• Journal of Korea Society of Digital Industry and Information Management
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• v.7 no.4
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• pp.7-14
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• 2011

Infinite failure NHPP models presented in the literature exhibit either constant, monotonic increasing or monotonic decreasing failure occurrence rate per fault (hazard function). This infinite non-homogeneous Poisson process is model which reflects the possibility of introducing new faults when correcting or modifying the software. In this paper, polynomial hazard function have been proposed, which can efficiency application for software reliability. Algorithm for estimating the parameters used to maximum likelihood estimator and bisection method. Model selection based on mean square error and the coefficient of determination for the sake of efficient model were employed. In numerical example, log power time model of the existing model in this area and the polynomial hazard function model were compared using failure interval time. Because polynomial hazard function model is more efficient in terms of reliability, polynomial hazard function model as an alternative to the existing model also were able to confirm that can use in this area.

• Bulletin of the Korean Mathematical Society
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• v.47 no.5
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• pp.933-950
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• 2010

We study a relationship between sets of uniqueness, weakly sufficient sets and sampling sets in the space $A^{-{\infty}}(\mathbb{B})$ of holomorphic functions with polynomial growth on the unit ball of $\mathbb{C}^n$ ($n\;{\geq}\;1$).

• Animal Bioscience
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• v.37 no.5
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• pp.817-825
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• 2024

Objective: The aim of this study was to identify suitable polynomial regression for modeling the average growth trajectory and to estimate the relative development of the rib eye area, scrotal circumference, and morphometric measurements of Guzerat young bulls. Methods: A total of 45 recently weaned males, aged 325.8±28.0 days and weighing 219.9±38.05 kg, were evaluated. The animals were kept on Brachiaria brizantha pastures, received multiple supplementations, and were managed under uniform conditions for 294 days, with evaluations conducted every 56 days. The average growth trajectory was adjusted using ordinary polynomials, Legendre polynomials, and quadratic B-splines. The coefficient of determination, mean absolute deviation, mean square error, the value of the restricted likelihood function, Akaike information criteria, and consistent Akaike information criteria were applied to assess the quality of the fits. For the study of allometric growth, the power model was applied. Results: Ordinary polynomial and Legendre polynomial models of the fifth order provided the best fits. B-splines yielded the best fits in comparing models with the same number of parameters. Based on the restricted likelihood function, Akaike's information criterion, and consistent Akaike's information criterion, the B-splines model with six intervals described the growth trajectory of evaluated animals more smoothly and consistently. In the study of allometric growth, the evaluated traits exhibited negative heterogeneity (b<1) relative to the animals' weight (p<0.01), indicating the precocity of Guzerat cattle for weight gain on pasture. Conclusion: Complementary studies of growth trajectory and allometry can help identify when an animal's weight changes and thus assist in decision-making regarding management practices, nutritional requirements, and genetic selection strategies to optimize growth and animal performance.

• Journal of Plant Biology
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• v.33 no.2
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• pp.141-146
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• 1990

Germination and vegetative growth of Enteromorpha multiramosa Bliding from Pyoson, Cheju Island were investigated in laboratory under various combinations of temperature (5-$25^{\circ}C$) and salinity (8-48$^{\circ}C$). Percent level of germination was relatively high at all combinations of the two factors. The highest value among the combinations was revealed at 15$^{\circ}C$ and 32$\textperthousand$. Dry weight also was fairly high at all levels of combination with maximum value at 2$0^{\circ}C$ and 32$\textperthousand$. Analysis of variance for germination and growth was completed respectively and polynomial prediction models were constructed. F ratio revealed that all factors had a significant effect (p<0.001) on percentage of germination and dry weight, and their interactions also were significant (p<0.001), although the F ratio of interactions was far less than that for either the separate effect of temperature or salinity. Response surface of polynomial equation represented that temperature influenced less than salinity on germination, while it effected remarkably on vegetative growth, so the Enteromorpha multiramosa was kept to visible macrothalli from winter to spring in Cheju Island.

• Phonetics and Speech Sciences
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• v.9 no.3
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• pp.17-23
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• 2017

The present study investigates the effect of Accentual Phrase on F0 using a subset of large-scale corpus of Seoul Korean. Four syllable words which were neither preceded nor followed by silent pauses were presumed to be canonical exemplars of Accentual Phrases in Korean. These four syllable words were extracted from female speakers' speech samples. Growth curve analyses, combination of regression and polynomial curve fitting, were applied to the four syllable words. Four syllable words were divided into four groups depending on the categorical status of the initial segment: voiceless obstruents, voiced obstruents, sonorants, and vowels. Results of growth curve analyses indicate that initial segment types have an effect on the F0 (in semitone) in the nucleus of the initial syllable, and the cubic polynomial term revealed that some of the medial low tones in the 4 syllable words may be guided by the principle of contrast maximization, while others may be governed by the principle of ease of articulation.

• KOREAN JOURNAL OF CROP SCIENCE
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• v.27 no.1
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• pp.84-86
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• 1982

The experiment was conducted with three varieties (Hicks, Burley 21, and Sohyang) and cultivation type (Improved mulching, general mulching, and non mulching) of NC 2326 to model to curve of tabacco growth against time. The basic growth data were obtained by harvest method at intervals of ten days from transplanting at 7-8 times and analyzed by polynomial regression, orthogonal polynomial, and logarithmic transformation. It is shown that the C model of growth curve: T = A +$\sqrt{(1.4 AK + K)}$2K provides an excellent fit.

• Journal of the Korean Geotechnical Society
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• v.21 no.2
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• pp.145-150
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• 2005

It is useful to select an appropriate mathematical model to predict landslide. Through observation and analysis of real-time measured time series, a reasonable mathematic model is chosen to do prediction of landslide. Two theoretical models, such as polynomial function and growth model, are suggested for the description and analysis of measured defermation from an active landslides. These models are applied herein to describe the main characteristics of defermation process for two types of landslide, namely polynomial and growth models. The TRS (tensiof rotation and settlement) sensors are applied to adopt two models, and the data analysis of two field (Neurpjae and Buksil) resulted in good coincidence between measured data and models.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.53 no.1
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• pp.40-49
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• 2004

In this paper, we propose a new architecture of Genetic Algorithms(GAs)-based Self-Organizing Polynomial Neural Networks(SOPNN), discuss a comprehensive design methodology and carry out a series of numeric experiments. The conventional SOPNN is based on the extended Group Method of Data Handling(GMDH) method and utilized the polynomial order (viz. linear, quadratic, and modified quadratic) as well as the number of node inputs fixed (selected in advance by designer) at Polynomial Neurons (or nodes) located in each layer through a growth process of the network. Moreover it does not guarantee that the SOPNN generated through learning has the optimal network architecture. But the proposed GA-based SOPNN enable the architecture to be a structurally more optimized network, and to be much more flexible and preferable neural network than the conventional SOPNN. In order to generate the structurally optimized SOPNN, GA-based design procedure at each stage (layer) of SOPNN leads to the selection of preferred nodes (or PNs) with optimal parameters- such as the number of input variables, input variables, and the order of the polynomial-available within SOPNN. An aggregate performance index with a weighting factor is proposed in order to achieve a sound balance between approximation and generalization (predictive) abilities of the model. A detailed design procedure is discussed in detail. To evaluate the performance of the GA-based SOPNN, the model is experimented with using two time series data (gas furnace and NOx emission process data of gas turbine power plant). A comparative analysis shows that the proposed GA-based SOPNN is model with higher accuracy as well as more superb predictive capability than other intelligent models presented previously.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.60 no.2
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• pp.398-406
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• 2011

In this paper, we introduce a new architecture of PSO-based Polynomial Neural Networks (PNN) and discuss its comprehensive design methodology. The conventional PNN is based on a extended Group Method of Data Handling (GMDH) method, and utilized the polynomial order (viz. linear, quadratic, and modified quadratic) as well as the number of node inputs fixed (selected in advance by designer) at Polynomial Neurons located in each layer through a growth process of the network. Moreover it does not guarantee that the conventional PNN generated through learning results in the optimal network architecture. The PSO-based PNN results in a structurally optimized structure and comes with a higher level of flexibility that the one encountered in the conventional PNN. The PSO-based design procedure being applied at each layer of PNN leads to the selection of preferred PNs with specific local characteristics (such as the number of input variables, input variables, and the order of the polynomial) available within the PNN. In the sequel, two general optimization mechanisms of the PSO-based PNN are explored: the structural optimization is realized via PSO whereas in case of the parametric optimization we proceed with a standard least square method-based learning. To evaluate the performance of the PSO-based PNN, the model is experimented with using Gas furnace process data, and pH neutralization process data. For the characteristic analysis of the given entire data with non-linearity and the construction of efficient model, the given entire system data is partitioned into two type such as Division I(Training dataset and Testing dataset) and Division II(Training dataset, Validation dataset, and Testing dataset). A comparative analysis shows that the proposed PSO-based PNN is model with higher accuracy as well as more superb predictive capability than other intelligent models presented previously.

• 모자간호학회지
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• v.1
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• pp.34-44
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• 1991

The purposes of this study were to predict newborn birthweight by use of gestational period and fundal height and to identify growth curve of fundal height according to gestational period and growth curve of newborn birthweight according to fundal height. The subjects for the study were 802 women who delivered the normal newborn babies at Seoul National University Hospital from Sep. 1, 1981 to Aug.31, 1986. The data were collected bit chart review and analyzed nth SPSS program. The results of study were as follows : 1. The multiple regression equation ($R^2$=0.416) used for the prediction of newborn birthweight was y=(newborn birthweight, kg)=-4.421+0.075$x_1$(fundal height, cm)+0.053$x_2$(gestational period, weeks)+0.016$x_3$(abdominal girth, cm)+0.010$x_4$(maternal height, cm) 2. The growth curve of fundal height according to gestational period was obtained by polynomial regression. The regression equation was Y(fundal height, cm)=-36.78+18.58$log_ex$(gestational period, weeks) The growth curve of newborn birth weight according to fundal height was obtained by polynomial regression. The regression equation was Y(newborn birthweight, kg)=-8.09+3.27$log_ex$ (Fundal Height, cm) 3. In the following subgroups no significant difference was found in fundal height : engaged vs. nonengaged presentation, and nulliparous vs. multiparous women.