• 보건행정학회지
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• 제25권1호
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• pp.31-39
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• 2015

Background: The purpose of this study is comparison of the results between regression and multi-level analysis to find out factors influencing outcome indicators (in-hospital death, length of stay, and medical charges) of stroke patients. Methods: By using patient sample data of Health Insurance Review & Assessment Service, patients admitted with stroke were selected as survey target and 15,864 patients and 762 hospitals were surveyed. Results: For the results of existing regression analysis and multi-level analysis, models were assessed through model suitability index value and as a result, the value of results of multi-level analysis decreased compared to the results of regression, showing it is a better model. Conclusion: Factors influencing in-hospital death of stroke patients were analyzed and as a result, intra-class correlation (ICC) was 13.6%. In factors influencing length of stay, ICC was 11.4%, and medical charges, ICC was 17.7%. It was found that factors influencing the outcome indicators of stroke patients may vary in every hospital. This study could carry out more accurate analysis than existing research findings through analysis of reflecting structure at patient level and hospital level factors and analysis on random effect.

• 한국정보처리학회:학술대회논문집
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• 한국정보처리학회 2015년도 춘계학술발표대회
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• pp.938-940
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• 2015

CCTV는 범죄상황 발생시 보안과 증거확보를 위해 사용되어 왔다. 그러나 실제 상황에서 범죄가 발생하기 전 예방을 하는 것 보다 사후 처리에 용도를 두고 있으며, 범죄 예방의 목적에 대해 미미한 효과를 보이고 있다. 본 논문에서는 CCTV로 수집된 보행자의 데이터를 통해 객체의 행동을 분석하여 위험도로 행동의 위험여부를 추정하기 위한 Flexible Multi-level Regression 모델을 제안하였다. 제안된 모델을 통해 관찰된 객체의 행동이 이상행동이라고 판단될 시 위험을 받는 객체에게 알림을 주어 범죄 발생 전 즉각적인 대응이 가능하며 빠른 상황판단이 가능할 것으로 예상된다.

• Genomics & Informatics
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• 제20권4호
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• pp.48.1-48.7
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• 2022

Penalized regression has been widely used in genome-wide association studies for joint analyses to find genetic associations. Among penalized regression models, the least absolute shrinkage and selection operator (Lasso) method effectively removes some coefficients from the model by shrinking them to zero. To handle group structures, such as genes and pathways, several modified Lasso penalties have been proposed, including group Lasso and sparse group Lasso. Group Lasso ensures sparsity at the level of pre-defined groups, eliminating unimportant groups. Sparse group Lasso performs group selection as in group Lasso, but also performs individual selection as in Lasso. While these sparse methods are useful in high-dimensional genetic studies, interpreting the results with many groups and coefficients is not straightforward. Lasso's results are often expressed as trace plots of regression coefficients. However, few studies have explored the systematic visualization of group information. In this study, we propose a multi-level polar Lasso (MP-Lasso) chart, which can effectively represent the results from group Lasso and sparse group Lasso analyses. An R package to draw MP-Lasso charts was developed. Through a real-world genetic data application, we demonstrated that our MP-Lasso chart package effectively visualizes the results of Lasso, group Lasso, and sparse group Lasso.

• Journal of Electrical Engineering and Technology
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• 제13권1호
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• pp.38-50
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• 2018

Spatio-temporal load forecasting (STLF) is a foundation for building the prediction-based power map, which could be a useful tool for the visualization and tendency assessment of urban energy application. Constructing one point-forecasting model for each electricity cell in the geographic space is possible; however, it is unadvisable and insufficient, considering the aggregation features of electricity cells and uncertainties in input variables. This paper presents a new STLF method, with a data-driven framework consisting of 3 subroutines: multi-level clustering of cells considering their aggregation features, load regression for each category of cells based on SLS-SVRNs (sparse least squares support vector regression networks), and interval forecasting of spatio-temporal load with sampled blind number. Take some area in Pudong, Shanghai as the region of study. Results of multi-level clustering show that electricity cells in the same category are clustered in geographic space to some extent, which reveals the spatial aggregation feature of cells. For cellular load regression, a comparison has been made with 3 other forecasting methods, indicating the higher accuracy of the proposed method in point-forecasting of spatio-temporal load. Furthermore, results of interval load forecasting demonstrate that the proposed prediction-interval construction method can effectively convey the uncertainties in input variables.

• 국제초고층학회논문집
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• 제11권4호
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• pp.315-320
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• 2022

With the rapid development of urbanization the problems of pedestrian-level wind comfort and natural ventilation of tall buildings are becoming increasingly prominent. The velocity at the pedestrian level ($\overline{MVR}$) and variation of wind pressure coefficients $\overline{{\Delta}C_p}$ between windward and leeward surfaces of tall buildings were investigated systematically through numerical simulations. The examined parameters included building density ρ, height ratio of building α_H, width ratio of building α_B, and wind direction θ. The linear and quadratic regression analyses of $\overline{MVR}$ and $\overline{{\Delta}C_p}$ were conducted. The quadratic regression had better performance in predicting $\overline{MVR}$ and $\overline{{\Delta}C_p}$ than the linear regression. $\overline{MVR}$ and $\overline{{\Delta}C_p}$ were optimized by the NSGA-II algorithm. The LINMAP and TOPSIS decision-making methods demonstrated better capability than the Shannon's entropy approach. The final optimal design parameters of buildings were ρ = 20%, α_H = 4.5, and α_B = 1, and the wind direction was θ = 10°. The proposed method could be used for the optimization of pedestrian-level wind comfort and natural ventilation in a residential area.

• 보건행정학회지
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• 제30권3호
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• pp.355-365
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• 2020

Background: This study is aimed to verify individual and regional-level factors affecting the depression of Koreans and to develop social programs for improving the depressive status. Methods: This study used individual-level variables from the Korean Community Health Survey (2018) and used the e-regional index of the Korean Statistical Information Service as the regional-level variable. A multi-level logistic regression was executed to identify individual and regional-level variables that were expected to affect the extent of depressive symptoms and to draw the receiver operating characteristic curve to compare the volume of impact between variables from both levels. Results: The results of the multi-level logistic regression analysis in regards to individual-level factors showed that older age, female gender, a lower income level, a lower education level, not having a spouse, the practice of walking, the consumption of breakfast higher levels of stress, and having high blood pressure or diabetes were associated with a greater increase in depressive symptoms. In terms of regional factors, areas with fewer cultural facilities and fewer car registration had higher levels of depressive symptoms. The comparison of area under the curve showed that individual factors had a greater influence than regional factors. Conclusion: This study showed that while both, individual and regional-level factors affect depression, the influence of the latter was relatively weaker as compared to the first. In this sense, it is necessary to develop programs focused on the individual, such as social prescribing at the local or community-level, rather than the city and nation-level approach that are currently prevalent.

• 보건행정학회지
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• 제30권3호
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• pp.399-408
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• 2020

Background: The purpose of this study was to examine the impact of the regional characteristics on the accessibility of emergency care and the impact of emergency medical accessibility on the patients' prognosis and the emergency medical expenditure. Methods: This study used the 13th beta version 1.6 annual data of Korea Health Panel and the statistics from the Korean Statistical Information Service. The sample included 8,119 patients who visited the emergency centers between year 2013 and 2017. The arrival time, which indicated medical access, was used as dependent variable for multi-level analysis. For ordinal logistic regression and multiple regression, the arrival time was used as independent variable while patients' prognosis and emergency medical expenditure were used as dependent variables. Results: The results for the multi-level analysis in both the individual and regional variables showed that as the number of emergency medical institutions per 100 km² area increased, the time required to reach emergency centers significantly decreased. Ordinal logistic regression and multiple regression results showed that as the arrival time increased, the patients' prognosis significantly worsened and the emergency medical expenses significantly increased. Conclusion: In conclusion, the access to emergency care was affected by regional characteristics and affected patient outcomes and emergency medical expenditure.

• 대한토목학회논문집
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• 제42권4호
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• pp.571-579
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• 2022

본 연구는 부산·울산·경남 지역의 관광호텔을 대상으로 에너지사용량과 이들의 개별적인 호텔특성 및 입지특성 간의 관계를 실증분석하였다. 복합적인 관계식 도출을 위해 다중회귀모형에서부터 다수준회귀분석(multi-level regression analysis)으로 모형을 확장하였고, 이를 통해 건축물의 개별적인 특성만을 고려한 대부분의 선행연구에서 나아가 호텔이 위치한 지역의 입지적 특성과 호텔-지역 간 위계적 구조를 고려하여 좀 더 개선된 모형을 도출하였다. 분석결과에 따르면, 호텔의 규모, 연한, 서비스 등급과 같은 개별적인 특성은 에너지사용량을 설명하는 주요 변수이고, 그들의 영향은 지역적으로 유의한 차이를 보이는 것으로 나타났다. 또한, 중심상업지에 인접하거나 다수의 관광호텔이 밀집한 지역에 위치할수록 에너지사용량은 달라지는 것으로 나타났으며, 이러한 입지특성 또한 개별호텔의 에너지사용량을 설명함에 있어 주요한 요인임을 확인하였다. 이와 같은 결과는 건축물단위의 에너지정책과 소비수준이 높고 에너지 집약시설이 밀집한 지역에 대한 지역단위의 에너지정책이 함께 고려될 필요성을 시사하며, 관광산업의 지속가능성을 높이기 위한 지역적 책임을 제언한다.

• 대한원격탐사학회:학술대회논문집
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• 대한원격탐사학회 2005년도 Proceedings of ISRS 2005
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• pp.116-119
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• 2005

As to the synthetical estimation of land covering parameters or the compounded land covering classification for multi-resolution satellite data, former researches mainly adopted linear or nonlinear regression models to describe the regression relationship of land covering parameters caused by the degradation of spatial resolution, in order to improve the retrieval accuracy of global land covering parameters based on 1;he lower resolution satellite data. However, these methods can't authentically represent the complementary characteristics of spatial resolutions among different satellite data at arithmetic level. To resolve the problem above, a new compounded land covering classification method at arithmetic level for multi-resolution satellite data is proposed in this .paper. Firstly, on the basis of unsupervised clustering analysis of the higher resolution satellite data, the likelihood distribution scatterplot of each cover type is obtained according to multiple-to-single spatial correspondence between the higher and lower resolution satellite data in some local test regions, then Parzen window approach is adopted to derive the real likelihood functions from the scatterplots, and finally the likelihood functions are extended from the local test regions to the full covering area of the lower resolution satellite data and the global covering area of the lower resolution satellite is classified under the maximum likelihood rule. Some experimental results indicate that this proposed compounded method can improve the classification accuracy of large-scale lower resolution satellite data with the support of some local-area higher resolution satellite data.

• 융합정보논문지
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• 제11권10호
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• pp.322-331
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• 2021

본 연구는 간호대학 신입생의 다문화수용성 수준을 파악하고 이에 영향을 주는 요인을 분석하고자 하였다. 연구 방법은 W 시 소재 K 대학 간호학과 1학년 학생 410명을 대상으로 2021년 3월 1일- 28일까지 설문조사하였고, 오픈소스 통계패키지 R을 이용하여 빈도, 신뢰도 분석, t-test, ANOVA, correlation, Multiple regression을 시행하였다. 연구결과 간호대학 신입생의 다문화수용성 수준은 평균 77.36점으로 다소 높은 다문화 수용성 능력을 가지는 것으로 나타났고, 다문화수용성 관련 요인의 영향을 분석한 결과 한국인 인정요건(𝛽=0.34, p<.001), 이주민에 대한 지각된 위협 인식(𝛽=0.29, p<.001), 다문화 교육 경험(𝛽=0.14, p<.001), 다문화 교육 적정 연령 인식(𝛽=0.20, p<.001)은 유의미하였다. 이러한 결과에 따라 간호대학생의 다문화 관련 정규 교육과정 및 프로그램을 개발하고 적극적으로 활용해야 할 필요가 있다.