• International Journal of Aeronautical and Space Sciences
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• 제3권1호
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• pp.19-29
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• 2002

Decentralized filtering for a formation flight instrumentation system by INS/GPS integration is considered in this paper. An elaborate tuning method of the measurement noise covariance is suggested to compensate modeling errors caused by decentralizing the extended Kalman filter. It does not require large data transfer between formation vehicles. Covariance analysis exhibits the superior performance of the proposed approach when compared with the existent decentralized filter and the global filter, which has the target-filter performance.

• 대한음성학회지:말소리
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• 제55권
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• pp.93-102
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• 2005

In this paper, sub-stream based eigenvoice method is proposed to overcome the weak points of conventional eigenvoice and dimensional eigenvoice. In the proposed method, sub-streams are automatically constructed by the statistical clustering analysis that uses the correlation information between dimensions. To obtain the reliable distance matrix from covariance matrix for dividing into optimal sub-streams, MAP adaptation technique is employed to the covariance matrix of training data and the sample covariance of adaptation data. According to our experiments, the proposed method shows $41\%$ error rate reduction when the number of adaptation data is 50.

• Communications for Statistical Applications and Methods
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• 제27권3호
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• pp.301-311
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• 2020

Schwarz's Bayesian information criterion (BIC) is one of the most popular criteria for model selection, that was derived under the assumption of independent and identical distribution. For correlated data in longitudinal studies, Jones (Statistics in Medicine, 30, 3050-3056, 2011) modified the BIC to select the best linear mixed effects model based on the effective sample size where the number of parameters in covariance structure was not considered. In this paper, we propose an extended Jones' modified BIC by considering covariance parameters. We conducted simulation studies under a variety of parameter configurations for linear mixed effects models. Our simulation study indicates that our proposed BIC performs better in model selection than Schwarz's BIC and Jones' modified BIC do in most scenarios. We also illustrate an example of smoking data using a longitudinal cohort of cancer patients.

• Nuclear Engineering and Technology
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• 제41권4호
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• pp.381-390
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• 2009

This article provides selects of outstanding problems in computational neutron transport, with some suggested approaches thereto, as follows: i) ray effect in discrete ordinates method, ii) diffusion synthetic acceleration in strongly heterogeneous problems, iii) method of characteristics extension to three-dimensional geometry, iv) fission source and $k_{eff}$ convergence in Monte Carlo, v) depletion in Monte Carlo, vi) nuclear data evaluation, and vii) uncertainty estimation, including covariance data.

• 전기전자학회논문지
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• 제27권1호
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• pp.38-44
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• 2023

STAP(space-time adaptive processing) 알고리즘의 성능은 CUT(cell under test) 내의 간섭에 대한 공분산 행렬 추정의 정확도가 결정적 역할을 한다. STAP 데이터는 일반적으로 많은 배열 소자 및 사용된 다수의 송신 펄스에 의해 결정되는 2차원 데이터 구조를 가지고 있다. 그러므로 공분산 행렬 추정의 정확도를 높이기 위해서는 매우 많은 트레이닝 데이터가 요구된다. 본 논문에서는 수신된 적은 개수의 데이터를 공간주파수-도플러 평면으로 변환한 후 가상의 트레이닝 데이터를 생성하는 알고리즘을 제시한다. 클러터 점유 위치를 이론적으로 유도하며 이에 근거해서 가상 트레이닝 데이터 생성 절차를 제시하고 STAP 시뮬레이션을 통해서 제시된 알고리즘이 STAP 성능을 개선할 수 있음을 보인다.

• Ocean and Polar Research
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• 제26권3호
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• pp.453-463
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• 2004

The Antarctic Peninsula is important in terms of global warming research due to pronounced increase of air temperature over the last century. The first eddy covariance system was established at King Sejong Station located in the northern region of the Antarctic Peninsula in December of 2002 and has been operated over one year. Here, we analyze turbulent characteristics to determine quality control criteria for turbulent sensible heat flux data as well as to diagnose the possibility of long term eddy covariance measurement under extreme weather conditions of the Antarctic Peninsula. We also report the preliminary result on sensible heat flux. Based on the analyses on turbulent characteristics such as integral turbulence characteristics of vertical velocity (w) and heat (T), stationarity test and investigation of correlation coefficient, they fallow the Monin-Obukhov similarity and eddy covariance flux data were reliable. ${\sim}47%$ of total retrieved sensible heat flux data could be used for further analysis. Daytime averaged sensible heat flux showed a pronounced seasonal variation, with a maximum of up to $300Wm^{-2}$ in summer. In conclusion, continuous and long-term eddy covariance measurement may be possible at the study site and the land surface may influence the atmosphere significantly through heat transport in summer.

• Journal of the Korean Data and Information Science Society
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• 제20권2호
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• pp.377-385
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• 2009

서울시에 거주하는 25명의 여대생을 대상으로 식이요법에 대한 체중감량 효과를 비교하고자 식이요법과 운동을 병행하는 그룹과 식이요법만 실시하는 그룹으로 나누어서, 8주간에 걸쳐서 2주 간격으로 측정을 실시하여 각 그룹별로 4회 반복측정실험자료를 얻었다. 이 실험자료를 바탕으로 반복측정에 관한 혼합모형을 이용하여 분석한 결과 처리별 Toeplitz 공분산형태가 가장 적절한 모형으로 선택되었다. 처리별 Toeplitz 공분산형태를 가정하여 분석한 결과, 식이요법 이전의 체중값과 시간의 차이에 따른 효과는 대단히 유의하지만, 처리와 시간 간의 교호작용은 유의하지 않은 것으로 나타났으며, 식이요법과 운동을 병행한 그룹의 학생들이 식이요법만 섭취한 그룹의 학생들보다 좀더 효과적인 체중감량의 효과가 있었음이 판명되었다.

• International journal of advanced smart convergence
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• 제9권4호
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• pp.42-51
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• 2020

In this paper, the authors propose the pseudo complex correlation coefficient (PCCC) of the two complex random variables (RV), because the four real correlation coefficients (RCC) of the corresponding four real RVs cannot be obtained only from the complex correlation coefficient (CCC) of given two complex RV. Such observation is motivated by the general statement; "The complex jointly-Gaussian random M-vector cannot be completely described by the complex covariance matrix, even though the real Gaussian random 2M-vector can be completely descried by the real covariance matrix. Therefore, in order to describe completely the complex jointly-Gaussian random M-vector, we need an additional matrix, namely the complex pseudo-covariance matrix, along with the complex covariance matrix." Then, we apply PCCC to correlated information sources (CIS) for non-orthogonal multiple access (NOMA) in 5G system, and investigate impact of the proposed PCCC on the achievable data rate of the stronger channel user in the conventional successive interference cancellation (SIC) NOMA with CIS. It is shown that for the given same CCC, the achievable data rates with the different PCCC are different, because the corresponding RCC are different. We also show that as the absolute value of the same CCC increases, the impact of the different PCCC becomes more significant.

• 한국농림기상학회지
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• 제1권1호
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• pp.60-71
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• 1999

The micrometeorological tower flux network is the cornerstone of the global terrestrial vegetation monitoring. The eddy covariance technique used for tower fluxes is derived from the conservation of mass and is most applicable for steady-state conditions over flat, extended, and uniform vegetation. This technique allows us to obtain surface fluxes of energy budget components, greenhouse and trace gases, and other pollutants. The quality-controlled flux data are invaluable to validate various models with temporal scales ranging from minutes to years and spatial scales ranging from a few meters to hundreds of kilometers. In this paper, we review the theoretical background of this important eddy covariance technique, examine the measurement criteria and corrections, and finally suggest some measurement strategies that may facilitate coordinated flux measurements among different disciplines and provide a strong infrastructure for the global flux network.

• Communications for Statistical Applications and Methods
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• 제23권6호
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• pp.575-585
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• 2016

Longitudinal studies repeatedly measure outcomes over time. Therefore, repeated measurements are serially correlated from same subject (within-subject variation) and there is also variation between subjects (between-subject variation). The serial correlation and the between-subject variation must be taken into account to make proper inference on covariate effects (Diggle et al., 2002). However, estimation of the covariance matrix is challenging because of many parameters and positive definiteness of the matrix. To overcome these limitations, we propose autoregressive moving average Cholesky decomposition (ARMACD) for the linear mixed models. The ARMACD allows a class of flexible, nonstationary, and heteroscedastic models that exploits the structure allowed by combining the AR and MA modeling of the random effects covariance matrix. We analyze a real dataset to illustrate our proposed methods.