• 한국시뮬레이션학회논문지
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• 제30권1호
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• pp.75-85
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• 2021

본 논문에서는 개인사업자 부도율을 보다 정확하게 예측하기 위한 새로운 방법으로 개인사업자의 기업 신용 및 개인 신용정보를 가공, 분석하여 입력 특성으로 활용하는 심층 신경망기반 예측 모델을 제시한다. 다양한 분야의 모델링 연구에서 특성 선택 기법은 특히 많은 특성을 포함하는 예측 모델에서 성능 개선을 위한 방법으로 활발히 연구되어 왔다. 본 논문에서는 부도율 예측 모델에 이용된 입력 변수인 거시경제지표(거시변수)와 신용정보(미시변수)에 대한 통계적 검증 이후 추가적으로 신용정보 특성 선택 방법을 통해 예측 성능을 개선하는 특성 집합을 확인할 수 있다. 제안하는 신용정보 특성 선택 방법은 통계적 검증을 수행하는 필터방법과 다수 래퍼를 결합 사용하는 반복적·하이브리드 방법으로, 서브 모델들을 구축하고 최대 성능 모델의 중요 변수를 추출하여 부분집합을 구성 한 후 부분집합과 그 결합셋에 대한 예측 성능 분석을 통해 최종 특성 집합을 결정한다.

• 한국지진공학회논문집
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• 제20권1호
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• pp.45-54
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• 2016

This study suggests a prediction model of ground motion spectral shape considering characteristics of earthquake records in Korea. Based on the Graizer and Kalkan's prediction procedure, a spectral shape model is defined as a continuous function of period in order to improve the complex problems of the conventional models. The approximate spectral shape function is then developed with parameters such as moment magnitude, fault distance, and average shear velocity of independent variables. This paper finally determines estimator coefficients of subfunctions which explain the corelation among the independent variables using the nonlinear optimization. As a result of generating the prediction model of ground motion spectral shape, the ground motion spectral shape well estimates the response spectrum of earthquake recordings in Korea.

• 한국산림과학회지
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• 제95권5호
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• pp.601-604
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• 2006

This study compared the accuracy of partial multivariate and vector autoregressive models for lumber demand prediction in Korea. The partial multivariate model has three explanatory variables; own price, construction permit area and dummy. The dummy variable reflected the boom of lumber demand in 1988, and the abrupt decrease in 1998. The VAR model consists of two endogenous variables, lumber demand and construction permit area with one lag. On the other hand, the prediction accuracy was estimated by Root Mean Squared Error. The results showed that the estimation by partial multivariate and vector autoregressive model showed similar explanatory power, and the prediction accuracy was similar in the case of using partial multivariate and vector autoregressive model.

• 한국수산경영학회:학술대회논문집
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• 한국수산경영학회 2007년도 추계학술발표회 및 심포지엄
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• pp.167-184
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• 2007

The objectives of this paper are to identify the causes of the corporate distress and to develop a distress prediction model with the financial information in fishery industry. In this study, the corporate distress is defined as economic failure and technical insolvency. Economic failure occurs by reduction, shut-down, or change of the business and technical insolvency results from failure to pay the financial debt of companies. The 33 distressed firms from 1991 to 2003 were composed by 14 economic failure companies, 15 technical insolvency companies. 4 companies applied to the both cases. The analysis of distress prediction of fishery companies were accomplished according to the distress definition. The analysis was carried out as two steps. The first step was the univariate analysis, which was used for checking the prediction power of individual financial variable. The t-test is used to identify the differences in financial variables between the distressed group and the non-distressed group. The second step was to develop distress prediction model with logistic regression. The variables showed the significant difference in univariate analysis were selected as the prediction variables. The financial ratios, used in the logistic regression model, were selected by backward elimination method. To test stability of the distress prediction model, the whole sample was divided as three sub-samples, period 1(1990$\sim$1993), period 2(1994$\sim$1997), period 3(1998$\sim$2002). The final model built from whole sample appled each three sub-samples. The results of the logistic analysis were as follows. the growth, profitability, stability ratios showed the significant effect on the distress. the some different result was found in the sub-sample (economic failure and technical insolvency). The growth and the profitability were important to predict the economic failure. The profitability and the activity were important to predict technical insolvency. It means that profitability is the really important factor to the fishery companies.

• 수산경영론집
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• 제39권2호
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• pp.61-79
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• 2008

The objectives of this paper are to identify the causes of the corporate distress and to develop a distress prediction model with the financial information in fishery industry. In this study, the corporate distress is defined as economic failure and technical insolvency. Economic failure occurs by reduction, shut - down, or change of the business and technical insolvency results from failure to pay the financial debt of companies. The 33 distressed firms from 1991 to 2003 were composed by 14 economic failure companies, 15 technical insolvency companies. 4 companies applied to the both cases. The analysis of distress prediction of fishery companies were accomplished according to the distress definition. The analysis was carried out as two steps. The first step was the univariate analysis, which was used for checking the prediction power of individual financial variable. The t - test is used to identify the differences in financial variables between the distressed group and the non - distressed group. The second step was to develop distress prediction model with logistic regression. The variables showed the significant difference in univariate analysis were selected as the prediction variables. The financial ratios, used in the logistic regression model, were selected by backward elimination method. To test stability of the distress prediction model, the whole sample was divided as three sub-samples, period 1(1990 - 1993), period 2(1994 - 1997), period 3(1998 - 2002). The final model built from whole sample appled each three sub - samples. The results of the logistic analysis were as follows. the growth, profitability, stability ratios showed the significant effect on the distress. the some different result was found in the sub - sample (economic failure and technical insolvency). The growth and the profitability were important to predict the economic failure. The profitability and the activity were important to predict technical insolvency. It means that profitability is the really important factor to the fishery companies.

• 한국소성가공학회:학술대회논문집
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• 한국소성가공학회 2003년도 춘계학술대회논문집
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• pp.180-183
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• 2003

In this research, on-line model for prediction of effective strain distribution hi strip on finishing mill process is prescribed. It has been developed using several selected non-dimensional parameters and previously made average effective strain model via series of finite element process simulations, $\Delta$$\varepsilon$ was introduced to describe the effective strain distribution in strip. To confirm adequate non-dimensional variables uniqueness test was done. And to decide the order of polynomial in on-line model equation tendency test for each variables was done. The prediction accuracy of the proposed model is examined through comparison with finite element calculation results.

• 응용통계연구
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• 제23권2호
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• pp.357-366
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• 2010

Many tree algorithms have been developed for regression problems. Although they are regarded as good algorithms, most of them suffer from loss of prediction accuracy when there are many noise variables. To handle this problem, we propose the multi-step GUIDE, which is a regression tree algorithm with a variable selection process. The multi-step GUIDE performs better than some of the well-known algorithms such as Random Forest and MARS. The results based on simulation study shows that the multi-step GUIDE outperforms other algorithms in terms of variable selection and prediction accuracy. It generally selects the important variables correctly with relatively few noise variables and eventually gives good prediction accuracy.

• KEPCO Journal on Electric Power and Energy
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• 제5권3호
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• pp.223-228
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• 2019

In order to integrate large amounts of variable generation resources such as wind and solar reliably into power grids, accurate renewable energy forecasting is necessary. Since renewable energy generation output is heavily influenced by environmental variables, accurate forecasting of power generation requires meteorological data at the point where the plant is located. Therefore, a spatial approach is required to predict the meteorological variables at the interesting points. In this paper, we propose the meteorological variable prediction model for enhancing renewable generation output forecasting model. The proposed model is implemented by three geostatistical techniques: Ordinary kriging, Universal kriging and Co-kriging.

• Asian-Australasian Journal of Animal Sciences
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• 제29권9호
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• pp.1215-1221
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• 2016

The prediction of carcass composition in Hanwoo steers is very important for value-based marketing, and the improvement of prediction accuracy and precision can be achieved through the analyses of independent variables using a prediction equation with a sufficient dataset. The present study was conducted to develop a prediction equation for Hanwoo carcass composition for which data was collected from 7,907 Hanwoo steers raised at a private farm in Gangwon Province, South Korea, and slaughtered in the period between January 2009 and September 2014. Carcass traits such as carcass weight (CWT), back fat thickness (BFT), eye-muscle area (EMA), and marbling score (MAR) were used as independent variables for the development of a prediction equation for carcass composition, such as retail cut weight and percentage (RC, and %RC, respectively), trimmed fat weight and percentage (FAT, and %FAT, respectively), and separated bone weight and percentage (BONE, and %BONE), and its feasibility for practical use was evaluated using the estimated retail yield percentage (ELP) currently used in Korea. The equations were functions of all the variables, and the significance was estimated via stepwise regression analyses. Further, the model equations were verified by means of the residual standard deviation and the coefficient of determination ($R^2$) between the predicted and observed values. As the results of stepwise analyses, CWT was the most important single variable in the equation for RC and FAT, and BFT was the most important variable for the equation of %RC and %FAT. The precision and accuracy of three variable equation consisting CWT, BFT, and EMA were very similar to those of four variable equation that included all for independent variables (CWT, BFT, EMA, and MAR) in RC and FAT, while the three variable equations provided a more accurate prediction for %RC. Consequently, the three-variable equation might be more appropriate for practical use than the four-variable equation based on its easy and cost-effective measurement. However, a relatively high average difference for the ELP in absolute value implies a revision of the official equation may be required, although the current official equation for predicting RC with three variables is still valid.

• Earthquakes and Structures
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• 제7권6호
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• pp.1061-1070
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• 2014

An iterative hybrid structural dynamic reliability prediction model has been developed under multiple-time interval loads with and without consideration of stochastic structural strength degradation. Firstly, multiple-time interval loads have been substituted by the equivalent interval load. The equivalent interval load and structural strength are assumed as random variables. For structural reliability problem with random and interval variables, the interval variables can be converted to uniformly distributed random variables. Secondly, structural reliability with interval and stochastic variables is computed iteratively using the first order second moment method according to the stress-strength interference theory. Finally, the proposed method is verified by three examples which show that the method is practicable, rational and gives accurate prediction.