• Journal of Korean Society on Water Environment
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• v.31 no.3
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• pp.272-285
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• 2015

Accurate assessment of chlorophyll-a (Chl-a) concentrations in inland waters using remote sensing is challenging due to the optical complexity of case 2 waters. and the inherent optical properties (IOPs) of natural waters are the most significant factors affecting light propagation within water columns, and thus play indispensable roles on estimation of Chl-a concentrations. Despite its importance, no IOPs retrieval model was specifically developed for inland water bodies, although significant efforts were made on oceanic inversion models. So we have applied and validated a recently developed Red-NIR three-band model and an IOPs Inversion Model for estimating Chl-a concentration and deriving inland water IOPs in Lake Uiam. Three band and IOPs based Chl-a estimation model accuracy was assessed with samples collected in different seasons. The results indicate that this models can be used to accurately retrieve Chl-a concentration and absorption coefficients. For all datasets the determination coefficients of the 3-band models versus Chl-a concentration ranged 0.65 and 0.88 and IOPs based model versus Chl-a concentration varied from 0.73 to 0.83 respectively. and Comparison between 3-band and IOPs based models showed significant performance with decrease of root mean square error from 18% to 33.6%. The results of this study provides the potential of effective methods for remote monitoring and water quality management in turbid inland water bodies using hyper-spectral remote sensing.

• Journal of Korean Society of Water and Wastewater
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• v.28 no.6
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• pp.735-745
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• 2014

This paper presents PKES(PuKyung -Excel based Simulator) for WWTPs(wastewater treatment plants) by using MS Excel and VBA(Visual Basic for Application). PKES is a user-friendly simulator for the design and optimization of the whole plant including biological and physico-chemical processes for the wastewater and sludge treatment. PKES calculates the performance under steady or dynamic state and allows changing the mathematical model by the user. Mathematical model implemented in PKES is a improved integration model based on ASM2d and ADM1 for simulation of AS(activated sludge) and AD(anaerobic digestion). Gaseous components of $N_2$, $N_2O$, $CO_2$ and $CH_4$ are added for estimation of GHGs(greenhouse gases) emission. The simulation results for comparison between PKES and Aquasim(EAWAG) showed about the same effluent concentrations. As a result of verification using by measured data of BOD, TSS, TN and TP for 2 years of operation, calculated effluent concentrations were similar to measured effluent concentrations. The values of average RMSE(root mean square error) were 1.9, 0.8, 1.6 and 0.2 mg/L for BOD, TSS, TN and TP, respectively. Total GHGs emission of WWTP calculated by PKES was 138.5 ton-$CO_2$/day and GHGs emissions of $N_2O$, $CO_2$ and $CH_4$ were calculated at 21.7, 28.9 and 87.9 ton-$CO_2$/day, respectively. GHGs emission of activated sludge was 32.5 % and that of anaerobic digestion was 67.5 %.

• Journal of Biosystems Engineering
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• v.42 no.3
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• pp.190-198
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• 2017

Purpose: A thin-layer drying equation was developed to analyze the drying processes of soybeans (white and black beans) and investigate drying conditions by verifying the suitability of existing grain drying equations. Methods: The drying rates of domestic soybeans were measured in a drying experiment using air at a constant temperature and humidity. The drying rate of soybeans was measured at two temperatures, 50 and $60^{\circ}C$, and three relative humidities, 30, 40 and 50%. Experimental constants were determined for the selected thin layer drying models (Lewis, Page, Thompson, and moisture diffusion models), which are widely used for predicting the moisture contents of grains, and the suitability of these models was compared. The suitability of each of the four drying equations was verified using their predicted values for white beans as well as the determination coefficient ($R^2$) and the root mean square error (RMSE) of the experiment results. Results: It was found that the Thompson model was the most suitable for white beans with a $R^2$ of 0.97 or greater and RMSE of 0.0508 or less. The Thompson model was also found to be the most suitable for black beans, with a $R^2$ of 0.97 or greater and an RMSE of 0.0308 or less. Conclusions: The Thompson model was the most appropriate prediction drying model for white and black beans. Empirical constants for the Thompson model were developed in accordance with the conditions of drying temperature and relative humidity.

• Korean Journal of Ecology and Environment
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• v.41 no.2
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• pp.247-263
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• 2008

A two-dimensional (2D) reservoir hydrodynamics and water quality model, CE-QUAL-W2, is employed to simulate the hydrothermal behavior and density current regime in Andong Reservoir. Observed data used for model forcing and calibration includes: surface water level, water temperature, dissolved oxygen and suspended solids concentration. The model was calibrated to the year of 2003 and verified with continuous run from 2000 till 2004. Without major adjustments, the model accurately simulated surface water levels including the events of large storm. Deep-water reservoirs, like Andong Reservoir, located in the Asian Monsoon region begin to stratify in summer and overturn in fall. This mixing pattern as well as the descending thermocline, onset and duration of stratification and timing of turnover phenomenon were well reproduced by the Andong Model. The temperature field and distinct thermocline are simulated to within $2^{\circ}C$ of observed data. The model performed well in simulating not only the dissolved oxygen profiles but also the metalimnetic dissolved minima phenomenon, a common1y occurring phenomenon in deep reservoirs of temperate regions. The Root Mean Square Error (RMSE) values of model calibration for surface water elevation, temperature and dissolved oxygen were 0.0095 m, $1.82^{\circ}C$, and $1.13\;mg\;L^{-1}$, respectively. The turbid storm runoff, during the summer monsoon, formed an intermediate layer of about 15 m thickness, moved along the metalimnion until being finally discharged from the dam. This mode of transport of density current, a common characteristic of various other large reservoirs in the Asian summer monsoon region, was well tracked by the model.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.30 no.2
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• pp.173-180
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• 2012

The most recent, 80 mega pixels digital camera appeared through the development of digital technology, and nonmetric digital cameras have been using in various field of photogrammetry. In this study, we experimented lens calibration using aerial photographs and ground control points. The aerial photographs were taken a non-metric digital camera which is CMOS(Complementary Metal Oxide Semiconductor) 21.1 mega pixels sensor and 35mm lens at a helicopter. And the ground control points were selected on the 1:1,000 plotting origin data. As a result, we calculated focal length, PPA(Principal Point of Autocollimation) and symmetric radial distortion coefficients from the lens. Also, RMSE(root mean square error) and maximum residual of the ground control points from the aerial triangulation were compared before and after calibration. And we found that the accuracy of the after calibration was improved very significantly.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.40 no.7
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• pp.1413-1419
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• 2015

Monopulse RADAR is the radar which detects the range of the target using a single transmitted signal. In this paper, using 9.41GHz X-band radar, the research for the phase comparison monopulse algorithm used in the marine environment is conducted. In addition, by applying the phase comparison monopulse algorithm, we calculate the RMSE for the various antenna spacings and the positions of the target. Based on that result, we compare the performance of the phase comparison monopulse algorithm in the uniform linear array with that in the non-uniform linear array. Finally, the differences in performance among the MUSIC algorithm, Bartlett method and the proposed phase comparison monopulse algorithm are analyzed.

• Korean Journal of Remote Sensing
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• v.36 no.4
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• pp.587-607
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• 2020

Soil moisture is essential information for meteorological and hydrological analyses. To date, many efforts have been made to achieve the two goals for soil moisture data, i.e., the improvement of accuracy and resolution, which is very challenging. We presented an ensemble downscaling method for quality improvement of gridded soil moisture data in terms of the accuracy and the spatial resolution by the integration of BMA (Bayesian model averaging) and ATPRK (area-to-point regression kriging). In the experiments, the BMA ensemble showed a 22% better accuracy than the data sets from ESA CCI (European Space Agency-Climate Change Initiative), ERA5 (ECMWF Reanalysis 5), and GLDAS (Global Land Data Assimilation System) in terms of RMSE (root mean square error). Also, the ATPRK downscaling could enhance the spatial resolution from 0.25° to 0.05° while preserving the improved accuracy and the spatial pattern of the BMA ensemble, without under- or over-estimation. The quality-improved data sets can contribute to a variety of local and regional applications related to soil moisture, such as agriculture, forest, hydrology, and meteorology. Because the ensemble downscaling method can be applied to the other land surface variables such as temperature, humidity, precipitation, and evapotranspiration, it can be a viable option to complement the accuracy and the spatial resolution of satellite images and numerical models.

• Korean Journal of Remote Sensing
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• v.36 no.4
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• pp.573-586
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• 2020

PM (particulate matter) is of interest to everyone because it can have adverse effects on human health by the infiltration from respiratory to internal organs. To date, many studies have made efforts for the prediction of PM10 and PM2.5 concentrations. Unlike previous studies, we conducted the prediction of tomorrow's PM10 concentration for the Air Korea stations using Chinese PM10 data in addition to the satellite AOD and weather variables. We constructed 230,639 matchups from the raw data over 3 million and built an RF (random forest) model from the matchups to cope with the complexity and nonlinearity. The validation statistics from the blind test showed excellent accuracy with the RMSE (root mean square error) of 9.905 ㎍/㎥ and the CC (correlation coefficient) of 0.918. Moreover, our prediction model showed a stable performance without the dependency on seasons or the degree of PM10 concentration. However, part of coastal areas had a relatively low accuracy, which implies that a dedicated model for coastal areas will be necessary. Additional input variables such as wind direction, precipitation, and air stability should also be incorporated into the prediction model as future work.

• Nuclear Engineering and Technology
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• v.51 no.8
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• pp.1871-1885
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• 2019

The deterministic MOC code STREAM of the Computational Reactor Physics and Experiment (CORE) laboratory of Ulsan National Institute of Science and Technology (UNIST), was initially designed for the calculation of pressurized water reactor two- and three-dimensional assemblies and cores. Since fast reactors play an important role in the generation-IV concept, it was decided that the code should be upgraded for the analysis of fast neutron spectrum reactors. This paper presents a coupled code - TULIP/STREAM, developed for the fast reactor assembly and core calculations. The TULIP code produces self-shielded multi-group cross-sections using a one-dimensional cylindrical model. The generated cross-section library is used in the STREAM code which solves eigenvalue problems for a two-dimensional assembly and a multi-assembly whole reactor core. Multiplication factors and steady-state power distributions were compared with the reference solutions obtained by the continuous energy Monte-Carlo code MCS. With the developed code, a sensitivity study of the number of energy groups, the order of anisotropic P_N scattering, and the multi-group cross-section generation model was performed on the k_eff and power distribution. The 2D core simulation calculations show that the TULIP/STREAM code gives a k_eff error smaller than 200 pcm and the root mean square errors of the pin-wise power distributions within 2%.

• KIPS Transactions on Software and Data Engineering
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• v.7 no.7
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• pp.267-274
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• 2018

This paper describes a head pose estimation method using PTZ(Pan-Tilt-Zoom) camera. When the external parameters of a camera is changed by rotation and translation, the estimated face pose for the same head also varies. In this paper, we propose a new method to estimate the head pose independently on varying the parameters of PTZ camera. The proposed method consists of 3 steps: face detection, feature extraction, and pose estimation. For each step, we respectively use MCT(Modified Census Transform) feature, the facial regression tree method, and the POSIT(Pose from Orthography and Scaling with ITeration) algorithm. The existing POSIT algorithm does not consider the rotation of a camera, but this paper improves the POSIT based on perspective projection in order to estimate the head pose robustly even when the external parameters of a camera are changed. Through experiments, we confirmed that RMSE(Root Mean Square Error) of the proposed method improve $0.6^{\circ}$ less then the conventional method.