• Membrane Journal
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• v.3 no.3
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• pp.108-116
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• 1993

In PVC/PS pelyblend laminated membranes, perrneabilities were increased as increasing the blend ratio of PS and selectivities were increased with increasing the blend ratio of PVC. The gas permeation mechanism was shifted from the combination of Poiseuille and Knudsen flow model to the solution-diffusion model as decreasing the PS blend ratio. The structure of polyblend laminated membranes showed series model, where PS rich phase was formed at air side and PVC rich phase was at water side. The model of permeation in the polyblend laminated membranes also showed series model structure.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.36 no.7
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• pp.703-711
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• 2008

In this paper, stabilizer which maintains the mixture ratio of gas generator of LRE has been introduced. Design criterion for the ideal performance of stabilizer was derived. Significant parameters on the performance of stabilizer were identified through mathematical model and gas generator system analysis. Also, simulation and test results of the gas generator system showed fair agreement, thus proving the validity of the mathematical model of the stabilizer.

• Proceedings of the Korean Nuclear Society Conference
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• 1998.05a
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• pp.738-744
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• 1998

본 논문에서는 DSNP로 개발된 EBR-II 시뮬레이션 프로그램을 이용하여 SHRT-45실험을 모의해석하고 실험결과와 비교 분석하였다. ULOF 사고시 노심과 계통에서 발생하늘 주요 현상학적인 특성과 이를 모의하기 위한 해석모델에 대하여 논의하였다. 특허 일차원적인 DSNP 코드로써 원자로 풀 내부에서 소듐의 혼합 및 성층화 현상과 같은 다차원적인 거동을 모의하는 방법을 검토하였다. 원자로 풀에서의 혼합모델을 적절히 조정함으로써 DSNP 코드는 일반적으로 ULOF 과도거동을 잘 예측하였다. SHRT-45 모의해석 결과, ULOF 발생시 금속핵연료를 사용하는 EBR-II노심의 고유 안전성과 피동 붕괴열 제거능력이 입증되었으며 이는 실험결과와 일치하였다.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.33 no.11
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• pp.57-64
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• 2005

Large Eddy Simulation (LES) of turbulent premixed combustion flow is performed by using the dynamic subgrid scale model based on -equation describing the flame front propagation. After introducing the LES governing equations with dynamic subgrid scale (DSGS) model newly introduced into the -equation, the turbulent premixed combustion flow over backward facing step is analyzed to validate present formulation. The calculated results can predict the velocity and temperature of the combustion flow in good agreement with the experiment data.

• Proceedings of the Korean Information Science Society Conference
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• 2012.06b
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• pp.396-398
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• 2012

대부분의 실세계 데이터는 시간성을 띄고 있으므로 시간성을 지닌 데이터를 분석할 수 있는 기계 학습 방법론은 매우 중요하다. 이런 관점에서 비디오 데이터는 다양한 모달리티가 결합된 대표적인 시간 데이터 이므로 비디오 데이터를 대상으로 하는 기계 학습 방법은 큰 의미를 갖는다. 본 논문에서는 음성 채널에기반한 비디오 데이터 분석 방법의 예비 연구로 비디오 데이터에 등장하는 화자를 인식할 수 있는 간단한 방법을 소개한다. 제안 방법은 MFCC (Mel-frequency cepstrum coefficients)를 이용하여 인간 음성 특성의 분포를 분석한 후 분석 결과를 신경망에 입력하여 목표한 화자를 인식하는 복합 신경망 모델을 특징으로 한다. 실제 TV 드라마 데이터에서 가우시안 혼합모델, 가우시안 혼합 신경망 모델, 제안 방법의 화자 인식 성능을 비교한 결과 제안 방법이 가장 우수한 인식 성능을 보임을 확인하였다.

• The Korean Journal of Applied Statistics
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• v.27 no.2
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• pp.263-275
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• 2014

The present study used a hierarchical Bayesian approach was used to develop a mixed effect model to describe the transitional behavior of subjects in time nonhomogeneous Markov chains. The posterior distributions of model parameters were not in analytically tractable forms; subsequently, a Gibbs sampling method was used to draw samples from full conditional posterior distributions. The proposed model was implemented with real data.

• The Journal of the Acoustical Society of Korea
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• v.24 no.7
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• pp.410-415
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• 2005

Recently, most of the speaker verification systems are based on the pattern recognition approach method. And performance of the pattern-classifier depends on how to classify a variety of speakers' feature parameters. In order to classify feature parameters efficiently and effectively, it is of great importance to enlarge variations between speakers and effectively measure distances between feature parameters. Therefore, this paper would suggest the positively mixed model scheme that can enlarge inter-speaker variation by searching the individual model with world model at the same time. During decision procedure, we can maximize inter-speaker variation by using the proposed mixed model scheme. We also make use of a symbol probability weighting function in this system so as to reduce vector quantization errors by measuring symbol probability derived from the distance rate of between the world codebook and individual codebook. As the result of our experiment using this method, we could halve the Detection Cost Function (DCF) of the system from $2.37\%\;to\;1.16\%$.

• Journal of Korean Society of Environmental Engineers
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• v.27 no.1
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• pp.67-74
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• 2005

Toxicity tests were performed to evaluate the feasibility of application with prediction models to 10 mixture chemicals (chloroneb, butylbenzylphthalate, pendimethaline, di-n-butylphthalate, di-iso-butylphthalate, diazinon, isofenphos, 2-chlorophenol, 2,4,6-trichlorophenol and p-octylphenol) detected in effluents from wastewater treatment plants (WWTPs). Ten chemicals were selected in the basis of their toxicities to Daphnia magna and the concentrations in effluents measured by GC/MS. Three models including concentration addition (CA), independent action (IA) and effect summation (ES) were employed for the comparison of the predicted and the observed mortality of D. magna exposed to 10 mixture chemicals for 48 hours. With a comparative study it was ineffective to predict the mortality through the CA and the ES prediction model, while the IA prediction model showed a high correlation($r^2\;=\;0.85$). Moreover, the ES model over-estimated the toxicity observed by bioassay experiments about five-fold. Consequently, IA model is a reasonable tool to predict the mixture toxicity of the discharging water from WWTPs.

• The Journal of the Acoustical Society of Korea
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• v.22 no.7
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• pp.519-527
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• 2003

Speech is much influenced by the existence of outliers which are introduced by such an unexpected happenings as additive background noise, change of speaker's utterance pattern and voice detection errors. These kinds of outliers may result in severe degradation of speaker recognition performance. In this paper, we proposed the GMM based on robust principal component analysis (RPCA-GMM) using M-estimation to solve the problems of both ouliers and high dimensionality of training feature vectors in speaker identification. Firstly, a new feature vector with reduced dimension is obtained by robust PCA obtained from M-estimation. The robust PCA transforms the original dimensional feature vector onto the reduced dimensional linear subspace that is spanned by the leading eigenvectors of the covariance matrix of feature vector. Secondly, the GMM with diagonal covariance matrix is obtained from these transformed feature vectors. We peformed speaker identification experiments to show the effectiveness of the proposed method. We compared the proposed method (RPCA-GMM) with transformed feature vectors to the PCA and the conventional GMM with diagonal matrix. Whenever the portion of outliers increases by every 2%, the proposed method maintains almost same speaker identification rate with 0.03% of little degradation, while the conventional GMM and the PCA shows much degradation of that by 0.65% and 0.55%, respectively This means that our method is more robust to the existence of outlier.

• Korean Journal of Remote Sensing
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• v.33 no.2
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• pp.135-147
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• 2017

We, for the first time, estimated daily and monthly surface nitrogen dioxide ($NO_2$) volume mixing ratio (VMR) using three regression models with $NO_2$ tropospheric vertical column density (OMIT-rop $NO_2$ VCD) data obtained from Ozone Monitoring Instrument (OMI) in Seoul in South Korea at OMI overpass time (13:45 local time). First linear regression model (M1) is a linear regression equation between OMI-Trop $NO_2$ VCD and in situ $NO_2$ VMR, whereas second linear regression model (M2) incorporates boundary layer height (BLH), temperature, and pressure obtained from Atmospheric Infrared Sounder (AIRS) and OMI-Trop $NO_2$ VCD. Last models (M3M & M3D) are a multiple linear regression equations which include OMI-Trop $NO_2$ VCD, BLH and various meteorological data. In this study, we determined three types of regression models for the training period between 2009 and 2011, and the performance of those regression models was evaluated via comparison with the surface $NO_2$ VMR data obtained from in situ measurements (in situ $NO_2$ VMR) in 2012. The monthly mean surface $NO_2$ VMRs estimated by M3M showed good agreements with those of in situ measurements(avg. R = 0.77). In terms of the daily (13:45LT) $NO_2$ estimation, the highest correlations were found between the daily surface $NO_2$ VMRs estimated by M3D and in-situ $NO_2$ VMRs (avg. R = 0.55). The estimated surface $NO_2$ VMRs by three modelstend to be underestimated. We also discussed the performance of these empirical modelsfor surface $NO_2$ VMR estimation with respect to otherstatistical data such asroot mean square error (RMSE), mean bias, mean absolute error (MAE), and percent difference. This present study shows a possibility of estimating surface $NO_2$ VMR using the satellite measurement.