• KSII Transactions on Internet and Information Systems (TIIS)
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• v.10 no.1
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• pp.321-340
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• 2016

The Compressive Video Sensing (CVS) is a useful technology for wireless systems requiring simple encoders but handling more complex decoders, and its rate-distortion performance is highly affected by the quantization of measurements and reconstruction of video frame, which motivates us to presents the Space-Time Quantization (ST-Q) and Motion-Aligned Reconstruction (MA-R) in this paper to both improve the performance of CVS system. The ST-Q removes the space-time redundancy in the measurement vector to reduce the amount of bits required to encode the video frame, and it also guarantees a low quantization error due to the fact that the high frequency of small values close to zero in the predictive residuals limits the intensity of quantizing noise. The MA-R constructs the Multi-Hypothesis (MH) matrix by selecting the temporal neighbors along the motion trajectory of current to-be-reconstructed block to improve the accuracy of prediction, and besides it reduces the computational complexity of motion estimation by the extraction of static area and 3-D Recursive Search (3DRS). Extensive experiments validate that the significant improvements is achieved by ST-Q in the rate-distortion as compared with the existing quantization methods, and the MA-R improves both the objective and the subjective quality of the reconstructed video frame. Combined with ST-Q and MA-R, the CVS system obtains a significant rate-distortion performance gain when compared with the existing CS-based video codecs.

• Asian-Australasian Journal of Animal Sciences
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• v.28 no.7
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• pp.936-942
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• 2015

The purpose of this study was to characterize genetic architecture of behavior patterns in Sapsaree dogs. The breed population (n=8,256) has been constructed since 1990 over 12 generations and managed at the Sapsaree Breeding Research Institute, Gyeongsan, Korea. Seven behavioral traits were investigated for 882 individuals. The traits were classified as a quantitative or a categorical group, and heritabilities ($h^2$) and variance components were estimated under the Animal model using ASREML 2.0 software program. In general, the $h^2$ estimates of the traits ranged between 0.00 and 0.16. Strong genetic ($r_G$) and phenotypic ($r_P$) correlations were observed between nerve stability, affability and adaptability, i.e. 0.9 to 0.94 and 0.46 to 0.68, respectively. To detect significant single nucleotide polymorphism (SNP) for the behavioral traits, a total of 134 and 60 samples were genotyped using the Illumina 22K CanineSNP20 and 170K CanineHD bead chips, respectively. Two datasets comprising 60 (Sap60) and 183 (Sap183) samples were analyzed, respectively, of which the latter was based on the SNPs that were embedded on both the 22K and 170K chips. To perform genome-wide association analysis, each SNP was considered with the residuals of each phenotype that were adjusted for sex and year of birth as fixed effects. A least squares based single marker regression analysis was followed by a stepwise regression procedure for the significant SNPs (p<0.01), to determine a best set of SNPs for each trait. A total of 41 SNPs were detected with the Sap183 samples for the behavior traits. The significant SNPs need to be verified using other samples, so as to be utilized to improve behavior traits via marker-assisted selection in the Sapsaree population.

• Journal of Korean Society of Environmental Engineers
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• v.28 no.1
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• pp.26-33
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• 2006

This study investigated the characteristics of the biofilm removal by free chlorine or monochloramine. The simulated drinking water distribution pipes on which biofilms had been formed were supplied with tap water containing 0.5, 1.0, 2.0 mg/L of free chlorine or monochloramine residuals. The biofilm removal was characterized by measurement of attached HPC and biomass on pipe surfaces. Chlorine was more effective in both inactivation of attached viable heterotrophic bacteria and removal of biofilm biomass compared to monochloramine. Biofilm matrix was not much eliminated from the surfaces by monochloramine disinfection. Free chlorine residual of 2.0 mg/L was found to be effective in biomass removal. However, biofilm level as low as $10CFU/cm^2$ of attached HPC and $5{\mu}g/cm^2$ of biomass still remained on the surfaces at 2.0 mg/L of chlorine residual. The measurement of biomass appeared to be a useful means in evaluating the characteristics of biofilm removal.

• Journal of the Korean Society of Marine Environment & Safety
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• v.19 no.6
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• pp.561-569
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• 2013

EOF analysis of tidal residual derived from 2003~2009 tide data was used to identify the spatio-temporal variability. The effect of sea surface air pressures and winds on the tidal residual was also investigated by the correlation analysis. The first mode accounting for 68 % of the total variance represented concurrent sea level rise or fall, and the second mode accounting for 21 % of the total variance explained alternative sea level rise and fall between West Sea coast and both South Sea and East Sea coasts. While northerly and southerly winds dominated the tidal residual in the eastern coast of Yellow Sea, the effect of sea surface air pressures on the tidal residual increased along the coastal regions from South Sea to East Sea.

• Journal of Astronomy and Space Sciences
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• v.25 no.4
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• pp.395-404
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• 2008

We explore the linkage between the long term variability of the Sun and earth's climate change by analysing periodicities of time series of solar proxies and global temperature anomalies. We apply the power spectral estimation method named as the periodgram to solar proxies and global temperature anomalies. We also decompose global temperature anomalies and reconstructed total solar irradiance into each local variability components by applying the EMD (Empirical Mode Decomposition) and MODWT MRA (Maximal Overlap Discrete Wavelet Multi Resolution Analysis). Powers for solar proxies at low frequencies are lower than those of high frequencies. On the other hand, powers for temperature anomalies show the other way. We fail to decompose components which having lager than 40 year variabilities from EMD, but both residuals are well decomposed respectively. We determine solar induced components from the time series of temperature anomalies and obtain 39% solar contribution on the recent global warming. We discuss the climate system can be approximated with the second order differential equation since the climate sensitivity can only determine the output amplitude of the signal.

• Korean Journal of Fisheries and Aquatic Sciences
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• v.35 no.3
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• pp.216-222
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• 2002

Autocorrelation in time series data can affect statistical inference in correlation or regression analyses. To improve a regression model from which the residuals are autocorrelated, Yule-Walker method, nonlinear least squares estimation, maximum likelihood method and 'prewhitening' method have been used to estimate the parameters in a regression equation. This study reviewed on the estimation methods of preventing spurious correlation in the presence of autocorrelation and applied the former three methods, Yule-Walker, nonlinear least squares and maximum likelihood method, to a 20-year real data set. Monte carlo simulation was used to compare the three parameter estimation methods. However, the simulation results showed that the mean squared error distributions from the three methods simulated do not differ significantly.

• 한국지구물리탐사학회:학술대회논문집
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• 2005.05a
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• pp.227-232
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• 2005

Least squares (${\ell}^2-norm$) solutions of seismic inversion tend to be very sensitive to data points with large errors. The ${\ell}^p-norm$ minimization for $1{\le}p<2$ gives more robust solutions, but usually with higher computational cost. Iteratively reweighted least squares (IRLS) gives efficient approximate solutions of these ${\ell}^p-norm$ problems. I propose a simple way to implement the IRLS method for a hybrid ${\ell}^1/{\ell}^2$ minimization problem that behaves as ${\ell}^2$ fit for small residual and ${\ell}^1$ fit for large residuals. Synthetic and a field-data examples demonstrates the improvement of the hybrid method over least squares when there are outliers in the data.

• Journal of radiological science and technology
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• v.22 no.2
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• pp.41-46
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• 1999

Osteoporosis, which causes mainly fracture of the spine, proximal femur and distal radius by minimal trauma, is a major public health problem and its prevalence is steadily increasing in Korea according to the development of public health care. There are reliable methods for diagnosis based on bone densitometry. Early detection and intervention are important for reducing the incidence of fractures. A consensus definition of osteoporosis, based on bone density measurement, has been developed by the World Health Organization(WHO). In this study, bone mineral density(RMD) was measured by dual energy x-ray absorptiometry(DEXA) at the proximal femur and lumbar spine in 132 post-menopausal women. The purpose of this study is to find influential factors on the BMD of the proximal femur and the lumbar spine and to analyze correlation between BMD and the problematic factors. We obtained the following results : 1. Mean BMD score, T-score and Z-score of the proximal femur were $0.81(g/cm^2)$, -2.45(S.D.) and -2.09(S.D.) respectively and in the lumbar spine were $0.83(g/cm^2)$, -2.02(S.D.), -2.43(S.D.) respectively. 2. In correlation analysis between BMD and many factors, correlation coefficients were -0.467, 0.212, -0.321 and 0.241 in age, height, duration after menopause respectively. BMI and the residuals were comparatively small. 3. Correlation coefficients to age matched BMD, in height and body weight were 0.222 and 0.241, in age and duration after menopause were -0.268, -0.282. 4. The fracture threshold of proximal femur BMD to the 90th percentile was $0.845(g/cm^2)$. 5. At the result of multiple regression analysis, age, body weight, $BMI(kg/m^2)$ and duration after menopause described as significant variables.

• The Korean Journal of Applied Statistics
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• v.18 no.2
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• pp.253-269
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• 2005

One of the basic assumptions of the regression models is that the parameter vector does not vary across sample observations. If the parameter vector is not constant for all observations in the sample, the statistical model is changed and the usual least squares estimators do not yield unbiased, consistent and efficient estimates. This study investigates the regression model with some or all parameters vary across partitions of the whole sample data when the model permits different response coefficients during unusual time periods. Since the usual test for overall homogeneity of regressions across partitions of the sample data does not explicitly identify the break points between the partitions, the testing the equality between subsets of coefficients in two or more linear regressions is generalized and combined with the test procedure to search the break point. The method is applied to find the possibility and the turning point of the structural change in the long-run unemployment rate in the usual static framework by using the regression model. The relationships between the variables included in the model are reexamined in the dynamic framework by using Vector Autoregression.

• The Korean Journal of Applied Statistics
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• v.29 no.3
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• pp.471-485
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• 2016

Least squares (LS) regression is a classic method for regression that is optimal under assumptions of regression and usual observations. However, the presence of unusual data in the LS method leads to seriously distorted estimates. Therefore, various robust estimation methods are proposed to circumvent the limitations of traditional LS regression. Among these, there are M-estimators based on maximum likelihood estimation (MLE), L-estimators based on linear combinations of order statistics and R-estimators based on a linear combinations of the ordered residuals. In this paper, robust regression estimators with high breakdown point and/or with high efficiency are compared under several simulated situations. The paper analyses and compares distributions of estimates as well as relative efficiencies calculated from mean squared errors (MSE) in the simulation study. We conclude that MM-estimators or GR-estimators are a good choice for the real data application.