• The Korean Journal of Applied Statistics
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• v.30 no.6
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• pp.957-981
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• 2017

A generalized linear mixed model is an extension of a generalized linear model that allows random effect as well as provides flexibility in developing a suitable model when observations are correlated or when there are other underlying phenomena that contribute to resulting variability. We describe maximum likelihood estimation methods for logistic regression models that include random effects - the Laplace approximation, Gauss-Hermite quadrature, adaptive Gauss-Hermite quadrature, and pseudo-likelihood. Applications are provided with social science problems by analyzing the effect of mental health and life satisfaction on volunteer activities from Korean welfare panel data; in addition, we observe that the inclusion of random effects in the model leads to improved analyses with more reasonable inferences.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.8 no.5
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• pp.516-524
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• 1997

The propagation properties of various pulse signal types(square pulse, Gaussian pulse, trapezoid pulse, RF pulse) on coupled microstrip lines are investigated. Numerical integration technique which has its accuracy and is easily simulated, is used to obtain the time domain response of pulse signals. Frequency-dependent characteristics of coupled microstrip line is obtained using Jansen's approximate equation. The propagation properties of pulse signal on coupled microstrip lines is analyzed regarding to its geometric structure (relative permittivity ${varepsilon}_r$ substrate height h, strip width w of the microstrip line) and pulse width ${\tau}$ of signal pulse. The simulation results show that space between two lines is very significant parameter in pulse distortion in comparison of any other parameters. The results of this paper are compatible to the trade-off determination of relative permittivity, substrate height, strip width and pulse width of signal pulse when a design of MIC and MMIC is necessary.

• Korean Journal of Remote Sensing
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• v.15 no.3
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• pp.195-208
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• 1999

In the classification of satellite images, the representative of training of classes is very important factor that affects the classification accuracy. Hence, in order to improve the classification accuracy, it is required to optimize pre-classification stage which determines classification parameters rather than to develop classifiers alone. In this study, the normality of training are calculated at the preclassification stage using SPOT XS and LANDSAT TM. A correlation coefficient of multivariate Q-Q plot with 5% significance level and a variance of initial training are considered as an object function of genetic algorithm in the training normalization process. As a result of normalization of training using the genetic algorithm, it was proved that, for the study area, the mean and variance of each class shifted to the population, and the result showed the possibility of prediction of the distribution of each class.

• Journal of The Institute of Information and Telecommunication Facilities Engineering
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• v.2 no.1
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• pp.22-29
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• 2003

A circuit model for the transition of stripline to dual slotline and a segmented method to analyze a notch antenna are presented. For the circuit model of the transition, the characteristic impedance, dispersions, and the shorted impedance of dual slotline are calculated and approximated with the closed-form expressions. The segmented analysis method allows to get readily an optimized results for the dual slotline-fed notch antenna. As a design example, a notch antenna is segmented into a 4'h order Marchand balun and a dual slot 134 notch antenna, and tested to show the validity of the proposed circuit model.

• Journal of Korea Society of Industrial Information Systems
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• v.22 no.1
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• pp.61-67
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• 2017

For a project network analysis, a fundamental problem is to estimate the distribution function of the project completion time. In this paper, we propose a method for evaluating moments(mean, variance, skewness, kurtosis) of the project completion time under the assumption that the durations of activities are independently and normally distributed. The proposed method utilizes the technique of discretization to replace the continuous probability density function(pdf) of activity duration with its discrete pdf and a random number generation. The proposed method is easy to use for large-sized project networks, and the computational results of the proposed method indicate that the accuracy is comparable to that of direct Monte Carlo simulation.

• Journal of the Korea Institute of Military Science and Technology
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• v.2 no.1
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• pp.30-37
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• 1999

Transmissivity and the signal loss in soil are computed. An electric field expression for the inhomogeneous complex media modelled by two layers is shown as an integral form. Volume scattering occurs in inhomogeneous media, and iterative Born approximation is used to analyze this scattering effect. The degree of randomness is controlled by specifying the variance and correlation length. Expression for the transmissivity and the signal loss is presented as the parameter of soil moisture contents, soil particle radius, temperature and frequency. The analysis shows that big deviation in signal loss depends on the temperature variation remarkably and the physical reason of unusual level is explained.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.12 no.1
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• pp.151-156
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• 2012

In this paper robust nonlinear image denoising algorithms are introduced for the distribution which is Gaussian in the center and Laplacian in the tails. The distribution is known as the least favorable ${\epsilon}$-contaminated normal distribution that maximizes the asymptotic variance. The proposed filter proves to be the maximum likelihood estimator under the heavy-tailed Gaussian noise environments. It is optimal in the respect of maximizing the efficacy under the above noise environment. Another filter for reducing impulsive noise is proposed by mixing with the myriad filter to propose an amplitude-limited myriad filter. Extensive experiment is conducted with images corrupted with ${\alpha}$-stable noise to analyze the behavior and performance of the proposed filters.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.25 no.7A
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• pp.1044-1049
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• 2000

Projection that can be used as a feature for image representation, includes much available informations such as approximated shape and location. But when we retrieve image using it, there are some disadvantage such as requiring much index data and making different length of projected vector for differenr image size. In order to overcome these problems, we propose a method of using block variance for the projected vector. We use block variance of the projection vector to localize the characteristics of image and to reduce the number of index data in database. Proposed algorithm can make use of statistical advantage through database including various size of images and be executed with fast response time in implementation.

• Proceedings of the Korea Water Resources Association Conference
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• 2022.05a
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• pp.4-4
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• 2022

낙동강 하구 환경/생태 복원을 위하여 "해수유입"으로 하구환경을 조성하는 사업이 추진되고 있으며, 해수 유입 규모와 빈도에 따른 생태환경변화를 예측하는 연구수요가 증가하고 있는 상황이다. 보다 구체적으로는 단기간의 해수유입에 의한 흐름 및 염분 확산범위 예측과 더불어 보다 장기간의 지형변화, 수질환경 변화, 생태환경 변화 등에 대한 예측이 필요한 상황이다. 그리고 그 예측의 대부분을 수치모델에 크게 의존하고 있는 상황이다. 그러나, 수치모형을 이용한 단기 예측은 가까운 미래에 대한 입력조건을 사용하여야 하기 때문에 입력조건에 대한 불확실성이 포함되고, 환경생태모형의 불확실성에 따른 예측 한계 등으로 인하여 오차가 누적되기 때문에 직접적인 활용에 크게 제한이 따를 수 있다. 또한 운영과정에서 어떤 분산, 편향 오차 등이 지속적으로 발생하는 경우, 모델 예측 결과에 대한 신뢰수준이 크게 감소하기 때문에 모델의 적절한 운영기법이 요구된다. 모델은 관심을 가지는 자연현상에 대한 근사(approximation)이고, 예상하지 못한 오차가 발생할 수 있기 때문에 관측 자료를 이용한 자료동화(data assimilation) 과정이 운영모델에서는 필수적인 부분이다. 이론적인 기반이 탄탄한 유체역학 기반 기상예측의 경우에도, 가용한 모든 지점의 관측 자료를 이용한 자료 동화과정을 통하여 모델 예측 결과를 개선하여 나가는 과정을 포함하여 운영하고 있다. 이 과정이 포함하는 중요한 개념은 수치모델이 가지고 있는 (예측 수준의) 한계를 인정하고, 수치모델에 전적으로 의존하는 것이 아니라 관측 자료를 이용하여 그 한계를 저감하여 나가는 과정이다. 모니터링은 모델의 한계를 알려주는 지표이다. 모델링과 모니터링의 불가피한 상호의존 관계를 의미하는 이 개념은 단기간의 흐름, 염분 확산 예측으로 한정되지 않고, 장기적인 변화가 예상되는 생태환경변화 모델에도 적용이 된다. 즉각적인 변화보다는 장기적인 관점에서 파악하여야 하는 생태학적인 변화는 보다 다양한 인자가 관여하기 때문에 어떤 측면에서는 모델보다는 적절한 빈도와 항목에 대한 관측계획 수립(monitoring design)이 더 중요하다고 할 수 있다. 이론적인 질량보존(mass conservation) 방정식을 기반으로 하는 모델은 다양한 현실적인 인자의 영향을 받기 때문에 모델의 한계를 인정하고, 모니터링 자료를 적극적으로 활용하여 불확실성을 저감하는 접근방식이 요구된다.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.33 no.5C
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• pp.413-421
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• 2008

The paper derives asymptotic formulas for the MSE distortion and the signal-to-noise ratio of a mismatched fixed-rate minimum MSE Laplacian quantizer. These closed-form formulas are expressed in terms of the number N of quantization points, the mean displacement $\mu$, and the ratio $\rho$ of the standard deviation of the source to that for which the quantizer is optimally designed. Numerical results show that the principal formula is accurate in that, for rate R=$log_2N{\geq}6$, it predicts signal-to-noise ratios within 1% of the true values for a wide range of $\mu$, and $\rho$. The new findings herein include the fact that, for heavy variance mismatch of ${\rho}>3/2$, the signal-to-noise ratio increases at the rate of $9/\rho$ dB/bit, which is slower than the usual 6 dB/bit, and the fact that an optimal uniform quantizer, though optimally designed, is slightly more than critically mismatched to the source. It is also found that signal-to-noise ratio loss due to $\mu$ is moderate. The derived formulas can be useful in quantization of speech or music signals, which are modeled well as Laplacian sources and have changing short-term variances.