• Journal of Information Technology Applications and Management
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• 제22권2호
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• pp.149-169
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• 2015

This study tried to find out how to overcome multicollinearity problems in the structural equation model by creating a hierarchical construct model about the repurchase intention of social commerce. This study selected, as independent variables, price, quality, service, and social influence, based on literature review about social commerce, and then, as detailed variables of independent variables, selected system quality, information quality, transaction safety, order fulfillment and after-sales service, communication, subjective norms, and reputation. As results of empirical analysis about hierarchical construct model, all the independent variables were accepted having a significant impact on repurchase intention of social commerce. Next, this study analyzed the competition model that eight independent variables of price, system quality, information quality, transaction safety, order fulfillment and after-sales service, communication, subjective norm, and reputation directly influence the repurchase intention of social commerce. As results of empirical analysis, system quality, information quality, transaction safety, communication appeared to be insignificant. This study showed that hierarchical construct model is useful to overcome the multicollinearity problem in structural equational model and to increase explanatory power.

• 대한간호학회지
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• 제43권3호
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• pp.312-320
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• 2013

Purpose: The purpose of this study was to test a model for quality of life among infertile women. This model was based primarily on the concept of the Fertility Quality of Life by Boivin et al. (2011) and the Infertility Resilience Model by Rindenour (2009). Methods: Fifteen measurable variables were used to estimate quality of life. They included endogenous variables such as fertility quality of life and resilience, and exogenous variables such as infertility related stress, depression, marital adjustment, and family support. Data sets (n=203) used for analysis were collected in a general hospital which had, on average, 400 assisted reproductive technologies per month. Results: The assessment of the modified model indicated acceptable fit, with $x^2/d.f$=2.07, GFI=.90, AGFI=.89, NFI=.89, CFI=.91, RMSEA=.07. Depression, infertility related stress, marital adjustment, resilience, and family support had direct influences on quality of life. Conclusion: The results of this study should contribute to the development of nursing intervention programs to enhance quality of life using factors that affect fertiQol (fertility quality of life) of infertile women.

• 기본간호학회지
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• 제3권2호
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• pp.183-199
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• 1996

The Purpose of this study is to develop and test a nursing model which can be applied to prediction of the quality of life for the patient receiving hemodialysis. A hypothetical model was constructed on Johns & Meleis's empowerment model framework which has 3 contsructs(stress, resource, empowerment). 6 Factors(perceived stress, self-esteem as personal resource, perceived social support as social resource, perceived fertigue, perceived health status & self efficacy as empowerment) were selected to pre dict the quality of life of receiving hemodialysis patients. 4 Factors(self-esteem, perceived social support, perceived health status & self efficacy) had direct effects on the quality of life significantly. Self-esteem had indirect effect on the quality of life via perceived heath status significantly. Perceived social support had indirect effect on the quality of life via self-effcacy significantly. Perceived stress had no direct and indirect effect on the quality of life significantly. Revised model from hypothetical model showed better fit to the data by eliminating unsignificant path. From results of this study we suggest that to improve quality of life of hemodialysis patient nurses provide nursing interventions which improve self-esteem, perceived social support, self-efficacy & perceived health status.

• 산업공학
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• 제23권3호
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• pp.229-238
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• 2010

Virtual metrology (VM), a critical component of semiconductor manufacturing, is an efficient way of assessing the quality of wafers not actually measured. This is done based on a model between equipment sensor data (obtained for all wafers) and the quality characteristics of wafers actually measured. This paper considers principal component regression (PCR), partial least squares regression (PLSR), kernel PCR (KPCR), and kernel PLSR (KPLSR) as VM models. For each regression model, two cases are considered. One utilizes all explanatory variables in developing a model, and the other selects significant variables using the genetic algorithm (GA). The prediction performances of 8 regression models are compared for the short- and long-term etch process data. It is found among others that the GA-KPLSR model performs best for both types of data. Especially, its prediction ability is within the requirement for the short-term data implying that it can be used to implement VM for real etch processes.

• 한국콘텐츠학회논문지
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• 제8권7호
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• pp.66-74
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• 2008

소프트웨어 개발 프로세스의 초기 단계에서 결함경향성이 많은 모듈들을 예측하는 위험도 예측 모델은 프로젝트 자원할당에 도움을 주어 전체 시스템의 품질을 개선시키는 역할을 한다. 설계 복잡도 메트릭에 기반을 둔 여러 예측 모델들이 제안 되었지만 대부분 훈련 데이터 집합을 필요로 하는 모델들이었고 훈련 데이터 집합을 보유하고 있지 않은 대부분의 개발 집단들은 이들을 사용할 수 없다는 문제점이 있었다. 본 논문에서는 잘 알려진 감독형 학습 모델인 오류 역전파 신경망 모델에 SDL 시스템 명세를 정량화하여 적용한 예측 모델을 개발하였으며, 기존 학습 모델들의 문제점을 해결하기 위해 이 모델을 여러 제약조건을 가지고 만든 가상 훈련데이터집합으로 학습시켰다. 제안 모델의 사용가능성을 알아보기 위해 몇가지 모의실험을 수행 하였으며, 그 결과 제안 모델이 훈련 데이터 집합이 없는 개발 집단에서는 실제 데이터로 훈련된 예측 모델의 대안으로 사용될 수 있음을 보였다.

• Journal of Information Display
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• 제5권3호
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• pp.35-40
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• 2004

In this study, we collected data used to formulate the relationship between quantitative metrological parameters in CRT display and the perceived focus quality. Human perception of the focusing quality was evaluated in terms of user feedback scores regarding the character legibility from four highly trained inspectors. Thirteen CRT monitors from five different manufacturers were compared relatively with respect to the norm monitor. The profile of electron beam such as spot size and the shape of distribution made by electron beam, contrast, convergence of RGB beams, and luminance characteristics were measured using a precision measurement system. Linear regression analysis and artificial neural network models were used to formulate the relationship between human perception and the quantitative measurements. The accuracy of the formulated linear regression model ($R^2$=0.515) was not satisfactory but the nonlinear neural network model ($R^2$=0.716) was fairly convincing and robust even the utilized data included subjective differences.

• 대한간호학회지
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• 제50권4호
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• pp.554-570
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• 2020

Purpose: This study aimed to develop and test a structural equation model of health-related quality of life among older women following bilateral total knee replacement based on a literature review and Wilson and Cleary's model of health-related quality of life. Methods: One hundred ninety three women who were diagnosed with osteoarthritis, were older than 65 years, and were between 13 weeks and 12 months of having a bilateral total knee replacement were recruited from an outpatient clinic. Data were collected from July 2017 to April 2018 using a structured questionnaire and medical records. Data were analyzed using SPSS/WIN 22.0, AMOS 22.0, and Smart PLS 3.2.4. Results: The fitness of the hypothetical model was good, with coefficients of determination (R²) ranging between .28 and .75 and predictive relevance (Q²) between .26 and .73. The standardized root mean square residual of the model fit indices for the hypothetical model was .04; which explained 64.2% of physical and 62.5% of mental health-related quality of life. Self-efficacy, symptom status, functional status, and general health perceptions had a significant direct effect on physical health-related quality of life, while social support, symptom status, and general health perceptions had a significant direct effect on participants' mental-health-related quality of life. Conclusion: To improve the physical and mental quality of life of older women who receive bilateral knee replacement, nursing-based intervention strategies that reduce symptoms, improve functional status, and increase health perceptions, self-efficacy, and social support are needed. The most important factor is the symptom status.

• 산업공학
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• 제15권2호
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• pp.209-217
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• 2002

Quality of Service(QoS) in the telecommunications can be evaluated by analyzing the opinion data which result from the surveyed opinions of respondents and quantify subjective satisfaction on the QoS from the customers' viewpoints. For analyzing the opinion data, MOS(mean opinion score) method and Cumulative Probability Curve method are often used. The methods are based on the scoring method, and therefore, have the intrinsic deficiency due to the assignment of arbitrary scores. In this paper, we propose an analysis method of the opinion data using logit models which can be used to analyze the ordinal categorical data without assigning arbitrary scores to customers' opinion, and develop an analysis procedure considering the usage of procedures provided by SAS(Statistical Analysis System) statistical package. By the proposed method, we can estimate the relationship between customer satisfaction and network performance parameters, and provide guidelines for network planning. In addition, the proposed method is compared with Cumulative Probability Curve method with respect to prediction errors.

• 정보관리학회지
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• 제35권1호
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• pp.33-59
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• 2018

본 연구는 최근 열린 정부 데이터에 대한 다차원 척도, 모델 개발 연구가 시작되고 있으나, 도서관에서는 관련 연구가 부족하다는 점을 고려하여 도서관에 적용할 수 있는 오픈 데이터 품질측정 모델개발을 목적으로 하였다. 본 연구는 모델개발과 모델평가 두 단계로 수행하였다. 모델개발은 델파이 기법을 적용하였으며, 모델평가는 도서관 오픈 데이터 이용자를 대상으로 설문조사를 실시하여 모델의 타당도와 신뢰도를 측정하였다. 모델개발은 델파이 기법을 적용하여 총 4차례 수행하여 3개 차원, 18개 요인, 133개 측정요소로 구성된 모델을 도출하였다. 모델평가는 델파이 기법으로 완성한 모델을 도서관 오픈 데이터 이용자인 국내 외 사서, 개발자, 오픈 데이터 활동가를 대상으로 적합성 설문조사를 실시하여 모델의 타당도와 신뢰도를 검증하였다. 그 결과 당초 18개 요인, 133개 측정요소는 15개 요인, 54개 측정요소가 타당성을 확보한 것으로 나타났다. 신뢰도는 차원별, 측정요인별로 모두 기준치인 0.6 이상의 결과를 보여주고 있어 높은 신뢰도를 확보한 것으로 나타났다. 모델평가를 통한 이용자 타당도, 신뢰도 분석으로 전문가가 구성한 평가모델은 현장에서 즉시 활용될 수 있을 정도로 정제되었다.

• 상하수도학회지
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• 제34권1호
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• pp.35-43
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• 2020

Turbidity has various effects on the water quality and ecosystem of a river. High turbidity during floods increases the operation cost of a drinking water supply system. Thus, the management of turbidity is essential for providing safe water to the public. There have been various efforts to estimate turbidity in river systems for proper management and early warning of high turbidity in the water supply process. Advanced data analysis technology using machine learning has been increasingly used in water quality management processes. Artificial neural networks(ANNs) is one of the first algorithms applied, where the overfitting of a model to observed data and vanishing gradient in the backpropagation process limit the wide application of ANNs in practice. In recent years, deep learning, which overcomes the limitations of ANNs, has been applied in water quality management. LSTM(Long-Short Term Memory) is one of novel deep learning algorithms that is widely used in the analysis of time series data. In this study, LSTM is used for the prediction of high turbidity(>30 NTU) in a river from the relationship of turbidity to discharge, which enables early warning of high turbidity in a drinking water supply system. The model showed 0.98, 0.99, 0.98 and 0.99 for precision, recall, F1-score and accuracy respectively, for the prediction of high turbidity in a river with 2 hour frequency data. The sensitivity of the model to the observation intervals of data is also compared with time periods of 2 hour, 8 hour, 1 day and 2 days. The model shows higher precision with shorter observation intervals, which underscores the importance of collecting high frequency data for better management of water resources in the future.