• Animal Bioscience
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• 제35권5호
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• pp.648-658
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• 2022

Objective: The identification of nonlinear mixed models that describe the growth trajectory of New Zealand rabbits was performed based on weight records and carcass measures obtained using ultrasonography. Methods: Phenotypic records of body weight (BW) and loin eye area (LEA) were collected from 66 animals raised in a didactic-productive module of cuniculture located in the southern Piaui state, Brazil. The following nonlinear models were tested considering fixed parameters: Brody, Gompertz, Logistic, Richards, Meloun 1, modified Michaelis-Menten, Santana, and von Bertalanffy. The coefficient of determination (R²), mean squared error, percentage of convergence of each model (%C), mean absolute deviation of residuals, Akaike information criterion (AIC), and Bayesian information criterion (BIC) were used to determine the best model. The model that best described the growth trajectory for each trait was also used under the context of mixed models, considering two parameters that admit biological interpretation (A and k) with random effects. Results: The von Bertalanffy model was the best fitting model for BW according to the highest value of R² (0.98) and lowest values of AIC (6,675.30) and BIC (6,691.90). For LEA, the Logistic model was the most appropriate due to the results of R² (0.52), AIC (783.90), and BIC (798.40) obtained using this model. The absolute growth rates estimated using the von Bertalanffy and Logistic models for BW and LEA were 21.51g/d and 3.16 cm², respectively. The relative growth rates at the inflection point were 0.028 for BW (von Bertalanffy) and 0.014 for LEA (Logistic). Conclusion: The von Bertalanffy and Logistic models with random effect at the asymptotic weight are recommended for analysis of ponderal and carcass growth trajectories in New Zealand rabbits. The inclusion of random effects in the asymptotic weight and maturity rate improves the quality of fit in comparison to fixed models.

• Economic and Environmental Geology
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• 제44권2호
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• pp.135-152
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• 2011

Nitrate concentrations in springs at the southern slope of Jeju Island were predicted using multiple linear regression (MLR) of spatial variables including hydrogeological parameters and land use characteristics. Springs showed wide range of nitrate concentrations from <0.02 to 86 mg/L with a mean of 20 mg/L. Spatial variables were generated for the circular buffer when the optimal buffer radius was assigned as 400 m. Selected regression models were tested using the p values and Durbin-Watson statistics. Explanatory variables were selected using the adjusted $R^2$, Cp (total squared error) and AIC (Akaike's Information Criterion), and significance. In addition, mutual linear relations between variables were also considered. Small portion of springs, usually <10% of total samples, were identified as outliers indicating limitations of MLR using circular buffers. Adjusted $R^2$ of the proposed models was improved from 0.75 to 0.87 when outliers were eliminated. In particular, the areal proportion of natural area had the greatest influence on the nitrate concentrations in springs. Among anthropogenic land uses, the influence of nitrate contamination is diminishing in the following order of orchard, residential area, and dry farmland. It is apparent quality of springs in the study area is likely to be controlled by land uses instead of hydrogeological parameters. Most of all, it is worth highlighting that the contamination susceptibility of springs is highly sensitive to nearby land uses, in particular, orchard.

• Nuclear Medicine and Molecular Imaging
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• 제41권3호
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• pp.234-240
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• 2007

Purpose: In this study we propose a block-iterative method for reconstructing Compton scattered data. This study shows that the well-known expectation maximization (EM) approach along with its accelerated version based on the ordered subsets principle can be applied to the problem of image reconstruction for Compton camera. This study also compares several methods of constructing subsets for optimal performance of our algorithms. Materials and Methods: Three reconstruction algorithms were implemented; simple backprojection (SBP), EM, and ordered subset EM (OSEM). For OSEM, the projection data were grouped into subsets in a predefined order. Three different schemes for choosing nonoverlapping subsets were considered; scatter angle-based subsets, detector position-based subsets, and both scatter angle- and detector position-based subsets. EM and OSEM with 16 subsets were performed with 64 and 4 iterations, respectively. The performance of each algorithm was evaluated in terms of computation time and normalized mean-squared error. Results: Both EM and OSEM clearly outperformed SBP in all aspects of accuracy. The OSEM with 16 subsets and 4 iterations, which is equivalent to the standard EM with 64 iterations, was approximately 14 times faster in computation time than the standard EM. In OSEM, all of the three schemes for choosing subsets yielded similar results in computation time as well as normalized mean-squared error. Conclusion: Our results show that the OSEM algorithm, which have proven useful in emission tomography, can also be applied to the problem of image reconstruction for Compton camera. With properly chosen subset construction methods and moderate numbers of subsets, our OSEM algorithm significantly improves the computational efficiency while keeping the original quality of the standard EM reconstruction. The OSEM algorithm with scatter angle- and detector position-based subsets is most available.

• Korean Journal of Remote Sensing
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• 제31권6호
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• pp.531-548
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• 2015

Atmospheric Motion Vector (AMV) from satellite images have shown Slow Speed Bias (SSB) in comparison with rawinsonde. The causes of SSB are originated from tracking, selection, and height assignment error, which is known to be the leading error. However, recent works have shown that height assignment error cannot be fully explained the cause of SSB. This paper attempts a new approach to examine the possibility of SSB reduction of COMS AMV by using a new target tracking algorithm. Tracking error can be caused by averaging of various wind patterns within a target and changing of cloud shape in searching process over time. To overcome this problem, Gaussian Mixture Model (GMM) has been adopted to extract the coldest cluster as target since the shape of such target is less subject to transformation. Then, an image filtering scheme is applied to weigh more on the selected coldest pixels than the other, which makes it easy to track the target. When AMV derived from our algorithm with sum of squared distance method and current COMS are compared with rawindsonde, our products show noticeable improvement over COMS products in mean wind speed by an increase of $2.7ms^{-1}$ and SSB reduction by 29%. However, the statistics regarding the bias show negative impact for mid/low level with our algorithm, and the number of vectors are reduced by 40% relative to COMS. Therefore, further study is required to improve accuracy for mid/low level winds and increase the number of AMV vectors.

• Journal of Korean Society of Coastal and Ocean Engineers
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• 제17권3호
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• pp.149-157
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• 2005

Typical water quality (WQ) parameters defined in the governing equation of the WQ model are the pollutant loads from atmosphere and watersheds, pollutant release rates from sediment, diffusion coefficient and reaction coefficient etc. The direct measurement of these parameters is very difficult as well as requires high cost. In this study, the pollutant budget equation including these parameters was used to construct the linear simultaneous equations. Based on these equations, the inverse problems were constructed and WQ parameter estimation method minimizing the sum of squared errors between the computed and observed amounts of the mass changes was suggested. WQ parameters, i.e., the atmospheric pollutant loads, sediment release rates, diffusion coefficients and reaction coefficient, were estimated using .this method by utilizing the vertical concentration profile data which has been observed in Cheonsu Bay and Ulsan Port. Values of the estimated parameters show a large temporal variation. However, this technique is persuasive in that the RHS (root mean square) error was less than $5.0\%$ of the observed value ranges and the agreement index was greater than 0.95.

• Journal of Korea Water Resources Association
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• 제40권10호
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• pp.811-821
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• 2007

The intensity-duration-frequency (IDF) curves by Talbot, Sherman and Japanese type formulas are widely used in South Korea since the parameters are easily estimated. However, these IDF curves' accuracies are relatively worse than those of the IDF curves developed by Lee et al. (1993) and Heo et al. (1999), and different parameters for the given return periods should be computed. In this study, parameter estimation method for the IDF curve by Heo et al. (1999) is suggested using genetic algorithm (GA). Quantiles computed by at-site frequency analysis using the rainfall data of 22 rainfall gauges operated by Korea Meteorological Administration are employed to estimate the parameters of IDF curves and minimizing root mean squared error (RMSE) and relative RMSE (RRMSE) of observed and computed quantiles are used as objective functions of GA. The comparison of parameter estimation methods between the empirical regression analysis and the suggested method show that the IDF curve in which the parameters are estimated by GA using RRMSE as an objective function is superior to the IDF curves using RMSE.

• Communications for Statistical Applications and Methods
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• 제17권4호
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• pp.515-525
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• 2010

We investigated design-based properties of the ordinary least square estimator(OLSE) and the weighted least square estimator(WLSE) in a panel regression model. Given a complex data we derive the magnitude of the design-based bias of two estimators and show that the bias of WLSE is smaller than that of OLSE. We also conducted a simulation study using Korean welfare panel data in order to compare design-based properties of two estimators numerically. In the study we found the followings. First, the relative bias of OLSE is nearly two times larger than that of WLSE and the bias ratio of OLSE is greater than that of WLSE. Also the relative bias of OLSE remains steady but that of WLSE becomes smaller as the sample size increases. Next, both the variance and mean square error(MSE) of two estimators decrease when the sample size increases. Also there is a tendency that the proportion of squared bias in MSE of OLSE increases as the sample size increase, but that of WLSE decreases. Finally, the variance of OLSE is smaller than that of WLSE in almost all cases and the MSE of OLSE is smaller in many cases. However, the number of cases of larger MSE of OLSE increases when the sample size increases.

• Journal of Korean Society for Atmospheric Environment
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• 제33권3호
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• pp.277-288
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• 2017

Previous studies of health effects of $PM_{2.5}$ performed spatial monitoring campaigns to assess spatial variability of $PM_{2.5}$ across people's residences. Highly reliable portable and cost-effective samplers will be useful for such campaigns. This study aimed to investigate applicability of the Deployable Particulate Impact Sampler(DPIS), one of the compact impact samplers, to spatial monitoring campaigns of $PM_{2.5}$ in Seoul, Korea. The investigation focused on the consistency of $PM_{2.5}$ concentrations measured by DPISs compared to those by the Low-volume Cyclone sampler (LCS). LCS has operated at a fixed site in the Seoul National University Yeongeon campus, Seoul, Korea since 2003 and provided qualified $PM_{2.5}$ data. $PM_{2.5}$ sampling of DPISs was carried out at the same site from November 17, 2015 through February 3, 2016. $PM_{2.5}$ concentrations were quantified by the gravimetric method. Using a duplicated DPIS, we confirmed the reliability of DPIS by computing relative precision and mean square error-based R squared value ($R^2$). Relative precision was one minus the difference of measurements between two samplers relative to the sum. For accuracy, we compared $PM_{2.5}$ concentrations from four DPISs (DPIS_Tg, DPIS_To, DPIS_Qg, and DPIS_Qo) to those of LCS. Four samplers included two types of collection filters(Teflon, T; quartz, Q) and impaction discs(glass fiber filter, g; pre-oiled porous plastic disc, o). We assessed accuracy using accuracy value which is one minus the difference between DPIS and LCS $PM_{2.5}$ relative to LCS $PM_{2.5}$ in addition to $R^2$. DPIS showed high reliability (average precision=97.28%, $R^2=0.98$). Accuracy was generally high for all DPISs (average accuracy=83.78~88.88%, $R^2=0.89{\sim}0.93$) except for DPIS_Qg (77.35~78.35%, 0.82~0.84). Our results of high accuracy of DPIS compared to LCS suggested that DPIS will help the assessment of people's individual exposure to $PM_{2.5}$ in extensive spatial monitoring campaigns.

• Journal of the Korean Wood Science and Technology
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• 제45권6호
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• pp.836-845
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• 2017

Gravimetric method is usually used to evaluate air-dry density, which is governing physical or mechanical properties of wood. Although it had high evaluation accuracy, the method is time consuming process. Thus, this study was conducted to estimate air-dry density of wood with high accuracy by using polychromatic X-ray and digital detector as alternative of gravimetric method. To quantify polychromatic X-ray projection for evaluating air-dry density, Lambert-Beer's law with the integral value of probability function was used. The integral value was used as weighting factor in the law, and it was determined by conducting simple test at various penetration depths and tube voltage. Mass attenuation coefficient (MAC) of wood also calculated by investigating polychromatic X-ray projection according to species, penetration depth and tube voltage. The species had not an effect on change of MAC. Finally, an air-dry density of wood was estimated by applying the integral value, MAC and Lambert-Beer's law to polychromatic X-ray projection. As an example, the relation of the integral value (${\alpha}$) according to penetration depth (t, cm) at tube voltage of 35 kV was ${\alpha}=-0.00091t{\times}0.0184$ while the regression of the MAC (${\mu}$, $cm^2/g$) was ${\mu}=0.5414{\exp}(-0.0734t)$. When calculation of root mean squared error (RMSE) was performed to check the estimation accuracy, RMSE at 35, 45 and 55 kV was 0.010, 0.013 and $0.009g/cm^3$, respectively. However, partial RMSE in relation to air-dry density was varied according to tube voltage. The partial RMSE below air-dry density of $0.41g/cm^3$ was $0.008g/cm^3$ when tube voltage of 35 kV was used. Meanwhile, the partial RMSE above air-dry density of $0.41g/cm^3$ decreased as tube voltage increased. It was conclude that the accuracy of estimation with polychromatic X-ray and digital detector was quite high if the integral value and MAC of wood were determined precisely or a condition of examination was chosen properly. It was seemed that the estimation of air-dry density by using polychromatic X-ray system can supplant the gravimetric method.

• Journal of the Institute of Electronics and Information Engineers
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• 제50권8호
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• pp.83-93
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• 2013

By comparing Wiener's MMSE on stochastic signal transmission with Shannon's mutual information first proved by C.E. Shannon in terms of information theory, connections between two approaches were investigated. What Wiener wanted to see in signal transmission in noisy channel is to try to capture fundamental limits for signal quality in signal estimation. On the other hands, Shannon was interested in finding fundamental limits of signal quantity that maximize the uncertainty in mutual information using the entropy concept in noisy channel. First concern of this paper is to show that in deriving limits of Shannon's point to point fundamental channel capacity, Shannon's mutual information obtained by exploiting MMSE combiner and Wiener filter's MMSE are interelated by integro-differential equantion. Then, At the meeting point of Wiener's MMSE and Shannon's mutual information the upper bound of spectral efficiency and the lower bound of energy efficiency were computed. Choosing a proper lattice-type code of a mod-${\Lambda}$AWGN channel model and MMSE estimation of ${\alpha}$ confirmed to lead to the fundamental Shannon capacity limits.