• Journal of The Korea Institute of Healthcare Architecture
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• v.19 no.2
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• pp.19-30
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• 2013

Purpose: The planning and design of hospital generally requires the participation and consultation of skilled experts since it has more complex space program than any other buildings. Therefore, the BIM systems for the planning of hospital have been tried continuously. The purpose of this study is to identify the precondition for space Program validation of healthcare architecture based on BIM, which is recently receiving wide attention. Method: For this study, United States, Australia and Finland's guidelines were analyzed among the description space program validation system in 14 overseas BIM Guidelines. And the propose precondition that can be applied to healthcare architecture from among these description of space program validation items, target, process etc for General building. Result: 1) spatial program validation is the following four evaluation phase. Step 1: Standard setting phase Step 2: BIM model accuracy assessment phase Step 3: space validation phase Step 4: Performance evaluation phase 2) The standards for the building elements at Standards Setting stage is considered to the standards for the architectural elements of General building. 3) Healthcare Architecture Area calculation method is considered to be reasonable that borrowing the area calculation standard of general architecture according to the UIA of international standards. However, Be proposed of measuring method that reflect the efficiency of the design process step-by-step area calculation method. The performance assessment indicators of reflect the Hospital uniqueness have to developed. And the research needs to be carried out continuously according to the purpose for healthcare architecture of feature-oriented. Implications: In this paper like to understanding that precondition of space program validation considering the BIM. As a result, understanding to condition about step of the evaluation, the evaluation standards. Is expected to keep the focus on the development of performance indicators that reflect the uniqueness of the hospital for the efficient evaluation of the Hospital building.

• Korean Journal of Materials Research
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• v.33 no.4
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• pp.164-174
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• 2023

The bandgap characteristics of semiconductor materials are an important factor when utilizing semiconductor materials for various applications. In this study, based on data provided by AFLOW (Automatic-FLOW for Materials Discovery), the bandgap of a semiconductor material was predicted using only the material's compositional features. The compositional features were generated using the python module of 'Pymatgen' and 'Matminer'. Pearson's correlation coefficients (PCC) between the compositional features were calculated and those with a correlation coefficient value larger than 0.95 were removed in order to avoid overfitting. The bandgap prediction performance was compared using the metrics of R² score and root-mean-squared error. By predicting the bandgap with randomforest and xgboost as representatives of the ensemble algorithm, it was found that xgboost gave better results after cross-validation and hyper-parameter tuning. To investigate the effect of compositional feature selection on the bandgap prediction of the machine learning model, the prediction performance was studied according to the number of features based on feature importance methods. It was found that there were no significant changes in prediction performance beyond the appropriate feature. Furthermore, artificial neural networks were employed to compare the prediction performance by adjusting the number of features guided by the PCC values, resulting in the best R² score of 0.811. By comparing and analyzing the bandgap distribution and prediction performance according to the material group containing specific elements (F, N, Yb, Eu, Zn, B, Si, Ge, Fe Al), various information for material design was obtained.

• The Korean Journal of Applied Statistics
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• v.34 no.2
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• pp.153-166
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• 2021

A main goal of pharmacogenomics studies is to predict individual's drug responsiveness based on high dimensional genetic variables. Due to a large number of variables, feature selection is required in order to reduce the number of variables. The selected features are used to construct a predictive model using machine learning algorithms. In the present study, we applied several hybrid feature selection methods such as combinations of logistic regression, ReliefF, TurF, random forest, and LASSO to a next generation sequencing data set of 400 epilepsy patients. We then applied the selected features to machine learning methods including random forest, gradient boosting, and support vector machine as well as a stacking ensemble method. Our results showed that the stacking model with a hybrid feature selection of random forest and ReliefF performs better than with other combinations of approaches. Based on a 5-fold cross validation partition, the mean test accuracy value of the best model was 0.727 and the mean test AUC value of the best model was 0.761. It also appeared that the stacking models outperform than single machine learning predictive models when using the same selected features.

• Korean Journal of Radiology
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• v.22 no.3
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• pp.334-343
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• 2021

Objective: We aimed to develop a prediction model for diagnosing severe aortic stenosis (AS) using computed tomography (CT) radiomics features of aortic valve calcium (AVC) and machine learning (ML) algorithms. Materials and Methods: We retrospectively enrolled 408 patients who underwent cardiac CT between March 2010 and August 2017 and had echocardiographic examinations (240 patients with severe AS on echocardiography [the severe AS group] and 168 patients without severe AS [the non-severe AS group]). Data were divided into a training set (312 patients) and a validation set (96 patients). Using non-contrast-enhanced cardiac CT scans, AVC was segmented, and 128 radiomics features for AVC were extracted. After feature selection was performed with three ML algorithms (least absolute shrinkage and selection operator [LASSO], random forests [RFs], and eXtreme Gradient Boosting [XGBoost]), model classifiers for diagnosing severe AS on echocardiography were developed in combination with three different model classifier methods (logistic regression, RF, and XGBoost). The performance (c-index) of each radiomics prediction model was compared with predictions based on AVC volume and score. Results: The radiomics scores derived from LASSO were significantly different between the severe AS and non-severe AS groups in the validation set (median, 1.563 vs. 0.197, respectively, p < 0.001). A radiomics prediction model based on feature selection by LASSO + model classifier by XGBoost showed the highest c-index of 0.921 (95% confidence interval [CI], 0.869-0.973) in the validation set. Compared to prediction models based on AVC volume and score (c-indexes of 0.894 [95% CI, 0.815-0.948] and 0.899 [95% CI, 0.820-0.951], respectively), eight and three of the nine radiomics prediction models showed higher discrimination abilities for severe AS. However, the differences were not statistically significant (p > 0.05 for all). Conclusion: Models based on the radiomics features of AVC and ML algorithms may perform well for diagnosing severe AS, but the added value compared to AVC volume and score should be investigated further.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.2
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• pp.504-519
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• 2023

Heart disease is becoming the top reason of death all around the world. Diagnosing cardiac illness is a difficult endeavor that necessitates both expertise and extensive knowledge. Machine learning (ML) is becoming gradually more important in the medical field. Most of the works have concentrated on the prediction of cardiac disease, however the precision of the results is minimal, and data integrity is uncertain. To solve these difficulties, this research creates an Integrated Accurate-Secure Heart Disease Prediction (IAS) Model based on Deep Convolutional Neural Networks. Heart-related medical data is collected and pre-processed. Secondly, feature extraction is processed with two factors, from signals and acquired data, which are further trained for classification. The Deep Convolutional Neural Networks (DCNN) is used to categorize received sensor data as normal or abnormal. Furthermore, the results are safeguarded by implementing an integrity validation mechanism based on the hash algorithm. The system's performance is evaluated by comparing the proposed to existing models. The results explain that the proposed model-based cardiac disease diagnosis model surpasses previous techniques. The proposed method demonstrates that it attains accuracy of 98.5 % for the maximum amount of records, which is higher than available classifiers.

• 제어로봇시스템학회:학술대회논문집
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• 1994.10a
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• pp.202-206
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• 1994

Mathematical model of the pollution rate of substation insulators is constructed, taking the model parameters as wind speed, wind direction, typhoon conditions and rainfall in an hourly basis. The main feature of model construction is to distinguish the effect of each parameter by separately analyzing the positive and negative pollution causing factors. Model parameters for the insulators of Karatsu substation, Saga, Japan were estimated and model validation was done using the actual data, in which the pollution deposits on the insulators were measured using pilot insulator and 'salt meter'. The proposed model of the pollution rate [mg/cm$^{2}$/hr] enables the identification of the effective parameters and prediction of the pollution rate so that it helps for the automatic decision making for insulator cleaning or the model can be used as a tool for the substation engineers to make precautionary measures.

• Earthquakes and Structures
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• v.27 no.3
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• pp.177-189
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• 2024

Accurately predicting the failure modes of reinforced concrete (RC) columns is essential for structural design and assessment. In this study, the challenges of imbalanced datasets and complex feature selection in machine learning (ML) methods were addressed through an optimized ML approach. By combining feature selection and oversampling techniques, the prediction of seismic failure modes in rectangular RC columns was improved. Two feature selection methods were used to identify six input parameters. To tackle class imbalance, the Borderline-SMOTE1 algorithm was employed, enhancing the learning capabilities of the models for minority classes. Eight ML algorithms were trained and fine-tuned using k-fold shuffle split cross-validation and grid search. The results showed that the artificial neural network model achieved 96.77% accuracy, while k-nearest neighbor, support vector machine, and random forest models each achieved 95.16% accuracy. The balanced dataset led to significant improvements, particularly in predicting the flexure-shear failure mode, with accuracy increasing by 6%, recall by 8%, and F1 scores by 7%. The use of the Borderline-SMOTE1 algorithm significantly improved the recognition of samples at failure mode boundaries, enhancing the classification performance of models like k-nearest neighbor and decision tree, which are highly sensitive to data distribution and decision boundaries. This method effectively addressed class imbalance and selected relevant features without requiring complex simulations like traditional methods, proving applicable for discerning failure modes in various concrete members under seismic action.

• Nuclear Engineering and Technology
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• v.42 no.3
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• pp.259-270
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• 2010

New generation nuclear reactors are designed using advanced safety analysis methods. A thorough understanding of different interacting physical phenomena is necessary to avoid underestimation and overestimation of consequences of off-normal transients in the reactor safety analysis results. This feature requires a multiphysics reactor simulation model. In this context, a coupled dynamics model based on a multiphysics formulation is developed indigenously for the transient analysis of large pressurized VVER reactors. Major simplifications are employed in the model by making several assumptions based on the physics of individual phenomenon. Space and time grids are optimized to minimize the computational bulk. The capability of the model is demonstrated by solving a series of international (AER) benchmark problems for VVER reactors. The developed model was used to analyze a number of reactivity transients that are likely to occur in VVER reactors.

• Journal of the Korea Society of Computer and Information
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• v.20 no.8
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• pp.35-43
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• 2015

Recently, internal information leaks is increasing rapidly by internal employees and authorized outsourcing personnel. In this paper, we propose a method to integrate internal control systems like system access control system and Digital Rights Managements and so on through expansion model of SIEM(Security Information Event Management system). this model performs a analysis step of security event link type and validation process. It develops unit scenarios to react illegal acts for personal information processing system and acts to bypass the internal security system through 5W1H view. It has a feature that derives systematic integration scenarios by integrating unit scenarios. we integrated internal control systems like access control system and Digital Rights Managements and so on through expansion model of Security Information Event Management system to defend leakage of internal information and customer information. We compared existing defense system with the case of the expansion model construction. It shows that expanding SIEM was more effectively.

• Journal of the Architectural Institute of Korea Structure & Construction
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• v.35 no.11
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• pp.155-162
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• 2019

The construction industry is the highest safety accident causing industry as 28.55% portion of all industries' accidents in Korea. In particular, falling is the highest accidents type composed of 60.16% among the construction field accidents. Therefore, we analyzed the factors of major disaster affecting the fall accident and then derived feature importances by considering various variables. We used data collected from Korea Occupational Safety & Health Agency (KOSHA) for learning and predicting in the proposed model. We have an effort to predict the degree of occupational fall accidents by using the machine learning model, i.e., Adaboost, short for Adaptive Boosting. Adaboost is a machine learning meta-algorithm which can be used in conjunction with many other types of learning algorithms to improve performance. Decision trees were combined with AdaBoost in this model to predict and classify the degree of occupational fall accidents. HyOperpt was also used to optimize hyperparameters and to combine k-fold cross validation by hierarchy. We extracted and analyzed feature importances and affecting fall disaster by permutation technique. In this study, we verified the degree of fall accidents with predictive accuracy. The machine learning model was also confirmed to be applicable to the safety accident analysis in construction site. In the future, if the safety accident data is accumulated automatically in the network system using IoT(Internet of things) technology in real time in the construction site, it will be possible to analyze the factors and types of accidents according to the site conditions from the real time data.