• Journal of Korea Technology Innovation Society
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• v.12 no.1
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• pp.113-136
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• 2009

The purpose of this study is to develop meta evaluating indicators for Defense R&D Programs in Korea. At first, the four components of this meta evaluation model were designed, which are evaluation context, evaluation input, evaluation process, evaluation output. And fifty two indicators were developed for this meta evaluation. Basic model of meta evaluation was derived from the literature review and brain storming. And this meta evaluation model was determined by adapting the result of experts who performed evaluations for Defense R&D Programs in recent years. Factor Analysis method was used to verify the validity of meta evaluation model. The survey of fifty three members turned out that Cronbach' ${\alpha}$ was over 0.6 in evaluation components and items. And the reliability of evaluation context was 0.877, that of evaluation input was 0.755, that of evaluation process was 0.755, that of evaluation output was 0.755 respectively. Analytic Hierarchy Process (AHP) method was used in assigning the evaluation weight. The survey of twenty two members showed that the Consistency Ratio was 0.09 in evaluation components and items.

• Journal of Ecology and Environment
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• v.38 no.2
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• pp.145-156
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• 2015

This study was based on water quality data of the Lake Doam watershed, monitored from 2010 to 2013 at eight different sites with multiple physiochemical parameters. The dataset was divided into two sub-datasets, namely, non-rainy and rainy. Principal component analysis (PCA) and factor analysis (FA) techniques were applied to evaluate seasonal correlations of water quality parameters and extract the most significant parameters influencing stream water quality. The first five principal components identified by PCA techniques explained greater than 80% of the total variance for both datasets. PCA and FA results indicated that total nitrogen, nitrate nitrogen, total phosphorus, and dissolved inorganic phosphorus were the most significant parameters under the non-rainy condition. This indicates that organic and inorganic pollutants loads in the streams can be related to discharges from point sources (domestic discharges) and non-point sources (agriculture, forest) of pollution. During the rainy period, turbidity, suspended solids, nitrate nitrogen, and dissolved inorganic phosphorus were identified as the most significant parameters. Physical parameters, suspended solids, and turbidity, are related to soil erosion and runoff from the basin. Organic and inorganic pollutants during the rainy period can be linked to decayed matters, manure, and inorganic fertilizers used in farming. Thus, the results of this study suggest that principal component analysis techniques are useful for analysis and interpretation of data and identification of pollution factors, which are valuable for understanding seasonal variations in water quality for effective management.

• Journal of the Korean Recycled Construction Resources Institute
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• v.9 no.4
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• pp.578-584
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• 2021

This study comparatively analyzed the properties of hydration reaction and strength development of four types of pond ash(PA) and fly ash(FA), aiming for the effective use of PA. The PA whose chlorine content was highest due to the seawater movement method had a faster setting time, higher cumulative heat, and greater initial strength development than those of FA due to the acceleration of the cement hydration reaction. However, the activity factor increase rate decreased after seven days of curing due to the rapid generation of early hydrates. The PA that contained impurities, such as a large amount of unburned carbon, had a delayed setting time due to the lower hydration reaction. Moreover, the strength was degraded in all curing ages. The PA whose chlorine content was lower due to the freshwater movement method and the amorphous content exhibited similar hydration reactivity and strength development characteristics compared to that of FA. The thermogravimetric analysis results verified that it had a similar level of Ca(OH)₂ consumption and pozzolanic reactivity with that of FA. Conclusively, it is necessary to expand the application of the freshwater movement method and manage the ignition loss to raise PA's usability.

• IE interfaces
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• v.14 no.1
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• pp.79-83
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• 2001

In this paper, we propose a simple method to reduce categorical variables into smaller, but significant numbers, and also demonstrate how the proposed method can be applied to the problem of reduction that empirical research often faces in the course of data processing. For the purpose, we introduce a concept of pseudo-correlation coefficient to make it possible to use factor analysis (FA) as a tool for reducing variables. The main idea of the concept is to deal with the measures of association of categorical variables in the sense of the concept of Pearson's correlation coefficient in order to meet the input requirement of FA. Upon examination of existing measures that could play as pseudo-correlation coefficients, Cramer's V coefficient is selected for the best result among them. To show the detailed procedure of the proposed method, a specific demonstration with the data from 329 R&D projects conducted in 18 private laboratories in electric and electronics industry is presented.

• Nuclear Engineering and Technology
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• v.54 no.12
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• pp.4731-4742
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• 2022

RBMK fuel assemblies differ from other LWR FA due to a specific arrangement of the fuel rods, the low enrichment, and the used burnable absorber - erbium. Therefore, there is a challenge to adapt modeling tools, developed for other LWR types, to solve RBMK problems. A set of 10 different depletion simulation schemes were tested to estimate the impact on reactivity and spent fuel composition of possible SCALE code options for the neutron transport modelling and the use of different nuclear data libraries. The simulations were performed using cross-section libraries based on both, VII.0 and VII.1, versions of ENDF/B nuclear data, and assuming continuous energy and multigroup simulation modes, standard and user-defined Dancoff factor values, and employing deterministic and Monte Carlo methods. The criticality analysis with burn-up credit was performed for the SFP loaded with RBMK-1500 FA. Spent fuel compositions were taken from each of 10 performed depletion simulations. The criticality of SFP is found to be overestimated by up to 0.08% in simulation cases using user-defined Dancoff factors comparing the results obtained using the continuous energy library (VII.1 version of ENDF/B nuclear data). It was shown that such discrepancy is determined by the higher U-235 and Pu-239 isotopes concentrations calculated.

• Journal of Korean Society on Water Environment
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• v.35 no.1
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• pp.19-27
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• 2019

The purpose of this study is to evaluate the water quality and phytoplankton community in Bukhan River which account for 44.4 % of the total inflow into Lake Paldang, using multivariate statistical techniques (i.e., correlation analysis, principal component analysis (PCA)/factor analysis (FA)). Water samples were collected from March to November 2015 and the following parameters measured; water temperature, pH, DO, EC, SS, BOD, Chl-a, COD, TN, $NO_3-N$, $NH_3-N$, TP, DTP, $PO_4-P$, and phytoplankton community. The water quality of the main stream and the tributaries were not significantly different apart from the relatively high concentration of BOD, COD and nutrients recorded in MH. The highest cell density of Stephanodiscus hantzschii and Merismopedia glauca dominated phytoplankton was observed in PD. Based on the correlation analysis, total phytoplankton and cyanophyceae were highly correlated with BOD, COD and nutrients. PCA/FA resulted in four main factors accounting for 82.240 % of the total variance in the water quality dataset. The group of component 1 (TN, DTN, DO, $NO_3-N$, water temperature) and component 2 ($PO_4-P$, T-P, DTP, SS) were classified as nutrient element factor whereas component 3 (Chl-a, COD, BOD, $NH_3-N$, pH) was related to organic substances. Hence, the identification of the main potential environmental pollution factors in Bukhan River will help policy makers make better and more informed decisions on how to improve the water quality.

• Journal of the Korea Convergence Society
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• v.13 no.3
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• pp.33-41
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• 2022

In this article, we proposed to predict natural gas (NG) leakage levels through feature selection based on a factor analysis (FA) of the integrating the Korean Meteorological Agency data and natural gas leakage data for considering complex factors. The paper has been divided into three modules. First, we filled missing data based on the linear interpolation method on the integrated data set, and selected essential features using FA with OrdinalEncoder (OE)-based normalization. The dataset is labeled by K-means clustering. The final module uses four algorithms, K-nearest neighbors (KNN), decision tree (DT), random forest (RF), Naive Bayes (NB), to predict gas leakage levels. The proposed method is evaluated by the accuracy, area under the ROC curve (AUC), and mean standard error (MSE). The test results indicate that the OrdinalEncoder-Factor analysis (OE-F)-based classification method has improved successfully. Moreover, OE-F-based KNN (OE-F-KNN) showed the best performance by giving 95.20% accuracy, an AUC of 96.13%, and an MSE of 0.031.

• Journal of Environmental Science International
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• v.25 no.6
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• pp.767-787
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• 2016

In this study, we analyzed on-site monitoring data for 15 tributaries in Geumho watersheds for 3 years (2011-2013) in order to sort out priorities on water quality characteristics and improvement. As a result of estimating contribution to contamination of the tributary rivers, Dalseocheon showed the highest load densities, despite the smallest watershed area, with 22.7% $BOD_5$, 30.7% $COD_{Mn}$, 31.3% TOC and 47.6% TP. After conducting PCA (principal component analysis) and FA (factor analysis) to analyze water quality characteristics of the tributary rivers, the first factor was classified as $COD_{Mn}$, TOC, EC, TP and $BOD_5$, the second factor as pH, Chl-a and DO, the third factor as water temperature and TN, and the fourth factor as SS and surface flow. In addition, arithmetical sum of each factor's scores based on grading criteria revealed that Dalseocheon and Namcheon were classified into Group A for their highest scores - 96 and 93, respectively -, and selected as rivers that require water environmental management measures the most. Also, water environmental contamination inspection showed that Palgeocheon had the most number of aquatic factors to be controlled: $BOD_5$, $COD_{Mn}$, SS, TOC, T-P, Chl-a, etc.

• Computers and Concrete
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• v.32 no.2
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• pp.119-138
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• 2023

Concrete carbonation is a prevalent phenomenon that leads to steel reinforcement corrosion in reinforced concrete (RC) structures, thereby decreasing their service life as well as durability. The process of carbonation results in a lower pH level of concrete, resulting in an acidic environment with a pH value below 12. This acidic environment initiates and accelerates the corrosion of steel reinforcement in concrete, rendering it more susceptible to damage and ultimately weakening the overall structural integrity of the RC system. Lower pH values might cause damage to the protective coating of steel, also known as the passive film, thus speeding up the process of corrosion. It is essential to estimate the carbonation factor to reduce the deterioration in concrete structures. A lot of work has gone into developing a carbonation model that is precise and efficient that takes both internal and external factors into account. This study presents an ML-based adaptive-neuro fuzzy inference system (ANFIS) approach to predict the carbonation depth of fly ash (FA)-based concrete structures. Cement content, FA, water-cement ratio, relative humidity, duration, and CO2 level have been used as input parameters to develop the ANFIS model. Six performance indices have been used for finding the accuracy of the developed model and two analytical models. The outcome of the ANFIS model has also been compared with the other models used in this study. The prediction results show that the ANFIS model outperforms analytical models with R-value, MAE, RMSE, and Nash-Sutcliffe efficiency index values of 0.9951, 0.7255 mm, 1.2346 mm, and 0.9957, respectively. Surface plots and sensitivity analysis have also been performed to identify the repercussion of individual features on the carbonation depth of FA-based concrete structures. The developed ANFIS-based model is simple, easy to use, and cost-effective with good accuracy as compared to existing models.

• Proceedings of the KOSOMBE Conference
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• v.1997 no.11
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• pp.575-578
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• 1997

Myocardial blood low (MBF) in human can be noninvasively quantified using dynamic N-13 ammonia PET and two-compartment tracer kinetic model. In this study, factor analysis was used to extract the "pure" blood-pool time-activity curves (TACs) and to generate actor images. ive human N-13 ammonia PET dynamic studies were obtained. Three actors and their corresponding actor images were extracted rom each study. The accuracy of MBF estimated by the actor analysis (FA/FA MBF) was examined by comparing to the values estimated using the conventional ROI method (ROI/ROI MBF). MBF obtained by the actor analysis linearly correlated with MBF obtained by the ROI method (slope=0.98, r=0.91). Input unctions obtained by the two methods agreed well. In conclusion, MBF can be measured accurately and noninvasively with dynamic N-13 ammonia PET imaging and actor analysis. This method is simple and acurate and can measure MBF without blood sampling, ROI drawing nor spillover correction.