• Journal of the Korean Society of Manufacturing Technology Engineers
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• v.25 no.4
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• pp.271-280
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• 2016

Predicting cutting forces in machine tools is essential to productivity improvement and process control in the manufacturing field. Furthermore, milling machining is more complicated than turning machining. Therefore, several studies have been conducted previously to simulate milling forces; this study aims to simulate the cutting forces in milling machines using multi-layered neural networks. In the experiments, the number of layers in these networks was 3 and 4 and the number of neurons in the hidden layers was varied from 20 to 200. The root mean square errors of simulated cutting force components were obtained from taught and untaught data for the various neural networks. Results show that the error trends for untaught data were non-uniform because of the complex nature of the cutting force components, which was caused by different cutting factors and nonlinear characteristics coming into play. However, trends for taught data showed a very good coincidence.

• Water for future
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• v.23 no.1
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• pp.109-118
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• 1990

In this study, the effectiveness of existing streamflow data collection networks in the Han and the Nakdong River Basin is evaluated for various gaging plans of 5, 10, 15 and 20years planning horizons by the nonlinear integer programming method, and also a technique for adjustment and planning of the existing network is provided for the purpose of increasing the efficiency of the network in terms of ecomony. The objective function is minimization of the average sampling mean square error of regional regression model with regression parameters estimated by generalized least squares method.

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.48 no.5
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• pp.571-579
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• 1999

In this paper, a Hadamard transform imaging spectrometer(HTIS) is proposed by using a grill spectrometer. And we reconfigure the system by using the grill sectrometer which uses a left cyclic S-matrix instead of the conventional right cyclic one. Then, we model the Hadamard transform imaging spectrometer and apply the mask characteristics compensation method, i.e. $ {T}^{-1}$ method, to complete fast algorithm. Also, through computer simulations the superiority of the proposed system in this paper to the conventional Hadamard transform spectrometer(HTS) is proved and the performance of the two systems are compared by introducing average mean square error(AMSE) as the algebraic criterion.

• Journal of the Korean Data and Information Science Society
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• v.25 no.3
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• pp.623-632
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• 2014

We shall consider the estimation for the parameter and the right tail probability in a general exponential distribution. We also shall consider the estimation of the reliability P(X < Y ) and the skewness trends of the density function of the ratio X=(X+Y) for two independent general exponential variables each having different shape parameters and known scale parameter. We then shall consider the estimation of the failure rate average and the hazard function for a general exponential variable having the density function with the unknown shape and known scale parameters, and for a bivariate density induced by the general exponential density.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.4 no.1
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• pp.37-42
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• 1986

This paper deals with comparison of simultaneous geodetic networks between with geographical coordinates and with plane coordinates. The adjustment computation is performed by variation of coordinates. Provisional values for observation equations are computed by extended Guass mid-latitude formula using, official coordinates ($\varphi,\lambda$) in geographical network abjustment, measurements are reduced to plane by origin scale factor (＝1.0000) Bessel ellipsoid and unit weight are adopted, and geographical coordinates are projected by Guass conformal double projection. The processing results of a test-network by distances yield the average root mean square error of position 6ㆍ2cm for adjustment with $\varphi,\lambda$ and 5.8cm for adjustment with X, Y. RMSE of discrepancy between two methods is 1.7cm. This result conform to required accracy.

• Korean Journal of Remote Sensing
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• v.32 no.6
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• pp.741-746
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• 2016

Albedo is one of the climate variables that modulate absorption of solar energy, and its retrieval is important process for climate change study. High spatial resolution and long-term consistent periods are important considerations in order to efficiently use the retrieved albedo data. This study retrieved surface broadband albedo based on Landsat 8 as high resolution which is consistent with Landsat 7. First of all, we analyzed consistency of Landsat 7 channel and Landsat 8 channel. As a result, correlation coefficient(R) on all channels is average 0.96. Based on this analysis, we used multiple linear regression model using Landsat 7 albedo, which is being used in many studies, and Landsat 8 reflectance channel data. The regression coefficients of each channel calculated by regression analysis were used to derive a formula for converting the Landsat 8 reflectance channel data to broadband albedo. After Landsat 8 albedo calculated using the derived formula is compared with Landsat 7 albedo data, we confirmed consistency of two satellite using Root Mean Square Error (RMSE), R-square ($R^2$) and bias. As a result, $R^2$ is 0.89 and RMSE is 0.003 between Landsat 7 albedo and Landsat 8 albedo.

• Journal of Environmental Impact Assessment
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• v.14 no.4
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• pp.157-164
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• 2005

Existing models that predict of Daily water supply include statistical models and neural network model. The neural network model was more effective than the statistical models. Only neural network model, which predict of Daily water supply, is focused on estimation of the operational control. Neural network model takes long learning time and gets into local minimum. This study proposes Neuro Genetic hybrid model which a combination of genetic algorithm and neural network. Hybrid model makes up for neural network's shortcomings. In this study, the amount of supply, the mean temperature and the population of the area supplied with water are use for neural network's learning patterns for prediction. RMSE(Root Mean Square Error) is used for a MOE(Measure Of Effectiveness). The comparison of the two models showed that the predicting capability of Hybrid model is more effective than that of neural network model. The proposed hybrid model is able to predict of Daily water, thus it can apply real time estimation of operational control of water works and water drain pipes. Proposed models include accidental cases such as a suspension of water supply. The maximum error rate between the estimation of the model and the actual measurement was 11.81% and the average error was lower than 1.76%. The model is expected to be a real-time estimation of the operational control of water works and water/drain pipes.

• Journal of the Korean Solar Energy Society
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• v.39 no.3
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• pp.59-66
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• 2019

New and renewable energy forecasts are key technology to reduce the annual operating cost of new and renewable facilities, and accuracy of forecasts is paramount. In this study, we intend to build a model for the prediction of short-term solar power generation for 1 hour to 3 hours. To this end, this study applied two time series technique, ARIMA model without considering seasonality and SARIMA model with considering seasonality, comparing which technique has better predictive accuracy. Comparing predicted errors by MAE measures of solar power generation for 1 hour to 3 hours at four locations, the solar power forecast model using ARIMA was better in terms of predictive accuracy than the solar power forecast model using SARIMA. On the other hand, a comparison of predicted error by RMSE measures resulted in a solar power forecast model using SARIMA being better in terms of predictive accuracy than a solar power forecast model using ARIMA.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.32 no.8C
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• pp.758-766
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• 2007

High peak-to-average power ratio(PAPR) is one of serious problems in the orthogonal frequency division multiplexing(OFDM) systems. This paper proposes a PAPR reduction technique for limited feedback multiple input multiple output(MIMO) OFDM systems. The proposed method is based on the null space of the MIMO channel where a dummy signal is made in the channel's null space and then, subtracted from the original signal to reduce the PAPR. First, we show that a problem occurs when the existing method is directly applied to limited feedback MIMO case. Then, a weight function for the dummy signal is proposed to mitigate the degradation of the receiver performance while still reducing PAPR significantly. The weight function is derived from a constrained nonlinear optimization problem to minimize the mean square error between the received signal and its ideal signal. Simulation results shows that the proposed technique provides about 2.5dB PAPR reduction with 0.2dB bit-error probability loss.

• Journal of Biosystems Engineering
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• v.39 no.4
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• pp.310-317
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• 2014

Purpose: This study was conducted to analyze the air flow characteristics in a plant factory with different inlet and outlet locations using computational fluid dynamics (CFD). Methods: In this study, the flow was assumed to be a steady-state, incompressible, and three-dimensional turbulent flow. A realizable k-${\varepsilon}$ turbulent model was applied to show more reasonable results than the standard model. A CFD software was used to perform the numerical simulation. For validation of the simulation model, a prototype plant factory ($5,900mm{\times}2,800mm{\times}2,400mm$) was constructed with two inlets (${\Phi}250mm$) and one outlet ($710mm{\times}290mm$), located on the top side wall. For the simulation model, the average air current speed at the inlet was $5.11m{\cdot}s^{-1}$. Five cases were simulated to predict the airflow pattern in the plant factory with different inlet and outlet locations. Results: The root mean square error of measured and simulated air current speeds was 13%. The error was attributed to the assumptions applied to mathematical modelling and to the magnitude of the air current speed measured at the inlet. However, the measured and predicted airflow distributions of the plant factory exhibited similar patterns. When the inlets were located at the center of the side wall, the average air current speed in the plant factory was increased but the spatial uniformity was lowered. In contrast, if the inlets were located on the ceiling, the average air current speed was lowered but the uniformity was improved. Conclusions: Based on the results of this study, it was concluded that the airflow pattern in the plant factory with multilayer cultivation shelves was greatly affected by the locations of the inlet and the outlet.