• 임상간호연구
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• 제30권1호
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• pp.11-23
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• 2024

Purpose: This study developed a scale of nurses' stress and examined the validity and reliability of the scale. Methods: The scale was developed according to DeVellis' scale development procedure. Based on the Nursing Work Environment Stress model and the results of focus group interviews, 43 preliminary items were generated. A survey was conducted with 208 clinical nurses to test the psychometric properties of the scale. Both exploratory factor analysis and confirmatory factor analysis were employed to figure out and confirm the scale's theoretical structure statistically. In addition, content, convergent, and discriminative validity were evaluated and Cronbach's α was calculated to test internal reliability. Results: The final scale consisted of 19 items and verified four-factor structures. The structure of the scale was confirmed using confirmatory factor analysis, and it showed moderate correlations with the Copenhagen Burnout Inventory and Korean Nurses Occupational Stress Scale. Cronbach's α was .87. Conclusion: A scale of nurses' stress to nursing work was developed to embrace a wide range of nurses' psychological responses to nursing work based on the theoretical model.

• Journal of Forest and Environmental Science
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• 제28권4호
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• pp.205-211
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• 2012

Yield tables are a frequently used data base for regional timber resource forecasting. A normal yield table is based on two independent variables, age and site (species constant), and applies to fully stocked (or normal) stands while empirical yield tables are based on average rather than fully stocked stands. Normal and empirical yield tables essentially have many limitations. The limitations of normal and empirical yield tables led to the development of variable density yield tables. Mathematical models for estimating timber yields are usually developed by fitting a suitable equation to observed data. The model is then used to predict yields for conditions resembling those of the original data set. It may be accurate for the specific conditions, but of unproven accuracy or even entirely useless in other circumstances. Thus, these models tend to be specific rather than general and require validation before applying to other areas. Dalbergia sissoo forms a major portion of irrigated plantations in the hot desert of India and is an important timber tree species where stem wood is primarily used as timber. Variable density yield model is not available for this species which is very crucial in long-term planning for managing the plantations on a sustained basis. Thus, the objective of this study was to develop variable density yield model based on the data collected from 30 sample plots of D. sissoo laid out in IGNP area of Rajasthan State (India) and measured annually for 5 years. The best approximating model was selected based on the fit statistics among the models tested in the study. The model develop was evaluated based on quantitative and qualitative statistical criteria which showed that the model is statistically sound in prediction. The model can be safely applied on D. sissooo plantations in the study area or areas having similar conditions.

• 한국의료질향상학회지
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• 제10권2호
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• pp.216-231
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• 2003

Objectives: To develop a model that predicts a death probability of acute myocardial infarction(AMI) patient, and to evaluate a performance of hospital services using the developed model. Methods: Medical records of 861 AMI patients in 7 general hospitals during 1996 and 1997 were reviewed by two trained nurses. Variables studied were risk factors which were measured in terms of severity measures. A risk model was developed by using the logistic regression, and its performance was evaluated using cross-validation and bootstrap techniques. The statistical prediction capability of the model was assessed by using c-statistic, $R^2$ as well as Hosmer-Lemeshow statistic. The model performance was also evaluated using severity-adjusted mortalities of hospitals. Results: Variables included in the model building are age, sex, ejection fraction, systolic BP, congestive heart failure at admission, cardiac arrest, EKG ischemia, arrhythmia, left anterior descending artery occlusion, verbal response within 48 hours after admission, acute neurological change within 48 hours after admission, and 3 interaction terms. The c statistics and $R^2$ were 0.887 and 0.2676. The Hosmer-Lemeshow statistic was 6.3355 (p-value=0.6067). Among 7 hospitals evaluated by the model, two hospitals showed significantly higher mortality rates, while other two hospitals had significantly lower mortality rates, than the average mortality rate of all hospitals. The remaining hospitals did not show any significant difference. Conclusion: The comparison of the qualities of hospital service using risk-adjusted mortality rates indicated significant difference among them. We therefore conclude that risk-adjusted mortality rate of AMI patients can be used as an indicator for evaluating hospital performance in Korea.

• 응용통계연구
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• 제21권4호
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• pp.639-647
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• 2008

본 연구는 국민건강보험공단의 건강검진데이터, 자격 및 보험료 그리고 진료비 데이터를 활용하여 고혈압 관리를 위한 맞춤형 고혈압 사후관리모형(고혈압 진료예측모형 및 고혈압 진료순응도세분화모형)을 개발하고자 하였다. 모형 개발에는 데이터마이닝의 로지스틱 회귀모형, 의사결정나무 그리고 앙상블 모형을 활용하였다. 고혈압 진료예측모형에서는 3가지 모형 중 로지스틱 회귀모형이 가장 우수한 모형으로 채택되었으며, 고혈압 진료순응도세분화모형은 의사결정나무모형을 통해 개발되었다. 본 연구는 전국 규모의 수년간 축적된 자료를 데이터마이닝을 활용함으로써 고혈압의 진료 및 진료순응도에 이르는 고혈압 사후관리 프로세스 전반에 걸친 결과를 도출함으로써 우리나라 고혈압 사후관리체계 구축에 기여할 것으로 사료된다.

• 대한기계학회논문집B
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• 제25권4호
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• pp.600-609
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• 2001

WSGGM based low-resolution spectral model for calculating radiation transfer in combustion gases is applied to estimate self-absorption of radiation energy in one-dimensional opposed flow flames. Development of such a model is necessary in order to enable detailed chemistry-radiation interaction calculations including self-absorption. Database of band model parameters which can be applied to various one-dimensional opposed flow diffusion and partially premixed flames is created. For the validation of the model and database, low resolution spectral intensities at fuel exit side are calculated and compared with the results of a narrow band model with those based on the Curtis-Godson approximation. Good agreements have been found between them. The resulting radiation model is coupled to the OPPDIF code to calculate the self-absorption of radiant energy and compared with the results of an optically thin calculation and the results of a discrete ordinates method in conjunction with the statistical narrow band model. Significant self-absorption of radiation is found for the flames considered here particularly for the fuel side of the reacting zone. However, the self-absorption does not have significant effects on the flame structure in this case. Even in the case of the low velocity diffusion flame and the partially premixed flame of low equivalence ratio, the effects of self-absorption of radiation on the flame temperature and production of minor species are not significant.

• 한국수자원학회:학술대회논문집
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• 한국수자원학회 2015년도 학술발표회
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• pp.225-225
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• 2015

Drought events usually evolve slowly in time and their impacts generally span a long period of time. This indicates that the sequence of drought is not completely random. The Hidden Markov Model (HMM) is a probabilistic model used to represent dependences between invisible hidden states which finally result in observations. Drought characteristics are dependent on the underlying generating mechanism, which can be well modelled by the HMM. This study employed a HMM with Gaussian emissions to fit the Standardized Precipitation Index (SPI) series and make multi-step prediction to check the drought characteristics in the future. To estimate the parameters of the HMM, we employed a Bayesian model computed via Markov Chain Monte Carlo (MCMC). Since the true number of hidden states is unknown, we fit the model with varying number of hidden states and used reversible jump to allow for transdimensional moves between models with different numbers of states. We applied the HMM to several stations SPI data in South Korea. The monthly SPI data from January 1973 to December 2012 was divided into two parts, the first 30-year SPI data (January 1973 to December 2002) was used for model calibration and the last 10-year SPI data (January 2003 to December 2012) for model validation. All the SPI data was preprocessed through the wavelet denoising and applied as the visible output in the HMM. Different lead time (T= 1, 3, 6, 12 months) forecasting performances were compared with conventional forecasting techniques (e.g., ANN and ARMA). Based on statistical evaluation performance, the HMM exhibited significant preferable results compared to conventional models with much larger forecasting skill score (about 0.3-0.6) and lower Root Mean Square Error (RMSE) values (about 0.5-0.9).

• 산업융합연구
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• 제20권12호
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• pp.179-186
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• 2022

본 연구에서는 태블릿PC 중고제품의 거래 시, 판매자와 구매자 모두에게 판매가격을 제시할 수 있는 예측모형을 개발하는 것을 목표로 한다. 모형 개발을 위하여 실제 태블릿PC 중고거래 데이터와 제품에 대한 상세 정보를 추가 수집한 데이터를 사용하였다. 데이터 분석을 통하여 여러 가지 예측모형을 개발하였으며, 이 중 태블릿PC 중고가격 예측 성능이 가장 뛰어난 모형을 최종 예측모형으로 선택하였다. 구체적으로 중고 태블릿의 판매가격을 종속변수로 하고, 통합된 데이터에서 판매가격과 연관성이 있는 변수들을 독립변수로 한 다중선형회귀모형, 교호작용을 포함한 다중선형회귀모형, 그리고 각 모형에서 단계적 변수 선택법을 통해 얻은 모형들을 고려하였다. 이들 모형 중 교차타당성을 통해 최종적으로 예측 성능이 가장 뛰어난 모형을 태블릿PC 중고가격을 예측하는 모형으로 선택하였다. 본 연구를 통하여 중고제품 판매가격을 예측하고 판매자와 구매자에게 적절한 중고 거래 가격을 제시해 볼 수 있을 것이다.

• 천문학회보
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• 제42권2호
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• pp.61.1-61.1
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• 2017

As the application of deep-learning methods has been succeeded in various fields, they have a high potential to be applied to space weather forecasting. Convolutional neural network, one of deep learning methods, is specialized in image recognition. In this study, we apply the AlexNet architecture, which is a winner of Imagenet Large Scale Virtual Recognition Challenge (ILSVRC) 2012, to the forecast of daily solar flare occurrence using the MatConvNet software of MATLAB. Our input images are SOHO/MDI, EIT $195{\AA}$, and $304{\AA}$ from January 1996 to December 2010, and output ones are yes or no of flare occurrence. We consider other input images which consist of last two images and their difference image. We select training dataset from Jan 1996 to Dec 2000 and from Jan 2003 to Dec 2008. Testing dataset is chosen from Jan 2001 to Dec 2002 and from Jan 2009 to Dec 2010 in order to consider the solar cycle effect. In training dataset, we randomly select one fifth of training data for validation dataset to avoid the over-fitting problem. Our model successfully forecasts the flare occurrence with about 0.90 probability of detection (POD) for common flares (C-, M-, and X-class). While POD of major flares (M- and X-class) forecasting is 0.96, false alarm rate (FAR) also scores relatively high(0.60). We also present several statistical parameters such as critical success index (CSI) and true skill statistics (TSS). All statistical parameters do not strongly depend on the number of input data sets. Our model can immediately be applied to automatic forecasting service when image data are available.

• 천문학회지
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• 제45권2호
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• pp.31-38
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• 2012

In this study we apply Support Vector Machine (SVM) to the prediction of geo-effective halo coronal mass ejections (CMEs). The SVM, which is one of machine learning algorithms, is used for the purpose of classification and regression analysis. We use halo and partial halo CMEs from January 1996 to April 2010 in the SOHO/LASCO CME Catalog for training and prediction. And we also use their associated X-ray flare classes to identify front-side halo CMEs (stronger than B1 class), and the Dst index to determine geo-effective halo CMEs (stronger than -50 nT). The combinations of the speed and the angular width of CMEs, and their associated X-ray classes are used for input features of the SVM. We make an attempt to find the best model by using cross-validation which is processed by changing kernel functions of the SVM and their parameters. As a result we obtain statistical parameters for the best model by using the speed of CME and its associated X-ray flare class as input features of the SVM: Accuracy=0.66, PODy=0.76, PODn=0.49, FAR=0.72, Bias=1.06, CSI=0.59, TSS=0.25. The performance of the statistical parameters by applying the SVM is much better than those from the simple classifications based on constant classifiers.

• 자원리싸이클링
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• 제26권4호
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• pp.56-61
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• 2017

본 연구에서는 토양내 BaA 농도로부터 BaP, DahA와 ${\Sigma}PAH$의 농도 예측식 개발과 토양 재활용을 위하여 통계적 접근을 시도하였다. 회귀분석 결과 높은 상관성($R^2$ > 0.90)과 BaA와 BaP(또는 DahA) 농도 사이에 밀접한 연관성을 보였다. 또한 개발된 회귀식을 다른 검증 연구에 적용한 결과 유사한 예측값을 얻었다. 통계적 분석에서 BaA가 BaP 예측에 높은 상관성을 보였으며 PAHs 예측에 중요한 인자로 여겨진다. 이들 예측식을 적용 할 경우 BaA 농도만을 이용하여 평균적인 BaP, DahA나 ${\Sigma}PAH$ 농도를 빨리 계산 할 수 있다.