• Korean Chemical Engineering Research
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• v.46 no.3
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• pp.486-491
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• 2008

This paper describes a model identification method that has been applied to a commercial 12-inch RTP (rapid thermal processing) equipment with an ultimate aim to develop a high-performance advanced controller. Seven thermocouples are attached on the wafer surface and twelve tungsten-halogen lamp groups are used to heat up the wafer. To obtain a MIMO balanced state space model, multiple SIMO (single-input multiple-output) identification with highorder ARX models have been conducted and the resulting models have been combined, transformed and reduced to a MIMO balanced state space model through a balanced truncation technique. The identification experiments were designed to minimize the wafer warpage and an output linearization block has been proposed for compensation of the nonlinearity from the radiation-dominant heat transfer. As a result from the identification at around 600, 700, and $800^{\circ}C$, respectively, it was found that $y=T(K)^2$ and the state dimension of 80-100 are most desirable. With this choice the root-mean-square value of the one-step-ahead temperature prediction error was found to be in the range of 0.125-0.135 K.

• Journal of the Korean Society of Radiology
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• v.11 no.7
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• pp.679-685
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• 2017

Computed tomography has widely been used to diagnose patient disease, and patient dose also increase rapidly. To reduce the patient dose by CT, various techniques have been applied. The iterative reconstruction is used in view of image reconstruction. Image quality of the reconstructed section image through algebraic reconstruction technique, one of iterative reconstruction methods, was examined by the normalized root mean square error. The computer program was written with the Visual C++ under the parallel beam geometry, Shepp-Logan head phantom of $512{\times}512$ size, projections of 360, and detector-pixels of 1,024. The forward and backward projection was realized by Joseph method. The minimum NRMS of 0.108 was obtained after 10 iterations in the regularization parameter of 0.09-0.12, and the optimum image was obtained after 8 and 6 iterations for 0.1% and 0.2% noise. Variation of optimum value of the regularization parameter was observed according to the phantom used. If the ART was used in the reconstruction, the optimal value of the regularization parameter should be found in the case-by-case. By finding the optimal regularization parameter in the algebraic reconstruction technique, the reconstruction time can be reduced.

• Asian-Australasian Journal of Animal Sciences
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• v.27 no.10
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• pp.1426-1435
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• 2014

Saponins have been considered as promising natural substances for mitigating methane emissions from ruminants. However, studies reported that addition of saponin-rich sources often arrived at contrasting results, i.e. either it decreased methane or it did not. The aim of the present study was to assess ruminal methane emissions through a meta-analytical approach of integrating related studies from published papers which described various levels of different saponin-rich sources being added to ruminant feed. A database was constructed from published literature reporting the addition of saponin-rich sources at various levels and then monitoring ruminal methane emissions in vitro. Accordingly, levels of saponin-rich source additions as well as different saponin sources were specified in the database. Apart from methane, other related rumen fermentation parameters were also included in the database, i.e. organic matter digestibility, gas production, pH, ammonia concentration, short-chain fatty acid profiles and protozoal count. A total of 23 studies comprised of 89 data points met the inclusion criteria. The data obtained were subsequently subjected to a statistical meta-analysis based on mixed model methodology. Accordingly, different studies were treated as random effects whereas levels of saponin-rich source additions or different saponin sources were considered as fixed effects. Model statistics used were p-value and root mean square error. Results showed that an addition of increasing levels of a saponin-rich source decreased methane emission per unit of substrate incubated as well as per unit of total gas produced (p<0.05). There was a decrease in acetate proportion (linear pattern; p<0.001) and an increase in propionate proportion (linear pattern; p<0.001) with increasing levels of saponin. Log protozoal count decreased (p<0.05) at higher saponin levels. Comparing between different saponin-rich sources, all saponin sources, i.e. quillaja, tea and yucca saponins produced less methane per unit of total gas than that of control (p<0.05). Although numerically the order of effectiveness of saponin-rich sources in mitigating methane was yucca>tea>quillaja, statistically they did not differ each other. It can be concluded that methane mitigating properties of saponins in the rumen are level- and source-dependent.

• Journal of Korea Water Resources Association
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• v.50 no.10
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• pp.691-701
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• 2017

The objective of this study is to evaluate the long-term drought outlook information based on long-term forecast data for the 2015 drought event. In order to estimate the Standardized Precipitation Index (SPI) for different durations (3-, 6-, 9-, 12-months), we used the observation precipitation of 59 Automated Synoptic Observing System (ASOS) sites, forecast and hindcast data of GloSea5. The Receiver Operating Characteristic (ROC) analysis and statistical analysis (Correlation Coefficient, CC; Root Mean Square Error, RMSE) were used to evaluate the utilization of drought outlook information for the forecast lead-times (1~6months). As a result of ROC analysis, ROC scores of SPI(3), SPI(6), SPI(9) and SPI(12) were estimated to be over 0.70 until the 2-, 3-, 4- and 5-months. The CC and RMSE values of SPI(3), SPI(6), SPI(9) and SPI(12) for forecast lead-time were estimated as (0.60, 0.87), (0.72, 0.95), (0.75, 0.95) and (0.77, 0.89) until the 2-, 4-, 5- and 6-months respectively.

• Journal of Korean Society of Transportation
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• v.28 no.1
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• pp.135-142
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• 2010

The convenient techniques for predicting the bus arrival time have used the data obtained from the buses belong to the same company only. Consequently, the conventional techniques have often failed to predict the bus arrival time at the downstream bus stops due to the lack of the data during congestion time period. The primary objective of this study is to overcome the weakness of the conventional techniques. The estimation model developed based on the data obtained from Bus Information System(BIS) and Bus management System(BMS). The proposed model predicts the bus arrival time at bus stops by using the data of all buses travelling same roadway section during the same time period. In the tests, the proposed model had a good accuracy of predicting the bus arrival time at the bus stops in terms of statistical measurements (e.g., root mean square error). Overall, the empirical results were very encouraging: the model maintains a prediction job during the morning and evening peak periods and delivers excellent results for the severely congested roadways that are of the most practical interest.

• Korean Journal of Agricultural and Forest Meteorology
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• v.15 no.4
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• pp.282-290
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• 2013

Biophysical and biochemical processes through which crops interact with the atmosphere have been simulated using land surface models and crop growth models. The Noah Multi Physics (MP) model and the CERES-Rice model, which are a land surface model, and a crop growth model, respectively, were used to simulate and compare rice growth and evapotranspiration (ET) in the areas near Haenam flux tower in Korea. Simulations using these models were performed from 2003 to 2012 during which flux measurements were obtained at the Haenam site. The Noah MP model failed to simulate the pattern of temporal change in leaf area index (LAI) after heading. The simulated aboveground biomass with the Noah MP model was underestimated by about 10% of the actual biomass. The ET simulated with the Noah MP model was as low as 21% of those with the CERES-Rice model. In comparison with actual ET measured at Haenam flux site, the root mean square error (RMSE) of the Noah MP model was 1.8 times larger than that of the CERES-Rice model. The Noah MP model seems to show less reliable simulation of crop growth and ET due to simplified phenology processes and assimilates partitioning compared with the CERES-Rice model. When ET was adjusted by the ratio between leaf biomass simulated using CERES-Rice model and Noah MP model, however, the RMSE of ET was reduced by 30%. This suggests that an improvement of the Noah MP model in representing rice growth in paddy fields would allow more reliable simulation of matter and energy fluxes.

• Korean Journal of Remote Sensing
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• v.32 no.5
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• pp.435-452
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• 2016

Land Surface Temperature (LST) has been operationally retrieved from the Communication, Ocean, and Meteorological Satellite (COMS) data by the spilt-window method (CSW_v2.0) developed by Cho et al. (2015). Although the CSW_v2.0 retrieved the LST with a reasonable quality compared to the Moderate Resolution Imaging Spectroradiometer (MODIS) LST data, it showed a relatively poor performance for the strong inversion and lapse rate conditions. To solve this problem, the LST retrieval algorithm (CSW_v2.0) was updated using the simulation results of radiative transfer model (MODTRAN 4.0) by considering the diurnal variations of air temperature. In general, the upgraded version, CSW_v3.0 showed a similar correlation coefficient between the prescribed LSTs and retrieved LSTs (0.99), the relatively smaller bias (from -0.03 K to-0.012 K) and the Root Mean Square Error (RMSE) (from 1.39 K to 1.138 K). Particularly, CSW_v3.0 improved the systematic problems of CSW_v2.0 that were encountered when temperature differences between LST and air temperature are very large and/or small (inversion layers and superadiabatic lapse rates), and when the brightness temperature differences and surface emissivity differences were large. The bias and RMSE of CSW_v2.0 were reduced by 10-30% in CSW_v3.0. The indirect validation results using the MODIS LST data showed that CSW_3.0 improved the retrieval accuracy of LST in terms of bias (from -0.629 K to -0.049 K) and RMSE (from 2.537 K to 2.502 K) compared to the CSW_v2.0.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.40 no.2
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• pp.257-264
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• 2020

Vectorization is currently the main method in feature collection (extraction) during digital mapping using UAV-Photogrammetry. However, this method is time consuming and prone to gross elevation errors when extracted from a DSM (Digital Surface Model), because three-dimensional feature coordinates are vectorized separately: plane information from an orthophoto and height from a DSM. Consequently, the demand for stereo plotting method capable of acquiring three- dimensional spatial information simultaneously is increasing. However, this method requires an expensive equipment, a Digital Photogrammetry Workstation (DPW), and the technology itself is still incomplete. In this paper, we evaluated the accuracy of low-cost stereo plotting system, Menci's StereoCAD, by analyzing its three-dimensional spatial information acquisition. Images were taken with a FC 6310 camera mounted on a Phantom4 pro at a 90 m altitude with a Ground Sample Distance (GSD) of 3 cm. The accuracy analysis was performed by comparing differences in coordinates between the results from the ground survey and the stereo plotting at check points, and also at the corner points by layers. The results showed that the Root Mean Square Error (RMSE) at check points was 0.048 m for horizontal and 0.078 m for vertical coordinates, respectively, and for different layers, it ranged from 0.104 m to 0.127 m for horizontal and 0.086 m to 0.092 m for vertical coordinates, respectively. In conclusion, the results showed 1: 1,000 digital topographic map can be generated using a stereo plotting system with UAV images.

• Ecology and Resilient Infrastructure
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• v.7 no.2
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• pp.114-125
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• 2020

Despite the various artificial neural networks that have been developed, most of the discharge models in previous studies have been developed using deep neural networks. This study aimed to develop a discharge model using a convolution neural network (CNN), which was used to solve classification problems. Furthermore, the applicability of CNN was evaluated. The photographs (pictures or images) for input data to CNN could not clearly show the characteristics of the study area as well as precipitation. Hence, the model employed in this study had to use numerical images. To solve the problem, the CN of NRCS was used to generate images as input data for the model. The generated images showed a good possibility of applicability as input data. Moreover, a new application of CN, which had been used only for discharge prediction, was proposed in this study. As a result of CNN training, the model was trained and generalized stably. Comparison between the actual and predicted values had an R² of 0.79, which was relatively high. The model showed good performance in terms of the Pearson correlation coefficient (0.84), the Nash-Sutcliffe efficiency (NSE) (0.63), and the root mean square error (24.54 ㎥/s).

• Korean Journal of Agricultural and Forest Meteorology
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• v.12 no.4
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• pp.232-237
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• 2010

While global solar radiation is an essential input variable in crop models, the observation stations are relatively sparse compared with other meteorological elements. Instead of using measured solar radiation, the Angstrom-Prescott model estimates have been widely used. Monthly data for solar radiation and sunshine duration are a convenient basis for deriving Angstrom-Prescott coefficients (a, b), but it is uncertain whether daily solar radiation could be estimated with a sufficient accuracy by the monthly data - derived coefficients. We derived the Angstrom-Prescott coefficients from the 25 years observed global solar radiation and sunshine duration data at 18 locations across South Korea. In order to figure out any improvements in estimating daily solar radiation by replacing monthly data with daily data, the coefficients (a, b) for each month were derived separately from daily data and monthly data. Local coefficients for eight validation sites were extracted from the spatially interpolated maps of the coefficients and used to estimate daily solar radiation from September 2008 to August 2009 when, pyranometers were operated at the same sites for validation purpose. Comparison with the measured radiation showed a better performance of the daily data - derived coefficients in estimating daily global solar radiation than the monthly data - derived coefficients, showing 9.3% decrease in the root mean square error (RMSE).