• Journal of The Korean Society of Agricultural Engineers
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• v.62 no.1
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• pp.39-50
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• 2020

The objective of this study was to assess the livestock nonpoint source pollutant impact on water quality in Namgang dam watershed using the HSPF (Hydrological Simulation Program-Fortran) model. The input data for the HSPF model was established using the landcover, digital elevation, and watershed and river maps. In order to apply the pollutant load to the HSPF model, the delivery load of the livestock nonpoint source in the Namgang dam watershed was calculated and used as a point pollutant input data for the HSPF model. The hydrologic and water quality parameters of HSPF model were calibrated and validated using the observed runoff data from 2007 to 2015 at Sancheong station. The R² (Determination Coefficient), RMSE (Root Mean Square Error), NSE (Nash-Sutcliffe efficiency coefficient), and RMAE (Relative Mean Absolute Error) were used to evaluate the model performance. The simulation results for annual mean runoff showed that R² ranged 0.79~0.81, RMSE 1.91~2.73 mm/day, NSE 0.7~0.71 and RMAE 0.37~0.49 mm/day for daily runoff. The simulation results for annual mean BOD for RMSE ranged 0.99~1.13 mg/L and RMAE 0.49~0.55 mg/L, annual mean TN for RMSE ranged 1.65~1.72 mg/L and RMAE 0.55 mg/L, and annual mean TP for RMSE ranged 0.043~0.055 mg/L and RMAE 0.552~0.570 mg/L. As a result of livestock nonpoint pollutant loading simulation for each sub-watersehd using the HSPF model, the BOD ranged 16.6~163 kg/day, TN ranged 27.5~337 kg/day, TP ranged 1.22~14.1 kg/day.

• Geomechanics and Engineering
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• v.34 no.6
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• pp.697-726
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• 2023

Brittleness as an important property of rock plays a crucial role both in the failure process of intact rock and rock mass response to excavation in engineering geological and geotechnical projects. Generally, rock brittleness indices are calculated from the mechanical properties of rocks such as uniaxial compressive strength, tensile strength and modulus of elasticity. These properties are generally determined from complicated, expensive and time-consuming tests in laboratory. For this reason, in the present research, an attempt has been made to predict the rock brittleness indices from simple, inexpensive, and quick laboratory test results namely dry unit weight, porosity, slake-durability index, P-wave velocity, Schmidt rebound hardness, and point load strength index using multiple linear regression, exponential regression, support vector machine (SVM) with various kernels, generating fuzzy inference system, and regression tree ensemble (RTE) with boosting framework. So, this could be considered as an innovation for the present research. For this purpose, the number of 39 rock samples including five igneous, twenty-six sedimentary, and eight metamorphic were collected from different regions of Iran. Mineralogical, physical and mechanical properties as well as five well known rock brittleness indices (i.e., B₁, B₂, B₃, B₄, and B₅) were measured for the selected rock samples before application of the above-mentioned machine learning techniques. The performance of the developed models was evaluated based on several statistical metrics such as mean square error, relative absolute error, root relative absolute error, determination coefficients, variance account for, mean absolute percentage error and standard deviation of the error. The comparison of the obtained results revealed that among the studied methods, SVM is the most suitable one for predicting B₁, B₂ and B₅, while RTE predicts B₃ and B₄ better than other methods.

• Journal of The Korean Society of Agricultural Engineers
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• v.60 no.5
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• pp.55-67
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• 2018

The objectives of this study were to monitor organic farming upland compared with conventional upland field and to evaluate nutrient loads reduction of surface cover effect with long-term historical climate data. APEX(Agricultural Policy Environmental eXtender) model was validated with experimental data and used for assessing surface cover scenarios for 30-year simulation periods. The validated values of RMSE(Root Mean Square Error), RMAE(Root Mean Absolute Error), $R^2$ and E(Nash-Sutcliffe efficiency) for runoff were 1.17-1.37 mm/day, 0.28-0.45 mm/day, 0.88-0.90 and 0.82-0.94 in two treatments, respectively. Those for water quality (nitrogen) were 0.05-0.16 kg/ha, 0.52-0.75 kg/ha, 0.67-0.72 and 0.32-0.70 in two treatments, respectively, and therefore the validated model showed good agreement with the observed runoff and nitrogen load for the study period. When decreasing the surface cover rate of organic farming field to 75%, 50%, 25%, and 0% (conventional field), average annual runoff increased by 7%, 15%, 23% and 31%, respectively. Under same condition of decreasing the surface cover rate, average annual nitrogen loads increased by 1.4 times, 1.7 times, 2.0 times, and 2.3 times compared with organic farming field, respectively. This study showed that it is possible to present an appropriate surface cover ratio to maintain conventional production and minimize nonpoint sources pollution for organic farming system, although long-term monitoring is needed to determine its effects on environmental concerns, crop competition, and other uncertainty.

• Bulletin of the Korean Space Science Society
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• 2011.04a
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• pp.28.3-28.3
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• 2011

Halo coronal mass ejections (HCMEs) are mainly responsible for the most severe geomagnetic storms. To minimize the projection effect of the HCMEs observed by coronagraphs, several cone models have been suggested. These models allow us to determine the geometrical and kinematic parameters of HCMEs : radial speed, source location, angular width, and the angle between the central axis of the cone and the plane of the sky. In this study, we compare these parameters form two representative cone models (the ice-cream cone model and the asymmetric cone model) using well-observed HCMEs from 2001 to 2002. And we obtain the root mean square error (rms error) between observed projection speeds and calculated projection speeds for both cone models. It is found that the average rms speed error (89 km/s) of the asymmetric cone model is a little smaller than that (107 km/s) of the ice-cream cone models, implying that the radial speeds from both models are reasonably estimated. We also find that the radial speeds obtained from two models are similar to each other with the correlation coefficient of about 0.8.

• Atmosphere
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• v.24 no.3
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• pp.419-432
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• 2014

An assessment of typhoon intensity predictability of numerical models was conducted to develop the typhoon intensity forecast guidance comparing with the RSMC-Tokyo best track data. Root mean square error, box plot analysis and time series of wind speed comparison were performed to evaluate the each model error level. One of noticeable fact is that all models have a trend of error increase as typhoon becomes stronger and the Global Forecast System showed the best performance among the models. In the detailed analysis in two typhoon cases [Danas (1324) and Haiyan (1330)], GFS showed good performance in maximum wind speed and intensity trend in the best track, however it could not simulate well the rapid intensity increasing period. On the other hand, ECMWF and Hurricane-WRF overestimated the typhoon intensity but simulated track trend well.

• Journal of the Korea Convergence Society
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• v.7 no.5
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• pp.175-180
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• 2016

The demand for accurate freeform apsheric surface is increasing to satisfy the optical performance. In this paper, we develop the algorithm for opto-mechatronics convergence, that reconstruct the surface 3D profiles from the curvarure data along two orthogonal directions. A synthetic freeform surface with 8.4 m diameter was simulated for the testing. The simulation results show that the reconstruction error is 0.065 nm PV(Peak-to-valley) and 0.013 nm RMS(Root mean square) residual difference. Finally the sensitivity to noise is diagnosed for probe position error, the simulation results proving that the suggested method is robust to position error.

• The Bulletin of The Korean Astronomical Society
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• v.36 no.2
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• pp.96.1-96.1
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• 2011

Halo coronal mass ejections (HCMEs) are major cause of the geomagnetic storms. To minimize the projection effect by coronagraph observations, we consider two CME cone models: an ice-cream cone model and an asymmetric cone model. These models allow us to determine three dimensional parameters of HCMEs such as radial speed, angular width, and the angle between sky plane and cone axis. In this study, we compare these parameters obtained from both models using 50 well-observed HCMEs from 2001 to 2002. Then we obtain the root mean square error (RMS error) between measured projection speeds and estimated ones for the models. As a result, we find that the radial speeds obtained from the models are well correlated with each other (R=0.89), and the correlation coefficient of angular width is 0.68. The correlation coefficient of the angle between sky plane and cone axis is 0.42, which is much smaller than what is expected. The reason may be due to the fact that the source locations of the asymmetric cone model are assumed to be near the center. The average RMS error of the asymmetric cone model (86.2km/s) is slightly smaller than that of the ice-cream cone model (88.6km/s).

• KIEAE Journal
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• v.10 no.6
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• pp.97-103
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• 2010

Sky simulator is a effective daylighting design tool that can evaluate three dimensional performance of lighting. Especially, the dome type sky simulator offer reliable and reproducible daylighting performance with different standard sky models. Recently, K university has developed the dome type sky simulator(sky dome) with the diameter of 6.5m and the height of 3.7m. The sky dome consists of a group of 145 large steel panels with 72 halogen lamps which are arranged in a circular array. The luminance distribution of the sky dome can be calibrated by changing the angle and the brightness of the lamps respectively. To allow more reliable prediction and evaluation of daylighting through the sky dome, It is essential to validate the sky luminance distribution of the sky dome. This study consider the validation of the comparisons between the measured and the calculated luminance values for the CIE standard overcast sky. Also, the error rate between the measured and the calculated luminance values were compared to the previous studies. The results indicated that the K university sky dome can reproduce reliable CIE standard overcast sky with the average relative error rate of 4.4% and root-mean-square error(RMSE) of 5.4%.

• Nuclear Engineering and Technology
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• v.53 no.9
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• pp.2788-2802
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• 2021

Multigroup cross section (MG XS) generation by the UNIST in-house Monte Carlo (MC) code MCS for fast reactor analysis using nodal diffusion codes is reported. The feasibility of the approach is quantified for two sodium fast reactors (SFRs) specified in the OECD/NEA SFR benchmark: a 1000 MW_th metal-fueled SFR (MET-1000) and a 3600 MW_th oxide-fueled SFR (MOX-3600). The accuracy of a few-group XSs generated by MCS is verified using another MC code, Serpent 2. The neutronic steady-state whole-core problem is analyzed using MCS/RAST-K with a 24-group XS set. Various core parameters of interest (core k_eff, power profiles, and reactivity feedback coefficients) are obtained using both MCS/RAST-K and MCS. A code-to-code comparison indicates excellent agreement between the nodal diffusion solution and stochastic solution; the error in the core k_eff is less than 110 pcm, the root-mean-square error of the power profiles is within 1.0%, and the error of the reactivity feedback coefficients is within three standard deviations. Furthermore, using the super-homogenization-corrected XSs improves the prediction accuracy of the control rod worth and power profiles with all rods in. Therefore, the results demonstrate that employing the MCS MG XSs for the nodal diffusion code is feasible for high-fidelity analyses of fast reactors.

• Journal of Environmental Science International
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• v.27 no.11
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• pp.1155-1167
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• 2018

We developed a small sensor observation system (SSOS) at a relatively low cost to observe the atmospheric boundary layer. The accuracy of the SSOS sensor was compared with that of the automatic weather system (AWS) and meteorological tower at the Korea Meteorological Administration (KMA). Comparisons between SSOS sensors and KMA sensors were carried out by dividing into ground and lower atmosphere. As a result of comparing the raw data of the SSOS sensor with the raw data of AWS and the observation tower by applying the root-mean-square-error to the error, the corresponding values were within the error tolerance range (KMA meteorological reference point: humidity ${\pm}5%$, atmospheric pressure ${\pm}0.5hPa$, temperature ${\pm}0.5^{\circ}C$. In the case of humidity, even if the altitude changed, it tends to be underestimated. In the case of temperature, when the altitude rose to 40 m above the ground, the value changed from underestimation to overestimation. However, it can be confirmed that the errors are within the KMA's permissible range after correction.