• Proceedings of the KSR Conference
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• 1998.11a
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• pp.361-368
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• 1998

This paper sums up the study of the soundproof facilities to be placed on the test track section within the Seoul-Pusan H.S.T project. The objective of this study is to determine optimum design of soundproof including height, length, location, sound absorbing materials for test track(chonan-taejon). This paper shows the model to design the shape and materials of noise barrier for high speed trains(TGV, ICE, etc). Noise prediction is to be conducted by MITHRA. Various parameters affecting the noise propagation outdoors are surveyed and discussed in relation to H.S.T. noise.

• Proceedings of the IEEK Conference
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• 2000.07b
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• pp.1067-1071
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• 2000

This paper presents a new 3-D ray tracing technique based on the image theory with newly defined ray tubes. The proposed method can be applied to indoor environments with arbitrary building layouts and has high computational efficiency compared to the precedent methods resorting to the ray launching scheme. Its predictions are in good agreement with the measurements

• Journal of electromagnetic engineering and science
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• v.1 no.1
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• pp.48-53
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• 2001

This paper presents a new 3-D ray tracing technique based on the image theory with newly defined ray tubes. The proposed method can be applied to indoor environments with arbitrary building layouts and has high computational efficiency compared to the precedent methods resorting to the ray launching scheme. It predictions are in good agreement with the measurements.

• Computers and Concrete
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• v.21 no.5
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• pp.505-511
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• 2018

This paper investigates the potential of a hybrid model which combines the least squares support vector machine (LSSVM) and an improved particle swarm optimization (IMPSO) techniques for prediction of concrete compressive strength. A modified PSO algorithm is employed in determining the optimal values of LSSVM parameters to improve the forecasting accuracy. Experimental data on concrete compressive strength in the literature were used to validate and evaluate the performance of the proposed IMPSO-LSSVM model. Further, predictions from five models (the IMPSO-LSSVM, PSO-LSSVM, genetic algorithm (GA) based LSSVM, back propagation (BP) neural network, and a statistical model) were compared with the experimental data. The results show that the proposed IMPSO-LSSVM model is a feasible and efficient tool for predicting the concrete compressive strength with high accuracy.

• Journal of the Korean Society for Precision Engineering
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• v.15 no.4
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• pp.15-26
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• 1998

Full car model is needed for investigating as a entire dynamics of vehicle. In this study, 7DOF of full car model's dynamics is selected. This paper proposes the output feedback controller based on optimal control theory. Input data and output data from the optimal controller are used for neural network system identification of the suspension system. To do system identification, neural network which has robustness against nonlinearities and disturbances is adapted. This study uses back-propagation algorithm to train a multil-layer neural network. After obtaining a neural network model of a suspension system, a neuro-controller is designed. Neuro-controller controls suspension system with off-line learning method and multistep ahead prediction model based on the neural network model and a neuro-controller. The optimal controller and the neuro-controller are designed and then, both performances are compared through. For simulation, sinusoidal and rectangular virtual bumps are selected.

• Journal of the Korean Society of Manufacturing Process Engineers
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• v.14 no.3
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• pp.65-73
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• 2015

This paper presents a method of predicting the machining parameters on the turning process of low carbon steel using a neural network with back propagation (BP) and particle swarm optimization (PSO). Cutting speed, feed rate, and depth of cut are used as input variables, while surface roughness and electric current consumption are used as output variables. The data from experiments are used to train the neural network that uses BP and PSO to update the weights in the neural network. After training, the neural network model is run using test data, and the results using BP and PSO are compared with each other.

• Proceedings of the Computational Structural Engineering Institute Conference
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• 2001.10a
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• pp.375-382
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• 2001

This study is attempted to predict experimentally the fatigue crack propagation behavior of two-span beams with steel fibrous for various steel fibrous contents. The static tests and the fatigue tests were performed on a series of SFRC(steel fibrous reinforced concrete) to investigate the fatigue behavior of SFRC varying with the steel fibrous contents. Through this test, the diagonal cracking loads, ultimate loads, deflections, strains of concrete and steels. Fatigue crack length were measured by the eye-observation. As a result of test, A model for S-N relationship, and propagation life of fatigue crack of SFRC was proposed. The crack growth and failure of SFRC beams were studied.

• Convergence Security Journal
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• v.8 no.4
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• pp.173-181
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• 2008

Recently, Worm epidemic models have been developed in response to the cyber threats posed by worms in order to analyze their propagation and predict their spread. Some of the most important ones involve mathematical model techniques such as Epidemic(SI), KM (Kermack-MeKendrick), Two-Factor and AAWP(Analytical Active Worm Propagation). However, most models have several inherent limitations. For instance, they target worms that employ random scanning in the network such as CodeRed worm and it was able to be applied to the specified threats. Therefore, we propose the probabilistic of worm propagation based on the Markov Chain, which can be applied to cyber threats such as Mass SQL Injection worm. Using the proposed method in this paper, we can predict the occurrence probability and occurrence frequency for each threats in the entire system.

• Journal of Korea Water Resources Association
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• v.43 no.12
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• pp.1011-1027
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• 2010

In hydrologic modeling, prediction uncertainty generally stems from various uncertainty sources associated with model structure, data, and parameters, etc. This study aims to assess the parameter uncertainty effect on hydrologic prediction results. For this objective, a distributed rainfall-sediment yield-runoff model, which consists of rainfall-runoff module for simulation of surface and subsurface flows and sediment yield module based on unit stream power theory, was applied to the mesoscale mountainous area (Cheoncheon catchment; 289.9 $km^2$). For parameter uncertainty evaluation, the model was calibrated by a multi-objective optimization algorithm (MOSCEM) with two different objective functions (RMSE and HMLE) and Pareto optimal solutions of each case were then estimated. In Case I, the rainfall-runoff module was calibrated to investigate the effect of parameter uncertainty on hydrograph reproduction whereas in Case II, sediment yield module was calibrated to show the propagation of parameter uncertainty into sedigraph estimation. Additionally, in Case III, all parameters of both modules were simultaneously calibrated in order to take account of prediction uncertainty in rainfall-sediment yield-runoff modeling. The results showed that hydrograph prediction uncertainty of Case I was observed over the low-flow periods while the sedigraph of high-flow periods was sensitive to uncertainty of the sediment yield module parameters in Case II. In Case III, prediction uncertainty ranges of both hydrograph and sedigraph were larger than the other cases. Furthermore, prediction uncertainty in terms of spatial distribution of erosion and deposition drastically varied with the applied model parameters for all cases.

• Computers and Concrete
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• v.25 no.2
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• pp.133-144
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• 2020

This paper aims to study vibration characteristics of chiral and zigzag double-walled carbon nanotubes entrenched on Donnell shell model. The Eringen's nonlocal elastic equations are being combined with Donnell shell theory to observe small scale response. Wave propagation is proposed technique to establish field equations of model subjected to four distinct end supports. A nonlocal model has been formulated to explore the frequency spectrum of both chiral and zigzag double-walled CNTs along with diversity of indices and nonlocal parameter. The significance of scale effect in relevance of length-to-diameter and thickness- to- radius ratios are discussed and displayed in detail. The numerical solution based on this nonlocal Donnell shell model can be further used to predict other frequency phenomena of double-walled and multi-walled CNTs.