• Journal of the Korea Society for Simulation
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• v.23 no.4
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• pp.1-8
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• 2014

Cloud computing helps big data processing to make various information using IT resources. The government has to start the RPS(Renewable Portfolio Standard) and induce the production of electricity using renewable energy equipment. And the government manages system to gather big data that is distributed geographically. The companies can purchase the REC(Renewable Energy Certificate) to other electricity generation companies to fill shortage among their duty from the system. Because of the RPS use voluntary competitive market in REC trade and the prices have the large variation, RPS is necessary to predict the equitable REC price using RPS big data. This paper proposed REC price prediction method base on fuzzy logic using the price trend and trading condition infra in REC market, that is modeled in cloud computing environment. Cloud computing helps to analyze correlation and variables that act on REC price within RPS big data and the analysis can be predict REC price by simulation. Fuzzy logic presents balanced REC average trading prices using the trading quantity and price. The model presents REC average trading price using the trading quantity and price and the method helps induce well-converged price in the long run in cloud computing environment.

• 전자공학회논문지 IE
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• v.43 no.4
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• pp.13-20
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• 2006

In the advanced country, It is forecasting farm prices using intelligence style of farming technique. In our country, It is offering basis research to prevent the prices rising and falling, But, It is impossible that no one can predict exactly for farming price. In this paper to improve forecasting farming price using neural network as a preprocessing. Also, we developed a fuzzy algorithm for real time forecasting as a postprocessing about unexpectable conditions. Computer simulation results preyed reducing pricing error which proposed farming price expecting system better than conventional demand forecasting system does not using fuzzy rules.

• Journal of the Korea Society for Simulation
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• v.17 no.3
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• pp.19-26
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• 2008

In this paper, we propose a dynamic prediction algorithm to predict the bond price using actual data set of treasure note (T-Note). The proposed algorithm is based on term structure model of the interest rates, which takes place in various financial modelling, such as the standard Gaussian Wiener process. To obtain cumulative distribution functions (CDFs) of actual data for the interest rate measurement used, we use the natural cubic spline (NCS) method, which is generally used as numerical methods for interpolation. Then we also use the random number generation scheme (RNGS) to calculate the pricing of bond through the obtained CDF. In empirical computer simulations, we show that the lower values of precision in the proposed prediction algorithm corresponds to sharper estimates. It is very reasonable on prediction.

• Journal of the Korea Computer Industry Society
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• v.6 no.2
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• pp.241-248
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• 2005

Conventional estimating methods forecast the future that it usually using the past statistical numerical value. In order to forecast the farming price, it must need many effort and accuracy knowledge. Therefore, to solve the these problems, this paper to improve forecasting farming price using fuzzy rules and neural network as a preprocessing. Also, we developed an intelligent farming expert system for real time forecasting as a postprocessing about unexpectable conditions. Computer simulation results proved reducing pricing error which proposed farming price expecting system better than conventional demand forecasting system does not using fuzzy rules.

• Journal of the Korea Society for Simulation
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• v.28 no.3
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• pp.49-56
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• 2019

In this paper, we deal with two pricing of bond options using the relationship between the forward rate model and the Libor rate model. First, we derive a formula for obtaining discounted bond prices using the restrictive condition of the Ritchken and Sankarasubramanian (RS), and then use the volatility function relationship of the forward rate and the Libor rate models to find the analytic solution (AS) of bond options pricing. Second, the price of the bond options is calculated by simulating several scenarios from the presented condition using Monte Carlo Simulation (MCS). Comparing the results of the implementation of the above two pricing methods, the relative error (RE) is obtained, which means the ratio of AS and MCS. From the results, we can confirm that the RE is around 3.9%, which means that the price of the bond options can be predicted very accurately using the MCS as well as AS.

• Journal of Internet Computing and Services
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• v.25 no.2
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• pp.93-99
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• 2024

In this paper, we derive the Partial Differential Bond Price Equation (PDBPE) by using Ito's Lemma to determine the pricing of bond on term-structure of interest rate (TSIR) model with jump. From PDBPE, the Maclaurin series (MS) and the moment-generating function (MGF) for the exponential function are used to obtain a numerical solution (NS) of the bond prices. And an algorithm for determining bond prices using Monte Carlo Simulation (MCS) techniques is proposed, and the pricing of bond is determined through the simulation process. Comparing the results of the implementation of the above two pricing methods, the relative error (RE) is obtained, which means the ratio of NS and MCS. From the results, we can confirm that the RE is less than around 2.2%, which means that the pricing of bond can be predicted very accurately using the proposed algorithms as well as numerical analysis. Moreover, it was confirmed that the bond price obtained using the MS has a relatively smaller error than the pricing of bond obtained by using the MGF.

• Proceedings of the KIEE Conference
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• 2003.07a
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• pp.596-598
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• 2003

본 논문에서는 CBP 가격을 기반으로, TWBP시장의 가격을 분석하였다. KPX가 발표한 SMP와 수요예측자료로 누적입찰자료를 추정하었으며, 이를 이용하여 계획예방정비에 따른 TWBP가격 변동을 시뮬레이션 하였다.

• Journal of Digital Convergence
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• v.11 no.2
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• pp.175-182
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• 2013

In this paper, the simulation model of house price is presented on the basis of pricing mechanism between the demand and the supply of apartments in seoul. The algorithm of house price simulation model for calculating the rate of price over time includes feedback control theory. The feedback control theory consists of stock variable, flow variable, auxiliary variable and constant variable. We suggest that the future price of apartment is simulated using mutual interaction variables which are demand, supply, price and parameters among them. In this paper we considers three items which include the behavior of apartment price index, the size of demand and supply, and the forecasting of the apartment price in the future economic scenarios. The proposed price simulation model could be used in public needs for developing a house price regulation policy using financial and non-financial aids. And the quantitative simulation model is to be applied in practice with more specific real data and Powersim Software modeling tool.

• Proceedings of the Korea Information Processing Society Conference
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• 2021.05a
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• pp.269-272
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• 2021

본 논문에서는 변수간의 다양한 관계 분석 또는 예측 모델에 많이 적용되는 베이지안 네트워크 모델에 대한 양자 회로를 설계하고, 설계한 양자 회로를 '모여봐요! 동물의 숲' 게임에서 진행되는 무 거래에 대한 무값을 예측하는 시나리오에 적용했다. 제안한 양자 가격 예측 모델은 양자 회로로 표현했으며 IBM 의 Qiskit 을 이용해 구현하였다. 구현한 회로는 시뮬레이션 백엔드 뿐만아니라 IBM 에서 클라우드로 제공하는 실제 양자 컴퓨터 2 종의 백엔드에 실행하였고, 실행 결과와 설계한 회로를 바탕으로 제안한 모델의 성능을 분석하여 제안 모델의 효용성을 보였다.

• 월간 기계설비
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• s.236
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• pp.94-95
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• 2010

예전에도 본 칼럼에서 한번 소개했지만 변화가 많을 것으로 예측될 경우에 유효한 경영 전략 중의 하나가 시나리오 경영이다. 즉, 미래에 관하여 몇 가지 경우의 수를 상정하고 시뮬레이션을 해본 후 각 상황에 맞는 가상 시나리오를 만드는 것이다. 미국의 쉘 정유사 등이 석유 파동시 큰 효과를 보아 유명해진 전략으로 자재 가격 변동이 심할 것으로 보이는 우리 설비 업계에도 적용해 볼 만하다. 본문에서 구체적으로 살펴보도록 하겠다.