• Tunnel and Underground Space
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• v.31 no.6
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• pp.593-609
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• 2021

We proposed a numerical method for the thermo-mechanical behavior of rock fracture using a grain-based distinct element model (GBDEM) and simulated thermally induced fracture slip. The present study is the benchmark simulation performed as part of DECOVALEX-2023 Task G, which aims to develop a numerical method to estimate the coupled thermo-hydro-mechanical processes within the crystalline rock fracture network. We represented the rock sample as an assembly of Voronoi grains and calculated the interaction of the grains (blocks) and their interfaces (contacts) using a distinct element code, 3DEC. Based on an equivalent continuum approach, the micro-parameters of grains and contacts were determined to reproduce rock as an elastic material. Then, the behavior of the fracture embedded in the rock was characterized by the contacts with Coulomb shear strength and tensile strength. In the benchmark simulation, we quantitatively examined the effects of the boundary stress and thermal stress due to heat conduction on fracture behavior, focusing on the mechanism of thermally induced fracture slip. The simulation results showed that the developed numerical model reasonably reproduced the thermal expansion and thermal stress increment, the fracture stress and displacement and the effect of boundary condition. We expect the numerical model to be enhanced by continuing collaboration and interaction with other research teams of DECOVALEX-2023 Task G and validated in further study experiments.

• Journal of Korean Society of Coastal and Ocean Engineers
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• v.34 no.1
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• pp.11-18
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• 2022

In order to improve the accuracy of particle tracking prediction techniques near the Korean Strait, this study compared and analyzed a particle tracking model based on a seawater flow numerical model and a machine learning based on a particle tracking model using field observation data. The data used in the study were the surface drifter buoy movement trajectory data observed in the Korea Strait, prediction data by machine learning (linear regression, decision tree) using the tide and wind data from three observation stations (Gageo Island, Geoje Island, Gyoboncho), and prediciton data by numerical models (ROMS, MOHID). The above three data were compared through three error evaluation methods (Correlation Coefficient (CC), Root Mean Square Errors (RMSE), and Normalized Cumulative Lagrangian Separation (NCLS)). As a final result, the decision tree model had the best prediction accuracy in CC and RMSE, and the MOHID model had the best prediction results in NCLS.

• Tunnel and Underground Space
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• v.30 no.6
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• pp.573-590
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• 2020

This study presents the current status of DECOVALEX-2023 project Task G and our research results so far. Task G, named 'Safety ImplicAtions of Fluid Flow, Shear, Thermal and Reaction Processes within Crystalline Rock Fracture NETworks (SAFENET)' aims at developing a numerical method to simulate the fracture creation and propagation, and the coupled thermohydro-mechanical processes in fracture in crystalline rocks. The first research step of Task G is a benchmark simulation, which is designed for research teams to make their modelling codes more robust and verify whether the models can represent an analytical solution for displacements of a single rock fracture. We reproduced the mechanical behavior of rock and embedded single fracture using a three-dimensional grain-based distinct element model for the simulations. In this method, the structure of the rock was represented by an assembly of rigid tetrahedral grains moving independently of each other, and the mechanical interactions at the grains and their contacts were calculated using 3DEC. The simulation results revealed that the stresses induced along the embedded fracture in the model were relatively low compared to those calculated by stress analysis due to stress redistribution and constrained fracture displacements. The fracture normal and shear displacements of the numerical model showed good agreement with the analytical solutions. The numerical model will be enhanced by continuing collaboration and interaction with other research teams of DECOVALEX-2023 Task G and validated using various experiments in a further study.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.11 no.3
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• pp.584-591
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• 2007

The recognition of human gestures in image sequences is an important and challenging problem that enables a host of human-computer interaction applications. This paper describes a gesture recognition algorithm based on the particle filters, namely CONDENSATION. The particle filter is more efficient than any other tracking algorithm because the tracking mechanism follows Bayesian estimation rule of conditional probability propagation. We used two models for the evaluation of particle filter and apply the MAILAB for the preprocessing of the image sequence. But we implement the particle filter using the C++ to get the high speed processing. In the experimental results, it is demonstrated that the proposed algorithm prove to be robust in the cluttered environment.

• Journal of Korean Society of Coastal and Ocean Engineers
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• v.34 no.3
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• pp.72-81
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• 2022

Positions of five drifting buoys deployed on August 2020 near southwestern area of Jeju Island and numerically predicted velocities were used to develop five Artificial Intelligence-based models (AI models) for the prediction of particle tracks. Five AI models consisted of three machine learning models (Extra Trees, LightGBM, and Support Vector Machine) and two deep learning models (DNN and RBFN). To evaluate the prediction accuracy for six models, the predicted positions from five AI models and one numerical model were compared with the observed positions from five drifting buoys. Three skills (MAE, RMSE, and NCLS) for the five buoys and their averaged values were calculated. DNN model showed the best prediction accuracy in MAE, RMSE, and NCLS.

• Proceedings of the KIEE Conference
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• 2006.10c
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• pp.217-219
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• 2006

본 연구는 정보입자와 유전알고리즘의 기호코딩을 통해 퍼지집합 기반 다항식 뉴럴네트워크(IG based gFSPNN)의 최적 설계 제안한다. 기존의 Furry Srt-based Polynomial Neural Networks의 최적설계를 위해 유전자 알고리즘의 이진코딩을 사용하였다. 이지코딩은 스티링 길이 때문에 연산시간이 급격히 증가되는 현상과 해밍절벽(Hamming Cliff)에 따른 급격한 비트변환이 힘들다는 단점이 내제 하였다. 이에 본 논문에서는 스티링 길이와 해밍절벽에 따른 문제를 해결 하기위해 기호코딩을 사용하였다._데이터들의 특성을 모델에 반영하기 위해 Hard C-Means(HCM)을 결합한 Information Granulation(IG)을 사용하여 최적모델 구축 속도를 빠르게 하였다. 실험적 예제를 통하여 제안된 모델의 성능을 평가한다.

• Journal of the Korean Society of Marine Environment & Safety
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• v.24 no.1
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• pp.119-125
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• 2018

The particle based computational fluid dynamics (CFD) method, which follow Lagrangian approach for fluid dynamics, fluid particle behavior by tracking all particle calculation physical quantities of each particle. According to basic concept of particle based CFD method, it is difficult to satisfy continuum theory and measure influences from neighboring particle. Article number density and weight function were used to solve aforementioned issue. Difficulties continuum mean simulate non-continuum particles such as solid including granular and sand. In this regard, the particle based CFD method modified solid particle problems by replacing viscous and surface tension forces friction and drag forces. In this paper, particle interaction model for solid particle friction model implemented to simulate solid particle problems. The broken dam problem, which is common to verify particle based CFD method, used fluid or solid particles. The angle of repose was observed in the simulation results the solid particle not fluid particle.

• Korean Chemical Engineering Research
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• v.58 no.3
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• pp.480-485
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• 2020

The extended Maxwell-Wagner polarization model is employed to describe the ER behavior of the conducting particle ER suspensions, and solutions to the equation of motion are obtained by dynamic simulation. The simulation results show the nonlinear ER behavior (Δτ∝Eⁿ, n≈1.5) of the conducting particle ER suspensions. The response point, where shear stress reaches steady-state, is the point where stable break-up and rebuild of the chain-like structure of particles reaches. Also, it shows the minimum of shear stress, which corresponds the start-up of random particle configuration. The shear stress reaches plateau as particle volume fraction increases.

• Water for future
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• v.53 no.8
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• pp.48-54
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• 2020

• Journal of the Korean Institute of Intelligent Systems
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• v.14 no.5
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• pp.539-544
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• 2004

We investigate a visual object tracking algorithm based upon particle filters, namely CONDENSATION, in order to combine multiple observation models such as active contours of digitally subtracted image and the particle measurement of object color. The former is applied to matching the contour of the moving target and the latter is used to independently enhance the likelihood of tracking a particular color of the object. Particle filters are more efficient than any other tracking algorithms because the tracking mechanism follows Bayesian inference rule of conditional probability propagation. In the experimental results, it is demonstrated that the suggested contour tracking particle filters prove to be robust in the cluttered environment of robot vision.