• Proceedings of the Korea Concrete Institute Conference
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• 1998.10b
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• pp.731-736
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• 1998

Deteriorations of concrete are governed by combined factors such as environmental stressors, processes and rates of deteriorations. Due to this reason, it's very difficult and important issue to predict quantitatively the service life of concrete structure. From this pont of views, the purpose of this study is to propose the approaches on the further development for predicting the remaining service life of concrete by analyzing the deteriorations mechanism and evaluating the existing models.

• Proceedings of the KAIS Fall Conference
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• 2008.05a
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• pp.354-357
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• 2008

In this paper, we develop business models for the robot industry that facilitates to explore new business opportunities in the convergence age. Considering the trend of convergence society, we analyze the market drivers. Analyzing market dynamics and value chain, we design a set of the business model for the robot industry focused on u-health robots. In addition, we describe the evolution path of the proposed business model in terms of technology development and market. Finally, we develop a framework for evaluating the effectiveness of the business model.

• The korean journal of orthodontics
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• v.45 no.2
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• pp.82-88
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• 2015

Objective: The aim of this study was to evaluate the reliability of measurements obtained after the superimposition of three-dimensional (3D) digital models by comparing them with those obtained from lateral cephalometric radiographs and photocopies of plaster models for the evaluation of upper molar distalization. Methods: Data were collected from plaster models and lateral cephalometric radiographs of 20 Class II patients whose maxillary first molars were distalized with an intraoral distalizer. The posterior movements of the maxillary first molars were evaluated using lateral cephalometric radiographs (group CP), photocopies of plaster models (group PH), and digitized 3D models (group TD). Additionally, distalization and expansion of the other teeth and the degrees of molar rotation were measured in group PH and group TD and compared between the two groups. Results: No significant difference was observed regarding the amount of molar distalization among the three groups. A comparison of the aforementioned parameters between group PH and group TD did not reveal any significant difference. Conclusions: 3D digital models are reliable to assess the results of upper molar distalization and can be considered a valid alternative to conventional measurement methods.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.20 no.6
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• pp.209-220
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• 2010

Evaluating performance in public sector aims to enhance the efficiency of organizations. Evaluating the efficiency which is the ratio between input and output, organizations set directions of improvement. This research applied Data Envelopment Analysis(DEA) useful to evaluating the efficiency of organizations in public sector. Decision Making Units(DMU) of this research are 21 Information Security Organizations of departments/agencies. As the results, the mean of efficiency score of 21 DMUs is a little more than 50%. Means of departments(8 DMUs) and agencies/committees(11 DMUs) are similar to the total efficiency score. For these results, the decision makers of the information security organizations in public sector have to strive to improve the inefficiency.

• Journal of the Korea Safety Management & Science
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• v.8 no.4
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• pp.205-218
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• 2006

Multi-criteria decision making is deducing the relative importance in the criterion of decision making and each alternative which is able to making a variety of choices measures the preferred degree in the series of town ranking criterions. Moreover, this is possible by synthesizing them systematically. In general, a fundamental problem decision maker solve for multi-criteria decision making is evaluating a set of activities which an considered as the target logically, and this kind of work is evaluated and synthesized by various criterions of the value which a chain of activities usually hold in common. In this paper, we use the compensatory models for the optimal decision making. For the purpose of optimal decision making, the data of five different car models are used in Europe.

• Journal of the Korean Burn Society
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• v.24 no.1
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• pp.1-6
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• 2021

Purpose: The purpose of this study was to evaluate the accuracy of four prediction models in adult burn patients. Methods: This retrospective study was conducted on 696 adult burn patients who were treated at burn intensive care unit (BICU) of Hallym University Hangang Sacred Heart Hospital from January 2017 to December 2019. The models are ABSI, APACHE IV, rBaux and Hangang score. Results: The discrimination of each prediction model was analyzed as AUC of ROC curve. AUC value was the highest with Hangang score of 0.931 (0.908~0.954), followed by rBaux 0.896 (0.867~0.924), ABSI 0.883 (0.853~0.913) and APACHE IV 0.851 (0.818~0.884). Conclusion: The results of evaluating the accuracy of the four models, Hangang score showed the highest prediction. But it is necessary to apply the appropriate prediction model according to characteristics of the burn center.

• Korean Chemical Engineering Research
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• v.58 no.2
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• pp.248-256
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• 2020

In this study, predictive models based on machine learning for evaluating the extent of hazardous zone of explosive gases are developed. They are able to provide important guidelines for installing the explosion proof apparatus. 1,200 research data sets including 12 combustible gases and their extents of hazardous zone are generated to train predictive models. The extent of hazardous zone is set to an output variable and 12 variables affecting an output are set as input variables. Multiple linear regression, principal component regression, and artificial neural network are employed to train predictive models. Mean absolute percentage errors of multiple linear regression, principal component regression, and artificial neural network are 44.2%, 49.3%, and 5.7% and root mean square errors are 1.389m, 1.602m, and 0.203 m respectively. Therefore, it can be concluded that the artificial neural network shows the best performance. This model can be easily used to evaluate the extent of hazardous zone for explosive gases.

• The Korean Journal of Physiology and Pharmacology
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• v.18 no.4
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• pp.279-288
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• 2014

Ulcerative colitis and Crohn's disease are a set of chronic, idiopathic, immunological and relapsing inflammatory disorders of the gastrointestinal tract referred to as inflammatory bowel disorder (IBD). Although the etiological factors involved in the perpetuation of IBD remain uncertain, development of various animal models provides new insights to unveil the onset and the progression of IBD. Various chemical-induced colitis models are widely used on laboratory scale. Furthermore, these models closely mimic morphological, histopathological and symptomatical features of human IBD. Among the chemical-induced colitis models, trinitrobenzene sulfonic acid (TNBS)-induced colitis, oxazolone induced-colitis and dextran sulphate sodium (DSS)-induced colitis models are most widely used. TNBS elicits Th-1 driven immune response, whereas oxazolone predominantly exhibits immune response of Th-2 phenotype. DSS-induced colitis model also induces changes in Th-1/Th-2 cytokine profile. The present review discusses the methodology and rationale of using various chemical-induced colitis models for evaluating the pathogenesis of IBD.

• The Transactions of the Korea Information Processing Society
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• v.4 no.7
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• pp.1706-1718
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• 1997

The rapid increase in the use of many commercial software products has necessitated a systematic and objective method of their evaluation and selection. Our study focuses on the assignment of weights and choice of proper models. First, the weights of attributes are assigned consistently by using the analytic hierarchy process. Second, many models, which can be suitable for the structure of evaluation and selection for software product, are collected, categorized into two types of model, and compared in terms of their strength and weakness. The models involved are four compensatory models and seven noncompensatory models. Finally, they are analyzed through the application of specific software products(database data modelers) in terms of their attributes. Our study enhances the applicability of models to a variety of user requirement utilizing the evaluating procedure and applications.

• Steel and Composite Structures
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• v.44 no.1
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• pp.49-63
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• 2022

This study aims to develop ensemble machine learning (ML) models for estimating the peak floor acceleration and maximum top drift of steel moment frames. For this purpose, random forest, adaptive boosting, gradient boosting regression tree (GBRT), and extreme gradient boosting (XGBoost) models were considered. A total of 621 steel moment frames were analyzed under 240 ground motions using OpenSees software to generate the dataset for ML models. From the results, the GBRT and XGBoost models exhibited the highest performance for predicting peak floor acceleration and maximum top drift, respectively. The significance of each input variable on the prediction was examined using the best-performing models and Shapley additive explanations approach (SHAP). It turned out that the peak ground acceleration had the most significant impact on the peak floor acceleration prediction. Meanwhile, the spectral accelerations at 1 and 2 s had the most considerable influence on the maximum top drift prediction. Finally, a graphical user interface module was created that places a pioneering step for the application of ML to estimate the seismic demands of building structures in practical design.