• Journal of The Korean Digital Architecture Interior Association
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• v.10 no.3
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• pp.11-18
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• 2010

Cost estimation through fast and correct quantity take offs are crucial in the process of construction project. The existing methods for cost estimation are mainly based on 2D-based drawings and the estimation result tends to be different according to the estimator's experience, the quality and quantity of used information and estimation time. To solve these problems, the domestic construction industry have recently tried to use the data extracted from 3D data modeling based on BIM(Building Information Modeling) in order to achieve more accurate and objective cost estimation. However it tends to increase dramatically the quantity of information that can be used in cost estimation by estimators. Therefore in order to achieve quality information data from 3D data modeling, the characteristics of the project should be reflected on the 3D model and it is most important to extract information only for cost estimation from the whole 3D model fast and accurately. Thus this study aims to propose the 3D modeling method through Data Normalization which maximizes the usability of 3D Data modeling in cost estimation process.

• Proceedings of the Computational Structural Engineering Institute Conference
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• 2008.04a
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• pp.96-99
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• 2008

When the design modification is occurred, at present, design process based on 2-D spend more time and effort than that based on 3-D to modify related structural details. To improve and develop these processes, therefore, the design possibility of civil structures based on virtual model of 3-D must be investigated. We designed reinforced concrete pier of 3-D model, containing parameters. The parameters was defined as structural details like area of the section, reinforcement specification for design modification and structural analysis. In this paper, we researched about the processes modeling of reinforced concrete bridge pier based on parameters, the extracting data from the virtual model of 3-D, and the reflection of data to virtual model throughout structural analysis.

• Journal of The Korean Society of Agricultural Engineers
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• v.57 no.5
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• pp.101-111
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• 2015

Establishment of appropriate data in certain formats is essential for agricultural water cycle analysis, which involves complex interactions and uncertainties such as climate change, social & economic change, and watershed environmental change. The main objective of this study was to develop web-based Data processing and Model linkage Techniques for Agricultural Water-Resource analysis (AWR-DMT). The developed techniques consisted of database development, data processing technique, and model linkage technique. The watershed of this study was the upper Cheongmi stream and Geunsam-Ri. The database was constructed using MS SQL with data code, watershed characteristics, reservoir information, weather station information, meteorological data, processed data, hydrological data, and paddy field information. The AWR-DMT was developed using Python. Processing technique generated probable rainfall data using non-stationary frequency analysis and evapotranspiration data. Model linkage technique built input data for agricultural watershed models, such as the TANK and Agricultural Watershed Supply (AWS). This study might be considered to contribute to the development of intelligent watercycle analysis by developing data processing and model linkage techniques for agricultural water-resource analysis.

• Journal of Korea Multimedia Society
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• v.21 no.12
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• pp.1407-1416
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• 2018

The purpose of this study was to train a model for the ulna and radius bone segmentation based on Convolutional Neural Networks and to verify the segmentation model. The data consisted of 840 training data, 210 tuning data, and 200 verification data. The learning model for the ulna and radius bone bwas based on U-Net (19 convolutional and 8 maximum pooling) and trained with 8 batch sizes, 0.0001 learning rate, and 200 epochs. As a result, the average sensitivity of the training data was 0.998, the specificity was 0.972, the accuracy was 0.979, and the Dice's similarity coefficient was 0.968. In the validation data, the average sensitivity was 0.961, specificity was 0.978, accuracy was 0.972, and Dice's similarity coefficient was 0.961. The performance of deep convolutional neural network based models for the segmentation was good for ulna and radius bone.

• Journal of the Korean Society for Heat Treatment
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• v.36 no.6
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• pp.374-381
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• 2023

This study develops and evaluates a deep learning model for predicting oxide and nitride layers based on plasma process data. We introduce a novel deep learning-based Varying Coefficient Regressor (VCR) by adapting the VCR, which previously relied on an existing unique function. This model is employed to forecast the oxide and nitride layers within the plasma. Through comparative experiments, the proposed VCR-based model exhibits superior performance compared to Long Short-Term Memory, Random Forest, and other methods, showcasing its excellence in predicting time series data. This study indicates the potential for advancing prediction models through deep learning in the domain of plasma processing and highlights its application prospects in industrial settings.

• Journal of Korean Library and Information Science Society
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• v.40 no.2
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• pp.87-110
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• 2009

This study examined and compared existing national library RFID data models, especially for Denmark, Finland, Netherlands, France, the U.S., Australia and South Korea. Four European country models(i.e., Danish, Finnish, Dutch, and French models) and South Korea use prescriptive data model(fixed encoding approach), while The U.S. and Australia adopt object-based data model, which is based on the data encoding rules of ISO/IEC 15962. This study expects to allow fertile ground for discussion on RFID data models in South Korean library environment.

• Journal of the Korean Data and Information Science Society
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• v.27 no.4
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• pp.1083-1090
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• 2016

In clinical studies, different types of outcomes (e.g. repeated measures data and time-to-event data) for the same subject tend to be observed, and these data can be correlated. For example, a response variable of interest can be measured repeatedly over time on the same subject and at the same time, an event time representing a terminating event is also obtained. Joint modelling using a shared random effect is useful for analyzing these data. Inferences based on marginal likelihood may involve the evaluation of analytically intractable integrations over the random-effect distributions. In this paper we propose a joint HGLM approach for analyzing such outcomes using the HGLM (hierarchical generalized linear model) method based on h-likelihood (i.e. hierarchical likelihood), which avoids these integration itself. The proposed method has been demonstrated using various numerical studies.

• ETRI Journal
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• v.44 no.2
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• pp.208-219
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• 2022

Crime amount prediction is crucial for optimizing the police patrols' arrangement in each region of a city. First, we analyzed spatiotemporal correlations of the crime data and the relationships between crime and related auxiliary data, including points-of-interest (POI), public service complaints, and demographics. Then, we proposed a crime amount prediction model based on 2D convolution and long short-term memory neural network (2DCONV-LSTM). The proposed model captures the spatiotemporal correlations in the crime data, and the crime-related auxiliary data are used to enhance the regional spatial features. Extensive experiments on real-world datasets are conducted. Results demonstrated that capturing both temporal and spatial correlations in crime data and using auxiliary data to extract regional spatial features improve the prediction performance. In the best case scenario, the proposed model reduces the prediction error by at least 17.8% and 8.2% compared with support vector regression (SVR) and LSTM, respectively. Moreover, excessive auxiliary data reduce model performance because of the presence of redundant information.

• Journal of Software Assessment and Valuation
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• v.17 no.1
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• pp.59-66
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• 2021

Recently, conceptual information model is changing fast, and these changes are coming about as a result of individual tendency, social cultural, new circumstances and societal shifts within big data environment. Despite the data is growing more and more, now is the time to commit ourselves to the development of renewable, invaluable information of social/live commerce. Because we have problems with various insoluble data, we propose about deep learning prediction model-based object classification in social commerce of big data environment. Accordingly, it is an increased need of social commerce platform capable of handling high volumes of multiple items by users. Consequently, responding to rapid changes in users is a very significant by deep learning. Namely, promptly meet the needs of the times, and a widespread growth in big data environment with the goal of realizing in this paper.

• Korean Medical Education Review
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• v.24 no.2
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• pp.156-171
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• 2022

The purpose of this study was to establish a model for constructing longitudinal data for medical school, and to structure cohort and longitudinal data using data from Yonsei University College of Medicine (YUCM) according to the established input-environment-output (I-E-O) model. The study was conducted according to the following procedure. First, the data that YUCM has collected was reviewed through data analysis and interviews with the person in charge of each questionnaire. Second, the opinions of experts on the validity of the I-E-O model were collected through the first expert consultation, and as a result, a model was established for each stage of medical education based on the I-E-O model. Finally, in order to further materialize and refine the previously established model for each stage of medical education, secondary expert consultation was conducted. As a result, the survey areas and time period for collecting longitudinal data were organized according to the model for each stage of medical education, and an example of the YUCM cohort constructed according to the established model for each stage of medical education was presented. The results derived from this study constitute a basic step toward building data from universities in longitudinal form, and if longitudinal data are actually constructed through this method, they could be used as an important basis for determining major policies or reorganizing the curricula of universities. These research results have implications in terms of the management and utilization of existing survey data, the composition of cohorts, and longitudinal studies for many medical schools that are conducting surveys in various areas targeting students, such as lecture evaluation and satisfaction surveys.