• Spatial Information Research
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• v.14 no.3 s.38
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• pp.321-334
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• 2006

3D spatial data are of increasing interest in landscape analysis, urban planning and map services based on Web, because of its reality. But the amount of 3D spatial data are very large in comparison with 2D spatial data. Therefore it is necessary to have a efficient methods to transfer and visualize 3D spatial data. The concept of Level of Detail in Computer Graphics is effective. This paper briefly presents two LOD data models of data transmission based on the spatial data model of international standards. First, it is separated LOD model that gives a LOD level to object. Second is Selective LOD model that gives a LOD level to object's element. We compared the efficiency of 3D data transmission based on two LOD model.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.17 no.2
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• pp.105-117
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• 2004

In each industry field, many engineers have tried to develop integrated environments using information technology. The core technology in building integrated environments is the database based on standardized information. To meet the requirements, this study builds a database with detailed design information as a part of integrating digital information generated from every work of steel bridges. The data model used to build the database was developed based on the international standard, namely ISO/STEP. The data model is classified into geometric and non-geometric parts to represent the design information of steel bridges. The geometric parts are represented by a three dimensional solid model so that they may be able to reuse existing information. Also, the non-geometric parts represent information requirements that are analyzed by the development method of standard data model. To verify the data model, this study validates the syntax of the model on EXPRESS Engine and verifies the validation of the model by applying the design data of Hannam bridge to the database.

• International conference on construction engineering and project management
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• 2024.07a
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• pp.949-956
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• 2024

While Building Information Modeling (BIM) is an important tool for digitization in the architecture industry, its introduction rate in the field of facility maintenance and management is still low. Accordingly, this study aims to spread BIM to this field. The introduction of BIM enables centralization of facility data that has been managed separately in two-dimensional data and allows analysis across data in three-dimensional space. This study includes three phases. Phase 1 is to create a BIM model of the head office building in Japan as an example, phase 2 is to link the BIM model with the building data, and phase 3 is to create an analysis environment based on the data-linked BIM created. The BIM model is linked to three sheets of data using Dynamo; data showing the seating ratio of the seats in the free-address office owned by the facility, the amount of electricity used, and the repair work history of the building. Finally, an analysis environment is created for using the BIM model with data linkage in actual facility maintenance and management operations. As the platform created in this study now makes it possible to analyze multiple sets of data in a three-dimensional environment, it is expected to provide multifaceted solutions through analysis across multiple datasets.

• Korean Journal of Computational Design and Engineering
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• v.2 no.1
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• pp.11-18
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• 1997

Scientific volume visualization addresses the representation, manipulation, and rendering of volumetric data sets, providing mechanisms for looking closely into structures and understanding their complexity and dynamics. In the past several years, a tremendous amount of research and development has been directed toward algorithms and data modeling methods for a scientific data visualization. But there has been very little work on developing a mathematical volume model that feeds this visualization. Especially, in flow visualization, the volume model has long been required as a guidance to display the very large amounts of data resulting from numerical simulations. In this paper, we focus on the mathematical representation of volumetric data sets and the method of extracting meaningful information from the derived volume model. For this purpose, a B-spline volume is extended to a high dimensional trivariate model which is called as a flow visualization model in this paper. Two three-dimensional examples are presented to demonstrate the capabilities of this model.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 2003.05a
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• pp.375-380
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• 2003

This study is concerned with the machine part grouping m cellular manufacturing. To group machines into the set of machine cells and parts into the set of part families, new p-median model considering the production data such as the operation sequences and production volumes for parts is proposed. Unlike existing p-median models relying on the classical binary part-machine incidence matrix which does not reflect the real production factors which seriously impact on machine-part grouping, the proposed p-median model reflects the production factors by adopting the new similarity coefficient based on the production data-based part-machine incidence matrix of which each non-binary entry indicates actual intra-cell or inter-cell flows to or from machines by parts. Computation test compares the proposed p median model favorably.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.2
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• pp.929-944
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• 2019

Many critical applications require accurate real-time human action recognition. However, there are many hurdles associated with capturing and pre-processing image data, calculating features, and classification because they consume significant resources for both storage and computation. To circumvent these hurdles, this paper presents a recognition machine learning (ML) based system model which uses reduced data structure features by projecting real 3D skeleton modality on virtual 2D space. The MMU VAAC dataset is used to test the proposed ML model. The results show a high accuracy rate of 97.88% which is only slightly lower than the accuracy when using the original 3D modality-based features but with a 75% reduction ratio from using RGB modality. These results motivate implementing the proposed recognition model on an embedded system platform in the future.

• Smart Structures and Systems
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• v.29 no.6
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• pp.799-805
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• 2022

Permanent magnet synchronous motors (PMSMs) are widely used in systems requiring high control precision, efficiency, and reliability. Predicting the remaining useful life (RUL) with health monitoring of PMSMs prevents catastrophic failure and ensures reliable operation of system. In this study, a model-based method for predicting the RUL of PMSMs using phase current and vibration signals is proposed. The proposed method includes feature selection and RUL prediction based on a particle filter with a degradation model. The Paris-Erdogan model describing micro fatigue crack propagation is used as the degradation model. An experimental set-up to conduct accelerated life test, capable of monitoring various signals was designed in this study. Phase current and vibration data obtained from an accelerated life test of the PMSMs were used to verify the proposed approach. Features extracted from the data were clustered based on monotonicity and correlation clustering, respectively. The results identify the effectiveness of using the current data in predicting the RUL of PMSMs.

• Journal of Information Processing Systems
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• v.15 no.3
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• pp.694-706
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• 2019

In this study, we applied the long short-term memory (LSTM) model to classify the cryptocurrency price time series. We collected historic cryptocurrency price time series data and preprocessed them in order to make them clean for use as train and target data. After such preprocessing, the price time series data were systematically encoded into the three-dimensional price tensor representing the past price changes of cryptocurrencies. We also presented our LSTM model structure as well as how to use such price tensor as input data of the LSTM model. In particular, a grid search-based k-fold cross-validation technique was applied to find the most suitable LSTM model parameters. Lastly, through the comparison of the f1-score values, our study showed that the LSTM model outperforms the gradient boosting model, a general machine learning model known to have relatively good prediction performance, for the time series classification of the cryptocurrency price trend. With the LSTM model, we got a performance improvement of about 7% compared to using the GB model.

• Journal of the military operations research society of Korea
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• v.8 no.1
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• pp.71-76
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• 1982

A procedure is established for combining a regression model and a time series model to fit to a set of autocorrelated data. This procedure is based on an iterative method to compute regression parameter estimates and time series parameter estimates simultaneously. The time series model which is discussed is basically AR(p) model, since MA(q) model or ARMA(p,q) model can be inverted to AR({$\infty$) model which can be approximated by AR(p) model. The procedure discussed in this articled is applied in general to any combination of regression model and time series model.

• Journal of Navigation and Port Research
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• v.46 no.3
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• pp.158-167
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• 2022

The International Maritime Organization's e-Navigation strategy introduces new technologies to ships to support easier and safer navigation. To implement the e-Navigation strategy, it was necessary to develop a common data model, that could meet various requirements across all aspects of the maritime information service. The International Hydrographic Organization's S-100 Universal Hydrographic Data Model was selected, as the basis for the standardization of maritime data products. Three S-100 based product specifications for weather information, collectively called "S-41X", are currently under development by the NOAA's Ocean Prediction Center, for use in the Electronic Chart Display and Information System (ECDIS). This paper describes a design of a grid based S-413 data model out of three S-41X product specifications. Other S-100 data products, which support the gridded data format, were considered. To verify the data model, an encoding test was conducted, using the Korean Meteorological Adminstration's numerical prediction model results.