• Journal of Information Technology Applications and Management
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• v.19 no.1
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• pp.1-12
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• 2012

Data mining is the process of searching and analyzing large quantities of data for finding out meaningful patterns and rules. Artificial Neural Network (ANN) is one of the tools of data mining which is becoming very popular in forecasting the future values. Some of the areas where it is used are banking, medicine, retailing and fraud detection. In finance, artificial neural network is used in various disciplines including stock market forecasting. In the stock market time series, due to high volatility, it is very important to choose a model which reads volatility and forecasts the future values considering volatility as one of the major attributes for forecasting. In this paper, an attempt is made to develop two models - one using feed forward back propagation Artificial Neural Network and the other using Autoregressive Conditional Heteroskedasticity (ARCH) technique for forecasting stock market returns. Various parameters which are considered for the design of optimal ANN model development are input and output data normalization, transfer function and neuron/s at input, hidden and output layers, number of hidden layers, values with respect to momentum, learning rate and error tolerance. Simulations have been done using prices of daily close of Sensex. Stock market returns are chosen as input data and output is the forecasted return. Simulations of the Model have been done using MATLAB$^{(R)}$ 6.1.0.450 and EViews 4.1. Convergence and performance of models have been evaluated on the basis of the simulation results. Performance evaluation is done on the basis of the errors calculated between the actual and predicted values.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.49 no.1
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• pp.112-118
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• 2012

In daily life, there are many information broadcasting by using voice information systems. If surrounding noises are mixed with the information signals, the clarity of the signal become down graded too much to understand. Surrounding noises are not uniformed, but very irregular signals always changing. Therefore, it is very hard to control the output signals along with the irregular signals. This paper suggests a method to change the level of the voice information adapting to the surround noise in the white noise environment. The surround noise level is measured by subtracting the stored output voice signal from the voice signal degraded by the noise. The noise is used to estimation of SNR. And, the method to change the output level of voice signal adapting to the noise level. The suggested adaptive voice information system has the advantage to improve listeners' speech perception and to use amplifier's energy effectively.

• Korean Journal of Agricultural and Forest Meteorology
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• v.19 no.4
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• pp.270-279
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• 2017

Regional assessment of crop productivity using a gridded simulation approach could aid policy making and crop management. Still, little effort has been made to develop the systems that allows gridded simulations of crop growth using ORYZA 2000 model, which has been used for predicting rice yield in Korea. The objectives of this study were to develop a series of data processing modules for creating input data files, running the crop model, and aggregating output files in a region of interest using gridded data files. These modules were implemented using C++ and R to make the best use of the features provided by these programming languages. In a case study, 13000 input files in a plain text format were prepared using daily gridded weather data that had spatial resolution of 1km and 12.5 km for the period of 2001-2010. Using the text files as inputs to ORYZA2000 model, crop yield simulations were performed for each grid cell using a scenario of crop management practices. After output files were created for grid cells that represent a paddy rice field in South Korea, each output file was aggregated into an output file in the netCDF format. It was found that the spatial pattern of crop yield was relatively similar to actual distribution of yields in Korea, although there were biases of crop yield depending on regions. It seemed that those differences resulted from uncertainties incurred in input data, e.g., transplanting date, cultivar in an area, as well as weather data. Our results indicated that a set of tools developed in this study would be useful for gridded simulation of different crop models. In the further study, it would be worthwhile to take into account compatibility to a modeling interface library for integrated simulation of an agricultural ecosystem.

• Proceedings of the KIEE Conference
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• 2006.04b
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• pp.365-368
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• 2006

The objective of this research is to investigate usage of 3KW photovoltaic-wind power hybrid generation system composed of 500W solar power generator and 400W wind power generator in a parallel circuit. In addition, solar radiation meter and wind monitor have been installed into each generation system to obtain the practical operating data that monitored in monthly, daily and hourly. These data that are independent to weather change and location would provide adequate generation output on average and cope with emergency situation in generation system In conclusion, based on this study, it could be considered for 3KW combined generation system to be gradually propagated to houses and small-size public facilities.

• Korean Journal of Hydrosciences
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• v.4
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• pp.1-9
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• 1993

It is a major objective for the management and operation of water resources system to forecast streamflows. The applicability of artificial neural network model to hydrologic system is analyzed and the performance is compared by statistical method with observed. Multi-layered perception was used to model rainfall-runoff process at Pyung Chang River Basin in Korea. The neural network model has the function of learning the process which can be trained with the error backpropagation (EBP) algorithm in two phases; (1) learning phase permits to find the best parameters(weight matrix) between input and output. (2) adaptive phase use the EBP algorithm in order to learn from the provided data. The generalization results have been obtained on forecasting the daily and hourly streamflows by assuming them with the structure of ARMA model. The results show validities in applying to hydrologic forecasting system.

• Korean Journal of Air-Conditioning and Refrigeration Engineering
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• v.22 no.12
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• pp.873-880
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• 2010

The present study developed a computer simulation program to determine the optimum strategy and capacity of a micro combined heat and power(CHP) system. This simulation program considered a part-load electrical/thermal efficiency and transient response characteristics of CHP unit. The result obtained from the simulation was compared with the actual operation of 30 kW gas engine driven micro CHP system. It was found that the simulation could reproduce the daily operation behavior, such as operating hours and mean load factor, closely to the actual behavior of the system and could predict the amount of electrical/thermal output and fuel consumption with the error of less than 12%.

• Current Photovoltaic Research
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• v.4 no.2
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• pp.80-86
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• 2016

Since the last five years it has become a lot of solar power plants installed. However, by installing the large-scale solar power station it is not easy to predict the actual generation years. Because there are a variety of factors, such as changes daily solar radiation, temperature and humidity. If the power output can be measured accurately it predicts profits also we can measure efficiency for solar power plants precisely. Therefore, Prediction of power generation is forecast to be a useful research field. In this study, out discovering the factors that can improve the accuracy of the prediction of the photovoltaic power generation presents the means to apply them to the power generation amount prediction.

• Proceedings of the Korean Society of Agricultural Engineers Conference
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• 2003.10a
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• pp.467-470
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• 2003

The size, scale, and number of subwatersheds can affect a watershed modeling process and subsequent results. The objective of this study was to determine the appropriate level of subwatershed division for simulating streamflow. The Soil and Water Assessment Tool(SWAT) model with a GIS interface(BASINS SWAT) was applied to Yongdam Dam watershed. Daily output was analyzed from simulation, which was executed for 10 years using climate data representing the 1987 to 1996 period. The optimal number of subwatersheds and HRUs to adequately predict streamflow was found to be around 15, 174. Increasing the number of subwatersheds and HRUs beyond this level does not significantly affect the computed streamflow. this number of subwatersheds and HRUs can be used to optimize SWAT input data preparation requirements and simplify the interpretation of results without compromising simulation accuracy.

• Journal of Environmental Impact Assessment
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• v.5 no.1
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• pp.95-105
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• 1996

Meteorological Joint Frequency Function required indispensably in long-term air quality prediction models were discussed for practical application in Korea. The algorithm, proposed by Turner(l964), is processed with daily solar insolation and cloudiness and height basically using Pasquill's atmospheric stability classification method. In spite of its necessity and applicability, the computer program, called STAR(STability ARray), had some significant difficulties caused from the difference in meteorological data format between that of original U.S. version and Korean's. To cope with the problems, revised STAR program for Korean users were composed of followings; applicability in any site of Korea with regard to local solar angle modification; feasibility with both of data which observed by two classes of weather service centers; and examination on output format associated with prediction models which should be used.

• Proceedings of the Korean Institute of Electrical and Electronic Material Engineers Conference
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• 2003.07a
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• pp.96-99
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• 2003

In this paper, ageing diagnostics in the large capacity oil transformer are investigated. Following items are investigated for the ageing diagnostic in transformer oils (leakage current of sensor, power consumption and temperature of transformer oil). All temperature data are gathered from daily report in the substation. The power consumption of transformer are gathered output report of APIS(Airport Power Information System). Especially, data of sensor leakage current are accumulated from the online diagnostic system for transformer oil. The temperature of transformer oils major change factor was ambient temperature and capacity of power load. The leakage current are change by oil temperature. The leakage current ware not more than 2 [nA] in summer,