• 한국유기농업학회지
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• 제29권2호
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• pp.159-171
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• 2021

The purpose of this paper is to analyze the relationship between the level of satisfaction of domestic Environment-friendly agricultural products and purchase intent of imported organic products. To accomplish the objective of the study a consumer survey was administered for quantitative analysis regarding consumption patterns. The bivariate probit with sample selection model was employed for empirical analysis on the relationship. The estimation results showed that to increase continuously the consumption, it is necessary to improve the quality satisfaction compared to the price, and that it is also necessary to increase the reliability of the certification system and the awareness that the consumption is helpful for health promotion to increase the quality satisfaction compared to price. In addition, it was concluded that in order to induce the purchase of domestic organic products rather than imported organic products, efforts to improve the safety of domestic products, remove the risk of residual pesticides, and increase the reliability of domestic products compared to imported products are needed. Therefore, to reduce the proportion of purchases of imported organic products and increase the consumption of domestic products, raising awareness that the consumption is conducive to health promotion, enhancing the safety of domestic products, and providing accurate information on the safety of imported products are required.

• 한국식품저장유통학회지
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• 제6권1호
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• pp.29-36
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• 1999

Using Fuji apple fruits cultivated in Kyungpook prefecture, the calibration model for firmness evaluation of fruits by near infrared(NIR) reflectance spectroscopy was developed, and the various influence factors such as instrument variety, measuring method, sample group, apple peel and selection of firmness point were investigated. Spectra of sample were recorded in wavelength range of 1100∼2500nm using NIR spectrometer (InfraAlyzer 500), and data were analyzed by stepwise multiple linear regression of IDAS program. The accuracy of calibration model was the highest when using sample group with wide range, and the firmness mean values obtained in graph by texture analyser(TA) were used as standard data. Chemometrics models were developed using a calibration set of 324 samples and an independent validation set of 216 samples to evaluate the predictive ability of the models. The correlation coefficients and standard error of prediction were 0.84 and 0.094kg, respectively. Using developed calibration model, it was possible to monitor the firmness change of fruits during storage frequently. Time, which was reached to firmness high value in graph by TA, is possible to use as new parameter for freshness of fruit surface during storage.

• 농촌계획
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• 제27권4호
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• pp.1-12
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• 2021

The purpose of this study is to analyze the effect of crop diversification on farm households' agricultural income. Abundant literature have explored the determinants and efficient strategies for crop diversification. Yet, there is a paucity of research studies that empirically test the effectiveness of crop diversification as a profitable farm management strategy. Utilizing the 2015 Agricultural Census, this study adopts a quasi-experimental research design to compare the outcomes between farm households that opted for crop diversification and farm households that did not engage in such a strategy. In doing so, this study applies the Heckman Selection Model and the decomposition technique to address the problem of selection bias and to identify the causal effect. Our empirical results show that farms that implement diversification are more likely to earn higher agricultural income than non-diversified farms, although the difference would not be much substantial. This study concludes with several policy proposals to stabilize agricultural income in conjunction with crop diversification.

• 응용통계연구
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• 제28권5호
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• pp.1035-1046
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• 2015

분산성분모형은 다양한 임의 요인들이 반응변수에 미치는 영향을 선형식의 형태로 나타내는 매우 유용하고 널리 사용되는 통계적 모형이다. 분산성분모형은 요인의 배치나 관측 자료의 구조에 따라 크게 교차배치와 지분배치로 나누어진다. 본 논문은 분산성분모형에서 요인의 배치구조와 분산성분의 크기에 따라 모형선택법의 경험적인 성질이 다르게 나타나는 현상을 체계적인 모의실험을 통하여 제시하고자 한다. 이원배치 분산성분모형에서 정보기준에 근거한 모형선택법, 즉 BIC 또는 AIC를 사용하는 경우 요인의 배치구조와 분산성분의 크기에 따라 모형선택법의 경험적인 성질이 다르게 나타나는 현상을 소규모 모의실험을 통하여 보여준다. 모의실험 결과에서 모형선택법의 경험적 성질이 요인의 배치 설계에 따라 다르게 나타난다는 사실을 확인하였으며 특히 요인의 배치구조가 지분 설계구조일때 내포된 요인의 분산성분의 상대적인 크기가 커짐에 따라 자료를 생성하는 모형보다 작은 모형을 선택하는 경향이 있다는 것이 모의실험으로 확인되었다.

• 한국산학기술학회논문지
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• 제20권10호
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• pp.76-91
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• 2019

그간 진화경제학적 관점에서 산업 고유의 특성에 따라 발생하는 기술혁신 패턴을 고찰하고자 하는 시도가 있어왔다. 본 연구는 국내 제조업을 기술집약도에 따라 고기술산업과 저기술산업으로 구분하고 제품혁신 패턴 및 혁신 성과 결정요인의 산업별 차이를 확인하였다. 기존 연구들은 연구개발 수행에 대한 기업의 의사결정 과정에서 연구개발을 수행하도록 만드는 결정요인을 분석에 반영하지 못했다는 지적이 있다. 본 연구에서는 이러한 문제를 극복하기 위해서 Heckman 표본선택모형과 허들모형을 대안으로 제시하고, "2014년 중소기업기술통계조사" 자료의 1,637개 기업에 대해 분석을 실시했다. 분석 결과 제조업의 중소기업이 수행하는 제품혁신 패턴과 제품혁신 성과에 영향을 미치는 결정요인들에 있어 고기술산업과 저기술산업 간 뚜렷한 차이가 있다는 것을 확인 할 수 있었다. 또한, 기존 연구의 한계점을 극복하기 위해 채택한 연구모형의 확장을 통해서 중소기업 연구개발 수행에 대한 의사결정 과정에서 표본선택편의 문제와 허들로 표현되는 문지방이 있다는 것을 발견할 수 있었다. 본 연구는 산업별 제품혁신 패턴의 특징과 제품혁신 성과 결정요인을 다각적으로 살펴보았고, 중소기업의 연구개발 수행에 대한 의사결정 과정을 더 깊이 이해할 수 있었다는 점에서 학술적 의의가 있다.

• Journal of Information Processing Systems
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• 제10권2호
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• pp.314-333
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• 2014

When analyzing default predictions in real estate companies, the number of non-defaulted cases always greatly exceeds the defaulted ones, which creates the two-class imbalance problem. This lowers the ability of prediction models to distinguish the default sample. In order to avoid this sample selection bias and to improve the prediction model, this paper applies a minority sample generation approach to create new minority samples. The logistic regression, support vector machine (SVM) classification, and neural network (NN) classification use an imbalanced dataset. They were used as benchmarks with a single prediction model that used a balanced dataset corrected by the minority samples generation approach. Instead of using prediction-oriented tests and the overall accuracy, the true positive rate (TPR), the true negative rate (TNR), G-mean, and F-score are used to measure the performance of default prediction models for imbalanced dataset. In this paper, we describe an empirical experiment that used a sampling of 14 default and 315 non-default listed real estate companies in China and report that most results using single prediction models with a balanced dataset generated better results than an imbalanced dataset.

• 한국정보처리학회논문지
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• 제4권10호
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• pp.2533-2543
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• 1997

컴퓨터 네트워크 장비의 객관적 선정을 위해서는 통신 서비스 품질(Quality of Service : QoS) 개념의 이해와 적용이 요구된다. ITU-T E.800 권고안은 공급자와 고객관점의 서비스 품질 및 만족도 평가 기준을 제시하고 있기 때문에 네트워크 제품 평가 및 선정에 이용될 수 있다. 본 연구에서는 QoS를 고려한 LAN 스위치 평가모형과 선정을 위한 소프트웨어 도구를 개발하였다. 이를 위한 구체적 평가방법으로 효과적인 집단 의사결정을 위한 다기준 평가 모형에서 유용하게 이용되는 Saaty의 계층적 분석방법(Analytic Hierarchy Process : AHP)을 적용하였다. 선정 평가를 위한 자료는 네트워크 전문가들에 대한 설문조사와 현장 조사 방법으로 수집하였다. 그리고 제안된 LAN 스위치 평가 모형에 따라 선정 도구를 구현하고 적용 결과를 분석하였다. 본 연구 결과는 LAN 스위치 선정을 위한 의사결정시 유용한 도구로 활용될 수 있을 것으로 기대되며, 아울러 컴퓨터 네트워크와 관련된 QoS 기반의 평가 및 선정을 위한 의사결정에 적용될 수 있을 것이다.

• 국제학술발표논문집
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• The 4th International Conference on Construction Engineering and Project Management Organized by the University of New South Wales
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• pp.432-438
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• 2011

The purpose of the present thesis is to develop bankruptcy prediction models capable of being applied to the Korean construction industry and to deduce an optimal model through comparative evaluation of final developed models. A study population was selected as general contractors in the Korean construction industry. In order to ease the sample securing and reliability of data, it was limited to general contractors receiving external audit from the government. The study samples are divided into a bankrupt company group and a non-bankrupt company group. The bankruptcy, insolvency, declaration of insolvency, workout and corporate reorganization were used as selection criteria of a bankrupt company. A company that is not included in the selection criteria of the bankrupt company group was selected as a non-bankrupt company. Accordingly, the study sample is composed of a total of 112 samples and is composed of 48 bankrupt companies and 64 non-bankrupt companies. A financial ratio was used as early predictors for development of an estimation model. A total of 90 financial ratios were used and were divided into growth, profitability, productivity and added value. The MDA (Multivariate Discriminant Analysis) model and BLRA (Binary Logistic Regression Analysis) model were used for development of bankruptcy prediction models. The MDA model is an analysis method often used in the past bankruptcy prediction literature, and the BLRA is an analysis method capable of avoiding equal variance assumption. The stepwise (MDA) and forward stepwise method (BLRA) were used for selection of predictor variables in case of model construction. Twenty two variables were finally used in MDA and BLRA models according to timing of bankruptcy. The ROC-Curve Analysis and Classification Analysis were used for analysis of prediction performance of estimation models. The correct classification rate of an individual bankruptcy prediction model is as follows: 1) one year ago before the event of bankruptcy (MDA: 83.04%, BLRA: 93.75%); 2) two years ago before the event of bankruptcy (MDA: 77.68%, BLRA: 78.57%); 3) 3 years ago before the event of bankruptcy (MDA: 84.82%, BLRA: 91.96%). The AUC (Area Under Curve) of an individual bankruptcy prediction model is as follows. : 1) one year ago before the event of bankruptcy (MDA: 0.933, BLRA: 0.978); 2) two years ago before the event of bankruptcy (MDA: 0.852, BLRA: 0.875); 3) 3 years ago before the event of bankruptcy (MDA: 0.938, BLRA: 0.975). As a result of the present research, accuracy of the BLRA model is higher than the MDA model and its prediction performance is improved.

• International Journal of Computer Science & Network Security
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• 제21권2호
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• pp.9-13
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• 2021

Type 2 diabetes mellitus (T2D) is a complex diabetes disease that is caused by high blood sugar, insulin resistance, and a relative lack of insulin. Many studies are trying to predict variant genes that causes this disease by using a sample disease model. In this paper we predict diabetic and normal persons by using fisher score feature selection, chi-2 feature selection and Logistic Regression supervised learning algorithm with best accuracy of 90.23%.

• 한국정보통신학회논문지
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• 제14권2호
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• pp.534-539
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• 2010

게놈 연구에서 수천 개의 특징들은 비교적 작은 샘플들로부터 모아진다. 게놈 연구의 목적은 미래 관찰들의 결과를 예측하는 분류기를 만드는 것이다. 분류기를 만들기 위해서는 특징 선택, 모델 선택 그리고 예측 평가 등의 3단계 과정을 거친다. 본 논문은 예측 평가에 초점을 맞추고 모든 슬라이드의 사분위수를 똑같게 맞추는 quantilenormalization 적용하여 마이크로어레이 데이터를 표준화 한 후 특징 선택에 앞서 예측 모델의 '진짜' 예측 에러를 평가하기 위해 몇 개의 방법들을 비교하는 시스템을 고안하고 방법들의 예측 에러를 비교 분석 하였다. LOOCV는 전체적으로 작은 MSE와 bias를 나타내었고, 크기가 작은 샘플에서 split 방법과 2-fold CV는 매우 좋지 않는 결과를 보였다. 계산적으로 번거로운 분석에 대해서는 10-fold CV가 LOOCV보다 오히려 더 낳은 경향을 보였다.