• Communications for Statistical Applications and Methods
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• v.30 no.4
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• pp.369-388
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• 2023

In this paper, we develop the two-step procedure that detects and estimates the position of structural changes for multivariate nonstationary time series, either on mean parameters or second-order structures. We first investigate the presence of mean structural change by monitoring data through the aggregated cumulative sum (CUSUM) type statistic, a sequential procedure identifying the likely position of the change point on its trend. If no mean change point is detected, the proposed method proceeds to scan the second-order structural change by modeling the multivariate nonstationary time series with a multivariate locally stationary Wavelet process, allowing the time-localized auto-correlation and cross-dependence. Under this framework, the estimated dynamic spectral matrices derived from the local wavelet periodogram capture the time-evolving scale-specific auto- and cross-dependence features of data. We then monitor the change point from the lower-dimensional approximated space of the spectral matrices over time by applying the dynamic principal component analysis. Different from existing methods requiring prior information on the type of changes between mean and covariance structures as an input for the implementation, the proposed algorithm provides the output indicating the type of change and the estimated location of its occurrence. The performance of the proposed method is demonstrated in simulations and the analysis of two real finance datasets.

• Geomechanics and Engineering
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• v.31 no.2
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• pp.183-195
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• 2022

Under the effects of the near-field earthquakes, the incident angle of the incoming wave could be different. In this study, the influences of some parameters such as incident angle, basin edge, peak ground acceleration level of the bedrock motion as well as different clay types with different consistency on the amplification behavior of the shallow basins are investigated. To attain this goal, the numerical analyses of the basins filled with three different clay types are performed using a fully nonlinear method. The two dimensional models of the basins are subjected to a set of strong ground motions with different peak ground acceleration levels and three different incident angles of 30◦, 45◦ and 90◦ with respect to the horizontal axes. The results show the dominant effect of the obliquely subjected waves at most cases. The higher effect of the 45◦ incident angle on the basin response was concluded. In the other part of this study, the spectral amplification curves of the surface points were compared. It was seen that the maximum spectral amplification of different surface points occurs at different periods. Also, it is affected by the increase in the peak acceleration level of the incoming motions.

• ETRI Journal
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• v.45 no.5
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• pp.768-780
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• 2023

The application of unmanned aerial vehicles (UAVs) has recently attracted considerable interest in various areas. A three-dimensional multiple-input multiple-output concentric two-hemisphere model is proposed to characterize the scattering environment around a vehicle in an urban UAV-to-vehicle communication scenario. Multipath components of the model consisted of lineof-sight and single-bounced components. This study focused on the key parameters that determine the scatterer distribution. A time-variant process was used to analyze the nonstationarity of the proposed model. Vital statistical properties, such as the space-time-frequency correlation function, Doppler power spectral density, level-crossing rate, average fade duration, and channel capacity, were derived and analyzed. The results indicated that with an increase in the maximum scatter radius, the time correlation and level-crossing rate decreased, the frequency correlation function had a faster downward trend, and average fade duration increased. In addition, with the increase of concentration parameter, the time correlation, space correlation, and level-crossing rate increased, average fade duration decreased, and Doppler power spectral density became flatter. The proposed model was compared with current geometry-based stochastic models (GBSMs) and showed good consistency. In addition, we verified the nonstationarity in the temporal and spatial domains of the proposed model. These conclusions can be used as references in the design of more reasonable communication systems.

• Geophysics and Geophysical Exploration
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• v.15 no.2
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• pp.57-65
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• 2012

Various numerical methods in simulation of seismic wave propagation have been developed. Recently an innovative numerical method called as the Spectral Element Method (SEM) has been developed and used in wave propagation in 3-D elastic media. The SEM that easily implements the free surface of topography combines the flexibility of a finite element method with the accuracy of a spectral method. It is generally used a weak formulation of the equation of motion which are solved on a mesh of hexahedral elements based on the Gauss-Lobatto-Legendre integration rule. Variational formulations of velocity-stress motion are newly modified in order to implement ADE-PML (Auxiliary Differential Equation of Perfectly Matched Layer) in wave propagation in 3-D elastic media, because a general weak formulation has a difficulty in adapting CFS (Complex Frequency Shifted) PML (Perfectly Matched Layer). SEM of Velocity-Stress motion having ADE-PML that is very efficient in absorbing waves reflected from finite boundary is verified with simulation of 1-D and 3-D wave propagation.

• Journal of Broadcast Engineering
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• v.9 no.1
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• pp.2-7
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• 2004

In this paper, we proposed the content-based music information retrieval technique using some MPEG-7 low-level descriptors. Especially, pitch information and timbral features can be applied in music genre classification, music retrieval, or QBH(Query By Humming) because these can be modeling the stochasticpattern or timbral information of music signal. In this work, we restricted the music domain as O.S.T of movie or soap opera to apply broadcasting system. That is, the user can retrievalthe information of the unknown music using only an audio clip with a few seconds extracted from video content when background music sound greeted user's ear. We proposed the audio feature set organized by MPEG-7 descriptors and distance function by vector distance or ratio computation. Thus, we observed that the feature set organized by pitch information is superior to timbral spectral feature set and IFCR(Intra-Feature Component Ratio) is better than ED(Euclidean Distance) as a vector distance function. To evaluate music recognition, k-NN is used as a classifier

• Journal of the korean society of oceanography
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• v.39 no.1
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• pp.46-56
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• 2004

Spectral attenuation of light and upwelling radiance were measured in the western coast of Korea on board the R/V Inchon 888 of the Korean National Fisheries Research and Development Institute(NFRDI) during four seasons. The goal of these efforts was to determine the spatial and temporal distribution of the inherent and apparent optical properties of the water, and the factors that control their distribution. Our data indicate that while stratification of the water column, phytoplankton, and wind stress determined the vertical distribution of the optical parameters offshore, it was the tidal current and sediment type that controlled both the vertical and horizontal distribution in the coastal areas. These findings led to the development of a model that estimates the spectral attenuation of light with respect to depth and time for the Yellow Sea. The model integrates water leaving radiance from satellites, sediment types, current vectors, sigma-t, bathymetry, and in situ optical measurements in a learning algorithm capable of extracting optical properties with only knowledge of the environmental conditions of the Yellow Sea. The performance of the model decreases with increase in depth. The mean absolute percentage error (MAPE) of the model is 2% for the upper five meters, 8-10% between 6 and 50 meters, and 15% below 51 meters.

• Journal of the Institute of Electronics and Information Engineers
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• v.53 no.5
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• pp.123-131
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• 2016

To capture color and near-infrared images simultaneously, a multi-spectral filter array(MSFA) sensor is used. This is because an NIR band gives additional invisible information to human eyes to see subject under extremely low light level. However, because lenses have different refractive indices for different wavelengths, lenses may fail to focus widely different rays to the same convergence point. This is why a chromatic aberration(CA) problem occurs and images are degraded. In this paper, the image restoration algorithm for an MSFA image, which removes the CA problem, is presented. The obtained MSFA image is filtered by the estimated low-pass kernel to generate a base image. This base image is used to remove CA problem in multi-spectral(MS) images. By modeling the image degradation process and by using the least squares approach of the difference between the high-frequencies of the base and MS images, the desired high-resolution MS images are reconstructed. The experimental results show that the proposed algorithm performs well in estimating the high-quality MS images and reducing the chromatic aberration problem.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.36 no.6
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• pp.515-523
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• 2018

In order to obtain consistent change detection result for multi-temporal satellite images, preprocessing must be performed. In particular, the preprocessing related to the spectral values can be performed by the radiometric normalization, and relative radiometric normalization is generally utilized. However, most relative radiometric normalization methods assume a linear relationship between the two images, and nonlinear spectral characteristics such as phenological differences are not considered. Therefore, this study proposes a relative radiometric normalization which assumes nonlinear relationships that can perform compositive normalization of radiometric and phenological characteristics. The proposed method selects the subject and reference images, and then extracts the radiometric control set samples through the no-change method. In addition, spectral indexes as well as pixel values are extracted in order to consider sufficient information, and modeling of nonlinear relationships is performed through multilayer perceptron. Finally, the proposed method is compared with the conventional relative radiometric normalization methods, which shows that the proposed method is visually and quantitatively superior.

• Proceedings of the Korean Society of Near Infrared Spectroscopy Conference
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• 2001.06a
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• pp.1041-1041
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• 2001

Removal of variability in spectra data before the application of chemometric modeling will generally result in simpler (and presumably more robust) models. Particularly for sparsely sampled data, such as typically encountered in diode array instruments, the use of Savitzky-Golay (S-G) derivatives offers an effective method to remove effects of shifting baselines and sloping or curving apparent baselines often observed with scattering samples. The application of these convolution functions is equivalent to fitting a selected polynomial to a number of points in the spectrum, usually 5 to 25 points. The value of the polynomial evaluated at its mid-point, or its derivative, is taken as the (smoothed) spectrum or its derivative at the mid-point of the wavelength window. The process is continued for successive windows along the spectrum. The original paper, published in 1964 [1] presented these convolution functions as integers to be used as multipliers for the spectral values at equal intervals in the window, with a normalization integer to divide the sum of the products, to determine the result for each point. Steinier et al. [2] published corrections to errors in the original presentation [1], and a vector formulation for obtaining the coefficients. The actual selection of the degree of polynomial and number of points in the window determines whether closely situated bands and shoulders are resolved in the derivatives. Furthermore, the actual noise reduction in the derivatives may be estimated from the square root of the sums of the coefficients, divided by the NORM value. A simple technique to evaluate the actual convolution factors employed in the calculation by the software will be presented. It has been found that some software packages do not properly account for the sampling interval of the spectral data (Equation Ⅶ in [1]). While this is not a problem in the construction and implementation of chemometric models, it may be noticed in comparing models at differing spectral resolutions. Also, the effects on parameters of PLS models of choosing various polynomials and numbers of points in the window will be presented.

• The Korean Journal of Applied Statistics
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• v.33 no.1
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• pp.25-46
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• 2020

This paper presents ordinal probit semiparametric regression models using Bayesian Spectral Analysis Regression (BSAR) method. Ordinal probit regression is a way of modeling ordinal responses - usually more than two categories - by connecting the probability of falling into each category explained by a combination of available covariates using a probit (an inverse function of normal cumulative distribution function) link. The Bayesian probit model facilitates posterior sampling by bringing a latent variable following normal distribution, therefore, the responses are categorized by the cut-off points according to values of latent variables. In this paper, we extend the latent variable approach to a semiparametric model for the Bayesian ordinal probit regression with nonparametric functions using a spectral representation of Gaussian processes based BSAR method. The latent variable is decomposed into a parametric component and a nonparametric component with or without a shape constraint for modeling ordinal responses and predicting outcomes more flexibly. We illustrate the proposed methods with simulation studies in comparison with existing methods and real data analysis applied to a Korean National Health and Nutrition Examination Survey (KNHANES) 2016 for investigating nonparametric relationship between smoking behavior and coffee intake.