• Clean Technology
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• v.17 no.2
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• pp.150-155
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• 2011

The removal process of heavy metal ion in aqueous solution by the functionalized silica bead was simulated using the finite difference method. Equilibrium model and non-equilibrium model were proposed and the effects of dimensionless groups and various parameters were investigated. Freundlich isotherm was used in equilibrium model and 1st order adsorption rate expression was assumed in non-equilibrium model. The comparison results by the predictions of equilibrium and non-equilibrium models showed good agreement. The predictions of equilibrium model were compared with experimental results reported in literature and showed the marginal agreement.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.39 no.3
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• pp.58-65
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• 2002

In order to reduce blocking artifacts in inter-frame coded images, we propose a new image restoration algorithm, which directly processes differential images before reconstruction. We note that blocking artifact in inter-frame coded images is caused by both 8$\times$8 DCT and 16$\times$16 macroblock based motion compensation, while that of intra-coded images is caused by 8$\times$8 DCT only. According to the observation, we Propose a new degradation model for differential images and the corresponding restoration algorithm that utilizes additional constraints and convex sets for discontinuity inside blocks. The proposed restoration algorithm is a modified version of standard regularization that incorporate!; spatially adaptive lowpass filtering with consideration of edge directions by utilizing a part of DCT coefficients. Most of video coding standard adopt a hybrid structure of block-based motion compensation and block discrete cosine transform (BDCT). By this reason, blocking artifacts are occurred on both block boundary and block interior For more complete removal of both kinds of blocking artifacts, the restored differential image must satisfy two constraints, such as, directional discontinuities on block boundary and block interior Those constraints have been used for defining convex sets for restoring differential images.

• Journal of computational fluids engineering
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• v.8 no.3
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• pp.50-55
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• 2003

Recent numerical simulation has a tendency to require the higher-order accuracy in time, as well as in space. This tendency is more true in LES and acoustic noise simulation. In the present work, the accuracy of a Fractional step method, which is widely used in LES simulation, has been increased to the fourth-order accurate compact Pade discretization. To validate the present code, the flow-field past a cylinder was simulated and compared with experiment. A good agreement with experiment was achieved.

• Proceedings of the KIEE Conference
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• 2011.07a
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• pp.1962-1963
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• 2011

본 연구에서는 패턴분류를 위해 최적화된 방사형 기저 함수 신경회로망(Radial Basis Function Neural Networks) 분류기를 제안한다. RBFNN은 입력층, 은닉층, 출력층의 3층 구조로 되어 있으며 Multi Dimension, Predictive ability, Robustness한 특징이 있다. RBFNN의 은닉층에는 기존의 활성함수가 아닌 Fuzzy C-means 클러스터링 알고리즘을 사용하여 입력 데이터의 특성을 고려한 적합도를 사용하였다. RBFNN은 은닉층의 노드수와 FCM 클러스터링의 퍼지화 계수, 연결가중치의 다항식 타입이 모델의 성능의 향상에 영향을 미치기 때문에 최적화가 필요하며 본 논문에서는 Differential Evolution(DE) 알고리즘을 사용하여 모델의 구조 및 파라미터를 최적화시켜 모델의 성능을 향상시켰다. 제안된 모델을 평가하기 위해 패턴분류에 많이 사용되는 Iris 데이터와 Wine 데이터를 이용하였다.

• Journal of the Korean Geotechnical Society
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• v.34 no.7
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• pp.29-38
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• 2018

Three-dimensional continuum modeling of dynamic soil-pile-structure interaction embedded in a liquefiable sand was carried out. Finn model which can model liquefaction behavior using effective stress method was adopted to simulate development of pore water pressure according to shear deformation of soil directly in real time. Finn model was incorporated into Non-linear elastic, Mohr-Coulomb plastic model. Calibration of proposed modeling method was performed by comparing the results with those of the centrifuge tests performed by Wilson (1998). Excess pore pressure ratio, pile bending moment, pile head displacement-time history according to depth calculated by numerical analysis agreed reasonably well with the test results. Validation of the proposed modeling method was later performed using another test case, and good agreement between the computed and measured values was observed.

• The Sea:JOURNAL OF THE KOREAN SOCIETY OF OCEANOGRAPHY
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• v.28 no.4
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• pp.133-142
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• 2023

In this paper, we built a data-driven artificial intelligence model using RNN-LSTM (Recurrent Neural Networks-Long Short-Term Memory) to predict the Lorenz system, and examined the possibility of whether this model can replace chaotic dynamic models. We confirmed that the data-driven model reflects the chaotic nature of the Lorenz system, where a small error in the initial conditions produces fundamentally different results, and the system moves around two stable poles, repeating the transition process, the characteristic of "deterministic non-periodic flow", and simulates the bifurcation phenomenon. We also demonstrated the advantage of adjusting integration time intervals to reduce computational resources in data-driven models. Thus, we anticipate expanding the applicability of data-driven artificial intelligence models through future research on refining data-driven models and data assimilation techniques for data-driven models.

• Water for future
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• v.29 no.5
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• pp.235-244
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• 1996

A one-dimensional eddy diffusion model and a mixed-layer model are developed and applied to simulate the vertical temperature profiles in lakes. Also the running results of each method are compared and analyzed. In an eddy diffusion model, molecular diffusivity is neglected and eddy diffusivity which does not need lake-specific fitting parameter and constant lake's level are applied. The heat exchanges at the water surface and the bottom are formulated by the energy balance and zero energy gradient, respectively. In a mixed-layer model, two layers approach which has a constant thickness is adopted. Application of these models which use explicit finite difference an Runge-Kutta methods respectively demonstrates that the models efficiently simulate water temperatures.

• Proceedings of the Korea Information Processing Society Conference
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• 2024.05a
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• pp.362-363
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• 2024

최근 자동차의 네트워크화와 연결성이 증가함에 따라, CAN(Controller Area Network) bus 의 설계상 취약점이 보안 위협으로 대두되고 있다. 이에 대응하여 CAN bus 의 취약점을 극복하고 보안을 강화하기 위해 머신러닝을 활용한 침입 탐지 시스템에 대한 연구가 필요하다. 본 논문은 XGBoost 를 활용한 비정상 분류 방법론을 제안한다. 고려대학교 해킹 대응 기술 연구실에서 개발한 데이터 세트를 기반으로 실험을 수행한 결과, 초기 모델의 정확도는 96%였다. 그러나 추가적으로 TimeDiff(발생 간격)과 DataDiff(바이트의 차분 값)을 모델에 통합하면서 정확도가 3% 상승하였다. 본 논문은 향후에 보다 정교한 머신러닝 알고리즘과 데이터 전처리 기법을 적용하여 세밀한 모델을 개발하고, 업체의 CAN Database 를 활용하여 데이터 분석을 보다 정확하게 수행할 계획이다. 이를 통해 보다 신뢰성 높은 자동차 네트워크 보안 시스템을 구축할 수 있을 것으로 기대된다.

• Journal of the Society of Naval Architects of Korea
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• v.33 no.2
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• pp.1-12
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• 1996

Hydrodynamic characteristics of shipping water on deck are investigated by using a simplified two-dimensional model. Formulation of the shipping water on deck leads to a nonlinear hyperbolic system of equations based on the shallow-water wave theory. Time-domain solution of these equations are obtained numerically using a finite difference method which adopts predictor-corrector method for time-marching and 2nd upwind differencing method for convection term calculation. To confirm the validity of the present numerical method, we calculated some shallow-water wave problems accompanying a bore and compared the obtained results with the analytic solutions. We found good agreements between them. Though the calculation results of shipping water on deck, we show that the shipping water flows into the deck as a rarefying wave arid grows into a bore after colliding with a deck structure. Also we examined the effects of acceleration and slope of deck and found that they have significant influences on the flow of shipping water.

• Journal of the Korean Institute of Intelligent Systems
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• v.23 no.5
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• pp.473-478
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• 2013

In this paper, we develop the Heavy Rain Advisory Decision Model based on intelligent neuro-fuzzy algorithm RBFNNs by using KLAPS(Korea Local Analysis and Prediction System) Reanalysis data. the prediction ability of existing heavy rainfall forecasting systems is usually affected by the processing techniques of meteorological data. In this study, we introduce the heavy rain forecast method using the pre-processing techniques of meteorological data are in order to improve these drawbacks of conventional system. The pre-processing techniques of meteorological data are designed by using point conversion, cumulative precipitation generation, time series data processing and heavy rain warning extraction methods based on KLAPS data. Finally, the proposed system forecasts cumulative rainfall for six hours after future t(t=1,2,3) hours and offers information to determine heavy rain advisory. The essential parameters of the proposed model such as polynomial order, the number of rules, and fuzzification coefficient are optimized by means of Differential Evolution.