• Journal of Radiation Protection and Research
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• v.31 no.4
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• pp.173-179
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• 2006

The Monte Carlo simulation code CEARPPU has been developed and updated to provide pulse pile-up correction spectra for high counting rate cases. For neutron activation analysis, CEARPPU correction spectra were used in library least-squares method to give better isotopic activity results than the convention library least-squares fitting with uncorrected spectra.

• Journal of the Korean Institute of Telematics and Electronics A
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• v.32A no.3
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• pp.100-109
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• 1995

A new Hydrodynamic model for the high energy tail electrons(Tail Electron Hydrodynamic Model : TEHD) is developed using the moment method. The Monte Carlo method is applied to a $n^{+}-n^{-}-n^{+}$ device to calibrate the TEHD equations. the discretization method and numerical procedures are explained. New models for the impact ionization and injection into the gate oxide using the tail electron density are proposed. The simulated results of the impact ionization rate for a $n^{+}-n^{-}-n^{+}$ device and MOSFET devices, and the gate injection experiment are shown to give good agreement with the Monte Carlo simulation and the measurements.

• KIEE International Transactions on Power Engineering
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• v.4A no.2
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• pp.69-72
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• 2004

Nowadays, one of the serious problems in the KEPCO system is a much higher fault current than the SCC (Short Circuit Capacity) of the circuit breaker. Since superconductivity technology has become more developed, the HTS-FCL (High Temperature Superconductor-Fault Current Limiter) may become an attractive alternative to solving the fault current problem. In order to achieve the best performance, the parameters of HTS-FCL should be designed optimally. Under this setting, this paper presents the optimal design method of parameters for resistive type HTS-FCL using the Monte Carlo technique.

• Smart Structures and Systems
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• v.26 no.2
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• pp.175-184
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• 2020

Conventional Monte Carlo simulation-based methods for seismic risk assessment of water networks often require excessive computational time costs due to the hydraulic analysis. In this study, an Artificial Neural Network-based surrogate model was proposed to efficiently evaluate the flow-based system reliability of water distribution networks. The surrogate model was constructed with appropriate training parameters through trial-and-error procedures. Furthermore, a deep neural network with hidden layers and neurons was composed for the high-dimensional network. For network training, the input of the neural network was defined as the damage states of the k-dimensional network facilities, and the output was defined as the network system performance. To generate training data, random sampling was performed between earthquake magnitudes of 5.0 and 7.5, and hydraulic analyses were conducted to evaluate network performance. For a hydraulic simulation, EPANET-based MATLAB code was developed, and a pressure-driven analysis approach was adopted to represent an unsteady-state network. To demonstrate the constructed surrogate model, the actual water distribution network of A-city, South Korea, was adopted, and the network map was reconstructed from the geographic information system data. The surrogate model was able to predict network performance within a 3% relative error at trained epicenters in drastically reduced time. In addition, the accuracy of the surrogate model was estimated to within 3% relative error (5% for network performance lower than 0.2) at different epicenters to verify the robustness of the epicenter location. Therefore, it is concluded that ANN-based surrogate model can be utilized as an alternative model for efficient seismic risk assessment to within 5% of relative error.

• Nuclear Engineering and Technology
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• v.52 no.10
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• pp.2361-2369
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• 2020

Compton cameras using large scintillators have been developed for high imaging sensitivity. These scintillator-based Compton cameras, however, mainly due to relatively low energy resolution, suffer from undesired background-radiation signals, especially when radioactive materials' activity is very low or their location is far from the Compton camera. To alleviate this problem for a large-size Compton camera, in the present study, a hybrid-type shielding system was designed that combines an active shield with a veto detector and a passive shield that surrounds the active shield. Then, the performance of the hybrid shielding system was predicted, by Monte Carlo radiation transport simulation using Geant4, in terms of minimum detectable activity (MDA), signal-to-noise ratio (SNR), and image resolution. Our simulation results show that, for the most cases, the hybrid shielding system significantly improves the performance of the large-size Compton camera. For the cases investigated in the present study, the use of the shielding system decreased the MDA by about 1.4, 1.6, and 1.3 times, increased the SNR by 1.2-1.9, 1.1-1.7, and 1.3-2.1 times, and improved the image resolution (i.e., reduced the FWHM) by 7-8, 1-6, and 3-5% for ¹³⁷Cs, ⁶⁰Co, and ¹³¹I point source located at 1-5 m from the imaging system, respectively.

• Computers and Concrete
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• v.3 no.2_3
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• pp.123-144
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• 2006

Reinforced concrete corbels are structural elements widely used in practical engineering. The complex response of these elements is described in design codes in a simplified manner. These formulations are not sufficient to show the real behavior, which, however, is an essential prerequisite for the manufacturing of numerous elements. Therefore, a deterministic and probabilistic study has been performed, which is described in this contribution. Real complex structures have been modeled by means of the finite element method supported primarily by experimental works. The main objective of this study was the detection of uncertainties effects and safety margins not captured by traditional codes. This aim could be fulfilled by statistical considerations applied to the investigated structures. The probabilistic study is based on advanced Monte Carlo simulation techniques and sophisticated nonlinear finite element formulations.

• The Bulletin of The Korean Astronomical Society
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• v.45 no.1
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• pp.68.2-68.2
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• 2020

UVO-Multiband Polarizing Imager System (UVOMPIS) is an ultraviolet to visible light multi-wavelength polarization/imaging system for Compact Advanced Satellite. We developed Linear Astigmatism Free-Three Mirror System (LAF-TMS) D200F2 as an optical system of UVOMPIS which has an entrance pupil diameter of 200 mm, a focal ratio of 2, a field of view of 2° × 4°. LAF-TMS is a confocal off-axis reflecting telescope system that removes linear astigmatism, and its all mirrors (M1, M2, M3) are optimized with the freeform surface to reduce high-order aberrations. Through the sensitivity analysis and Monte-Carlo simulation as the tolerance analysis, we can confirm the feasibility of the system, relatively sensitive parameters (tilt, decenter, despace, surface RMS error), and considerations for optomechanical design. From the sensitivity analysis, we can discover the relatively sensitive optical alignment parameters to a single perturbation. Further more, in the monte-carlo simulation, we investigate the minimum tolerance budget satisfying the required optical performance and whether the tolerance range is satisfied within manufacturing error.

• 한국정보디스플레이학회:학술대회논문집
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• 2006.08a
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• pp.371-374
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• 2006

We developed new quantum chemical molecular dynamics and kinetic Monte Carlo programs to simulate the destruction processes of MgO protecting layer in plasma display panel. Our simulation results proposed that MgO(111) surface with nano-dot structures covered by (001) facets has the highest stability, which is against the previous knowledge. The formation of nano-dot structures on the MgO(111) surface covered by (001) facets was found to be the reason for the high stability of the MgO(111) surface. Furthermore, the effect of grain boundary on the stability of MgO surfaces was also clarified.

• Transactions of the Korean Society of Mechanical Engineers B
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• v.31 no.4
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• pp.320-329
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• 2007

Electrokinesis and dielectrophoresis are important transport phenomena produced by external electric field applied to a microchannel containing a conductive fluid. We developed a CFD code to predict electrokinetic and dielectrophoretic flows in a microchannel with a uniform circular post array. Using the code, we calculated particle velocities driven by electrokinesis and dielectrophoresis, and conducted Monte Carlo simulations to visualize the particle motions. The code was validated by comparing the results with those from previous studies in literature. At a low electric field, electrokinesis and diffusion is the dominant transport mechanism. At a moderate electric field, dielectrophoresis is balanced with electrokinesis and diffusion, resulting in flowing filaments of particles in the microchannels. However, dielectrophoresis overwhelms the flow at a high electric field and traps particles locally. These results provide useful insight for optimizing design parameters of a microfluidic chip for biochemical analysis, especially for development of on-chip sample pretreatment techniques using electrokinetic and dielectrophoretic effects.

• Smart Structures and Systems
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• v.21 no.5
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• pp.601-609
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• 2018

The estimated probabilistic model of wind data based on the conventional approach may have high discrepancy compared with the true distribution because of the uncertainty caused by the instrument error and limited monitoring data. A sequential quadratic programming (SQP) algorithm-based finite mixture modeling method has been developed in the companion paper and is conducted to formulate the joint probability density function (PDF) of wind speed and direction using the wind monitoring data of the investigated bridge. The established bivariate model of wind speed and direction only represents the features of available wind monitoring data. To characterize the stochastic properties of the wind parameters with the subsequent wind monitoring data, in this study, Bayesian inference approach considering the uncertainty is proposed to update the wind parameters in the bivariate probabilistic model. The slice sampling algorithm of Markov chain Monte Carlo (MCMC) method is applied to establish the multi-dimensional and complex posterior distribution which is analytically intractable. The numerical simulation examples for univariate and bivariate models are carried out to verify the effectiveness of the proposed method. In addition, the proposed Bayesian inference approach is used to update and optimize the parameters in the bivariate model using the wind monitoring data from the investigated bridge. The results indicate that the proposed Bayesian inference approach is feasible and can be employed to predict the bivariate distribution of wind speed and direction with limited monitoring data.