• The Korean Journal of Ecology
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• v.9 no.4
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• pp.185-192
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• 1986

The production and decomposition rate of litters from the three different locations, Quercus acutissium forest at 630 m, Q. mongolica forests at 1, 005m and 1, 490 m of Mt. Dokyoo, were estimated by Olson model. The contents of N, P, K, Ca and Na in soils were measured and the relationships among them were elucidated. The amounts of litter production in Q. mangolica were the lowest, 378.96g/$m^2$ at 1, 490 m and the highest, 876.12g/$m^2$ at 1, 005 m. And the amounts of litter production in Q. acutissima at 630 m was 686.16 g/$m^2$. The decay rate of litters in Q. mongolica was the smallest, 0.123 at 1, 490 m, and the largest, 0.222 at 1, 005 m. And that in Q. acutissima was 0.169 at 630 m which was the medium rate. The production and decay rate of litters decreased with the ascending altitude. The values at 630 m were maller than those at 1, 005 m. This might be due to the fact that the tree species at 630 m was Q. acutissima was 0.169 at 630 m which was the medium rate. The production and decay rate of litters decreased with the ascending altitude. The values at 630 m was Q. acutissima which was different from Q. mongolica at 1, 005 m and 1, 490 m. The half-0life of litter decay in Q. monglica was 5, 634 years at 1, 490 m and 3.134 years at 1, 005 m. And that in Q. acutissima was 4.132 years at 630 m. The decay rates of litters were tend to be inversely proportional to the ascending altitude. The annual standing stocks of mineral and their amounts returned to the soil were proportional to the decay rate of organic matters.

• Journal of IKEEE
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• v.20 no.1
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• pp.94-102
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• 2016

This paper proposes a new prediction method to reduce times and labor of repetitive multi-physics simulation. To achieve exact results from the whole simulation processes, complex modeling and huge amounts of time are required. Current multi-physics analysis focuses on the simulation method itself and the simulation environment to reduce times and labor. However this paper proposes an alternative way to reduce simulation times and labor by exploiting machine learning algorithm trained with data set from simulation results. Through comparing each machine learning algorithm, Gaussian Process Regression showed the best performance with under 100 training data and how similar results can be achieved through machine-learning without a complex simulation process. Given trained machine learning algorithm, it's possible to predict the result after changing some features of the simulation model just in a few second. This new method will be helpful to effectively reduce simulation times and labor because it can predict the results before more simulation.