• Maxillofacial Plastic and Reconstructive Surgery
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• 제28권3호
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• pp.229-236
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• 2006

The biologic principle of guided bone regeneration(GBR) has been studied extensively in hopes of regenerating alveolar bone. Various materials have been utilized as regenerative membranes and grafting materials in implant surgery. To improve the ability of membranes, several types of membrane have been developed. Various materials have been utilized as regenerative membranes; however, all materials have disadvantages, and the ideal membrane material is yet to be identified. In these cases, a homologous gelatinized bone matrix(GBM) were used as a regenerative material in conjunction with the placement of endosseous root implants. 22 patients participated in this study, and 42 implants were inserted. The result of 1st operative surgery was uneventful, inflammatory reaction and dehiscences were not observed except for only one case. After the final protheses, all implants were functioning successfully. The major advantages in the use of GBMs for guided bone regeneration are of very wide application such as membrane and graft material, and that a second procedure to remove the material is not necessary, and the GBMs are accepted by the surrounding tissues without complications. The purpose of this study was to observe the usefulness of GBMs in dental implant surgery.

• 응용통계연구
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• 제31권6호
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• pp.801-810
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• 2018

확산은 금융이나 물리적 현상의 모형화에 이용되는 확률과정이다. 반복적으로 관측된 확산과정에 대하여 통계적인 모형을 구축할 때, 확률효과를 고려할 필요가 있다. 이 연구에서는 Ornstein-Uhlenbeck 확산모형과 geometric Brownian motion 확산모형에 대하여 확률효과를 도입한다. 모형모수에 대한 최도우도추정법을 적용하기 위하여, 확률효과에 대한 적절한 분포를 가정하여 닫힌 형태로 우도함수를 얻는 방법을 탐색하였다. 1991년부터 2017년까지 27년간 일일 단위로 기록된 다우존스 산업지수에 대하여 확률효과 모형을 적용하였다.

• 한국지반신소재학회논문집
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• 제18권1호
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• pp.1-9
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• 2019

본 연구에서는 인공어초의 침하 및 세굴을 저감하고자 다양한 보강재로 보강된 해저 지반의 침하 및 세굴 거동 특성을 알아보았다. 지반에 적용한 보강재는 총 3가지로서 지오그리드(geogrid), 지오그리드-대나무 매트(geogrid-bamboo mat, GBM) 및 해초-지지봉 매트(seaweed-pile mat, SPM)를 각각 보강하여 실험을 수행하였다. 모래, 실트 및 점토 지반에 대해 지지력 실험, 대형 수조 침하 실험, 2차원 흐름 수조 세굴 실험 등 다양한 실내 실험을 수행하였다. 실험 결과 보강재의 보강에 따라 인공어초의 지지력 증진, 침하 및 세굴이 저감되는 효과를 보였으며, 모래나 실트 지반보다 점토 지반과 같은 연약 지반에서 보강효과가 더 크게 나타나는 경향을 보였다.

• 한국멀티미디어학회논문지
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• 제24권3호
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• pp.448-459
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• 2021

In this paper, we propose a method of predicting the next-day stock price fluctuations of 10 KOSDAQ-listed companies in 5G, autonomous driving, and electricity sectors by training SVM, XGBoost, and LightGBM models from macroeconomic·financial market indicators, technical indicators, social interest indicators, and daily positive indices extracted from online news. In the three experiments to find out the usefulness of social interest indicators and daily positive indices, the average accuracy improved when each indicator and index was added to the models. In addition, when feature selection was performed to analyze the superiority of the extracted features, the average importance ranking of the social interest indicator and daily positive index was 5.45 and 1.08, respectively, it showed higher importance than the macroeconomic financial market indicators and technical indicators. With the results of these experiments, we confirmed the effectiveness of the social interest indicators as alternative data and the daily positive index for predicting stock price fluctuation.

• Journal of Information Processing Systems
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• 제18권3호
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• pp.302-310
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• 2022

Efficient use of limited blood products is becoming very important in terms of socioeconomic status and patient recovery. To predict the appropriateness of patient-specific transfusions for the intensive care unit (ICU) patients who require real-time monitoring, we evaluated a model to predict the possibility of transfusion dynamically by using the Medical Information Mart for Intensive Care III (MIMIC-III), an ICU admission record at Harvard Medical School. In this study, we developed an explainable machine learning to predict the possibility of red blood cell transfusion for major medical diseases in the ICU. Target disease groups that received packed red blood cell transfusions at high frequency were selected and 16,222 patients were finally extracted. The prediction model achieved an area under the ROC curve of 0.9070 and an F1-score of 0.8166 (LightGBM). To explain the performance of the machine learning model, feature importance analysis and a partial dependence plot were used. The results of our study can be used as basic data for recommendations related to the adequacy of blood transfusions and are expected to ultimately contribute to the recovery of patients and prevention of excessive consumption of blood products.

• Structural Engineering and Mechanics
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• 제84권5호
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• pp.605-618
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• 2022

Glass fiber reinforced polymer (GFRP) elastic gridshells consist of long continuous GFRP tubes that form elastic deformations. In this paper, a method for the form-finding of gridshell structures is presented based on the interpretable machine learning (ML) approaches. A comparative study is conducted on several ML algorithms, including support vector regression (SVR), K-nearest neighbors (KNN), decision tree (DT), random forest (RF), AdaBoost, XGBoost, category boosting (CatBoost), and light gradient boosting machine (LightGBM). A numerical example is presented using a standard double-hump gridshell considering two characteristics of deformation as objective functions. The combination of the grid search approach and k-fold cross-validation (CV) is implemented for fine-tuning the parameters of ML models. The results of the comparative study indicate that the LightGBM model presents the highest prediction accuracy. Finally, interpretable ML approaches, including Shapely additive explanations (SHAP), partial dependence plot (PDP), and accumulated local effects (ALE), are applied to explain the predictions of the ML model since it is essential to understand the effect of various values of input parameters on objective functions. As a result of interpretability approaches, an optimum gridshell structure is obtained and new opportunities are verified for form-finding investigation of GFRP elastic gridshells during lifting construction.

• Computers and Concrete
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• 제32권4호
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• pp.373-381
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• 2023

Fly ash, granulated blast furnace slag, marble waste powder, etc. are just some of the by-products of other sectors that the construction industry is looking to include into the many types of concrete they produce. This research seeks to use surrogate machine learning methods to forecast the compressive strength of self-compacting concrete. The surrogate models were developed using Gradient Boosting Machine (GBM), Support Vector Machine (SVM), Random Forest (RF), and Gaussian Process Regression (GPR) techniques. Compressive strength is used as the output variable, with nano silica content, cement content, coarse aggregate content, fine aggregate content, superplasticizer, curing duration, and water-binder ratio as input variables. Of the four models, GBM had the highest accuracy in determining the compressive strength of SCC. The concrete's compressive strength is worst predicted by GPR. Compressive strength of SCC with nano silica is found to be most affected by curing time and least by fine aggregate.

• 정보처리학회 논문지
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• 제13권5호
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• pp.217-220
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• 2024

지진 발생은 정확히 예측하기 어렵고, 이러한 무작위성을 갖는 사건에 대비하여 모든 건물에 내진 설계를 도입하는 것은 현실적으로 어려운 과제이다. 건물의 특징 분석을 통한 건물 손상 예측을 기반으로 건물의 취약점을 보완한다면, 내진 설계를 도입하지 않은 건물에서도 피해를 최소화할 수 있으므로 건물 손상 예측 모델의 효율성을 분석하는 연구가 필요하다. 본 논문에서는 2015년 네팔 대지진으로 인해 손상된 건물 데이터를 활용하여 Random Forest, Extreme Gradient Boosting, LightGBM, CatBoost 기계학습 분류 알고리즘을 사용하여 지진 피해 예측 모델의 정확도를 비교하였다.

• 방사선산업학회지
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• 제17권4호
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• pp.551-557
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• 2023

In recent years, radiation has become a socially important issue, increasing the need for accurate prediction of radiation levels. In this study, machine learning-based models such as Multiple Linear Regression (MLR), Random Forest (RF), XGBoost, and LightGBM, which predict the dose rate by time(nSv h^-1) by selecting only important variables, were used, and the correlation between temperature, humidity, cumulative precipitation, wind direction, wind speed, local air pressure, sea pressure, solar radiation, and radiation dose rate (nSv h^-1) was analyzed by collecting weather data and radiation dose rate for about 6 months in Jangseong, Jeollanam-do. As a result of the evaluation based on the RMSE (Root Mean Squared Error) and R-Squared (R-Squared coefficient of determination) scores, the RMSE of the XGBoost model was 22.92 and the R-Squared was 0.73, showing the best performance among the models used. As a result of optimizing hyperparameters of all models using the GridSearch method and comparing them by adding variables inside the measuring instrument, it was confirmed that the performance improved to 2.39 for RMSE and 0.99 for R-Squared in both XGBoost and LightGBM.

• 농업과학연구
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• 제50권3호
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• pp.527-538
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• 2023

In this study, we aimed to apply multispectral imaging (713 - 920 nm, 10 bands) for maturity classification and recognition of oriental melons grown in hydroponic greenhouses. A total of 20 oriental melons were selected, and time series multispectral imaging of oriental melons was 7 - 9 times for each sample from April 21, 2023, to May 12, 2023. We used several approaches, such as Savitzky-Golay (SG), standard normal variate (SNV), and Combination of SG and SNV (SG + SNV), for pre-processing the multispectral data. As a result, 713 - 759 nm bands were preprocessed with SG for the maturity classification of oriental melons. Additionally, a Light Gradient Boosting Machine (LightGBM) was used to train the recognition model for oriental melon. R² of recognition model were 0.92, 0.91 for the training and validation sets, respectively, and the F-scores were 96.6 and 79.4% for the training and testing sets, respectively. Therefore, multispectral imaging in the range of 713 - 920 nm can be used to classify oriental melons maturity and recognize their fruits.