• 대한간호학회지
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• 제41권5호
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• pp.633-641
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• 2011

Purpose: The purpose of this study was to propose and test a predictive model that could explain and predict Korean nurses' turnover intentions. Methods: A survey using a structured questionnaire was conducted with 445 nurses in Korea. Six instruments were used in this model. The data were analyzed using SPSS 15.0 and Amos 7.0 program. Results: Based on the constructed model, organizational commitment, and burnout were found to have a significant direct effect on turnover intention of nurses. In addition, factors such as empowerment, job satisfaction, and organizational commitment were found to indirectly affect turnover intention of nurse. The final modified model yielded ${\chi}^2$=402.30, p<.001), ${\chi}^2$/df=2.94, RMSEA=0.07, RMR=0.03, GFI=0.90, AGF=0.87, NFI=0.88, CFI=0.92 and good fit indices. Conclusion: This structural equational model is a comprehensive theoretical model that explains the related factors and their relationship with turnover intention in Korean nurses. Findings from this study can be used to design appropriate strategies to further decrease the nurses' turnover intention in Korea.

• Geomechanics and Engineering
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• 제34권5호
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• pp.507-527
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• 2023

Accurately estimation of the geo-mechanical parameters in Artificial Ground Freezing (AGF) is a most important scientific topic in soil improvement and geotechnical engineering. In order for this, one way is using classical and conventional constitutive models based on different theories like critical state theory, Hooke's law, and so on, which are time-consuming, costly, and troublous. The others are the application of artificial intelligence (AI) techniques to predict considered parameters and behaviors accurately. This study presents a comprehensive data-mining-based model for predicting the Young's Modulus of frozen sand under the triaxial test. For this aim, several single and hybrid models were considered including additive regression, bagging, M5-Rules, M5P, random forests (RF), support vector regression (SVR), locally weighted linear (LWL), gaussian process regression (GPR), and multi-layered perceptron neural network (MLP). In the present study, cell pressure, strain rate, temperature, time, and strain were considered as the input variables, where the Young's Modulus was recognized as target. The results showed that all selected single and hybrid predicting models have acceptable agreement with measured experimental results. Especially, hybrid Additive Regression-Gaussian Process Regression and Bagging-Gaussian Process Regression have the best accuracy based on Model performance assessment criteria.

• 한국터널지하공간학회 논문집
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• 제20권2호
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• pp.255-268
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• 2018

해저터널은 시공중 예측치 못한 고수압으로 인한 해수 침투가 발생할 가능성이 매우 크다. 이에 고수압조건에서 차수 및 보강효과가 탁월한 인공 동결 공법의 적용이 대두되고 있다. 본 연구에서는 인공 동결 공법에 필요한 냉매량을 산정하기 위해 열흐름 에너지 이론 모델에 의한 이론적인 값을 계산하고, 동결 챔버 실험결과 및 수치해석결과와의 비교를 통해 적정성을 검증하였다. 염분과 수압에 따른 열적 역학적 특성 변화를 규명하기 위해 동결용 챔버를 제작하여 염분과 수압 조건에 따라 사질토의 동결 시간을 파악하였다. 또한, 이론값과 수치해석 결과의 동결 시간은 유사한 경향을 확인하였다. 동결공법의 냉매량은 수치해석의 결과를 기반으로, 동결 챔버 실험을 통해 동결 효율의 결과와 이론식을 통한 동결 유지를 위한 에너지 비율을 적용하여 산정하였다. 동결유지를 위한 에너지 비율은 해저터널의 토피고와 해저면의 수온에 따라 좌우될 것으로 판단된다.