• Proceedings of the Korean Society of Agricultural Engineers Conference
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• 2005.10a
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• pp.673-678
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• 2005

The objective of this paper is to develop the microorganism concentration simulation model for the health related effect analysis while farmers and water managers reuse the wastewater for agricultural irrigation. This model consists of the CE-QUAL-R1 model and the CREAMS-PADDY model. The CE-QUAL-R1 model is the 1-D numerical model to analyze the water quality of the reservoir and the CREAMS-PADDY model is modified from CREAMS model for considering the hydrologic cycles in paddy field. This model was applied to examine the application by the observed data from 2003 in Byoungjum study area. From this research, the average root mean square error (RMSE) for the simulated concentration during the calibration period was 0.51 MPN/100ml and correlation coefficient $(R^2)$ was 0.71. And the RMSE for the simulated concentration during the verification period was 0.46 MPN/100ml and $R^2$ was 0.73. This simulation results show that the coliform inflow concentrations by the wastewater irrigation wield great influence upon the temporal coliform concentrations in paddy field.

• Proceedings of the Korean Society of Surveying, Geodesy, Photogrammetry, and Cartography Conference
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• 2010.04a
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• pp.251-252
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• 2010

Lately, airborne digital camera and airborne laser scanner in field of airborne surveying is used to build geography information such as DEM generation and terrain analysis. In this study, 3D position accuracy is compared medium format CCD camera RCD105 with high resolution airborne digital camera DMC. For this, test area was decided for aerial photograph and ground control points was selected in 1/1,000 scale digital map. In Result, Root Mean Square Error(RMSE) was analyzed between RCD105 and DMC after aerial triangulation.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.30 no.6_2
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• pp.643-651
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• 2012

Object recognition belongs to high-level processing that is one of the difficult and challenging tasks in computer vision. Digital photogrammetry based on the computer vision paradigm has begun to emerge in the middle of 1980s. However, the ultimate goal of digital photogrammetry - intelligent and autonomous processing of surface reconstruction - is not achieved yet. Object recognition requires a robust shape description about objects. However, most of the shape descriptors aim to apply 2D space for image data. Therefore, such descriptors have to be extended to deal with 3D data such as LiDAR(Light Detection and Ranging) data obtained from ALS(Airborne Laser Scanner) system. This paper introduces extension of chain code to 3D object space with hierarchical approach for segmenting point cloud data. The experiment demonstrates effectiveness and robustness of the proposed method for shape description and point cloud data segmentation. Geometric characteristics of various roof types are well described that will be eventually base for the object modeling. Segmentation accuracy of the simulated data was evaluated by measuring coordinates of the corners on the segmented patch boundaries. The overall RMSE(Root Mean Square Error) is equivalent to the average distance between points, i.e., GSD(Ground Sampling Distance).

• Magazine of the Korean Society of Agricultural Engineers
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• v.43 no.5
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• pp.70-82
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• 2001

This study was conducted to derive the regional design rainfall by the regional frequency analysis based on the regionalization of the precipitation suggested by the first report of this project. Using the L-moment ratios and Kolmogorov-Smirnov test, the underlying regional probability distribution was identified to be the Generalized extreme value distribution among applied distributions. Regional and at-site parameters of the generalized extreme value distribution were estimated by the linear combination of the probability weighted moments, L-moment. The regional and at-site analysis for the design rainfall were tested by Monte Carlo simulation. Relative root-mean-square error(RRMSE), relative bias(RBIAS) and relative reduction(RR) in RRMSE were computed and compared with those resulting from at-site Monte Carlo simulation. All show that the regional analysis procedure can substantially reduce the RRMSE, RBIAS and RR in RRMSE in the prediction of design rainfall. Consequently, optimal design rainfalls following the legions and consecutive durations were derived by the regional frequency analysis.

• KOREAN JOURNAL OF CROP SCIENCE
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• v.53 no.2
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• pp.161-170
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• 2008

Interest is growing in applying simulation models for the South Texas conditions, to better assess crop water use and production with different crop management practices. The Environmental Policy Integrated Climate (EPIC) model was used to evaluate its application as a decision support tool for irrigation management of com (Zea mays L.) in South Texas of the U.S. We measured actual crop evapotranspiration (ETc) using a weighing lysimeter, soil moisture using a neutron probe, and grain yield by field sampling. The model was then validated using the measured data. Simulated ETc using the Hargreaves-Samani equation was in agreement with the lysimeter measured ETc. Simulated soil moisture generally matched with the measured soil moisture. The EPIC model simulated the variability in grain yield with different irrigation regimes with $r^2$value of 0.69 and root mean square error of $0.5\;ton\;ha^{-1}$. Simulation results with farm data demonstrate that EPIC can be used as a decision support tool for com under irrigated conditions in South Texas. EPIC appears to be effective in making long term and pre-season decisions for irrigation management of crops, while reference ET and phenologically based crop coefficients can be used for inseason irrigation management.

• Journal of Biosystems Engineering
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• v.29 no.6 s.107
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• pp.495-500
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• 2004

This study was performed to develop thin layer drying equations for low temperature. Thin layer drying tests of short grain rough rice were conducted at three low temperature levels of 15, 25, $35^{\circ}C$ and two relative humidity levels of 30, $50\%$, respectively. The measured moisture ratios were fitted to the selected four drying models (Page, Thompson, Simplified diffusion and Lewis model) using stepwise multiple regression analysis. The overall drying rate increased as the drying air temperature was increased and as relative humidity was decreased, but the effect of temperature increase was dominant. Half response time (Moisture ratio=0.5) of drying was affected by both drying temperature and relative humidity at drying temperature of below $25^{\circ}C$, but at $35^{\circ}C$ was mainly affected by drying temperature. The results of comparing coefficients of determination and root mean square error of moisture ratio for low drying models showed that Page model was found to fit adequately to all drying test data.

• Atmosphere
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• v.21 no.2
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• pp.151-162
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• 2011

One-layer solar radiation(GWNU; Gangneung-Wonju National University) model is developed in order to resolve the lack of vertical observations and fast calculation with high resolution. GWNU model is based on IQBAL(Iqbal, 1983) and NREL(National Renewable Energy Laboratory) methods and corrected by precise multi-layer LBL(Line-by-line) model. Input data were used 42 atmospheric profiles from Garand et al.(2001) for calculation of global radiation by the Multi-layer and one-layer solar radiation models. GWNU model has error of about -0.10% compared with LBL model while IQBAL and NREL models have errors of about -3.92 and -2.57%, respectively. Global solar radiation was calculated by corrected GWNU solar model with satellites(MODIS, OMI and MTSAT-1R), RDPS model prediction data in Korea peninsula in 2009, and the results were compared to surface solar radiation observed by 22 KMA solar sites. All models have correlation($R^2$) of 0.91 with the observed hourly solar radiation, and root mean square errors of IQBAL, NREL and GWNU models are 69.16, 69.74 and $67.53W/m^2$, respectively.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.42 no.4
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• pp.723-730
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• 2017

In this paper, we consider an online compensation algorithm considering the mutual coupling and suggest a new GPS antenna array to apply. To evaluate the anti-jamming performance for the proposed antenna array, ULA and URA, we divide direction finding of multiple jamming signals into environments. 1. there is no mutual coupling. 2. there is mutual coupling but no compensation. 3. mutual coupling is compensated. RMSE analysis showed that the online compensation algorithm works and that peak detection is possible for multiple jamming signals.

• Nuclear Engineering and Technology
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• v.52 no.11
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• pp.2678-2685
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• 2020

The pipe bends and elbows in nuclear power plants (NPPs) are vulnerable to degradation mechanisms and can cause wall-thinning defects. As it is difficult to detect both the defects generated inside the wall-thinned pipes and the preliminary signs, the wall-thinning defects should be accurately estimated to maintain the integrity of NPPs. This paper proposes a deep fuzzy neural network (DFNN) method and estimates the collapse moment of wall-thinned pipe bends and elbows. The proposed model has a simplified structure in which the fuzzy neural network module is repeatedly connected, and it is optimized using the least squares method and genetic algorithm. Numerical data obtained through simulations on the pipe bends and elbows with extrados, intrados, and crown defects were applied to the DFNN model to estimate the collapse moment. The acquired databases were divided into training, optimization, and test datasets and used to train and verify the estimation model. Consequently, the relative root mean square (RMS) errors of the estimated collapse moment at all the defect locations were within 0.25% for the test data. Such a low RMS error indicates that the DFNN model is accurate in estimating the collapse moment for wall-thinned pipe bends and elbows.

• 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.