• Analytical Science and Technology
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• v.25 no.2
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• pp.133-145
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• 2012

The quantitative composition of total suspended particulates (TSP) in the atmosphere is identified to consist mainly of ions, organic carbon (OC), element carbon (EC), and metals. In terms of environmental analysis, the rest of the TSP composition may be defined as unknown fraction (${\Sigma}X$) which is yet difficult to analyze both quantitatively and qualitatively. In this study, the major components of TSP were measured at an urban residential area (Gang Seo) in Seoul, Korea from February to December 2009. These TSP data were analyzed in various respects to explain the relationship between known and unknown constituents. During this study period, TSP was comprised mainly of unknown compounds (48.6%) followed by ions, OC, EC, and metals. The results of this study indicate that the distribution of ${\Sigma}X$ exhibits a strong similarity with ${\Sigma}Anions$, as they both increase with increasing TSP levels. However, if the concentrations of ${\Sigma}X$ and ${\Sigma}Anion$ are normalized against TSP, they exhibit a strong inverse correlation with each other due probably to larges differences in solubility. To establish a better strategy for air quality control in urban atmosphere, more efforts are needed to characterize unidentified proportion of particulate matters.

• Journal of the Korean Institute of Telematics and Electronics C
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• v.36C no.10
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• pp.66-74
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• 1999

In this paper, we propose a generalized model of statistical Hopfield neural network applicable to solving the well known NP-Complete problem, TSP. Van Den Bout's method to simplify the energy function through normalization has severe weak points that it does not consider the necessary perturbation effects. In proposed model, the improved energy function is used and 5 kinds of perturbation effects and the ratio between perturbation effects are considered including van Den Bout's 2 kinds and one more kind of Park. Through the simulation of randomly generated distribution of 10-city, it is found that our model shows 90 out of 100 cases reach the optimum and near optimum solution(within 5% error). We show the simulation of the large scale, 30-city and 50-city.