• 한국안전학회지
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• 제37권6호
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• pp.128-135
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• 2022

The comparative evaluation of occupational accident fatality rates (OAFRs) of different countries is complicated owing to the differences in their level of socio-economic development. However, such evaluation is necessary to assess the national occupational safety and health system of a country. This study proposes a statistical method to compare the OAFRs of countries taking into consideration the difference in their level of socio-economic development. We first collected data on the socio-economic indicators and OAFRs of 11 countries over a 30-year period. Next, based on literature survey and statistical correlation analysis, we selected the significant independent variables and built multiple linear regression models to predict OAFR. We also determined the groups of countries having heterogeneous relationships between the independent variables and OAFRs, which are represented by the regression models. The proposed method is demonstrated by comparing the OAFR of Korea with the OAFRs of 10 other developed countries.

• Smart Structures and Systems
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• 제32권1호
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• pp.61-81
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• 2023

This study presents an ensemble learning based Bayesian model updating approach for structural damage diagnosis. In the developed framework, the structure is initially decomposed into a set of substructures. The autoregressive moving average (ARMAX) model is established first for structural damage localization based structural motion equation. The wavelet packet decomposition is utilized to extract the damage-sensitive node energy in different frequency bands for constructing structural surrogate models. Four methods, including Kriging predictor (KRG), radial basis function neural network (RBFNN), support vector regression (SVR), and multivariate adaptive regression splines (MARS), are selected as candidate structural surrogate models. These models are then resampled by bootstrapping and combined to obtain an ensemble model by probabilistic ensemble. Meanwhile, the maximum entropy principal is adopted to search for new design points for sample space updating, yielding a more robust ensemble model. Through the iterations, a framework of surrogate ensemble learning based model updating with high model construction efficiency and accuracy is proposed. The specificities of the method are discussed and investigated in a case study.

• 한국경제지리학회지
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• 제14권3호
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• pp.377-393
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• 2011

본 연구의 목적은 우리나라 농업공간에 나타나는 변화의 공간적 특징을 파악하는 것이다. 특히 세계화에 따라 가속되는 농산물 시장개방과 함께 안전한 먹거리와 지속가능성에 대한 수요 증가에 따라 부상하고 있는 친환경농업의 공간적 확산 양상과 그 지리적 특징을 분석한다. 이를 위하여 우리나라 농가와 친환경 농가의 공간적 분포 양상에 나타나는 변화를 분석하고, 특히 친환경농가의 공간적 특성을 파악하기 위하여 입지계수(LQ: Location Quotient)와 LISA(Local Indicator of Spatial Association) 분석을 적용하였다. 분석결과 2000년의 경우 우리나라의 친환경농업의 주요 집적지가 수도권과 충청권을 중심으로 분포하여 매우 불균등하게 나타났으나 2005년에는 전라권과 경상권 지역으로 확산되면서 특정지역의 공간집중이 비교적 완화되었다. 다중회귀분석을 이용하여 친환경농가 분포에 작용하는 지리적 변수들과의 관계를 분석한 결과 우리나라의 2005년 현재 친환경농업 분포는 농가 경영자의 연령, 농업관련 사업 경영농가, 농가의 정보화, 농가 경영자 교육수준 등에 영향을 받는 것으로 나타난다.

• 한국경제지리학회지
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• 제16권3호
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• pp.512-527
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• 2013

본 연구는 현대 생활환경의 필수 인프라인 금융산업의 공간적 분포에 대하여 연구하였다. 특히 금융기관의 성격에 따른 분포 차이를 확인하기 위하여 제도권 금융기관인 은행과 비 제도권 금융기관인 대부업체의 공간적 분포양상을 분석하였다. 먼저 커널밀도를 통하여 각 금융기관 분포의 집중도를 분석하고 분포양상을 비교분석하였다. 또한 공간적 자기상관분석을 통하여 은행과 대부업의 군집패턴의 차이를 확인하였다. 이와 더불어 각 금융기관의 분포에 영향을 미치는 지역의 사회 경제적 요인과의 관계를 파악하기 위하여 다중회귀모형을 구축하였다. 이러한 공간적 분포분석 결과를 바탕으로 수도권 지역의 금융소외문제를 검토하였다.

• 한국임상약학회지
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• 제28권2호
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• pp.107-116
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• 2018

Objective: This study analyzed the national claims data of veterans to generate scientific evidence of the trends and appropriateness of their drug utilization in an outpatient setting. Methods: The claims data were provided by the Health Insurance Review & Assessment (HIRA). Through sampling and matching data, we selected two comparable groups; Veterans vs. National Health Insurance (NHI) patients and Veterans vs. Medical Aid (MAID) patients. Drug use and costs were compared between groups by using multivariate gamma regression models to account for the skewed distribution, and therapeutic duplication was analyzed by using multivariate logistic regression models. Results: In equivalent conditions, veteran patients made fewer visits to medical institutions (0.88 vs. 1), had 1.86 times more drug use, and paid 1.4 times more drug costs than NHI patients (p<0.05); similarly, veteran patients made fewer visits to medical institutions (0.96 vs. 1), had 1.11 times more drug use, and paid 0.95 times less drug costs than MAID patients (p<0.05). The risk of therapeutic duplication was 1.7 times higher (OR=1.657) in veteran patients than in NHI patients and 1.3 times higher (OR=1.311) than in MAID patients (p<0.0001). Conclusion: Similar patterns of drug use were found in veteran patients and MAID patients. There were greater concerns about the drug use behavior in veteran patients, with longer prescribing days and a higher rate of therapeutic duplication, than in MAID patients. Efforts should be made to measure if any inefficiency exists in veterans' drug use behavior.

• 대한화학회지
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• 제47권1호
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• pp.19-25
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• 2003

흡수 분광법에 의해 얻은 스펙트럼을 주성분분석(principal analysis, PCA) 으로 자료를 요약하여 주성분 회귀분서(principal component regression, PCR)과 부분 최소자승법(partial least squares, PLS)으로 음이온과 비이온 계면활성제(anionic and nonionic surfactant)를 동시에 정량하는 방법에 대하여 연구하였다. 두 가지 계면활성제가 서로 다른 농도로 혼합되어 있는 26개의 시료용액을 400~700 nm 범위에서 스펙트럼을 얻었고, 이를 이용하여 PCR과 PLS회귀모델을 얻었다. 두 가지 계면활성제가 서로 다른 농도로 포함된 5개의 외부검정용 시료들의 스펙트럼들을 이용해서 회귀모델의 적합성을 검정하기 위하여 외부검정용 시료의 농도를 계산하였다. 계산된 농도를 이용하여 relative standard error of prediction(RSEP$_{\alpha}$)를 구하여 회귀모델의 적합성을 검정하였다.

• 한국전자통신학회논문지
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• 제19권2호
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• pp.383-388
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• 2024

본 논문은 전라남도에서 측정한 태양광 발전 데이터를 기반으로 발전량 예측값을 도출하기 위한 연구이다. 발전량 측정을 위해 인버터에서 직류, 교류, 환경데이터와 같은 다변량 변수를 측정하였고, 측정값의 안정성과 신뢰성 확보를 위한 전처리 작업을 수행하였다. 상관관계 분석은 부분자기상관함수(PACF: Partial Autocorrelation Function)을 활용하여 시계열 데이터에서 발전량과 상관성이 높은 데이터만을 예측을 위해 사용하였다. 태양광 발전량 예측을 위해 딥러닝 모델을 이용하여 발전량을 측정했고, 예측 정확도를 높이기 위해 각 다변량 변수의 상관관계 분석 결과를 이용하였다. 정제된 데이터를 활용한 학습은 기존 데이터를 그대로 사용했을 때 보다 안정되었고, 상관관계 분석 결과를 반영하여 다변량 변수 중 상관성이 높은 변수만을 활용하여 태양광 발전량 예측 알고리즘을 개선하였다.

• 한국병원경영학회지
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• 제25권3호
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• pp.67-77
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• 2020

Background: As of 2016, average Koreans sleep 7 hours and 42 minutes, the lowest figure among Organization for Economic Cooperation and Development(OECD) countries, and the number of people with sleep disorders reached 561,000. Accordingly, the government has promoted the provision of 'Multiple Sleep Test' to strengthen the diagnosis service for patients with 'sleep disorder' in july 2018. As a result, healthcare costs for patients with sleep disorder is on the rise every year. In this study, we utilized 'Appropriate Sleep' criteria of United States's National Sleep Foundation(NSF) then investigated Korean's sleep pertinence using ｢7th National Health and Nutrition Survey for 2016-2018」 by different occupational type, demographic characteristics, socio-economic characteristics, and health behaviors. Methods: We performed descriptive analysis to examine differences of sleep appropriateness by various sample characteristics. Multivariate logistic regression models were used to examine sleep appropriateness by occupational type and other variables. We also analyzed subgroup models to investigate. Results: As a result, a total of 1,948 (18.37%) study subjects experienced in-appropriate sleep. Results of the Multivariate logistic regression analysis revealed that blue color group had a higher odds ratio (OR) for experiencing in-appropriate sleep (OR=1.179). In addition, the odds ratio of experienced in-appropriate sleep among the elderly aged 70 and over was 2.698, and the odds ratio of the overstressed group was 1.299. Furthermore, sub-group analysis showed that blue color job of female(Or=1.334), high school or below(OR=1.404), divorce/death/separation(OR=2.039), 25%ile-50%lie income group(OR=1.411) more likely experienced in-appropriate sleep. Conclusion: Growing sleep disorder patients and related health care costs are expected. Government should apply detailed 'total periodic sleep disorder management policy' including pre-consultation, examination, diagnosis, treatment, post-consultation, self-management especially to vulnerable population that this study found.

• 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.

• Computers and Concrete
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• 제27권4호
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• pp.305-317
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• 2021

The bond between the concrete and bar is a main factor affecting the performance of the reinforced concrete (RC) members, and since the steel corrosion reduces the bond strength, studying the bond behavior of concrete and GFRP bars is quite necessary. In this research, a database including 112 concrete beam test specimens reinforced with spliced GFRP bars in the splitting failure mode has been collected and used to estimate the concrete-GFRP bar bond strength. This paper aims to accurately estimate the bond strength of spliced GFRP bars in concrete beams by applying three soft computing models including multivariate adaptive regression spline (MARS), Kriging, and M5 model tree. Since the selection of regularization parameters greatly affects the fitting of MARS, Kriging, and M5 models, the regularization parameters have been so optimized as to maximize the training data convergence coefficient. Three hybrid model coupling soft computing methods and genetic algorithm is proposed to automatically perform the trial and error process for finding appropriate modeling regularization parameters. Results have shown that proposed models have significantly increased the prediction accuracy compared to previous models. The proposed MARS, Kriging, and M5 models have improved the convergence coefficient by about 65, 63 and 49%, respectively, compared to the best previous model.