• Computers and Concrete
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• v.31 no.3
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• pp.253-265
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• 2023

In this research, the gene expression programming (GEP) technique was employed to provide a new model for predicting the maximum loading capacity of concrete-encased steel (CES) columns. This model was developed based on 96 CES column specimens available in the literature. The six main parameters used in the model were the compressive strength of concrete (f_c), yield stress of structural steel (f_ys), yield stress of steel rebar (f_yr), and cross-sectional areas of concrete, structural steel, and steel rebar (A_c, A_s and A_r respectively). The performance of the prediction model for the ultimate load-carrying capacity was investigated using different statistical indicators such as root mean square error (RMSE), correlation coefficient (R), mean absolute error (MAE), and relative square error (RSE), the corresponding values of which for the proposed model were 620.28, 0.99, 411.8, and 0.01, respectively. Here, the predictions of the model and those of available codes including ACI ITG, AS 3600, CSA-A23, EN 1994, JGJ 138, and NZS 3101 were compared for further model assessment. The obtained results showed that the proposed model had the highest correlation with the experimental data and the lowest error. In addition, to see if the developed model matched engineering realities and corresponded to the previously developed models, a parametric study and sensitivity analysis were carried out. The sensitivity analysis results indicated that the concrete cross-sectional area (A_c) has the greatest effect on the model, while parameter (f_yr) has a negligible effect.

• Journal of Preventive Medicine and Public Health
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• v.56 no.1
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• pp.41-49
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• 2023

Objectives: Rwanda reported a stunting rate of 33% in 2020, decreasing from 38% in 2015; however, stunting remains an issue. Globally, child deaths from malnutrition stand at 45%. The best options for the early detection and treatment of stunting should be made a community policy priority, and health services remain an issue. Hence, this research aimed to develop a model for predicting stunting in Rwandan children. Methods: The Rwanda Demographic and Health Survey 2019-2020 was used as secondary data. Stratified 10-fold cross-validation was used, and different machine learning classifiers were trained to predict stunting status. The prediction models were compared using different metrics, and the best model was chosen. Results: The best model was developed with the gradient boosting classifier algorithm, with a training accuracy of 80.49% based on the performance indicators of several models. Based on a confusion matrix, the test accuracy, sensitivity, specificity, and F1 were calculated, yielding the model's ability to classify stunting cases correctly at 79.33%, identify stunted children accurately at 72.51%, and categorize non-stunted children correctly at 94.49%, with an area under the curve of 0.89. The model found that the mother's height, television, the child's age, province, mother's education, birth weight, and childbirth size were the most important predictors of stunting status. Conclusions: Therefore, machine-learning techniques may be used in Rwanda to construct an accurate model that can detect the early stages of stunting and offer the best predictive attributes to help prevent and control stunting in under five Rwandan children.

• Journal of Preventive Medicine and Public Health
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• v.56 no.3
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• pp.248-254
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• 2023

Objectives: Measuring the quality of care is paramount to inform policies for healthcare services. Nevertheless, little is known about the quality of primary care and acute care provided in Korea. This study investigated trends in the quality of primary care and acute care. Methods: Case-fatality rates and avoidable hospitalization rates were used as performance indicators to assess the quality of primary care and acute care. Admission data for the period 2008 to 2020 were extracted from the National Health Insurance Claims Database. Case-fatality rates and avoidable hospitalization rates were standardized by age and sex to adjust for patients' characteristics over time, and significant changes in the rates were identified by joinpoint regression. Results: The average annual percent change in age-/sex-standardized case-fatality rates for acute myocardial infarction was -2.3% (95% confidence interval, -4.6 to 0.0). For hemorrhagic and ischemic stroke, the age-/sex-standardized case-fatality rates were 21.8% and 5.9%, respectively in 2020; these rates decreased since 2008 (27.1 and 8.7%, respectively). The average annual percent change in age-/sex-standardized avoidable hospitalization rates ranged from -9.4% to -3.0%, with statistically significant changes between 2008 and 2020. In 2020, the avoidable hospitalization rates decreased considerably compared with the 2019 rate because of the coronavirus disease 2019 pandemic. Conclusions: The avoidable hospitalization rates and case-fatality rates decreased overall during the past decade, but they were relatively high compared with other countries. Strengthening primary care is an essential requirement to improve patient health outcomes in the rapidly aging Korean population.

• Proceedings of the Korea Water Resources Association Conference
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• 2020.06a
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• pp.9-9
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• 2020

하천공간은 홍수방어와 용수사용을 위한 수자원관리 측면의 공간에서 벗어나 국민들의 삶의 질 제고와 이용편익, 생활환경의 중심 공간으로 자리매김하고 있다. 국민의 하천에 대한 눈높이가 상승하고 있음에도 하천공간이 주는 여러 가치에 대해서는 학술적 검토가 미진하였으며, 그에 따라 정책에도 제한적으로 반영되고 있다. 본 연구에서는 설문조사, 요인분석 등을 통해 주민 입장에서 부여할 수 있는 하천의 다면적 가치를 식별하고 주민친화적 관리를 위한 성과지표를 개발하고자 하였다. 특히 본 연구가 개발하고자 하는 성과지표 체계는 기존 방식대로 객관화·정량화가 불가능한 주민의 정성적·주관적 만족도를 측정할 수 있는 지표를 개발하는 것에 목적이 있다. 먼저, 관련된 각종 선행연구 및 도로 등 타분야 사례, 일본 등 해외 사례 등을 종합하여 하천의 4가지 사업부문인 치수, 이수, 환경, 친수를 모두 포괄하는 지표체계안을 구축하였다. 다만 본 연구가 주민친화적 하천관리를 목적으로 하므로 상대적으로 환경·친수 부문 지표의 비중이 높았으며, 총 지표 개수는 25개로 도출하였다. 도출한 지표체계안을 바탕으로 주민 대상 설문조사를 실시하였으며, 요인분석법을 활용하여 지표체계를 재정리하였다. 요인분석법은 학술적·실무적으로 가장 널리 사용되는 PCA 추출법과 Varimax 회전법을 채택하였으며, KMO 및 Barlett's 검정법을 활용하여 자료 검정을 수행하였다. 그 결과, 하천 부문 주민만족 요인은 3가지로 나타났으며, 이를 각각 '최소친수기반요인', '안전·청결기반요인', '친수활동지원요인'으로 정의하였다. 요인 분석 결과를 바탕으로, 3개 영역, 6개 세부 영역, 24개 평가항목으로 구성된 성과지표 체계를 도출하였다. 마련된 성과지표의 항목별 중요도를 전국 대상 설문조사를 통해 분석한 결과, 영역 중에서는 '안전·청결기반요인'이 가장 중요한 것으로 나타났으며, 특징적으로는 CCTV, 장애·위험물 제거, 수질·공간청결성, 공공화장실 만족성 등이 중요한 것으로 나타났다. 또한 대전지역 국가하천과 지방하천을 사례지역으로 성과지표를 비교한 결과, 국가하천의 성과가 대체로 높게 나타났으며 '최소친수기반요인' 영역의 성과가 상대적으로 높은 것으로 나타났다.

• Journal of Korean Society for Quality Management
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• v.51 no.2
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• pp.155-170
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• 2023

Purpose: Nowadays, the risk assessment system is widely used in many industrial and public areas to reduce the possible risks. The system is used to determine the priorities of the government quality assurance works in Defense Agency for Technology and Quality. However, as the risk assessment system is used for other purposes, there are some items that need improvement, and in this study, we propose improvement plans by benchmarking the risk assessment systems of other institutions. Methods: In this paper, first, the procedures of risk assessment system used in many industrial sites were reviewed, and how each institution specialized and applied the system. Afterwards, by benchmarking various risk assessment systems, an improvement plan on how to operate the risk assessment system in the case of government quality assurance for centrally procured military supplies was presented, and practical application cases were presented to prove the usefulness of the improvement plan. Results: The proposed risk assessment system differs from the existing system in five major aspects. First, inputs, outputs, and key performance indicators were specified from the systematic point of view. Second, risk analysis was analyzed in four dimensions: probability of occurrence, impact, detection difficulty. Third, risk mitigation measures were classified, control, transfer, and sharing. Fourth, the risk mitigation measures were realized through document verification, product verification, process verification, and quality system evaluation. Finally, risk mitigation measures were implemented and the effectiveness of the risk mitigation measures was evaluated through effectiveness evaluation. Conclusions: In order for the risk assessment procedure proposed in this study to be applied to actual work, it is necessary to obtain the consent of the person involved in the work due to the increased time for risk identification and preparation of the government quality assurance log, and a change in the information system that performs the actual work is required. Therefore, the authors of this study plan to actively perform internal seminar presentations and work improvement suggestions to apply these research outputs to actual work.

• International conference on construction engineering and project management
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• 2020.12a
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• pp.463-481
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• 2020

Cutter suction dredgers (CSDs) are widely used in various dredging constructions such as channel excavation, wharf construction, and reef construction. During a CSD construction, the main operation is to control the swing speed of cutter to keep the slurry concentration in a proper range. However, the slurry concentration cannot be monitored in real-time, i.e., there is a "time-lag effect" in the log of slurry concentration, making it difficult for operators to make the optimal decision on controlling. Concerning this issue, a solution scheme that using real-time monitored indicators to predict current slurry concentration is proposed in this research. The characteristics of the CSD monitoring data are first studied, and a set of preprocessing methods are presented. Then we put forward the concept of "index class" to select the important indices. Finally, an ensemble learning algorithm is set up to fit the relationship between the slurry concentration and the indices of the index classes. In the experiment, log data over seven days of a practical dredging construction is collected. For comparison, the Deep Neural Network (DNN), Long Short Time Memory (LSTM), Support Vector Machine (SVM), Random Forest (RF), Gradient Boosting Decision Tree (GBDT), and the Bayesian Ridge algorithm are tried. The results show that our method has the best performance with an R² of 0.886 and a mean square error (MSE) of 5.538. This research provides an effective way for real-time predicting the slurry concentration of CSDs and can help to improve the stationarity and production efficiency of dredging construction.

• Proceedings of the Korean Society of Crop Science Conference
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• 2022.10a
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• pp.88-88
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• 2022

Detection of stress responses in crops is important to diagnose crop growth and evaluate yield. Also, the multi-spectral sensor is effectively known to evaluate stress caused by nutrient and moisture in crops or biological agents such as weeds or diseases. Therefore, in this experiment, multispectral images were taken by an unmanned aerial vehicle(UAV) under field condition. The experiment was conducted in the long-term fertilizer field in the National Institute of Crop Science, and experiment area was divided into different status of NPK(Control, N-deficiency, P-deficiency, K-deficiency, Non-fertilizer). Total 11 vegetation indices were created with RGB and NIR reflectance values using python. Variations in nutrient content in plants affect the amount of light reflected or absorbed for each wavelength band. Therefore, the objective of this experiment was to evaluate vegetation indices derived from multispectral reflectance data as input into machine learning algorithm for the classification of nutritional deficiency in rice. RandomForest model was used as a representative ensemble model, and parameters were adjusted through hyperparameter tuning such as RandomSearchCV. As a result, training accuracy was 0.95 and test accuracy was 0.80, and IPCA, NDRE, and EVI were included in the top three indices for feature importance. Also, precision, recall, and f1-score, which are indicators for evaluating the performance of the classification model, showed a distribution of 0.7-0.9 for each class.

• Journal of Korean Library and Information Science Society
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• v.54 no.3
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• pp.249-270
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• 2023

The purpose of this study is to identify the characteristics of school library services using the RGT (repertory grid technique), and to examine whether there are differences in the perceptions and internal experiences of school library services between human resources (librarian teachers, subject teachers, and librarians). I used indicators such as element intensity, construct extremity, discrepant constructs, and implicative dilemmas to analyze the characteristics of school library services. The elements of the repertory grid were set to 9, and 14 constructs were set through focus group interviews with 3 librarian-teachers. The GRIDCOR 6.0, which can be accessed online, was used to complete the repertory grid for 30 graduate students of the College of Education, and 6 of them were selected and analyzed, considering the demographic characteristics. From the perspective of element intensity, it was found that the following school library services are important and influential: resource management, teacher-librarian collaboration, and reading and information counseling services. The clarity of one's role, the actual performance, and self-regulatory ability were ranked high from the perspective of constructs intensity.

• Journal of Biomedical Engineering Research
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• v.44 no.5
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• pp.346-353
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• 2023

In this paper, we propose an innovative approach that leverages deep learning to find optimal reference points for achieving precise tooth segmentation in three-dimensional tooth point cloud data. A dataset consisting of 350 aligned maxillary and mandibular cloud data was used as input, and both end coordinates of individual teeth were used as correct answers. A two-dimensional image was created by projecting the rendered point cloud data along the Z-axis, where an image of individual teeth was created using an object detection algorithm. The proposed algorithm is designed by adding various modules to the Unet model that allow effective learning of a narrow range, and detects both end points of the tooth using the generated tooth image. In the evaluation using DSC, Euclid distance, and MAE as indicators, we achieved superior performance compared to other Unet-based models. In future research, we will develop an algorithm to find the reference point of the point cloud by back-projecting the reference point detected in the image in three dimensions, and based on this, we will develop an algorithm to divide the teeth individually in the point cloud through image processing techniques.

• Journal of Venture Innovation
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• v.5 no.2
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• pp.15-34
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• 2022

Many countries, including Korea, have established and operated various startup supporting programs to revitalize youth entrepreneurship. This study aims to identify the current status and characteristics of the major startup supporting programs currently operated in Korea and propose development plans for future startup supporting programs through analysis of the startup supporting systems of major countries. By analyzing the success factors of domestic and foreign startup supporting systems, we suggested improvements that can be operated more effectively in the areas of financial support, selection process, education and mentoring, networking, publicity and branding, and follow-up management by operated startup supporting programs. In addition, improvements for performance evaluation indicators of startup supporting programs were suggested and limitations of the study were presented.