• Journal of the Korean Wood Science and Technology
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• v.38 no.2
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• pp.85-93
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• 2010

Japanese cedar has low density and poor mechanical performance. Manufacturing glue-laminated timber (glulam) is the best way to compensate for its poor mechanical performance. The Korean Standard (KS) confines outermost lamina of glulam to higher grade than E8, but the yield of higher than grade E8 from logs is only 6.5%. Therefore, the aim of this study is to investigate the possibility of non-Korean-Standard glulam in structural applications. Allowable stresses determined by both hand-calculation and Monte-Carlo simulation show a higher allowable stress than that of the KS-standard glulam of 6S-22B. In the Korean Standard (KS), knot characteristics are not taken into account. Japanese cedar has relatively small knots. We believe that the small knots in Japanese cedar contribute to a higher allowable stress than the KS-standard glulam would predict. The species classification of KS is required to be further subdivided into sub-species groups based on knot characteristics.

• Journal of Korea Multimedia Society
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• v.23 no.12
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• pp.1486-1495
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• 2020

The analysis of digital microscopy images plays a vital role in computer-aided diagnosis (CAD) and prognosis. The main purpose of this paper is to develop a machine learning technique to predict the histological grades in prostate biopsy. To perform a multiclass classification, an AI-based deep learning algorithm, a multichannel convolutional neural network (MCCNN) was developed by connecting layers with artificial neurons inspired by the human brain system. The histological grades that were used for the analysis are benign, grade 3, grade 4, and grade 5. The proposed approach aims to classify multiple patterns of images extracted from the whole slide image (WSI) of a prostate biopsy based on the Gleason grading system. The Multichannel Convolution Neural Network (MCCNN) model takes three input channels (Red, Green, and Blue) to extract the computational features from each channel and concatenate them for multiclass classification. Stain normalization was carried out for each histological grade to standardize the intensity and contrast level in the image. The proposed model has been trained, validated, and tested with the histopathological images and has achieved an average accuracy of 96.4%, 94.6%, and 95.1%, respectively.

• Journal of Cadastre & Land InformatiX
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• v.51 no.2
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• pp.141-150
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• 2021

Fine dust is a substance that greatly affects human health, and various studies have been conducted in this regard. Due to the human influence of particulate matter, various studies are being conducted to predict particulate matter grade using past data measured in the monitoring network of Seoul city. In this paper, predictive model have focused on particulate matter concentration in May, 2019, Seoul. The air pollutant variables were used to training such as SO₂, CO, NO₂, O₃. The predictive model based on Adaboost, and training model was dividing PM₁₀ and PM_2.5. As a result of the prediction performance comparison through confusion matrix, the Adaboost model was more conformable for predicting the particulate matter concentration grade. Although air pollutant variables have a higher correlation with PM2.5, training model need to train a lot of data and to use additional variables such as traffic volume to predict more effective PM₁₀ and PM_2.5 distribution grade.

• Structural Engineering and Mechanics
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• v.81 no.5
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• pp.565-574
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• 2022

The infrastructures such as offshore, bridges, power plant, oil and gas piping and aircraft operate in a harsh environment during their service life. Structural integrity of engineering components used in these industries is paramount for the reliability and economics of operation. Two regression models based on the concept of Gaussian process regression (GPR) and Minimax probability machine regression (MPMR) were developed to predict stress intensity factor range (𝚫K). Both GPR and MPMR are in the frame work of probability distribution. Models were developed by using the fatigue crack growth data in MATLAB by appropriately modifying the tools. Fatigue crack growth experiments were carried out on Eccentrically-loaded Single Edge notch Tension (ESE(T)) specimens made of API 5L X65 Grade steel in inert and corrosive environments (2.0% and 3.5% NaCl). The experiments were carried out under constant amplitude cyclic loading with a stress ratio of 0.1 and 5.0 Hz frequency (inert environment), 0.5 Hz frequency (corrosive environment). Crack growth rate (da/dN) and stress intensity factor range (𝚫K) values were evaluated at incremental values of loading cycle and crack length. About 70 to 75% of the data has been used for training and the remaining for validation of the models. It is observed that the predicted SIF range is in good agreement with the corresponding experimental observations. Further, the performance of the models was assessed with several statistical parameters, namely, Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Coefficient of Efficiency (E), Root Mean Square Error to Observation's Standard Deviation Ratio (RSR), Normalized Mean Bias Error (NMBE), Performance Index (ρ) and Variance Account Factor (VAF).

• Journal for History of Mathematics
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• v.24 no.4
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• pp.97-118
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• 2011

This study aimed finding effective models for the teaching the concept of function. We selected two models. One is discrete model which focuses on the 'corresponding relation of the elements of the sets(domain and range). The other is continuous model which focuses on the dependent relationship of the two variables connected in variable phenomenon. A vending machine model was used as a discrete model, and a water bucket model was used as a continuous model in our study. We taught 2 times about the concept of function using two models to the 60 students (7th grade, 2 classes) living in Taebak city, and tested it twice, after class and about 3 months later. A vending machine model was helpful in understanding the definition of function in the 7th grade math textbook. Also, it was helpful to making concept image and to recalling it. On the other hand, students who used the water bucket model had a difficultly in understanding the all independent variables of the domain corresponding to the dependent variables. But they excelled in tasks making formula expression and understanding changing situations.

• Journal of the Korean GEO-environmental Society
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• v.23 no.8
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• pp.5-10
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• 2022

Ground Subsidence has been continuously occurring in densely populated downtown. The main cause of ground subsidence is the damaged underground facility like sewer. Currently, ground subsidence is being dealt with by discovering cavities in ground using GPR. However, this consumes large amount of manpower and cost, so it is necessary to predict hazardous area for efficient operation of GPR. In this study, ◯◯city is divided into 500 m×500 m grids. Then, data set was constructed using the characteristics of the underground facility and ground subsidence in grids. Data set used to machine learning model for ground subsidence risk grade prediction. The purposed model would be used to present a ground subsidence risk map of target area.

• Journal of The Korean Society of Integrative Medicine
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• v.12 no.2
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• pp.155-166
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• 2024

Purpose : This study aims to identify key factors for predicting dropout risk at the university level and to provide a foundation for policy development aimed at dropout prevention. This study explores the optimal machine learning algorithm by comparing the performance of various algorithms using data on college students' dropout risks. Methods : We collected data on factors influencing dropout risk and propensity were collected from N University. The collected data were applied to several machine learning algorithms, including random forest, decision tree, artificial neural network, logistic regression, support vector machine (SVM), k-nearest neighbor (k-NN) classification, and Naive Bayes. The performance of these models was compared and evaluated, with a focus on predictive validity and the identification of significant dropout factors through the information gain index of machine learning. Results : The binary logistic regression analysis showed that the year of the program, department, grades, and year of entry had a statistically significant effect on the dropout risk. The performance of each machine learning algorithm showed that random forest performed the best. The results showed that the relative importance of the predictor variables was highest for department, age, grade, and residence, in the order of whether or not they matched the school location. Conclusion : Machine learning-based prediction of dropout risk focuses on the early identification of students at risk. The types and causes of dropout crises vary significantly among students. It is important to identify the types and causes of dropout crises so that appropriate actions and support can be taken to remove risk factors and increase protective factors. The relative importance of the factors affecting dropout risk found in this study will help guide educational prescriptions for preventing college student dropout.

• Journal of the Korean Wood Science and Technology
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• v.43 no.4
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• pp.456-462
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• 2015

365 pieces of domestic $38{\times}140{\times}3600mm$ Red pine structural lumber were machine graded conforming to a softwood structural lumber standard (KS F 3020). The allowable bending stresses calculated for each grade were compared with the values currently tabulated in the standard. Four calculation methods for lower $5^{th}$ percentile bending stress were non-parametric estimation with 75% confidence level, 2-parameter and 3-parameter Weibull distribution fit, and bending modulus of rupture (MOR)-modulus of elasticity (MOE) regression based method. Only the data set of Grades E8, E9, and E10 were statistically eligible for the $5^{th}$ percentile calculation. The MOR-MOE regression based method only was able to estimate the lower $5^{th}$ percentile values theoretically for the full range of grades. The results showed that all allowable bending stresses calculated were lower than the design values tabulated in the standard. This implies that the current machine grading system has the pitfall of structural safety. Improvement in current machine grading system could be achieved by introducing the bending strength and stiffness combination grade system.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.43 no.3
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• pp.397-411
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• 2023

The number of deteriorated bridges with a service period of more than 30 years has been rapidly increasing in Korea. Accordingly, the importance of advanced maintenance technologies through the predictions of age-induced deterioration degree, condition, and performance of bridges is more and more noticed. The prediction method of the safety grade of bridges was proposed in this study using the classification models of the Decision Tree and the Random Forest based on machine learning. As a result of analyzing these models for the 8,850 bridges located in national roads with various evaluation indexes such as confusion matrix, balanced accuracy, recall, ROC curve, and AUC, the Random Forest largely showed better predictive performance than that of the Decision Tree. In particular, random under-sampling in the Random Forest showed higher predictive performance than that of other sampling techniques for the C and D grade bridges, with the recall of 83.4%, which need more attention to maintenance because of the significant deterioration degree. The proposed model can be usefully applied to rapidly identify the safety grade and to establish an efficient and economical maintenance plan of bridges that have not recently been inspected.

• Journal of the Korean Wood Science and Technology
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• v.43 no.4
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• pp.446-455
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• 2015

Based on comparative studies on standards and grading procedures of machine graded lumber in Korea and other countries, this study proposed a procedure of determining the grade classification and design strengths of domestic machine graded lumber. Differences between machine stress rated lumber and E-rated laminations were detailed in order to clarify the need for the procedure improvement. To this improvement the use of average MOE requirement for grading was introduced instead of the fixed minimum MOE requirement which is currently used in the Korean standards. It was found that the fixed minimum MOE requirement method was easier for an inspector to grade but, less efficient as a strength predictor than the average MOE requirement method. The advantage of average MOE requirement method is statistically MOR-MOE regression-based MOR prediction and highly efficient in quality control though it requires a computer-aided operation system in an initial setup. A major weakness of the current Korean grading system was found that different strength characteristics depending on wood species were not reflected on the grade classification and the tabulated allowable design stress. The proposed procedures were developed taking advantages of respective merits of both methods and based on MOR-MOE regression analysis. Through this procedure, the grades of machine stress rated lumber should be revised to become interchangeable with E-rated lamination, which would be beneficial to the cost competitiveness of domestic machine graded lumber and glued laminated timber industry.