• Journal of Korea Water Resources Association
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• v.44 no.9
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• pp.711-720
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• 2011

SWAT (Soil and Water Assessment Tool) model was calibrated for the flow rate of the Deachung lake with a large area of 3108.29 $km^2$. Application of SWAT model requires significant number of input data and is prone to result in uncertainties due to errors in input data, model structure and model parameters. The SUFI-2 (Sequential Uncertainty Fitting Ver. 2) program and GLUE (Generalized Likelihood Uncertainty Estimation) program in SWAT-CUP (SWAT-Calibration and Uncertainty Program) are used to select the best parameters for SWAT model. Optimal combination of parameter values was determined through 2,000 iterative SWAT model runs. The Nash-Sutcliffe values and $R^2$ values were 0.87 and 0.89 respectively indicating both methods show good agreements with observed data successfully. RMSE and MSE values also showed similar results for both programs. It seems the SWAT-CUP has a great practical appeal for parameter optimization especially for large basin area and it also can be used for less experienced SWAT model users.

• Journal of Gastric Cancer
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• v.9 no.3
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• pp.78-87
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• 2009

Serum pepsinogen (sPG) is a marker of gastric mucosal atrophy, a condition that has been associated with an increased risk of gastric neoplasia. A low sPGI level and a low PG I/II ratio have been associated with severe gastric atrophy, and are frequently found in gastric cancer. Because the prevalence of gastric cancer is high in Korea, it would be convenient if a good biomarker for gastric cancer were developed. Two studies recently investigated the efficacy of sPG along with Helicobacter pylori (H. pylori) as a screening tool for gastric cancer. In these studies, sPG was measured using a Latex enhanced Turbidimetric Immunoassay. We found that H. pylori IgG status, age and gender were associated with serum pepsinogen levels. Thus, to increase the ability of the PG I/II ratio to detect atrophic gastritis, the cutoff value for the PG I/II ratio should be stratified according to the H. pylori IgG status. In addition, a PG I/II ratio ($\leq3.0$), which has been widely used as an international standard for gastric cancer, was found to be a reliable marker for the detection of gastric dysplasia or gastric cancer, especially of the intestinal type. The efficacy of the test in Korea was lower than the efficacy in Japan. However, the detecting power of a PG I/II ratio ($\leq3.0$) was significantly increased in the presence of H. pylori. The ratio together with H. pylori psotivitiy could provide a means of identifying persons at high risk of developing gastric cancer in Korea.

• Korean Journal of Construction Engineering and Management
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• v.14 no.1
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• pp.82-90
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• 2013

In the construction industry, there are so many qualitative factors affecting the performance of a project. So it is crucial to measure the factors in an effective way in order to analyze the interrelationship among the various factors. To improve the performance level of a project, it is also important to identify the most appropriate management practices which are inter-linked with the subject project. The purpose of this study is to develop a project performance management system (PPMS) to quantitatively analyze the variety of project performance data and identify the best management practice to increase the potential level of a particular performance area. Using a comparative statistical method, this study developed a quantification method and web-based computerized system to enhance the usage of the system. The system, however, is still under the validation stage because of the shortage of data set. In the future, when more and more completed project data are stored in the system, the system would play a crucial role in predicting the performance level and matching the best management practice for a subject project. In addition, the system can also be modified as a tool for a business- or industry-level system by incorporating the existing enterprise resource programs.

• Korean Chemical Engineering Research
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• v.46 no.2
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• pp.233-247
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• 2008

Process monitoring technology is able to detect the faults and the process changes which occur in a process unpredictably, which makes it possible to find the reasons of the faults and get rid of them, resulting in a stable process operation, high-quality product. Statistical process monitoring method based on data set has a main merit to be a tool which can easily supervise a process with the statistics and can be used in the analysis of process data if a high quality of data is given. Because a real process has the inherent characteristics of nonlinearity, non-Gaussianity, multiple operation modes, sensor faults and process changes, however, the conventional multivariate statistical process monitoring method results in inefficient results, the degradation of the supervision performances, or often unreliable monitoring results. Because the conventional methods are not easy to properly supervise the process due to their disadvantages, several advanced monitoring methods are developed recently. This review introduces the theories and application results of several remarkable monitoring methods, which are a nonlinear monitoring with kernel principle component analysis (KPCA), an adaptive model for process change, a mixture model for multiple operation modes and a sensor fault detection and reconstruction, in order to tackle the weak points of the conventional methods.

• Journal of Radiation Protection and Research
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• v.32 no.3
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• pp.117-122
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• 2007

The hydrodynamic behaviors of the clarifier with an interior baffle in a wastewater treatment plant was investigated by using a radiotracer $^{99m}Tc$(30 40 mCi) to verify the results of CFD(computational fluid dynamics) modelling in the previous study. The clarifier model was manufactured with consideration to the hydraulic similarity(1/21) of a real plant($L{\times}W{\times}H:2.6{\times}0.4{\times}0.2m$). By installation of an interior baffle to the clarifier, the strong density current at the bottom of the clarifier decreased substantially and increased the area of sludge settling zone, which were visualized successfully from the radiotracer experiment. Also the portion of short circuit stream changed from 48 % to 32 % and the mean residence time of sludge decreased from 940 sec to 810 sec, which corresponds to the results of CFD modelling. As a result, it is anticipated that radiotracer technology can be used as an important tool for designing new wastewater treatment plants and verifying their performances after structural modifications.

• Asian Pacific Journal of Cancer Prevention
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• v.14 no.10
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• pp.6019-6023
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• 2013

Lung cancer, one of the leading causes of cancer-related deaths, usually appears as solitary pulmonary nodules (SPNs) which are hard to diagnose using the naked eye. In this paper, curvelet-based textural features and clinical parameters are used with three prediction models [a multilevel model, a least absolute shrinkage and selection operator (LASSO) regression method, and a support vector machine (SVM)] to improve the diagnosis of benign and malignant SPNs. Dimensionality reduction of the original curvelet-based textural features was achieved using principal component analysis. In addition, non-conditional logistical regression was used to find clinical predictors among demographic parameters and morphological features. The results showed that, combined with 11 clinical predictors, the accuracy rates using 12 principal components were higher than those using the original curvelet-based textural features. To evaluate the models, 10-fold cross validation and back substitution were applied. The results obtained, respectively, were 0.8549 and 0.9221 for the LASSO method, 0.9443 and 0.9831 for SVM, and 0.8722 and 0.9722 for the multilevel model. All in all, it was found that using curvelet-based textural features after dimensionality reduction and using clinical predictors, the highest accuracy rate was achieved with SVM. The method may be used as an auxiliary tool to differentiate between benign and malignant SPNs in CT images.

• Journal of Advanced Navigation Technology
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• v.15 no.4
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• pp.523-535
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• 2011

The precipitable water vapor (PW) was estimated using Global Navigation Satellite System (GNSS) from several GNSS stations within the Korean Peninsula. Nearby radiosonde sites covering the GNSS stations were used for the comparison and validation of test results. GNSS data recorded under typical and severe weather conditions were used to generalize our approach. Based on the analysis, we have confirmed that the derived PW values from the GNSS observables were well agreed on the estimates from the radiosonde observables within 10 mm level. Assuming that the GNSS observables could be a good weather monitoring tool, we further tested the performance of the current WAAS tropospheric delay model, UNB3, in the Korean Peninsula. Especially, the wet zenith delays estimated from the GNSS observables and from UNB3 delay model were compared. Test results showed that the modelled approach for the troposphere (i.e., UNB3) did not perform well especially under the wet weather conditions in the Korean Peninsula. It was suggested that a new model or a near real-time model (e.g., based on regional model from GNSS or numerical weather model) would be highly desirable for the Korean WA-DGNSS to minimize the effects of the tropospheric delay and hence to achieve high precision vertical navigation solutions.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.21 no.4
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• pp.236-247
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• 2020

This study was conducted with the aim of developing inspection tools to select and measure human resource figures and appropriate core competency of the educational goals pursued by S universities. Therefore, key competency factors were defined consistent with concept of talented figures and educational objectives of S universities, inspection tools were developed to measure core competences, and directions are presented for future education policies and curriculum compilation and securing data for rational operation. Based on key competency indicators, questions were developed in a self-reporting form that measures the consent level of the respondents by comprising seven areas of competence, 16 small areas (sub-capacity), and 46 questions. The main Test conducted an analysis of the questionnaires based on the survey results of 2,486 people to ensure the validity of the inspection by conducting a positive factor analysis and reliability analysis. The core competency diagnostic tool of S university is meaningful in this study which verifies the effectiveness of the curriculum and programs conducted at S university and as the first step for the rational operation of the core competency certification system to improve the competency appropriate for students.

• Journal of Korea Water Resources Association
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• v.40 no.6 s.179
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• pp.459-468
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• 2007

To estimate the groundwater recharge, the fully distributed parameter based model, MIKE SHE was applied to the Gyeongan-cheon watershed which is one of the tributaries of Han River Basin, and covers approximately $260km^2$ with about 49 km main stream length. To set up the model, spatial data such as topography, land use, soil, and meteorological data were compiled, and grid size of 200m was applied considering computer ability and reliability of the results. The model was calibrated and validated using a split sample procedure against 4-year daily stream flows at the outlet of the watershed. Statistical criteria for the calibration and validation results indicated a good agreement between the simulated and observed stream flows. The annual recharges calculated from the model were compared with the values from the conventional groundwater recession curve method, and the simulated groundwater levels were compared with the observed values. As a result, it was concluded that the model could reasonably simulate the groundwater level and recharge, and could be a useful tool for estimating spatially/temporally the groundwater recharges, and enhancing the analysis of the watershed water cycle.

• Korean Journal of Agricultural and Forest Meteorology
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• v.22 no.3
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• pp.194-204
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• 2020

Cultivar parameters, which are key inputs to a crop growth model, have been estimated using observation data in good quality. Observation data with high quality often require considerable labor and cost, which makes it challenging to gather a large quantity of data for calibration of cultivar parameters. Alternatively, data in sufficient quantity can be collected from the reports on the evaluation of cultivars by region although these data are of questionable quality. The objective of our study was to assess the quality of crop and management data available from the reports on the regional adaptation trials for rice cultivars. We also aimed to propose the measures for improvement of the data quality, which would aid reliable estimation of cultivar parameters. DatasetRanker, which is the tool designed for quantitative assessment of the data for parameter calibration, was used to evaluate the quality of the data available from the regional adaptation trials. It was found that these data for rice cultivars were classified into the Silver class, which could be used for validation or calibration of key cultivar parameters. However, those regional adaptation trial data would fall short of the quality for model improvement. Additional information on management, e.g., harvest and irrigation management, can increase the quantitative quality by 10% with the minimum effort and cost. The quality of the data can also be improved through measurements of initial conditions for crop growth simulations such as soil moisture and nutrients. In addition, crop model improvement can be facilitated using crop growth data in time series, which merits further studies on development of approaches for non-destructive methods to monitor the crop growth.