• 한국농림기상학회지
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• 제21권2호
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• pp.75-84
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• 2019

이 연구의 목적은 random forest 를 활용하여 기상요소만을 이용하여 우리나라 전체의 벼 평균수량을 예측하는데 있다. Random forest 는 예측에 사용되는 각 predictor variable 을 분리할 수 있는데 이를 통해 분리된 시계열 상의 추세가 비정상적인 증가형태를 보였다. 이는 결국 예측능력의 저하로 이어지기 때문에 이를 제거할 필요가 있고 본 연구에서는 이동 평균을 이용하여 제거한 후 예측을 하였다. 1991 년부터 2005 년까지의 기상자료와 수량자료를 학습에 사용하였고 2006 년부터 2015 년까지의 자료들을 검증용으로 사용하였다. 학습자료에 대해서는 상당히 정확한 예측 능력을 보여주었으나 검증 자료에서는 그렇지 못하였다. 그 이유를 분석하기 위해 학습 자료와 검증자료에 대해서 각각 변수 중요도를 산출하여 비교한 결과 두 자료 간에 월별 기상 자료에 대한 중요도가 변동되었음을 발견하였다. 이러하 차이가 발생한 이유는 학습자료와 검증 자료에서의 전국적으로 표준이앙기가 이동하여 벼의 생육기간 자체가 변하였기 때문이다. 따라서, 정확한 예측을 위해서는 지역별 파종기 또는 이앙기에 대한 자료가 필요하며 단순히 기상 자료만을 활용한 예측은 어려운 것으로 생긱된다.

• 산업기술연구
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• 제28권B호
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• pp.149-156
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• 2008

A reliability model of Level II AFDA is developed to analyze the stability of armor units on the sloped coastal structures. Additionally, the partial safety factors of random variables related to armor units can be straightforwardly evaluated by applying the inverse-reliability method in which influence coefficients and uncertainties of random variables, and target probability of failure are combined directly. In particular, a design equation for armor units is derived in terms of the same criteria as deterministic design method in order to apply the reliability-based design method of Level I without some understanding to the reliability analysis. Finally, it is confirmed that several results redesigned by the reliability-based design method of Level I have satisfactorily agreement with results of CEM as well as those of Level II AFDA.

• 한국유기농업학회지
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• 제23권1호
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• pp.31-42
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• 2015

This thesis has analyzed the determination factor for the crop insurance of rice focused on paddy rice. The analysis on each farmer has been used with integrated probit model & random effects probit model. It has shown in the analysis result of determination factor for buying the crop insurance of paddy rice farmer through integrated probit model & random effects probit model that the higher age, degree of education, cultivated area, and amount of received insurance money and the lower in a number of family member have revealed the higher possibility to buy the crop insurance in the integrated probit model. While the random effects probit model has shown a higher possibility to buy the crop insurance as the higher age, cultivated area, and amount of received insurance money.

• 한국시뮬레이션학회논문지
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• 제10권4호
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• pp.41-50
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• 2001

When optimizing a complex system by determining the optimum condition of the system parameters of interest, we often employ the process of estimating the unknown objective function, which is assumed to be a second order spline function. In doing so, we normally use common random numbers for different set of the controllable factors resulting in more accurate parameter estimation for the objective function. In this paper, we will show some mathematical result for the analysis of variance when using common random numbers in terms of the regression error, the residual error and the pure error terms. In fact, if we can realize the special structure of the covariance matrix of the error terms, we can use the result of analysis of variance for the uncorrelated experiments only by applying minor changes.

• Journal of the Korean Data and Information Science Society
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• 제28권4호
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• pp.927-936
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• 2017

To analyze longitudinal count data, Poisson linear mixed models are commonly used. In the models the random effects covariance matrix explains both within-subject variation and serial correlation of repeated count outcomes. When the random effects covariance matrix is assumed to be misspecified, the estimates of covariates effects can be biased. Therefore, we propose reasonable and flexible structures of the covariance matrix using autoregressive and moving average Cholesky decomposition (ARMACD). The ARMACD factors the covariance matrix into generalized autoregressive parameters (GARPs), generalized moving average parameters (GMAPs) and innovation variances (IVs). Positive IVs guarantee the positive-definiteness of the covariance matrix. In this paper, we use the ARMACD to model the random effects covariance matrix in Poisson loglinear mixed models. We analyze epileptic seizure data using our proposed model.

• Current Optics and Photonics
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• 제3권5호
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• pp.390-400
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• 2019

Encrypted images obtained through double random phase-encoding (DRPE) occupy considerable storage space. We propose efficient compression schemes to reduce the size of the encrypted data. In the proposed schemes, two state-of-art compression methods of JPEG and JP2K are applied to the quantized encrypted phase images obtained by combining the DRPE algorithm with the virtual photon counting imaging technique. We compute the nonlinear cross-correlation between the registered reference images and the compressed input images to verify the performance of the compression of double random phase-encoded images. We show quantitatively through experiments that considerable compression of the encrypted image data can be achieved while security and authentication factors are completely preserved.

• International journal of advanced smart convergence
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• 제10권2호
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• pp.73-78
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• 2021

Recently, the number of people who visit the hospital due to diabetes is increasing. According to the Korean Diabetes Association, it is statistically indicated that one in seven adults aged 30 years or older in Korea suffers from diabetes, and it is expected to be more if the pre-diabetes, fasting blood sugar disorders, are combined. In the last study, the validity of Triglyceride and Cholesterol associated with diabetes was confirmed and analyzed using Random Forest. Random Forest has a disadvantage that as the amount of data increases, it uses more memory and slows down the speed. Therefore, in this paper, we compared and analyzed Random Forest and XGBoost, focusing on improvement of learning speed and prevention of memory waste, which are mainly dealt with in machine learning. Using XGBoost, the problem of slowing down and wasting memory was solved, and the accuracy of the diabetes recognition factor was further increased.

• 한국해안해양공학회지
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• 제19권4호
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• pp.336-344
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• 2007

항만 구조물의 피복재를 설계하는 경우 경험식의 불확실성에 따른 영향뿐만 아니라 사용년수에 따른 파고분포한수를 직접적으로 고려할 수 있는 부분안전계수 산정 모형이 개발되었다. 동일한 재현기간 그리고 사용년수에서 구조물의 파괴에 대한 목표수준이 증가함에 따라 저항력과 파고에 대한 부분안전계수가 커지는 등의 특성들을 잘 재현하고 있다. 따라서 본 연구에서 산정 된 각 경험식들의 저항력 그리고 파고에 대한 부분안전계수를 이용하면 현행의 결정론적 설계법과 동일한 형태의 설계식을 사용하면서도, 확률변수들의 불확실성, 사용년수 그리고 파괴에 대한 목표수준을 고려하여 설계하는 것이 가능하다

• 대한간호학회지
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• 제33권4호
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• pp.457-463
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• 2003

Purpose. This cross-sectional survey was conducted to describe the sexuality of Korean women after menopause using a national sample, and to examine relationships between the sexuality and demographic, body mass index, and life style factors including smoking, alcohol use, and physical activity. Method. From Dec. 20, 1998 to April 30, 1999, 2196 naturally postmenopausal women aged between 41 and 65 years were recruited by a disproportional stratified random sampling method from 7 metropolitans and 6 provinces in Korea. The questionnaire was used to obtain information on the demographic characteristics, life style factors, body mass index, and sexual activities. Result. The findings show that the frequency of intercourse after menopause decreased among most of postmenopausal Korean women (64.5%). The frequency of women reported their sexual activity as satisfactory was higher among women doing physical activity, not smoking, with higher educational status, with middle socioeconomic status, without sleep disturbance, with lower body mass index, and with good subjective health status. Conclusion. Further studies need to be designed as the longitudinal studies with larger random samples and better measures of sexuality.

• 한국물환경학회지
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• 제35권5호
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• pp.425-431
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• 2019

Recently, people have been attracting attention to the good quality of water resources as well as water welfare. to improve the quality of life. This study is a papers on the prediction of benthic macroinvertebrate index (BMI), which is a aquatic ecological health, using the machine learning based CFS (Correlation-based Feature Selection) method and the random forest model to compare the measured and predicted values of the BMI. The data collected from the Han River's branch for 10 years are extracted and utilized in 1312 data. Through the utilized data, Pearson correlation analysis showed a lack of correlation between single factor and BMI. The CFS method for multiple regression analysis was introduced. This study calculated 10 factors(water temperature, DO, electrical conductivity, turbidity, BOD, $NH_3-N$, T-N, $PO_4-P$, T-P, Average flow rate) that are considered to be related to the BMI. The random forest model was used based on the ten factors. In order to prove the validity of the model, $R^2$, %Difference, NSE (Nash-Sutcliffe Efficiency) and RMSE (Root Mean Square Error) were used. Each factor was 0.9438, -0.997, and 0,992, and accuracy rate was 71.6% level. As a result, These results can suggest the future direction of water resource management and Pre-review function for water ecological prediction.