• 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.

• International Journal of Computer Science & Network Security
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• v.21 no.4
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• pp.65-74
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• 2021

The rapid change in gold price is an issue of concern in the global economy and financial markets. Gold has been used as a means for trading and transaction around the world for long period of time and it plays an integral role in monetary, business, commercial and financial activities. More importantly, it is used as economic measure for the global economy and will continue to play an important economic vital role - both locally and globally. There has been an explosive growth in demand for efficient and effective scheme to predict gold price due its volatility and fluctuation. Hence, there is need for the development of gold price prediction scheme to assist and support investors, marketers, and financial institutions in making effective economic and monetary decisions. This paper primarily proposed an intelligent based system for predicting and characterizing the gold market trend. The simulation result shows that the proposed intelligent gold price scheme has been able to predict the gold price with high accuracy and precision, and ultimately it has significantly reduced the prediction error when compared to baseline neural network (NN).

• Journal of Electrical Engineering and Technology
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• v.12 no.6
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• pp.2166-2176
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• 2017

Electricity price prediction plays a crucial part in making the schedule and managing the risk to the competitive electricity market participants. However, it is a difficult and challenging task owing to the characteristics of the nonlinearity, non-stationarity and uncertainty of the price series. This study proposes a hybrid improved strategy which incorporates data preprocessor components and a forecasting engine component to enhance the forecasting accuracy of the electricity price. In the developed forecasting procedure, the Seasonal Adjustment (SA) method and the Ensemble Empirical Mode Decomposition (EEMD) technique are synthesized as the data preprocessing component; the Coupled Simulated Annealing (CSA) optimization method and the Least Square Support Vector Regression (LSSVR) algorithm construct the prediction engine. The proposed hybrid approach is verified with electricity price data sampled from the power market of New South Wales in Australia. The simulation outcome manifests that the proposed hybrid approach obtains the observable improvement in the forecasting accuracy compared with other approaches, which suggests that the proposed combinational approach occupies preferable predication ability and enough precision.

• The Journal of the Korea Contents Association
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• v.12 no.2
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• pp.472-482
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• 2012

There are many investors in the stock market, and more and more people get interested in the stock investment. In order to avoid risks and make profit in the stock investment, we have to determine several aspects using various information. That is, we have to select profitable stocks and determine appropriate buying/selling prices and holding period. This paper proposes a data mining tool for the investors' decision support. The data mining tool makes stock investors apply machine learning techniques and generate stock price prediction model. Also it helps determine buying/selling prices and holding period. It supports individual investor's own decision making using past data. Using the proposed tool, users can manage stock data, generate their own stock price prediction models, and establish trading policy via investment simulation. Users can select technical indicators which they think affect future stock price. Then they can generate stock price prediction models using the indicators and test the models. They also perform investment simulation using proper models to find appropriate trading policy consisting of buying/selling prices and holding period. Using the proposed data mining tool, stock investors can expect more profit with the help of stock price prediction model and trading policy validated on past data, instead of with an emotional decision.

• 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.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2014.04a
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• pp.366-370
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• 2014

Considering a countermeasure against vibration along the existing line, in many cases, it may be the most efficient method to replace existing fasteners with high resilient fasteners because of the restriction of the construction of a new track type. There are many types of high resilient fasteners which are different in price and performance. Therefore it is important to choose the high resilient fastener which has best vibration-proof performance per price. In this study, a prediction method which can exactly evaluate the performance of a fastener in the reduction the ground borne noise in the existing line is presented, which is constructed based on the combination of the measurement and the simulation. A numerical example using the presented method is introduced to evaluate the vibration-proof performance for the solution of the vibration problem in the existing line.

• The Journal of Fisheries Business Administration
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• v.47 no.4
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• pp.65-75
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• 2016

The purpose of this study was to analyze the production and marketing control effects of aqua-cultured flounder required for stable income growth of aqua-cultured household. We analyzed the supply and demand structure of cultured flounder using the partial equilibrium model approach. And we estimated the optimal yield of cultured flounder and analyzed the effect of marketing control through constructed model. The main results of this study are summarized as follows. First, the fitness and predictive power of the estimated model showed that the RMSPE and MAPE values were less than 5% and Theil's inequality coefficient was very close to 0 rather than 1. It was evaluated that the prediction ability of the aqua-cultured flounder supply and demand model by dynamic simulation was excellent. Second, dynamic simulation based on policy simulation was conducted to analyze the price increase effect of production and shipment control of cultured flounder. As a result, if the annual production volume is reduced by 1%, 5%, and 10% among 32,852~37,520 tons, it is analyzed that the price increase effect is from 1.2% to 12.5%. Finally, this study suggests that the production and marketing control can increase the price of aqua-cultured flounder in the market. In this paper, we propose a policy implementation of the total supply system instead of conclusions.

• The Korean Journal of Applied Statistics
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• v.17 no.2
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• pp.219-228
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• 2004

In this paper we construct prediction intervals for nonlinear time series models using the bootstrap. We compare these prediction intervals to traditional asymptotic prediction intervals using quasi-score estimation function and M-quasi-score estimating function comprising bounded functions. Simulation results show that the bootstrap method leads to improved accuracy. The accuracy of the bootstrap is empirically demonstrated with the consumer price index.

• Structural Engineering and Mechanics
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• v.45 no.5
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• pp.693-702
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• 2013

Recently, artificial intelligence tools are most used for structural engineering and mechanics. In order to predict reserve prices and prices of awards, this study proposed a novel regression prediction model by the intelligent Kalman filtering method. An artificial intelligent multiple regression model was established using categorized data and then a prediction model using intelligent Kalman filtering. The rather precise construction bid price model was selected for the purpose of increasing the probability to win bids in the simulation example.

• The Transactions of the Korean Institute of Electrical Engineers P
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• v.58 no.4
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• pp.483-489
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• 2009

Electrolytic capacitors are used in variety of equipments as smoothening element of the power converters because it has high capacitance for its size and low price. Electrolytic capacitors, which is most of the time affected by aging effect, plays a very important role for the power electronics system quality and reliability. Therefore it is important to estimate the parameter of an electrolytic capacitor to predict the failure. This objective of this paper is to propose a new method to detect the rise of equivalent series resistor(ESR) in order to realize the online failure prediction of electrolytic capacitor for DC output filter of power converter. The ESR of electrolytic capacitor estimated from RMS result of filtered waveform(BPF) of the ripple capacitor voltage/current. Therefore, the preposed online failure prediction method has the merits of easy ESR computation and circuit simplicity. Simulation and experimental results are shown to verify the performance of the proposed on-line method.