• The Korean Journal of Applied Statistics
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• v.20 no.2
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• pp.333-343
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• 2007

The auxiliary renewal function has an important role in analyzng queues in which the either of the inter-arrival time and the service time of customers is not exponential. As like the renewal function, the auxiliary renewal function is hard to compute although it can be defined theoretically. In this paper, we suggest two approximations for auxiliary renewal function and compare the two with the true value of auxiliary renewal function which can be computed in some special cases.

• Environmental and Resource Economics Review
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• v.30 no.1
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• pp.107-128
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• 2021

It has been recognized that implementing the marginal price mechanism to CBP is not acceptable due to the lack of revenue of the marginal generators. This study shows that it is not the problem of marginal price mechanism but the structural problems originated by the suspension of restructuring, the technical limits of RSC program and inaccuracy of the generation cost estimation method. This study explains the method to calculate the cost function in operating modes of the CC generators and proposes the modeling for the CC generators in RSC program. To implementing the cost function in operating modes could give an opportunity to change the price setting mechanism from average to marginal cost. The price setting mechanism based on the marginal cost will be one of the main points to provide the right price signals and to introduce a real-time and A/S markets to prepare the energy transition era.

• The Journal of the Korea Contents Association
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• v.22 no.7
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• pp.55-62
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• 2022

The perovskite solar cell is an active part of research in renewable energy fields such as solar energy, wind, hydroelectric power, marine energy, bioenergy, and hydrogen energy to replace fossil fuels such as oil, coal, and natural gas, which will gradually disappear as power demand increases due to the increase in use of the Internet of Things and Virtual environments due to the 4th industrial revolution. The perovskite solar cell is a solar cell device using an organic-inorganic hybrid material having a perovskite structure, and has advantages of replacing existing silicon solar cells with high efficiency, low cost solutions, and low temperature processes. In order to optimize the light absorption layer thin film predicted by the existing empirical method, reliability must be verified through device characteristics evaluation. However, since it costs a lot to evaluate the characteristics of the light-absorbing layer thin film device, the number of tests is limited. In order to solve this problem, the development and applicability of a clear and valid model using machine learning or artificial intelligence model as an auxiliary means for optimizing the light absorption layer thin film are considered infinite. In this study, to estimate the light absorption layer thin-film optimization of perovskite solar cells, the regression models of the support vector machine's linear kernel, R.B.F kernel, polynomial kernel, and sigmoid kernel were compared to verify the accuracy difference for each kernel function.