• 한국물환경학회지
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• 제23권1호
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• pp.161-168
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• 2007

The main aim of this work is focus on the Geum river water quality evaluation of pollution data obtained by monitoring measurement during the period 2001-2005. The complex data matrix 19 (entire monitoring stations)*13 (parameters), 60 (month)*13 (parameters) and 20 (season)*13 (parameters) were treated with different multivariate techniques such as factor analysis/principal component analysis (FA/PCA). FA/PCA identified two factor (19*13) classified pollutant Loading factor (BOD, COD, pH, Cond, T-N, T-P, $NH_3$-N, $NO_3$-N, $PO_4$-P, Chl-a), seasonal factor (water temp, SS) and three Factor (60*13, 20*13) classified pollutant Loading factor (BOD, COD, Cond, T-N, T-P, $NH_3$-N, $NO_3$-N, $PO_4$-P), seasonal factor (water temp, SS) and metabolic factor (Chl-a, pH). Loadings of pollutant factor is potent influence main factor in the Geum river which is explained by loadings of pollutant factor at whole sampling stations (71.16%), month (52.75%) and season (56.57%) of main water quality stations. Result of this study is that pollutant loading factor is affected at Gongju 1, 2, Buyeo 1, 2, Gangkyeong, Yeongi stations by entire stations and entire month (Gongju 1, Cheongwon stations), April, May, July and August (buyeo 1) by month. Also the pollutant Loading factor is season gives an influence in winter (Gongju 1, buyeo 1) from main sampling stations, but Cheongwon characteristic is non-seasonal influenced. This study presents necessity and usefulness of multivariate statistic techniques for evaluation and interpretation of large complex data set with a view to get better information data effective management of water sources.

• 대한간호학회지
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• 제43권1호
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• pp.123-132
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• 2013

Purpose: To develop and test the validity and reliability of the Korean version of the Family Management Measure (Korean FaMM) to assess applicability for families with children having chronic illnesses. Methods: The Korean FaMM was articulated through forward-backward translation methods. Internal consistency reliability, construct and criterion validity were calculated using PASW WIN (19.0) and AMOS (20.0). Survey data were collected from 341 mothers of children suffering from chronic disease enrolled in a university hospital in Seoul, South Korea. Results: The Korean version of FaMM showed reliable internal consistency with Cronbach's alpha for the total scale of .69-.91. Factor loadings of the 53 items on the six sub-scales ranged from 0.28-0.84. The model of six subscales for the Korean FaMM was validated by expiratory and confirmatory factor analysis (${\chi}^2$ <.001, RMR<.05, GFI, AGFI, NFI, NNFI>.08). Criterion validity compared to the Parental Stress Index (PSI) showed significant correlation. Conclusion: The findings of this study demonstrate that the Korean FaMM showed satisfactory construct and criterion validity and reliability. It is useful to measure Korean family's management style with their children who have a chronic illness.

• 정보처리학회논문지:소프트웨어 및 데이터공학
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• 제11권3호
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• pp.107-114
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• 2022

본 논문에서는 질병관리청 국민건강영양조사(KNHANES: Korea National Health and Nutrition Examination Survey) 데이터베이스에서 특징선택 방법으로 고혈압을 감지 예측하는 방법을 개선했다. 또한 만성 고혈압과 관련된 다양한 위험 요인을 확인하였다. 본 논문은 3가지로 나누어, 첫째 결측값을 제거하고 Z-변환을 하는 데이터 전처리 단계이다. 다음은 데이터 셋에서 특징선택법을 기반으로 하는 요인분석(FA)을 사용하는 특징선택 단계이며, 특징선택을 기반으로 다중공선형 분석(MC)와 특징중요도(FI)을 비교했다. 마지막으로 예측분석단계에서 고혈압 위험을 감지하고 예측하는데 적용했다. 본 연구에서는 각 분류 모델에 대해 ROC 곡선(AUC) 아래의 평균 표준 오차(MSE), F1 점수 및 면적을 비교한다. 테스트 결과 제안한 MC-FA-RF모델은 80.12% 가장 높은 정확도를 보이고, MSE, f-score, AUC 모델의 경우 각각 0.106, 83.49%의, 85.96% 으로 나타났다. 이러한 결과는 고혈압위험 예측에 대한 제안된 MC-FA-RF 방법이 다른 방법에 비해 우수함을 보이고 있다.

• Journal of the Korean Data and Information Science Society
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• 제15권2호
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• pp.297-306
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• 2004

It is usual that multivariate data analysis produces related (small number of) artificial variates for data reduction. Among them, refer to MDS(multidimensional scaling), MDPREF(multidimensional preference analysis), CDA(canonical discriminant analysis), CCA(canonical correlation analysis) and FA(factor analysis). Varimax rotation of artificial variables which is originally invented in FA for easy interpretations is applied to diverse multivariate techniques mentioned above. Real data analysisis is performed in order to manifest that rotation improves interpretations of artificial variables.

• Nuclear Engineering and Technology
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• 제46권3호
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• pp.291-298
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• 2014

This paper concerns estimating uncertainties of the core neutronics design parameters of power reactors by direct sampling method (DSM) calculations based on the two-step McCARD/MASTER design system in which McCARD is used to generate the fuel assembly (FA) homogenized few group constants (FGCs) while MASTER is used to conduct the core neutronics design computation. It presents an extended application of the uncertainty propagation analysis method originally designed for uncertainty quantification of the FA FGCs as a way to produce the covariances between the FGCs of any pair of FAs comprising the core, or the covariance matrix of the FA FGCs required for random sampling of the FA FGCs input sets into direct sampling core calculations by MASTER. For illustrative purposes, the uncertainties of core design parameters such as the effective multiplication factor ($k_{eff}$), normalized FA power densities, power peaking factors, etc. for the beginning of life (BOL) core of Yonggwang nuclear unit 4 (YGN4) at the hot zero power and all rods out are estimated by the McCARD/MASTER-based DSM computations. The results are compared with those from the uncertainty propagation analysis method based on the McCARD-predicted sensitivity coefficients of nuclear design parameters and the cross section covariance data.

• 한국물환경학회지
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• 제29권4호
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• pp.504-513
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• 2013

Water quality trends for major tributaries (66 sites) in the Yeongsan River basin of Korea were examined for 12 parameters based on water quality data collected every month over a period of 12 months. The complex data matrix was treated with multivariate analysis such as PCA, FA and CA. PCA/FA identified four factors, which are responsible for the structure explaining 78.2% of the total variance. The first factor accounting 27.3% of the total variance was correlated with BOD, TN, TP, and TOC, and weighting values were allowed to these parameters for grade classification. CA rendered a dendrogram, where monitoring sites were grouped into 5 clusters. Cluster 2 corresponds to high pollution from domestic wastewater, wastewater treatment and run-off from livestock farms. For grade classification of tributaries, scores to 10 indexes were calculated considering the weighting values to 3 parameters as BOD, TN and TP which were categorized as the first factor after FA. The highest-polluted group included 10 tributaries such as Gwangjucheon, Jangsucheon, Daejeoncheon, Gamjungcheon, Yeongsancheon. The results indicate that grade classification method suggested in this study is useful in reliable classification of tributaries in the study area.

• Structural Engineering and Mechanics
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• 제62권1호
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• pp.1-9
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• 2017

This paper describes laboratory tests carried out to evaluate the influence of class F fly ash (FA) on fracture toughness of plain concretes, specified at the third model fracture. Composites with the additives of: 0%, 20% and 30% siliceous FA were analysed. Fracture toughness tests were performed on axial torsional machine MTS 809 Axial/Torsional Test System, using the cylindrical specimens with dimensions of 150/300 mm, having an initial circumferential notch made in the half-height of cylinders. The studies examined effect of FA additive on the critical stress intensity factor $K_{IIIc}$. In order to determine the fracture toughness $K_{IIIc}$ a special device was manufactured.The analysis of the results revealed that a 20% FA additive causes increase in $K_{IIIc}$, while a 30% FA additive causes decrease in fracture toughness. Furthermore, it was observed that the results obtained during fracture toughness tests are convergent with the values of the compression strength tests.

• 공학교육연구
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• 제8권1호
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• pp.31-45
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• 2005

본 논문에서는, 강의 평가에 필요한 설문을 효과적이며 체계적으로 얻기 위한, 대표 문항 추출 방법을 비교하였다. 비교에 사용한 방법은 요인분석(Factor Analysis: FA), FCM(Fuzzy c-Means) 알고리즘과 군집분석(Cluster Analysis : CA) 등으로 이러한 방법들을 사용하여 고려할 수 있는 다양한 형태의 많은 문항들로부터 적은 수의 문항을 추출한다. 추출된 문항은 많은 수의 문항들이 형성하는 클러스터의 대표 문항을 이루고 있다. 이를 위해 여러 개의 설문지로부터 얻은 120 문항의 강의 평가서를 명지대학교 외 3 개 대학교 646명의 학생들에게 평가를 실시하여 데이터를 얻었는데 학생들은 주어진 문항에 대하여 "매우 그렇다", "그렇다", "보통이다", "그렇지 않다", "매우 그렇지 않다", 그리고 "해당 없다"까지의 6등급으로 응답하였다. 각 문항에 대한 학생들의 응답 성향을 분석하여 약 25문항을 추출하였다. 실험 결과 본 논문에서 비교 분석한 요인분석, FCM알고리즘과 군집분석 등의 기법은 매우 유사한 설문을 추출할 수 있었다.

• 한국항만경제학회지
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• 제20권1호
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• pp.141-151
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• 2004

Although the small and medium ports are actually competing with various strategies, the definition and structural understanding of small and medium ports are not known very much. Therefore this study has launched from this fact, and has the objective of obtaining the structural model for increasing shippers' attraction of small and medium ports. The process began by abstracting the components that composed the success factors through recent research, and grouping it by FA(Factor Analysis) method. Also, by using the FSM(Fuzzy Structural Modeling) method to understand the structure of the grouped components, and the structural model for increasing shippers' attraction of small and medium ports was able to obtain as the result. When analyzing the obtained structural model, easiness of shipment, connection to hubport and efficiency of hinterland network came out to be the most important component groups.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2003년도 ICCAS
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• pp.2217-2220
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• 2003

It is known that an EEG is characterized by the unique and personal characteristics of an individual. Little research has been done to take into account these personal characteristics when analyzing EEG signals. Often the EEG has frequency components which can describe most of the significant characteristics. These combinations are often unique like individual human beings and yet they have an underlying basic characteristics as well. We think that these combinations are the personal characteristics frequency components of the EEG. In this seminar, the EEG analysis method by using the Genetic Algorithms (GA), Factor Analysis (FA), and the Neural Networks (NN) is proposed. The GA is used for selecting the personal characteristic frequency components. The FA is used for extracting the characteristics data of the EEG. The NN is used for estimating the characteristics data of the EEG. Finally, in order to show the effectiveness of the proposed method, classifying the EEG pattern is carried out via computer simulations. The EEG pattern is evaluated under 4 conditions: listening to Rock music, Schmaltzy Japanese ballad music, Healing music, and Classical music. The results, when personal characteristics frequency components are NOT used, gave over 80 % accuracy versus a 95 % accuracy when personal characteristics frequency components are used. This result of our experiment shows the effectiveness of the proposed method.