• Journal of the Korean Ceramic Society
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• v.36 no.1
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• pp.50-54
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• 1999

We have measured the x-ray photoemission spectroscopy of cation deficient La0.970Mn0.970O3 as a function of temperature. Detailed results on the chemical shifts and changes in Mn 2p and Lp 3d core levels due to variation of temperature have been obtained. The Mn 2p 3/2 and 1/2 main peaks and La 3d core spectrum shift to lower binding energy levels with increasing temperature. This XPS behavior is correlated with the strength of localization of Mn3+. The Jahn-Teller effect due to Mn3+ besides the conventional random potential effects is likely to localize charge carriers in La-.970Mn0.970O3.

• Journal of the korean Society of Automotive Engineers
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• v.14 no.1
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• pp.25-37
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• 1992

Interior noise in a car is known to have an important influence on product acceptability. This noise is closely correlated with structural-acoustic vibration. When considering noise problem, the structural-acoustic relation of a vehicle cabin model needs to be identified. However, it is very difficult to get the modal parameters of this kind of cabin structure composed of thin plates: because it not only can be excited by the acoustic vibration of cavity, but also tends to have decoupling effects of one plate from another. In order to obtain modal parameters more precisely, various excitation techniques, i.e. impact, pure random, burst random, and swept sine testing are applied for the first step. In the case of the cabin model, impact and swept sine testing show good results. Next, the determination of the excitation point by trial- and-error and the accurate measurements of FRF's are performed with these methods. The modal parameter extraction is carried out for the final step. This paper proposes a new approach to find the modal parameters more reliably in the case of high decoupling effects. That is, the convergence of MIF and MCF in each panel, which provide some criteria for the validity of the obtained modal parameters, is observed. And from those results, the pretty accurate modal parameters can be determined. A comparative assessment between the modal testing and the FEM is also performed.

• Journal of the Korean Data and Information Science Society
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• v.23 no.2
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• pp.385-392
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• 2012

Logistic regression is a well known binary classification method in the field of statistical learning. Mixed-effect regression models are widely used for the analysis of correlated data such as those found in longitudinal studies. We consider kernel extensions with semiparametric fixed effects and parametric random effects for the logistic regression. The estimation is performed through the penalized likelihood method based on kernel trick, and our focus is on the efficient computation and the effective hyperparameter selection. For the selection of optimal hyperparameters, cross-validation techniques are employed. Numerical results are then presented to indicate the performance of the proposed procedure.

• Communications for Statistical Applications and Methods
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• v.23 no.6
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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.

• Communications for Statistical Applications and Methods
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• v.30 no.2
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• pp.119-133
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• 2023

With the rapid growth of the economy and fossil fuel consumption, the concentration of air pollutants has increased significantly and the air pollution problem is no longer limited to small areas. We conduct statistical analysis with the actual data related to air quality that covers the entire of South Korea using R and Python. Some factors such as SO₂, CO, O₃, NO₂, PM₁₀, precipitation, wind speed, wind direction, vapor pressure, local pressure, sea level pressure, temperature, humidity, and others are used as covariates. The main goal of this paper is to predict air quality index (AQI) spatio-temporal data. The observations of spatio-temporal big datasets like AQI data are correlated both spatially and temporally, and computation of the prediction or forecasting with dependence structure is often infeasible. As such, the likelihood function based on the spatio-temporal model may be complicated and some special modelings are useful for statistically reliable predictions. In this paper, we propose several methods for this big spatio-temporal AQI data. First, random effects with spatio-temporal basis functions model, a classical statistical analysis, is proposed. Next, neural networks model, a deep learning method based on artificial neural networks, is applied. Finally, random forest model, a machine learning method that is closer to computational science, will be introduced. Then we compare the forecasting performance of each other in terms of predictive diagnostics. As a result of the analysis, all three methods predicted the normal level of PM_2.5 well, but the performance seems to be poor at the extreme value.

• The Korean Journal of Applied Statistics
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• v.28 no.2
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• pp.335-342
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• 2015

Joint hierarchical generalized linear models proposed by Molas et al. (2013) extend the simple longitudinal model into multiple models fitted jointly. It can easily handle the correlation of multivariate longitudinal data. In this paper, we apply this method to analyze KoGES cohort dataset. Fixed unknown parameters, random effects and variance components are estimated based on a standard framework of h-likelihood theory. Furthermore, based on the conditional Akaike information criterion the correlated covariance structure of random-effect model is selected rather than an independent structure.

• Earthquakes and Structures
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• v.22 no.3
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• pp.219-230
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• 2022

Knowing that the variability of soil properties is an important source of uncertainty in geotechnical analyses, we will study in this paper the effect of this variability on the seismic response of a structure within the framework of Soil Structure Interaction (SSI). We use the proposed and developed model (N2-ISS, Mekki et al., 2014). This approach is based on an extension of the N2 method by determining the capacity curve of the fixed base system oscillating mainly in the first mode, then modified to obtain the capacity curve of the system on a flexible basis using the concept of the equivalent nonlinear oscillator. The properties of the soil that we are interested in this paper will be the shear wave velocity and the soil damping. These parameters will be modeled at first, as independent random fields, then, the two parameters will be correlated. The results obtained showed the importance of the use of random field in the study of SSI systems. The variability of soil damping and shear wave velocity introduces significant uncertainty not only in the evaluation of the damping of the soil-structure system but also in the estimation of the displacement of the structure and the base-shear force.

• Interdisciplinary Bio Central
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• v.6 no.1
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• pp.1.1-1.6
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• 2014

A growing body of evidence shows that most psychological traits are polygenic, that is they involve the action of many genes with small effects. However, the study of selection has disproportionately been on one or a few genes and their associated sweep signals (rapid and large changes in frequency). If our goal is to study the evolution of psychological variables, such as intelligence, we need a model that explains the evolution of phenotypes governed by many common genetic variants. This study illustrates simple statistical tools to detect signals of recent polygenic selection: a) ANOVA can be used to reveal significant deviation from random distribution of allele frequencies across racial groups. b) Principal component analysis can be used as a tool for finding a factor that represents the strength of recent selection on a phenotype and the underlying genetic variation. c) Method of correlated vectors: the correlation between genetic frequencies and the average phenotypes of different populations is computed; then, the resulting correlation coefficients are correlated with the corresponding alleles' genome-wide significance. This provides a measure of how selection acted on genes with higher signal to noise ratio. Another related test is that alleles with large frequency differences between populations should have a higher genome-wide significance value than alleles with small frequency differences. This paper fruitfully employs these tools and shows that common genetic variants exhibit subtle frequency shifts and that these shifts predict phenotypic differences across populations.

• Nutritional Sciences
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• v.9 no.4
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• pp.280-287
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• 2006

The aims of this study were to describe the effects of glucose-sweetened drinks on blood glucose, energy, and water intake at a meal 3 hours later. The effect of blood glucose on prandial energy intake and the relationship between water and energy intake during a meal were also determined. Twenty healthy normal-weight men were fed pizza test meals 3h after consuming four drinks of 0, 50g, 65g, and 75g glucose in random order, within-subjects design. Blood samples were measured at baseline and every 30 min after ingestion of drinks and 30min after the end of the test meal and the appetite was also assessed by visual analog test at the same interval. The results of this study showed that various glucose drinks altered blood glucose responses compared with that of water control(p<0.0001). Blood glucose areas under the curve(AUC) for glucose-sweetened drinks were significantly(p<0.05) higher than that for the control over 3 hours after a drink and 30 min after the test meal. Consumption of the glucose-sweetened drinks significantly increased(p<0.05) energy and water intake at a test meal compared with the water control, except the drink containing 75g glucose. For all drinks combined, the energy intake was negatively correlated with the blood glucose and positively correlated with the volume of water consumed at a test meal at 3 hours later.

• 한국지구물리탐사학회:학술대회논문집
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• 2007.06a
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• pp.81-86
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• 2007

Spectral features of the seismic wave propagation from Odaesan Earthquake were evaluated based on the commonly treated random error between the observed data and the prediction values by the stochastic point-source ground-motion spectral model regarding the source, path and site effects. Radiation pattern of the error according to azimuth angle was found to be similar to the theoretical estimate. It was also observed that the spatial distribution of the errors was correlated with the geological map and the Q0 map which are indicatives of seismic boundaries.