알 수 있었다.

• Bulletin of the Korean Chemical Society
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• v.31 no.4
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• pp.845-849
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• 2010

A gold nanoparticles modified carbon paste electrode (GN-CPE) has been used for the determination of atenolol (ATN) in drug formulations by cyclic voltammetry (CV), differential pulse voltammetry (DPV) and chronocoulometric methods. The results revealed that the modified electrode shows an electrocatalytic activity toward the anodic oxidation of atenolol by a marked enhancement in the current response in buffered solution at pH 10.0. The anodic peak potential shifts by -80.0 mV when compared with the potential using bare carbon paste electrde. A linear analytical curve was observed in the range of $1.96\;{\times}\;10^{-6}$ to $9.09\;{\times}\;10^{-4}\;mol\;L^{-1}$. The detection limit for this method is $7.3\;{\times}\;10^{-8}\;mol\;L^{-1}$. The method was then successfully applied to the determination of atenolol in tablets and human urine. The percent recoveries in urine ranged from 92.0 to 110.0%.

• Journal of Welding and Joining
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• v.11 no.2
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• pp.27-41
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• 1993

The estimation of nugget sizes was attempted by utilizing the artificial neural networks method. Artificial neural networks is a highly simplified model of the biological nervous system. Artificial neural networks is composed of a large number of elemental processors connected like biological neurons. Although the elemental processors have only simple computation functions, because they are connected massively, they can describe any complex functional relationship between an input-output pair in an autonomous manner. The electrode head movement signal, which is a good indicator of corresponding nugget size was determined by measuring the each test specimen. The sampled electrode movement data and the corresponding nugget sizes were fed into the artificial neural networks as input-output pairs to train the networks. In the training phase for the networks, the artificial neural networks constructs a fuctional relationship between the input-output pairs autonomusly by adjusting the set of weights. In the production(estimation) phase when new inputs are sampled and presented, the artificial neural networks produces appropriate outputs(the estimates of the nugget size) based upon the transfer characteristics learned during the training mode. Experimental verification of the proposed estimation method using artificial neural networks was done by actual destructive testing of welds. The predicted result by the artifficial neural networks were found to be in a good agreement with the actual nugget size. The results are quite promising in that the real-time estimation of the invisible nugget size can be achieved by analyzing the process variable without any conventional destructive testing of welds.