• Journal of the Korean Geotechnical Society
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• v.35 no.4
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• pp.27-35
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• 2019

This study confirmed the applicability to the field of geotechnical engineering for relatively complicated space and many target design variables in back analysis. The Sharan's equation and the Blum's method were used for the tunnel field and the retaining wall as a model for the multi-variate problem of geotechnical engineering. Optimization methods are generally divided into a deterministic method and a stochastic method. In this study, Simulated Annealing Method (SA) was selected as a deterministic method and Differential Evolution Algorithm (DEA) and Particle Swarm Optimization Method (PSO) were selected as stochastic methods. The three selected optimization methods were compared by applying a multi-variate model. The problem of deterministic method has been confirmed in the multi-variate back analysis of geotechnical engineering, and the superiority of DEA can be confirmed. DEA showed an average error rate of 3.12% for Sharan's solution and 2.23% for Blum's problem. The iteration number of DEA was confirmed to be smaller than the other two optimization methods. SA was confirmed to be 117.39~167.13 times higher than DEA and PSO was confirmed to be 2.43~6.91 times higher than DEA. Applying a DEA to the multi-variate back analysis of geotechnical problems can be expected to improve computational speed and accuracy.

• The Journal of the Korean dental association
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• v.9 no.12
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• pp.819-822
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• 1971

For this investigation, author isolated Streptococcus mitis strain SD-9 from the bacterial flora in the human dental plaque, which was incubated in brain-heart infusion media containing 5% sucrose at 37℃ for 24 hours. For the cytochemical demonstration of polysaccharide produced by this strain, a modified thiosemicarbazide osmium method (Critchley et al., 1967) was used. After fixation with this reagent, the harvested cells was suspended in 1% agar for the higher concentration of cells(Kellenberger et al., 1964). And they were dehydrated in the various concentration of ethanol, and embedded in Epon 812(Luft, 1961). Sectioning was done with the Sorvall MT-2 Porter Blum ultramicrotome by means of a glass knife, and the sections were stained with saturated uranyl acetate and lead citrate (Raynolds, 1963). All preparations were examined in a electron microscope, Hitachi HU-ll E-1 type. The morphological features of extracellular polysaccharide produced by Streptococcus mitis strain SD-9 were appeared in 3 structurally different forms, those are, electron dense fibrillar material linearly arranged adjacent to the outer surface of cell wall, highly electron dense globular material adjacent to the outer surface of cell wall, and strutureless fluffy meshwork of possible very fine filament.

• Journal of Educational Research in Mathematics
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• v.11 no.1
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• pp.157-177
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• 2001

Modern society's technological and economical changes require high-level education that involve critical thinking, problem solving, and communication with others. Thus, today's perspective of mathematics and mathematics learning recognizes a potential symbolic relationship between concrete and abstract mathematics. If the problems engage students' interests and aspiration, mathematical problems can serve as a source of their motivation. In addition, if the problems stimulate students'thinking, mathematical problems can also serve as a source of meaning and understanding. From these perspectives, the purpose of my study is to prove that mathematical modeling tasks can provide opportunities for students to attach meanings to mathematical calculations and procedures, and to manipulate symbols so that they may draw out the meanings out of the conclusion to which the symbolic manipulations lead. The review of related literature regarding mathematical modeling and model are performed as a theoretical study. I especially concentrated on the study results of Freudenthal, Fischbein, Lesh, Disessea, Blum, and Niss's model systems. We also investigate the emphasis of mathematising, the classified method of mathematical modeling, and the cognitive nature of mathematical model. And We investigate the purposes of model construction and the instructive meaning of mathematical modeling. In conclusion, we have presented the methods that promote students' effective model construction ability. First, the teaching and the learning begins with problems that reflect reality. Second, if students face problems that have too much or not enough information, they will construct useful models in the process of justifying important conjecture by attempting diverse models. Lastly, the teachers must understand the modeling cycle of the students and evaluate the effectiveness of the models that the students have constructed from their classroom observations, case study, and interaction between the learner and the teacher.