• Journal of Biosystems Engineering
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• v.41 no.3
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• pp.232-239
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• 2016

Purpose: The aims of this study were to define the drying characteristics of rapeseed and to determine the optimum thin-layer drying model for rapeseed by considering the effects of drying temperature and relative humidity. Methods: The thin-layer drying experiments were conducted at different combinations of drying air temperature levels of 40, 50, and $60^{\circ}C$ and relative humidity levels of 30, 45, and 60%, on both of which drying rate depends. The drying rate increased with increasing air temperature as well as decreasing relative humidity. The 13 models were fitted to the experimental data. Results: From the results of the regression analysis for empirical constants of the Page model, the values of $R^2$ were the highest (ranging from 0.9924 to 0.9966) and the values of RMSE were the lowest (ranging from 0.0169 to 0.0296). Conclusions: For all drying conditions considered, the Page model was determined to be the most suitable model for describing the thin-layer drying of rapeseed (P-value < 0.01). The moisture diffusion coefficients were calculated using the moisture diffusion equation for a spherical shape, based on Fick's second law.

• Proceedings of the KSRS Conference
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• 2007.10a
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• pp.153-155
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• 2007

Korea Meteorological Administration (KMA) has operationally produced Atmospheric Motion Vector (AMV) from the consecutive MTSAT-1R satellite image dataset. Comparing with radiosonde data, our current AMV scheme shows more than 10 m/s RMSE. Therefore we need to improve continuously its accuracy. Many AMV producers have stated that the bad performance of the Height Assignment (HA) algorithm is the main reason of degrading the accuracy of AMV. The uncertainties in AMV HA can occur in the algorithm itself, used NWP profiles, and the performance of Radiative Transfer Model (RTM) etc. This study introduces currently operated AMV HA schemes and the impacts of NWP profile data and RTM that these schemes use were investigated. Finally we analyzed the relationship between vectors by vector tracking and heights assigned to each vector by using collocated wind profile dataset with radiosonde data. This study is a preliminary work to improve the accuracy of AMV by removing or decreasing the uncertainties in AMV estimation.

• Journal of The Korean Society of Agricultural Engineers
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• v.51 no.3
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• pp.1-8
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• 2009

The objectives of this study are to apply univariate search and genetic algorithm to tank model, and compare the two optimization methods. Hydrologic data of Baran watershed during 1996 and 1997 were used for correction the tank model, and the data of 1999 to 2000 were used for validation. RMSE and R2 were used for the tank model's optimization. Genetic algorithm showed better result than univariate search. Genetic algorithm converges to general optima, and more population of potential solution made better result. Univariate search was easy to apply and simple but had a problem of convergence to local optima, and the problem was not solved although search the solution more minutely. Therefore, this study recommend genetic algorithm to optimize tank model rather than univariate search.

• Journal of The Korean Society of Agricultural Engineers
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• v.56 no.6
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• pp.139-147
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• 2014

Forecasting salinity in an estuary reservoir is essential to promise irrigation water for the reclaimed land. The objective of the research was to assess salinity balance and its temporal and spatial variations in the Iwon estuary reservoir which has been issued by its high contents of salinity in spite of desalination process for four years. Seepage flows through the see dikes which could be one of possible reason of high salinity level of the reservoir was calculated based on the salinity balance in the reservoir, and used as input data for salinity modeling. A three-dimensional hydrodynamic model, Environmental Fluid Dynamics Code (EFDC), was used to simulate salinity level in the reservoir. The model was calibrated and validated based on weekly or biweekly observed salinity data from 2006 to 2010 in four different locations in the reservoir. The values of $R^2$, RMSE and RMAE between simulated and observed salinity were calculated as 0.70, 2.16 dS/m, and 1.72 dS/m for calibration period, and 0.89, 1.15 dS/m, and 0.89 dS/m for validation period, respectively, showing that simulation results was generally consistent with the observation data.

• Journal of Biosystems Engineering
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• v.37 no.6
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• pp.385-397
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• 2012

Purpose: Drying characteristics of sawdust was studied under batch mode using lab scale microwave dryer. The objective of this study was to investigate the effect of material load and microwave output power on drying characteristics of sawdust. Methods: Material load and microwave output power were varied from 23 to 186 g and 530 to 370 W respectively. Different kinetic models were tested to fit the drying rates of sawdust. Similarly, the activation energy was calculated by employing the Arrhenius equation. Results: The drying efficiency increased considerably, whereas the specific energy consumption significantly decreased with increase in material load and microwave output power. The cumulative energy efficiency increased by 9%, and the specific energy consumption decreased by 8% when the material load was increased from 23 to 186 g. The effective diffusivity increased with decrease in material load and increase in microwave output power. The previously published model gave the best fit for data points with $R^2$ and RMSE values of 0.999 and 0.01, respectively. Conclusions: The data obtained from this study could be used as a basis for modeling of large scale industrial microwave dryers for the pellet production.

• Journal of Environmental Impact Assessment
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• v.21 no.2
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• pp.239-246
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• 2012

Daily net radiation is essential for heat budget analysis for environmental impact assessment in the coastal zone and longwave radiation is an important element of net radiation because there is a significant exchange of radiant energy between the earth's surface and the atmosphere in the form of radiation at longer wavelengths. However, radiation data is not commonly available, and there has been no direct measurement for most areas where coastal environmental impact assessment is usually most needed. Often an empirical equation, e.g., Penman and FAO-24 formulae is used to estimate longwave radiation using temperature, humidity, and sunshine hour data but local calibration may be needed. In this study, local recalibration was performed to have best fit from a widely used longwave equation using the measured longwave radiation data in Korea Global Atmospheric Watch Center (KGAWC). The results shows recalibration can provided better performance AE=0.23($W/m^2$) and RMSE=14.73($W/m^2$). This study will contribute to improve the accuracy of the heat budget analysis in the coastal area.

• Bulletin of the Korean Chemical Society
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• v.28 no.9
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• pp.1472-1476
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• 2007

Artificial neural networks (ANNs) are successfully developed for the modeling and prediction of normalized polarity parameter (ETN) of 216 various solvents with diverse chemical structures using a quantitative-structure property relationship. ANN with architecture 5-9-1 is generated using five molecular descriptors appearing in the multi-parameter linear regression (MLR) model. The most positive charge of a hydrogen atom (q+), total charge in molecule (qt), molecular volume of solvent (Vm), dipole moment (μ) and polarizability term (πI) are input descriptors and its output is ETN. It is found that properly selected and trained neural network with 192 solvents could fairly represent the dependence of normalized polarity parameter on molecular descriptors. For evaluation of the predictive power of the generated ANN, an optimized network is applied for prediction of the ETN values of 24 solvents in the prediction set, which are not used in the optimization procedure. Correlation coefficient (R) and root mean square error (RMSE) of 0.903 and 0.0887 for prediction set by MLR model should be compared with the values of 0.985 and 0.0375 by ANN model. These improvements are due to the fact that the ETN of solvents shows non-linear correlations with the molecular descriptors.

• The Journal of Asian Finance, Economics and Business
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• v.7 no.11
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• pp.83-93
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• 2020

This empirical research aims to modeling and improving the forecasting accuracy of the volatility pattern by employing the Saudi Arabia stock market (Tadawul)by studying daily closed price index data from October 2011 to December 2019 with a number of observations being 2048. In order to achieve significant results, this study employs many mathematical functions which are non-linear spectral model Maximum overlapping Discrete Wavelet Transform (MODWT) based on the best localized function (Bl14), autoregressive integrated moving average (ARIMA) model and generalized autoregressive conditional heteroskedasticity (GARCH) models. Therefore, the major findings of this study show that all the previous events during the mentioned period of time will be explained and a new forecasting model will be suggested by combining the best MODWT function (Bl14 function) and the fitted GARCH model. Therefore, the results show that the ability of MODWT in decomposition the stock market data, highlighting the significant events which have the most highly volatile data and improving the forecasting accuracy will be showed based on some mathematical criteria such as Mean Absolute Percentage Error (MAPE), Mean Absolute Scaled Error (MASE), Root Means Squared Error (RMSE), Akaike information criterion. These results will be implemented using MATLAB software and R- software.

• Journal of Environmental Science International
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• v.23 no.4
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• pp.585-594
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• 2014

This study Analyzed four of seven runoffs which had happened in 2012 in comparison with the runoffs shown in Kalesto data, using the fixed surface image velocimetry (FSIV) installed at Oedo stream, Jeju Island. As a result of identifying a runoff curve graph, it was analyzed that the flood runoffs calculated with two observation devices were almost equivalent. As the differences in peak flows were 10 $m^3/s$, 0.7 $m^3/s$ and 3 $m^3/s$, the very similar result values were calculated. Even though there were errors in RMSE(Root Mean Square Error) made by two observation devices according to the degree of the peak flow, the values of $R^2$ by flood event were 0.89, 0.87, 0.86 and 0.82, showing the result values almost close to 1. Therefore, there was a very high correlation in flood runoffs calculated with two observation devices. This research method was considered to be a very suitable method to measure unexpected flood runoffs which could happen in the island area such as Jeju island during bad weather.

• Journal of Biosystems Engineering
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• v.29 no.1
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• pp.59-64
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• 2004

This study was performed to determine drying equations of sarcodon aspratus. Drying tests for sarcodon aspratus were conducted in an experimental dryer equiped with an air conditioning unit. The drying tests were performed at three air temperatures of 30$^{\circ}C$, 40$^{\circ}C$ and 50$^{\circ}C$, and two relative humidities of 30% and 50%. Measured moisture ratio data were fitted with the selected four drying models(Page, Thompson, Lewis and simplified diffusion models) using stepwise multiple regression analysis. When the coefficients of determination and root mean square errors of moisture ratio were evaluated for four drying models, the Page model was found to fit adequately to all the drying test data with coefficient of determination of 0.9996 and RMSE of 0.00523.