• International Journal of Highway Engineering
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• v.8 no.1 s.27
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• pp.139-152
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• 2006

Many experimental and numerical approaches have been developed to evaluate paving materials and to predict pavement response and distress. Micromechanical simulation modeling is a technology that can reduce the number of physical tests required in material formulation and design and that can provide more details, e.g., the internal stress and strain state, and energy evolution and dissipation in simulated specimens with realistic microstructural features. A clustered distinct element modeling (DEM) approach was implemented In the two-dimensional particle flow software package (PFC-2D) to study the complex behavior observed in asphalt mixture fracturing. The relationship between continuous and discontinuous material properties was defined based on the potential energy approach. The theoretical relationship was validated with the uniform axial compression and cantilever beam model using two-dimensional plane strain and plane stress models. A bilinear cohesive displacement-softening model was implemented as an intrinsic interface and applied for both homogeneous and heterogeneous fracture modeling in order to simulate behavior in the fracture process zone and to simulate crack propagation. A disk-shaped compact tension test (DC(T)) with heterogeneous microstructure was simulated and compared with the experimental fracture test results to study Mode I fracture. The realistic arbitrary crack propagation including crack deflection, microcracking, crack face sliding, crack branching, and crack tip blunting could be represented in the fracture models. This micromechanical modeling approach represents the early developmental stages towards a 'virtual asphalt laboratory,' where simulations of laboratory tests and eventually field response and distress predictions can be made to enhance our understanding of pavement distress mechanisms, such its thermal fracture, reflective cracking, and fatigue crack growth.

• Journal of the Korean Institute of Gas
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• v.16 no.5
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• pp.58-65
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• 2012

Various models are currently applied to predict the dispersion of leaked combustible gas and overpressure from a vapor cloud explosion(VCE). However, those models use simple approaches where topography and barriers of anti-leakage facilities and the effects of buildings were not sufficiently taken into considerations. For this reason, this study has proposed the dispersion process of leaked gas, distribution patterns, and flames and overpressure generated from gas explosions in 2D and 3D virtual spaces by reviewing more accurately analyzable computational fluid dynamics (CFD) model by considering various variables including combustion types of leaked substances, geometry of facility, warm currents, barriers, the influence of wind, and others. The CFD analysis results are anticipated to be usefully applied for the risk analysis of explosion and for the risk-based design.

• The KIPS Transactions:PartD
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• v.9D no.5
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• pp.827-836
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• 2002

Moving object management systems manage spatiotemporal data, which change their location over tine such as people, animals, and cars. These moving object management systems can be applied to vehicle location tracking, digital battlefield, location-based service, and so on. The existing moving object management systems only manage past or future location of the moving objects separately. Therefore, they cannot suggest estimation method of uncertain past or future location of the moving objects. In this paper, we propose a moving object management system, which not only manages historical data of the moving objects, but also predicts past and future location of the moving objects using historical data stored in database. We define the moving objects for vehicle location tracking and propose a moving object database structure. Finally, we suggest an execution model of the proposed system and apply the execution model to a virtual scenario for vehicle tracking.

• Journal of the Korea Society of Computer and Information
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• v.16 no.7
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• pp.49-58
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• 2011

Augmented Reality(AR) assists human's cognitive ability through the information visualization by substantiating information about virtual situation. This technology is studied in a variety of ways including education, design, industry, and so on, by various supply of information devices equipped with cameras and display monitors. Since the most of the AR system depends on limited interaction that responds to the order from user, it can not reflect diverse real world situation. In this study, we suggest a knowledge based augmented reality system, which is composed of context awareness agent that provides recognized context information, along with knowledge based component that provides intelligent capability by utilizing domain knowledges. With this capability, the augmented object can generate dynamic model intelligently by reflecting context information, and can make the interaction possible among the multiple objects. We developed rule based context awareness system along with 3D model generation, and tested interaction among the augmented objects. And we suggest a framework that can provide a convenient way of developing augmented reality system for user.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.26 no.3
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• pp.396-402
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• 2022

User interactions are being developed in various forms, and in particular, interactions using human gestures are being actively studied. Among them, hand gesture recognition is used as a human interface in the field of realistic media based on the 3D Hand Model. The use of interfaces based on hand gesture recognition helps users access media media more easily and conveniently. User interaction using hand gesture recognition should be able to view images by applying fast and accurate hand gesture recognition technology without restrictions on the computer environment. This paper developed a fast and accurate user hand gesture recognition algorithm using the open source media pipe framework and machine learning's k-NN (K-Nearest Neighbor). In addition, in order to minimize the restriction of the computer environment, a stereoscopic image control system based on user hand gesture recognition was designed and implemented using a web service environment capable of Internet service and a docker container, a virtual environment.

• Journal of Platform Technology
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• v.11 no.2
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• pp.15-25
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• 2023

This study analyzes the limitations of the insider threat datasets used for insider threat detection research and compares and analyzes the solution-based insider threat data with public insider threat data using a security solution to overcome this. Through this, we design a data format suitable for insider threat detection and implement a system that can safely share insider threat information between different institutions and companies using blockchain technology. Currently, there is no dataset collected based on actual events in the insider threat dataset that is revealed to researchers. Public datasets are virtual synthetic data randomly created for research, and when used as a learning model, there are many limitations in the real environment. In this study, to improve these limitations, a private blockchain was designed to secure information sharing between institutions of different affiliations, and a method was derived to increase reliability and maintain information integrity and consistency through agreement and verification among participants. The proposed method is expected to collect data through an outflow threat collector and collect quality data sets that posed a threat, not synthetic data, through a blockchain-based sharing system, to solve the current outflow threat dataset problem and contribute to the insider threat detection model in the future.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.38 no.5
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• pp.741-749
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• 2018

The purpose of this study is to propose a rational and scientific damage calculation model in relation to damages caused by bid rigging in construction projects. Previous studies and precedents in relation to calculating damages from bid rigging suggest that the main issue was the lack of consideration in standards for deciding successful bids, selection of inadequate standard comparative markets, insufficiency in analyzing the appropriateness of competitive bid price influence factors, and absence of calculation model verification. In order to improve on these issues, a damage calculation method on alternative tenders for construction projects was proposed. For this calculation model, first, a standard market adequate to the successful bid selection standards was determined, second, an appropriate factor was selected by analyzing the correlation between competitive bid price influence factors, and third, a regression analysis was conducted on the selected factor. Lastly, this was demonstrated through verification of appropriateness, significance & normality of the proposed model and application of actual bid rigging cases. Through the proposed calculation model, this study seeks to serve as a base to prevent opportunity damages for parties involved in related court cases by early resolution of disputes and relief from issues of unfair damage burdens on a particular party.

• Journal of Broadcast Engineering
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• v.26 no.3
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• pp.269-282
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• 2021

As the distribution of 3D content such as augmented reality and virtual reality increases, the importance of real-time computer animation technology is increasing. However, the computer animation process consists mostly of manual or marker-attaching motion capture, which requires a very long time for experienced professionals to obtain realistic images. To solve these problems, animation production systems and algorithms based on deep learning model and sensors have recently emerged. Thus, in this paper, we study four methods of implementing natural human movement in deep learning model and kinect camera-based animation production systems. Each method is chosen considering its environmental characteristics and accuracy. The first method uses a Kinect camera. The second method uses a Kinect camera and a calibration algorithm. The third method uses deep learning model. The fourth method uses deep learning model and kinect. Experiments with the proposed method showed that the fourth method of deep learning model and using the Kinect simultaneously showed the best results compared to other methods.

• Cartoon and Animation Studies
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• s.43
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• pp.363-386
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• 2016

This research is to design basic framework for developing teaching and learning method in the field of art and design major in university. Especially taking off from oneway e-learning teaching method, it is focused on increasing reality of student learning situation with applying AR contents process to augment virtual condition on reality condition. The processing of framework design and developing teaching and learning method are about practice education based on AR for model experiment research which was published "The E-Learning for Practice Training Using Augmented Reality in the College Education". This thesis is supposed to be a precedent study of the pre-published, and the purpose of those two studies were for experiment test in earnest in studying effect research. The classification of learning method divided basically as Face-to-Face Learning(FFL), Blended Learning(BL), fully E-Learning(EL), and Augmented Reality based E-Learning(AREL). This research compares and analyzes each frame of FFL, BL, and EL. And then, designed framework lead to the over-all conclusion with the type of AREL. Additionally, AREL for PBE suggests the ways of advanced teaching learning.

• Bulletin of the Korean Chemical Society
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• v.32 no.1
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• pp.201-207
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• 2011

Peroxisome proliferator-activated receptors (PPARs) are members of nuclear receptors and their activation induces regulation of fatty acid storage and glucose metabolism. Therefore, the $PPAR\gamma$ is a major target for the treatment of type 2 diabetes mellitus. In order to generate pharmacophore model, 1080 known agonists database was constructed and a training set was selected. The Hypo7, selected from 10 hypotheses, contains four features: three hydrogen-bond acceptors (HBA) and one general hydrophobic (HY). This pharmacophore model was validated by using 862 test set compounds with a correlation coefficient of 0.903 between actual and estimated activity. Secondly, CatScramble method was used to verify the model. Hence, the validated Hypo7 was utilized for searching new lead compounds over 238,819 and 54,620 chemical structures in NCI and Maybridge database, respectively. Then the leads were selected by screening based on the pharmacophore model, predictive activity, and Lipinski's rules. Candidates were obtained and subsequently the binding affinities to $PPAR\gamma$ were investigated by the molecular docking simulations. Finally the best two compounds were presented and would be useful to treat type 2 diabetes.