• Journal of Preventive Medicine and Public Health
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• 제56권4호
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• pp.291-302
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• 2023

Objectives: Disability weights require regular updates, as they are influenced by both diseases and societal perceptions. Consequently, it is necessary to develop an up-to-date list of the causes of diseases and establish a survey panel for estimating disability weights. Accordingly, this study was conducted to calculate, assess, modify, and validate disability weights suitable for Korea, accounting for its cultural and social characteristics. Methods: The 380 causes of disease used in the survey were derived from the 2019 Global Burden of Disease Collaborative Network and from 2019 and 2020 Korean studies on disability weights for causes of disease. Disability weights were reanalyzed by integrating the findings of an earlier survey on disability weights in Korea with those of the additional survey conducted in this study. The responses were transformed into paired comparisons and analyzed using probit regression analysis. Coefficients for the causes of disease were converted into predicted probabilities, and disability weights in 2 models (model 1 and 2) were rescaled using a normal distribution and the natural logarithm, respectively. Results: The mean values for the 380 causes of disease in models 1 and 2 were 0.488 and 0.369, respectively. Both models exhibited the same order of disability weights. The disability weights for the 300 causes of disease present in both the current and 2019 studies demonstrated a Pearson correlation coefficient of 0.994 (p=0.001 for both models). This study presents a detailed add-on approach for calculating disability weights. Conclusions: This method can be employed in other countries to obtain timely disability weight estimations.

• 대한토목학회논문집
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• 제29권6D호
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• pp.675-683
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• 2009

본 논문에서는 도로와 철도에서 자동차와 열차주행시에 발생하는 진동에너지를 건축-토목 구조물에 활용 가능한 전기에너지로 변환하는 진동력 발전시스템 인프라 구축을 위한 기초 개념에 대해 연구하였다. 본 연구에서는 자기유도기술을 활용하여 진동에너지를 전기에너지로 변환하는 진동력 발전장치를 제안하고, 자기유도기술의 기본 원리 및 진동력 발전장치의 동적특성에 대해 설명하였다. 마지막으로 외력과 진동속도에 따라 진동력 발전장치에서 발생하는 기전력의 세기와의 상관관계를 분석하기 위해 다양한 변수를 두고 실내실험을 수행하였고, 이러한 실험결과를 토대로 시험용 진동력 발전장치의 적용성과 효용성을 분석하여, 최대 가용 전력을 획득하기 위한 진동력 발전시스템의 최적설계에 대한 기초 자료를 확보하였다.

• 한국노년학
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• 제30권3호
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• pp.895-910
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• 2010

본 연구는 지역사회에 거주하고 있는 65세 이상 빈곤노인의 사회적 고립이 생활만족도에 어떠한 영향을 미치는지 살펴보고자 하였고, 사회적 고립이 생활만족도에 미치는 영향에서 사회적 인식이 매개효과를 가지는지를 검증하였다. 이를 위해 기장복지네트워크 조사연구(2009) 자료를 활용하였고, 이 자료중 부산시 기장군에 거주하고 있는 기초생활 수급대상자 또는 1,2종의 의료보호 대상자이면서 65세 이상 노인 256명의 자료를 분석대상으로 하였다. 자료 분석을 위해 상관관계 분석과 구조방정식에 의한 매개효과검증을 하였다. 연구결과에 의하면 조사대상노인의 5.5%는 극도의 사회적 고립을 경험하였고, 조사대상의 31%가 사회적 고립을 경험하고 있는 것으로 나타났다. 그리고 지역사회인식은 사회적 고립과 생활만족도와의 관계에서 매개효과를 가지는 것으로 나타났다. 이러한 연구결과들을 바탕으로 노인의 사회적 고립을 예방하기 위한 지역사회개입과 노인들의 사회참여 및 사회관계를 강조한 사회적고립 예방프로그램의 개발이 필요하다고 제안하였다.

• Geomechanics and Engineering
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• 제31권2호
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• pp.129-147
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• 2022

The properties of soil are naturally highly variable and thus, to ensure proper safety and reliability, we need to test a large number of samples across the length and depth. In pile foundations, conducting field tests are highly expensive and the traditional empirical relations too have been proven to be poor in performance. The study proposes a state-of-art Particle Swarm Optimization (PSO) hybridized Artificial Neural Network (ANN), Extreme Learning Machine (ELM) and Adaptive Neuro Fuzzy Inference System (ANFIS); and comparative analysis of metaheuristic models (ANN-PSO, ELM-PSO, ANFIS-PSO) for prediction of bearing capacity of pile foundation trained and tested on dataset of nearly 300 dynamic pile tests from the literature. A novel ensemble model of three hybrid models is constructed to combine and enhance the predictions of the individual models effectively. The authenticity of the dataset is confirmed using descriptive statistics, correlation matrix and sensitivity analysis. Ram weight and diameter of pile are found to be most influential input parameter. The comparative analysis reveals that ANFIS-PSO is the best performing model in testing phase (R² = 0.85, RMSE = 0.01) while ELM-PSO performs best in training phase (R² = 0.88, RMSE = 0.08); while the ensemble provided overall best performance based on the rank score. The performance of ANN-PSO is least satisfactory compared to the other two models. The findings were confirmed using Taylor diagram, error matrix and uncertainty analysis. Based on the results ELM-PSO and ANFIS-PSO is proposed to be used for the prediction of bearing capacity of piles and ensemble learning method of joining the outputs of individual models should be encouraged. The study possesses the potential to assist geotechnical engineers in the design phase of civil engineering projects.

• 한국비블리아학회지
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• 제34권4호
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• pp.125-147
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• 2023

본 연구는 문헌연구와 국가도서관통계시스템 데이터를 중심으로 공공도서관에서 진행되는 독서프로그램의 현황을 분석하여 시사점을 도출하고 이를 기반으로 향후 공공도서관의 독서프로그램 활성화를 위해 필요한 정책 방향 등을 제안하고자 하였다. 분석을 통해 도출된 시사점을 바탕으로 첫째, 공공도서관 독서 활동 데이터 확보와 공유 방안을 제안하였다. 둘째, 지속가능한 독서프로그램 추진을 위한 방안 제안을 위해 독서프로그램 기획과 운영에 관련있는 요소의 상관관계 분석결과를 제시하였다. 셋째, 독서프로그램 평가지표 개선 및 질적 평가의 확산을 언급하고 문화체육관광부, 한국도서관협회 등에 상시적으로 공공도서관 현장의 의견을 수렴할 수 있는 공식 창구 마련 등을 제안하였다. 넷째, 독서대전 개최지역 공공도서관들 간 협의체 구성 운영을 제안하였다.

• Korean Journal of Radiology
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• 제22권12호
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• pp.2017-2025
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• 2021

Objective: To evaluate the accuracy and clinical efficacy of a hybrid Greulich-Pyle (GP) and modified Tanner-Whitehouse (TW) artificial intelligence (AI) model for bone age assessment. Materials and Methods: A deep learning-based model was trained on an open dataset of multiple ethnicities. A total of 102 hand radiographs (51 male and 51 female; mean age ± standard deviation = 10.95 ± 2.37 years) from a single institution were selected for external validation. Three human experts performed bone age assessments based on the GP atlas to develop a reference standard. Two study radiologists performed bone age assessments with and without AI model assistance in two separate sessions, for which the reading time was recorded. The performance of the AI software was assessed by comparing the mean absolute difference between the AI-calculated bone age and the reference standard. The reading time was compared between reading with and without AI using a paired t test. Furthermore, the reliability between the two study radiologists' bone age assessments was assessed using intraclass correlation coefficients (ICCs), and the results were compared between reading with and without AI. Results: The bone ages assessed by the experts and the AI model were not significantly different (11.39 ± 2.74 years and 11.35 ± 2.76 years, respectively, p = 0.31). The mean absolute difference was 0.39 years (95% confidence interval, 0.33-0.45 years) between the automated AI assessment and the reference standard. The mean reading time of the two study radiologists was reduced from 54.29 to 35.37 seconds with AI model assistance (p < 0.001). The ICC of the two study radiologists slightly increased with AI model assistance (from 0.945 to 0.990). Conclusion: The proposed AI model was accurate for assessing bone age. Furthermore, this model appeared to enhance the clinical efficacy by reducing the reading time and improving the inter-observer reliability.

• Computers and Concrete
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• 제33권1호
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• pp.55-75
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• 2024

The present study introduces an optimum performance soft computing model for predicting the compressive strength of high-performance concrete (HPC) by comparing models based on conventional (kernel-based, covariance function-based, and tree-based), advanced machine (least square support vector machine-LSSVM and minimax probability machine regressor-MPMR), and deep (artificial neural network-ANN) learning approaches using a common database for the first time. A compressive strength database, having results of 1030 concrete samples, has been compiled from the literature and preprocessed. For the purpose of training, testing, and validation of soft computing models, 803, 101, and 101 data points have been selected arbitrarily from preprocessed data points, i.e., 1005. Thirteen performance metrics, including three new metrics, i.e., a20-index, index of agreement, and index of scatter, have been implemented for each model. The performance comparison reveals that the SVM (kernel-based), ET (tree-based), MPMR (advanced), and ANN (deep) models have achieved higher performance in predicting the compressive strength of HPC. From the overall analysis of performance, accuracy, Taylor plot, accuracy metric, regression error characteristics curve, Anderson-Darling, Wilcoxon, Uncertainty, and reliability, it has been observed that model CS4 based on the ensemble tree has been recognized as an optimum performance model with higher performance, i.e., a correlation coefficient of 0.9352, root mean square error of 5.76 MPa, and mean absolute error of 4.1069 MPa. The present study also reveals that multicollinearity affects the prediction accuracy of Gaussian process regression, decision tree, multilinear regression, and adaptive boosting regressor models, novel research in compressive strength prediction of HPC. The cosine sensitivity analysis reveals that the prediction of compressive strength of HPC is highly affected by cement content, fine aggregate, coarse aggregate, and water content.

• 농업과학연구
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• 제50권4호
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• pp.675-696
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• 2023

Terpenoids, also referred to as terpenes, are a large family of naturally occurring chemical compounds present in the essential oils extracted from medicinal plants. In this study, a nondestructive methodology was created by combining ATR-FT-IR (attenuated total reflectance-Fourier transform infrared), and Raman spectroscopy for the terpenoids assessment in medicinal plants essential oils from ten different geographical locations. Partial least squares regression (PLSR) and support vector regression (SVR) were used as machine learning methodologies. However, a deep learning based model called as one-dimensional convolutional neural network (1D CNN) were also developed for models comparison. With a correlation coefficient (R²) of 0.999 and a lowest RMSEP (root mean squared error of prediction) of 0.006% for the prediction datasets, the SVR model created for FT-IR spectral data outperformed both the PLSR and 1 D CNN models. On the other hand, for the classification of essential oils derived from plants collected from various geographical regions, the created SVM (support vector machine) classification model for Raman spectroscopic data obtained an overall classification accuracy of 0.997% which was superior than the FT-IR (0.986%) data. Based on the results we propose that FT-IR spectroscopy, when coupled with the SVR model, has a significant potential for the non-destructive identification of terpenoids in essential oils compared with destructive chemical analysis methods.

• 한국의상디자인학회지
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• 제25권4호
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• pp.77-92
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• 2023

The purpose of this study is to investigate the public perception of Gorpcore through Big Aata analytics. The study was conducted based on the collection of Big Data on the word 'Gorpcore' through Textom from July 24, 2017 to March 31, 2023. As a result, 63,386 words were collected from a total of 18,879 posts, and the top 50 words were determined based on frequency of appearance. Based on the collected words, centrality measures and CONCOR algorithm were performed in Ucinet 6. The research results are as follows. 1) The frequency of appearance was high in the order of 'Gorpcore look', 'fashion', 'coordination', 'clothes', 'outdoor', 'Musinsa', 'look', 'trend', 'brand' and 'ahjussi (middle-aged old man in Korean)'. These words had high TF-IDF scores, which leads to the conclusion that these are key words that are recognized as important. 2) Network centrality shows that 'Gorpcore look', 'fashion', 'outdoor', 'coordination', 'clothes', 'trend', 'look' and 'style' have a high correlation with other words. Through this, it was found that the public thinks it is important to create a variety of fashions by styling high-performance outdoor wear and casual wear, and that they are highly interested in clothes and in brands leading the Gorpcore trend. 3) As a result of the CONCOR algorithm, four significant groups were formed. The words that appear in each group are as follows. Group 1 - 'outdoor', 'Gorp', 'Normcore', 'hiking', 'functionality', 'new', 'sports', 'casual wear', 'activity', 'generation', 'collaboration'. Group 2 - 'fashion', 'trend', 'look', 'brand', 'style', 'shoes', 'ugly', 'item', 'trend', 'product', 'Salomon', 'padded jacket', 'stylishness', 'utilization', 'Winter', 'street', 'design', 'retro', 'popular', 'styling'. Group 3 - 'Gorpcore look', 'coordination', 'Musinsa', 'windbreaker', 'recommendation', 'Arcteryx', 'pants', 'man'. Group 4 - 'clothes' 'ahjussi', 'jacket', 'launching', 'spring', 'The North Face', 'collection', 'utility', 'jumper'. As a result, it can be seen that the Gorpcore is also regarded as a part of outdoor, fashion, coordination, and casual wear.

• 한국컴퓨터정보학회논문지
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• 제29권4호
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• pp.39-46
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• 2024

많은 연구자들이 다양한 모델을 이용하여 물의 수질을 평가하기 위해 노력하고 있다. 평가 모델에는 결측값이 없는 데이터셋이 필요하지만, 관측 데이터셋에는 결측값이 다수 포함되는 것이 현실이다. 단순히 결측값을 삭제하는 방법은 경우에 따라 기저 데이터의 분포를 왜곡시키고 모델의 예측성능에도 편의(bias)를 불러올 위험성이 있다. 본 연구에서는 수질 데이터의 결측값 처리에 적합한 기법을 탐색하기 위해, 기존의 KNN과 MICE Imputation, 그리고 생성형 신경망 모델인 Autoencoder와 Denoising Autoencoder를 기반으로 몇 가지 대치 기법을 실험하였다. 실험 결과, KNN과 MICE Imputation의 결과를 평균한 Combined Imputation이 실측치에 가장 가깝게 값을 추정하였으며, 이 기법을 적용하여 결측값을 처리한 관측 데이터셋을 support vector machine과 ensemble 기반의 분류 모델로 평가한 결과, 결측값을 삭제했을 때에 비해 Accuracy, F1 score, ROC-AUC score, 그리고 MCC(Mathews Correlation Coefficient) 지표가 향상되었다.