• Korean Journal of Construction Engineering and Management
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• v.23 no.3
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• pp.104-115
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• 2022

Safety accidents in the construction industry account for the highest percentage of all industries; and thus, it is encouraged to introduce a virtual reality (VR)-based experiential education system into the basic occupational safety and health training for construction as a way to solve the problem. However, there are some limitations such as a lack of competent workforce, and insufficient content and equipment for VR-based construction safety education. In this background, this study aimed to analyze the difference in learning effect between the CG-based experiential VR education (direct method, type B) and the existing photo-based audiovisual VR education (indirect method, type A), in which a CAMIL (Cognitive & Affective Model of Immersive Learning) theory was used as objective assessment tool. The learning effect of the direct education method (type B) was found to be superior to that of the indirect education method (type A) in terms of all areas in the CAMIL theory. It is expected that the VR-based experiential construction safety education will increase the learning effect of construction workers.

• The Journal of the Convergence on Culture Technology
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• v.8 no.6
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• pp.971-978
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• 2022

As a marketing method in a non-face-to-face society, the purpose of this study is to test how AR experience affects purchase intention in the process of consumers recognizing product information to purchase products and to secure the basis for the effectiveness of developing and introducing augmented reality functions in future product brand applications. Literary research methods and empirical research methods were used to verify the research purpose, and to measure this, an application of domestic tableware brand 'Odense', which implements augmented reality functions, was produced and used as an experimental tool. Also, a direct causal relationship was attempted by constituting a questionnaire by deriving a measurement scale for perceived usefulness, perceived ease, perceived pleasure, and purchase, which are factors of technology acceptance theory (TAM), and empirical analysis was conducted using the SPSS 25.0 statistical package to achieve the purpose of the study. As a result of the study, significant results were derived from all factors in the effect of perceived usefulness, ease, and pleasure on purchase intention, and several significant differences were found among factors according to gender, age, and internet shopping usage time in general characteristics. In conclusion, the user experience of the medium in which the augmented reality function is introduced in the information recognition stage of the product has a positive effect on purchase compared to the user experience of existing applications.

• Journal of the Korea Society of Computer and Information
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• v.27 no.6
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• pp.131-137
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• 2022

As the age increases, the oral cavity, that is, the teeth and periodontium, also begin to age, and accordingly, a preparation process is required. The preparation process is an important period for oral health management to start continuously with oral health education consisting of knowledge, attitude, and behavior from the 20s. Therefore, to design a clinical dental hygiene course for patients who visited a dental clinic in Gyeonggi-do and received continuous care in an oral health care room after treatment, we tried to analyze the data of the dental hygiene assessment. As a dental hygiene assessment tool, based on personal information and general medical history, dental visit experience, bleeding on probing(BOP), bad breath measurement, phase contrast microscopy, and O'Leary index were performed. The number of subjects who had dental visits was 75.4% and those without experience were 24.6%, and as a result of the periodontal examination, generally bleeding was found in 76.3%. In preventive oral care, the stage of dental hygiene assessment in the 20s is an important first step. From this point on, it is an important time to be systematically habituated so that you can take responsibility for your own oral condition. Therefore, in this study, the results of dental hygiene assessment through oral examinations of subjects in their 20s are derived and presented as basic data for the development of dental hygiene performance competency of dental hygienists during the clinical dental hygiene process in oral health education and oral health management.

• International conference on construction engineering and project management
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• 2022.06a
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• pp.1253-1253
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• 2022

Although the construction industry is changing from a 2D-based to a 3D BIM-based management process, 2D drawings are still used as standards for permits and construction. For this reason, 2D deliverables extracted from 3D BIM are one of the essential achievements of BIM projects. However, due to technical and institutional problems that exist in practice, the process of extracting 2D deliverables from BIM requires additional work beyond generating 3D BIM models. In addition, the consistency of data between 3D BIM models and 2D deliverables is low, which is a major factor hindering work productivity in practice. To solve this problem, it is necessary to build BIM data that meets information requirements (IRs) for extracting 2D deliverables to minimize the amount of work of users and maximize the utilization of BIM data. However, despite this, the additional work that occurs in the BIM process for drawing creation is still a burden on BIM users. To solve this problem, the purpose of this study is to increase the productivity of the BIM process by automating the process of extracting 2D deliverables from BIM and securing data consistency between the BIM model and 2D deliverables. For this, an expert interview was conducted, and the requirements for automation of the process of extracting 2D deliverables from BIM were analyzed. Based on the requirements, the types of drawings and drawing expression elements that require automation of drawing generation in the design development stage were derived. Finally, the method for developing automation technology targeting elements that require automation was classified and analyzed, and the process for automatically extracting BIM-based 2D deliverables through templates and rule-based automation modules were derived. At this time, the automation module was developed as an add-on to Revit software, a representative BIM authoring tool, and 120 rule-based automation rulesets, and the combinations of these rulesets were used to automatically generate 2D deliverables from BIM. Through this, it was possible to automatically create about 80% of drawing expression elements, and it was possible to simplify the user's work process compared to the existing work. Through the automation process proposed in this study, it is expected that the productivity of extracting 2D deliverables from BIM will increase, thereby increasing the practical value of BIM utilization.

• Journal of Korean Clinical Nursing Research
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• v.16 no.2
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• pp.95-105
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• 2010

Purpose: The purpose of this study was to identify the research trend of 341 studies published in Journal of Korean Clinical Nursing Research from 1995 to 2008. Methods: This study was conducted from May to November, 2009. The tool for analysis developed through literature reviews and discussions of researchers consisted of 7 categories such as characteristics of researchers, research designs, subjects, data collection methods, ethical consideration, nursing interventions, and dependent variables. Data were analyzed by frequency and percentage. Results: The mean number of author per study was 4.7, and clinical nurses were the most frequent as the first author (79.7%) and as a corresponding author (67.1%). Quantitative study was 97.6% and nonexperimental study was 51.7%. The most frequently used research designs were quasi-experimental study (73.9%) among experimental studies and survey study (79.1%) among nonexperimental studies. Patients were the most frequent subjects for studies (55.8%). 46.1% of studies gathered data with questionnaire, 57.7% of studies had consent from patients, and 44.3% of studies used nursing skills for nursing interventions, and 60.0% of studies used clinical end points for dependent variables. Conclusion: The research trend of clinical nursing studies shows that clinical nurses mostly perform quasi-experimental researches to solve patients' problem and frequently use nursing skills for nursing intervention and explore the effect of nursing interventions on clinical end points.

• Journal of the Korean Geotechnical Society
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• v.22 no.6
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• pp.15-26
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• 2006

Although numerous investigations have been performed over the years to predict the behavior and bearing capacity of piles, the mechanisms are not yet entirely understood. The prediction of bearing capacity is a difficult task, because large numbers of factors affect the capacity and also have complex relationship one another. Therefore, it is extremely difficult to search the essential factors among many factors, which are related with ground condition, pile type, driving condition and others, and then appropriately consider complicated relationship among the searched factors. The present paper describes the application of Artificial Neural Network (ANN) in predicting the capacity including its components at the tip and along the shaft from dynamic load test of the driven piles. Firstly, the effect of each factor on the value of bearing capacity is investigated on the basis of sensitivity analysis using ANN modeling. Secondly, the authors use the design methodology composed of ANN and genetic algorithm (GA) to find optimal neural network model to predict the bearing capacity. The authors allow this methodology to find the appropriate combination of input parameters, the number of hidden units and the transfer structure among the input, the hidden and the out layers. The results of this study indicate that the neural network model serves as a reliable and simple predictive tool for the bearing capacity of driven piles.

• Nuclear Engineering and Technology
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• v.55 no.1
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• pp.339-352
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• 2023

Prediction of the time-related traits of pebble flow inside pebble-bed HTGRs is of great significance for reactor operation and design. In this work, an image-driven approach with the aid of a convolutional neural network (CNN) is proposed to predict the remaining time of initially loaded pebbles and the time interval of paired flow images of the pebble bed. Two types of strategies are put forward: one is adding FC layers to the classic classification CNN models and using regression training, and the other is CNN-based deep expectation (DEX) by regarding the time prediction as a deep classification task followed by softmax expected value refinements. The current dataset is obtained from the discrete element method (DEM) simulations. Results show that the CNN-aided models generally make satisfactory predictions on the remaining time with the determination coefficient larger than 0.99. Among these models, the VGG19+DEX performs the best and its CumScore (proportion of test set with prediction error within 0.5s) can reach 0.939. Besides, the remaining time of additional test sets and new cases can also be well predicted, indicating good generalization ability of the model. In the task of predicting the time interval of image pairs, the VGG19+DEX model has also generated satisfactory results. Particularly, the trained model, with promising generalization ability, has demonstrated great potential in accurately and instantaneously predicting the traits of interest, without the need for additional computational intensive DEM simulations. Nevertheless, the issues of data diversity and model optimization need to be improved to achieve the full potential of the CNN-aided prediction tool.

• Asia-pacific Journal of Multimedia Services Convergent with Art, Humanities, and Sociology
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• v.7 no.8
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• pp.189-199
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• 2017

The purpose of this study is to compare the grid usage that is common to Leonardo da Vinci and J.N.L. Durand in the process of designing the architectural plan. In the days when there was no proper measurement tool, auxiliary lines relied entirely on the architect's personal mindset and design convenience. Therefore, it is considered that studying the auxiliary lines drawn by the architects will be useful for studying the human perception system. Among auxiliary lines, the grid has been used by many architects. Leonardo da Vinci and J.N.L. Durand are famous. However, these two show a significant different grid usage. As auxiliary grid and space ares added the center of the Leonardo da Vinci grid continues to move, and the grid in his sketch is becoming a building space itself. So I call it 'conceptual grid'. In the case of J.N.L. Durand, the one center of the grid is always at the center of the drawing. That is, all the positions of the grid can be determined in phase around a common point, and all of the same specifications are assumed. The grid is a kind of filter. That's why his grid is a visual abstraction of the process of thinking. In this paper, I will call the grid of J.N.L. Durand as 'abstract grid'.

• Journal of Intelligence and Information Systems
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• v.29 no.2
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• pp.241-265
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• 2023

A corporate insolvency prediction model serves as a vital tool for objectively monitoring the financial condition of companies. It enables timely warnings, facilitates responsive actions, and supports the formulation of effective management strategies to mitigate bankruptcy risks and enhance performance. Investors and financial institutions utilize default prediction models to minimize financial losses. As the interest in utilizing artificial intelligence (AI) technology for corporate insolvency prediction grows, extensive research has been conducted in this domain. However, there is an increasing demand for explainable AI models in corporate insolvency prediction, emphasizing interpretability and reliability. The SHAP (SHapley Additive exPlanations) technique has gained significant popularity and has demonstrated strong performance in various applications. Nonetheless, it has limitations such as computational cost, processing time, and scalability concerns based on the number of variables. This study introduces a novel approach to variable selection that reduces the number of variables by averaging SHAP values from bootstrapped data subsets instead of using the entire dataset. This technique aims to improve computational efficiency while maintaining excellent predictive performance. To obtain classification results, we aim to train random forest, XGBoost, and C5.0 models using carefully selected variables with high interpretability. The classification accuracy of the ensemble model, generated through soft voting as the goal of high-performance model design, is compared with the individual models. The study leverages data from 1,698 Korean light industrial companies and employs bootstrapping to create distinct data groups. Logistic Regression is employed to calculate SHAP values for each data group, and their averages are computed to derive the final SHAP values. The proposed model enhances interpretability and aims to achieve superior predictive performance.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.29 no.6B
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• pp.503-512
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• 2009

Watershed models, which are a tool for water cycle mechanism, are classified as the distributed model and the lumped model. Currently, the distributed models have been more widely used than lumped model for many researches and applications. The lumped model estimates the parameters in the conceptual and empirical sense, on the other hand, in the case of distributed model the first-guess value is estimated from the grid-based watershed characteristics and rainfall data. Therefore, the distributed model needs more detailed parameter adjustment in its calibration and also one should precisely understand the model parameters' characteristics and sensitivity. This study uses Jungnang basin as a study area and $Vflo^{TM}$ model, which is a physics-based distributed hydrologic model, is used to analyze its parameters' sensitivity. To begin with, 100 years frequency-design rainfall is derived from Huff's method for rainfall duration of 6 hours, then the discharge is simulated using the calibrated parameters of $Vflo^{TM}$ model. As a result, hydraulic conductivity and overland's roughness have an effect on runoff depth and peak discharge, respectively, while channel's roughness have influence on travel time and peak discharge.