• Journal of Animal Science and Technology
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• v.55 no.3
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• pp.165-171
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• 2013

Genomic relationship matrix (GRM) was constructed using whole genome SNP markers of swine and genomic breeding value was estimated by substitution of the numerator relationship matrix (NRM) based on pedigree information to GRM. Genotypes of 40,706 SNP markers from 448 pure Landrace pigs were used in this study and five kinds of GRM construction methods, G05, GMF, GOF, $GOF^*$ and GN, were compared with each other and with NRM. Coefficients of GOF considering each of observed allele frequencies showed the lowest deviation with coefficients of NRM and as coefficients of GMF considering the average minor allele frequency showed huge deviation from coefficients of NRM, movement of mean was expected by methods of allele frequency consideration. All GRM construction methods, except for $GOF^*$, showed normally distributed Mendelian sampling. As the result of breeding value (BV) estimation for days to 90 kg (D90KG) and average back-fat thickness (ABF) using NRM and GRM, correlation between BV of NRM and GRM was the highest by GOF and as genetic variance was overestimated by $GOF^*$, it was confirmed that scale of GRM is closely related with estimation of genetic variance. With the same amount of phenotype information, accuracy of BV based on genomic information was higher than BV based on pedigree information and these symptoms were more obvious for ABF then D90KG. Genetic evaluation of animal using relationship matrix by genomic information could be useful when there is lack of phenotype or relationship and prediction of BV for young animals without phenotype.

• Korean journal of applied entomology
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• v.55 no.2
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• pp.119-128
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• 2016

This study was conducted to develop temperature-driven models for a population model of turnip aphid, Lipaphis erysimi: nymphal development rate models and apterious adult's oviposition (larviparous) model. Nymphal development and the longevity and fecundity of adults were examined on cabbage at six constant temperatures (10, 15, 20, 25, 30, $35{\pm}1^{\circ}C$, 16L:8D). L. erysimi nymphs did not survive at $10^{\circ}C$. Development time of nymphs increased with increasing temperature up to $30^{\circ}C$ and thereafter slightly decreased, ranging from 18.5 d at $15^{\circ}C$ to 5.9 d at $30^{\circ}C$. The lower threshold temperature and thermal constant were estimated as $7.9^{\circ}C$ and 126.3 degree days, respectively. The nonlinear model of Lactin 2 fitted well for the relationship between the development rate and temperature of small (1+2 instar), large (3+4 instar) and total nymph (all instars). The Weibull function provided a good fit for the distribution of development times of each stage. Temperature affected the longevity and fecundity of L. erysimi. Adult longevity decreased as the temperature increased and ranged from 24.4 d at $20^{\circ}C$ to 16.4 d at $30.0^{\circ}C$ with abnormal longevity 18.2 d at $15^{\circ}C$, which was used to estimate adult aging rate model for the calculation of adult physiological age. L. erysimi showed a maximum fecundity of 91.6 eggs per female at $20^{\circ}C$. In this study, we provided three temperature-dependent components for an oviposition model of L. erysimi: total fecundity, age-specific cumulative oviposition rate, and age-specific survival rate.

• Korean journal of applied entomology
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• v.42 no.1
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• pp.43-51
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• 2003

This study was carried out to develop the forecasting model of Pseudococcus comtocki Kuwana for timing spray. Field phonology and temperature-dependent development of p. comstocki were studied, and its stage transition models were developed. p comstocki occurred three generations a year in Suwon. The 1 st adults occurred during mid to late June, and the 2nd adults were abundant during mid to late August. The 3rd adults were observed after late October. The development times of each instar of p. comstocki decreased with increasing temperature up to 25$^{\circ}C$, and thereafter the development times increased. The estimated low-threshold temperatures were 14.5, 8.4, 10.2, 11.8, and 10.1$^{\circ}C$ for eggs, 1st+2nd nymphs, 3rd nymphs, preoviposition, and 1st nymphs to preoviposition, respectively. The degree-days (thermal constants) for completion of each instar development were 105 DD for egg,315 DD for 1st+2nd nymph, 143 DD for 3rd nymph, 143 DD for preoviposition, and 599 DD for 1 st nymph to preoviposition. The stage transition models of p. comstocki, which simulate the proportion of individuals shifted from a stage to the next stage, were constructed using the modified Sharpe and DeMichele model and the Weibull function. In field validation, degree-day models using mean-minus-base, sine wave, and rectangle method showed 2-3d, 1-7d, and 0-6 d deviation with actual data in predicting the peak oviposition time of the 1st and 2nd generation adults, respectively. The rate summation model, in which daily development rates estimated by biophysical model of Sharpe and DeMichele were accumulated, showed 1-2 d deviation with actual data at the same phonology predictions.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.42 no.6
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• pp.1-8
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• 2005

In this paper, we propose a two-stage head tracking algorithm adequate for real-time active camera system having pan-tilt-zoom functions. In the color convergence stage, we first assume that the shape of a head is an ellipse and its model color histogram is acquired in advance. Then, the min-shift method is applied to roughly estimate a target position by examining the histogram similarity of the model and a candidate ellipse. To reflect the temporal change of object color and enhance the reliability of mean-shift based tracking, the target histogram obtained in the previous frame is considered to update the model histogram. In the updating process, to alleviate error-accumulation due to outliers in the target ellipse of the previous frame, the target histogram in the previous frame is obtained within an ellipse adaptively shrunken on the basis of the model histogram. In addition, to enhance tracking reliability further, we set the initial position closer to the true position by compensating the global motion, which is rapidly estimated on the basis of two 1-D projection datasets. In the subsequent stage, we refine the position and size of the ellipse obtained in the first stage by using shape information. Here, we define a robust shape-similarity function based on the gradient direction. Extensive experimental results proved that the proposed algorithm performs head hacking well, even when a person moves fast, the head size changes drastically, or the background has many clusters and distracting colors. Also, the propose algorithm can perform tracking with the processing speed of about 30 fps on a standard PC.

• Korean Journal of Mineralogy and Petrology
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• v.33 no.3
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• pp.273-291
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• 2020

The strength characteristics of the three orthogonal splitting planes, known as rift, grain and hardway planes in granite quarries, were examined. R, G and H specimens were obtained from the block samples of Jurassic granites in Geochang and Hapcheon areas. The directions of the long axes of these three specimens are perpendicular to each of the three planes. First, The chart, showing the scaling characteristics of three graphs related to the uniaxial compressive strengths of R, G and H specimens, were made. The graphs for the three specimens, along with the increase of strength, are arranged in the order of H < G < R. The angles of inclination of the graphs for the three specimens, suggesting the degree of uniformity of the texture within the specimen, were compared. The above angles for H specimens(θ^H, 24.0°~37.3°) are the lowest among the three specimens. Second, the scaling characteristics related to the three graphs of RG, GH and RH specimens, representing a combination of the mean compressive strengths of the two specimens, were derived. These three graphs, taking the various N-shaped forms, are arranged in the order of GH < RH < RG. Third, the correlation chart between the strength difference(Δσ_t) and the angle of inclination(θ) was made. The above two parameters show the correlation of the exponential function with an exponent(λ) of -0.003. In both granites, the angle of inclination(θ^RH) of the RH-graph is the lowest. Fourth, the six types of charts, showing the correlations among the three kinds of compressive strengths for the three specimens and the five parameters for the two sets of microcracks aligned parallel to the compressive load applied to each specimen, were made. From these charts for Geochang and Hapcheon granites, the mean value(0.877) of the correlation coefficients(R²) for total density(Lt), along with the frequency(N, 0.872) and density(ρ, 0.874), is the highest. In addition, the mean values(0.829) of correlation coefficients associated with the mean compressive strengths are more higher than the minimum(0.768) and maximum(0.804) compression strengths of three specimens. Fifth, the distributional characteristics of the Brazilian tensile strengths measured in directions parallel to the above two sets of microcracks in the three specimens from Geochang granite were derived. From the related chart, the three graphs for these tensile strengths corresponding to the R, G and H specimens show an order of H(R1+G1) < G(R2+H1) < R(R1+G1). The order of arrangement of the three graphs for the tensile strengths and that for the compressive strengths are mutually consistent. Therefore, the compressive strengths of the three specimens are proportional to the three types of tensile strengths. Sixth, the values of correlation coefficients, among the three tensile strengths corresponding to each cumulative number(N=1~10) from the above three graphs and the five parameters corresponding to each graph, were derived. The mean values of correlation coefficients for each parameter from the 10 correlation charts increase in the order of density(0.763) < total length(0.817) < frequency(0.839) < mean length(Lm, 0.901) ≤ median length(Lmed, 0.903). Seventh, the correlation charts among the compressive strengths and tensile strengths for the three specimens were made. The above correlation charts were divided into nine types based on the three kinds of compressive strengths and the five groups(A~E) of tensile strengths. From the related charts, as the tensile strength increases with the mean and maximum compressive strengths excluding the minimum compressive strength, the value of correlation coefficient increases rapidly.