• 수산경영론집
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• 제42권2호
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• pp.15-30
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• 2011

This study is designed to measure the relative efficiency of regional fishery cooperatives based on Data Envelopment Analysis(DEA) methods. Selecting 40 regional fishery cooperatives in Busan as Decision Making Units (DMUs), the study uses their panel data from 2007 to 2008 to rank the relative efficiency of the DMUs. First, the efficiency score of the DMUs are calculated using CCR, SBM, and super-SMB model. Within the model, input variables are the number of employees and area of fishery cooperatives. Output variables are the amount of deposit money, loan and profit. Based on the efficiency scores calculated from super-SMB model, the efficiency ranking of the DMUs is determined. Second, the differences in average efficiency calculated from the three DEA models are tested using a pair-wise mean comparison test. The results based on the efficiency scores evaluated from super-SMB model show that seven out of the forty DMUs are efficient; among the efficient DMUs, the DMUs that can be benchmarked for inefficient DMUs through the frequency analysis of reference set being identified. Third, the differences in average efficiency of the three DEA models between 2007 and 2008 are tested using pair-wise mean comparison test and the study estimates the efficiency change of the DMUs between 2007 and 2008 using Malmquist productivity index(MPI). Finally, the paper suggests an improved composite DMU superior to the inefficient DMUs evaluated by Super-SBM model.

• Steel and Composite Structures
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• 제27권6호
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• pp.717-725
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• 2018

One of the most important design criteria in military tunnels and armoured doors is to resist the blast loads with minimum structural weight. This can be achieved by using steel sandwich panels. In this paper, the nonlinear behaviour of steel sandwich panels, with different core materials: (1) Hollow (no core material); (2) Rigid Polyurethane Foam (RPF); and (3) Vulcanized Rubber (VR) under free air blast loads, was investigated using detailed 3D nonlinear finite element models in Ansys Autodyn. The accuracy of the finite element model proposed was verified using available experimental test data of a similar steel sandwich panel tested. The results show the developed finite element model can be reliably used to simulate the nonlinear behaviour of the steel sandwich panels under free air blast loads. The verified finite element model was used to examine the different parameters of the steel sandwich panel with different core materials. The result shows that the sandwich panel with RPF core material is more efficient than the VR sandwich panel followed by the Hollow sandwich panels. The average maximum displacement of RPF sandwich panel under different ranges of TNT charge (1 kg to 10 kg at a standoff distance of 1 m) is 49% and 53% less than the VR and Hollow sandwich panels, respectively. Detailed empirical design equations were provided to quantify the maximum deformation of the steel sandwich panels with different core materials and core thickness under a different range of blast loads. The developed equations can be used as a guide for engineer to design steel sandwich panels with RPF and VR core material under a different range of free air blast loads.

• 대한교통학회지
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• 제36권2호
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• pp.141-154
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• 2018

지속적으로 증가하는 국제선 항공수요에 대웅하기 위해 지방 광역권에도 새로운 공항 건설 및 기존 공항 확장 계획이 이루어지고 있다. 그러나 기존 항공수요예측은 우리나라 전체 항공수요 또는 주요 도시 간의 항공수요에 대해서 수행되어 왔으며, 지방의 고유 특성을 고려한 지역별 항공수요예측은 많이 이루어지지 않았다. 본 연구에서는 영남권 국제선 항공수요를 대상으로 하였고, 현실적으로 관측하기 어려운 지방 광역권의 고유 특성을 반영할 수 있는 패널 자료를 활용한 fixed-effects model을 최적 모형으로 제안하였다. 모형 검증결과를 살펴보면 패널 자료 분석은 시계열 특성을 가지는 몇 개의 거시 사회경제지표만을 사용한 모형에서 다루기 어려운 허구적 회귀와 미관찰 이질성을 효과적으로 처리하고 있음을 알 수 있다. 다양한 통계적 검증과 적합성 평가를 통해서 본 연구에서 제안한 fixed-effects model이 다른 계량경제 모형들에 비해서 영남권 국제선 수요예측에 있어서 우수함을 증명하였다.

• 한국안전학회지
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• 제33권3호
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• pp.77-82
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• 2018

This paper aims to analyze the accident based on changing patterns of traffic culture index. For this purpose, this paper particularly focuses on classifying the traffic culture patterns and developing the traffic accidents using panel count data model. The main results are as follows. First, the traffic culture patterns are divided into 'increasing', 'decreasing' and 'other' patterns. The null hypotheses that the number of accident are the same over patterns are rejected. Second, 4 fixed effect negative binomial models which are all statistically significant are developed. Third, the regions with 'increasing' pattern are analyzed to be mostly the counties, and to demand the traffic law enforcement. Fourth, the regions with 'decreasing' pattern are evaluated to be mainly the districts and to require such the traffic culture as turn signal usage. Finally, the regions with 'other' pattern are analyzed to be mostly the cities and to ask for enhancing the level of traffic culture.

• Structural Engineering and Mechanics
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• 제78권4호
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• pp.387-401
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• 2021

This paper provides a methodology to analyze the seismic performance of different component designs in hybrid masonry structures (HMS). HMS, comprised of masonry panels, steel frames and plate connectors is a relatively new structural system with potential applications in high seismic areas. HMS dissipate earthquake energy through yielding in the steel components and damage in the masonry panels. Currently, there are no complete codes to assist with the design of the energy dissipation components of HMS and there have been no computational studies performed to aid in the understanding of the system energy dissipation mechanisms. This paper presents parametric studies based on calibrated computational models to extrapolate the test data to a wider range of connector strengths and more varied reinforcement patterns and reinforcement ratios of the masonry panels. The results of the numerical studies are used to provide a methodology to examine the effect of connector strength and masonry panel design on the energy dissipation in HMS systems. We use as test cases two story structures subjected to cyclic loading due to the availability of experimental data for these configurations. The methodology presented is however general and can be applied to arbitrary panel geometries, and column and story numbers.

• Journal of Preventive Medicine and Public Health
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• 제56권6호
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• pp.515-522
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• 2023

Objectives: The pandemic caused by coronavirus disease 2019 (COVID-19) has exerted an unprecedented impact on the health of populations worldwide. However, the adverse health consequences of the pandemic in terms of infection and mortality rates have varied across countries. In this study, we investigate whether COVID-19 mortality rates across a group of developed nations are associated with characteristics of their healthcare systems, beyond the differential policy responses in those countries. Methods: To achieve the study objective, we distinguished healthcare systems based on the extent of healthcare decommodification. Using available daily data from 2020, 2021, and 2022, we applied quantile regression with non-additive fixed effects to estimate mortality rates across quantiles. Our analysis began prior to vaccine development (in 2020) and continued after the vaccines were introduced (throughout 2021 and part of 2022). Results: The findings indicate that higher testing rates, coupled with more stringent containment and public health measures, had a significant negative impact on the death rate in both pre-vaccination and post-vaccination models. The data from the post-vaccination model demonstrate that higher vaccination rates were associated with significant decreases in fatalities. Additionally, our research indicates that countries with healthcare systems characterized by high and medium levels of decommodification experienced lower mortality rates than those with healthcare systems involving low decommodification. Conclusions: The results of this study indicate that stronger public health infrastructure and more inclusive social protections have mitigated the severity of the pandemic's adverse health impacts, more so than emergency containment measures and social restrictions.

• 유통과학연구
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• 제17권3호
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• pp.85-95
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• 2019

Purpose - The existing marketing studies using Social Network Analysis have assumed that network structure variables are time-invariant. However, a node's network position can fluctuate considerably over time and the node's network structure can be changed dynamically. Hence, if such a dynamic structural network characteristics are not specified for virtual goods purchase model, estimated parameters can be biased. In this paper, by comparing a time-invariant network structure specification model(base model) and time-varying network specification model(proposed model), the authors intend to prove whether the proposed model is superior to the base model. In addition, the authors also intend to investigate whether coefficients of network structure variables are random over time. Research design, data, and methodology - The data of this study are obtained from a Korean social network provider. The authors construct a monthly panel data by calculating the raw data. To fit the panel data, the authors derive random effects panel tobit model and multi-level mixed effects model. Results - First, the proposed model is better than that of the base model in terms of performance. Second, except for constraint, multi-level mixed effects models with random coefficient of every network structure variable(in-degree, out-degree, in-closeness centrality, out-closeness centrality, clustering coefficient) perform better than not random coefficient specification model. Conclusion - The size and importance of virtual goods market has been dramatically increasing. Notwithstanding such a strategic importance of virtual goods, there is little research on social influential factors which impact the intention of virtual good purchase. Even studies which investigated social influence factors have assumed that social network structure variables are time-invariant. However, the authors show that network structure variables are time-variant and coefficients of network structure variables are random over time. Thus, virtual goods purchase model with dynamic network structure variables performs better than that with static network structure model. Hence, if marketing practitioners intend to use social influences to sell virtual goods in social media, they had better consider time-varying social influences of network members. In addition, this study can be also differentiated from other related researches using survey data in that this study deals with actual field data.

• 지능정보연구
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• 제24권2호
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• pp.111-124
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• 2018

본 연구는 만성질환 중의 하나인 고지혈증 유병을 예측하는 분류모형을 개발하고자 한다. 이를 위해 SVM과 meta-learning 알고리즘을 이용하여 성과를 비교하였다. 또한 각 알고리즘에서 성과를 향상시키기 위해 변수선정 방법을 통해 유의한 변수만을 선정하여 투입하여 분석하였고 이 결과 역시 각각 성과를 비교하였다. 본 연구목적을 달성하기 위해 한국의료패널 2012년 자료를 이용하였고, 변수 선정을 위해 세 가지 방법을 사용하였다. 먼저 단계적 회귀분석(stepwise regression)을 실시하였다. 둘째, 의사결정나무(decision tree) 알고리즘을 사용하였다. 마지막으로 유전자 알고리즘을 사용하여 변수를 선정하였다. 한편, 이렇게 선정된 변수를 기준으로 SVM, meta-learning 알고리즘 등을 이용하여 고지혈증 환자분류 예측모형을 비교하였고, TP rate, precision 등을 사용하여 분류 성과를 비교분석하였다. 이에 대한 분석결과는 다음과 같다. 첫째, 모든 변수를 투입하여 분류한 결과 SVM의 정확도는 88.4%, 인공신경망의 정확도는 86.7%로 SVM의 정확도가 좀 더 높았다. 둘째, stepwise를 통해 선정된 변수만을 투입하여 분류한 결과 전체 변수를 투입하였을 때보다 각각 정확도가 약간 높았다. 셋째, 의사결정나무에 의해 선정된 변수 3개만을 투입하였을 때 인공신경망의 정확도가 SVM보다 높았다. 유전자 알고리즘을 통해 선정된 변수를 투입하여 분류한 결과 SVM은 88.5%, 인공신경망은 87.9%의 분류 정확도를 보여 주었다. 마지막으로, 본 연구에서 제안하는 meta-learning 알고리즘인 스태킹(stacking)을 적용한 결과로서, SVM과 MLP의 예측결과를 메타 분류기인 SVM의 입력변수로 사용하여 예측한 결과, 고지혈증 분류 정확도가 meta-learning 알고리즘 중에서는 가장 높은 것으로 나타났다.

• 한국직업건강간호학회지
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• 제25권4호
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• pp.277-289
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• 2016

Purpose: This study aims to investigate the cross-sectional association of company size and self-rated health using representative data on Korean workers. Methods: We used the data from 2,884 wage workers collected by Korean Labor and Income Panel Study (17th). The association between company size and self-rated health was analyzed using logistic regression with covariates including demographic characteristics, work environment, job satisfaction, and health-related behaviors. Resulst: Odds ratio (OR) for better health status among workers in large-sized company was 1.351 (CI. 1.054~1.731), compared to workers in small-sized company. We performed three separate models stratified by firm size (small, medium, and large companies). Occupation variables showed different effect on health depending on firm sizes. OR for better health of white-color job (referred to blue-color job) was 1.693 in medium-sized company model but it was 0.615 in large company model. OR for better health of the workers working shift work showed 0.606 in large company model but it was not significant in small and medium company models. Conclusion: We found that small-sized company workers have significantly poor self-rated health compared to large-sized firm workers. This study revealed that there exist differences among health related factors depending on firm sizes.

• 마케팅과학연구
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• 제11권
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• pp.129-146
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• 2003

본 연구는 소비자들의 선택모형에서 널리 사용된 NBD (Negative Binomial Distribution) 타입의 계랑적 모델 접근법이 온라인 상에서 소비자들이 특정한 기업의 웹사이트를 방문하는 행위를 설명하는데 적용될 수 있는지를 탐구한다. 본 연구에서는 다음의 두 가지 연구 주제를 다루고 있다. 첫째, 소비자들이 웹사이트를 반복하여 방문하는 행위의 빈도에 관한 분포를 확률적으로 규정하며, 둘째로는 그러한 소비자들의 반복된 이용빈도의 분포에 소비자들의 일반적인 인터넷 사용패턴과 인구 통계적인 변수들이 어떤 영향을 미치는지를 조사하고 있다. 일련의 실증적 분석을 통하여, 이 논문은 마케팅의 선택모형 (Choice Model)들에서 널리 사용된 NBD 타입의 모댈들이 인터넷상의 사이트 방문빈도 연구에도 잘 적용될 수 있음을 보여주고 있다. 그리고 이 연구는 이러한 소비자들의 이용빈도에 관한 모델개발이 온라인 기업의 당면문제에 어떠한 영향을 미치는 지를 설명한다. 특히 본 연구는 반복된 이용빈도와 소비자들의 일반적인 인터넷사용 특징 및 인구 통계적인 변수들과의 상호관계를 규명했다. 본 연구에서 제시된 모델들을 추정하고 검정하기 위해 800,000번의 방문 기록과 1000개 이상의 상이한 방문사이트 수로 구성된 웹 패널 데이터를 사용하여 실증분석을 연구에서 제시하는 모델을 개발하고 검증하였다.