• Geomechanics and Engineering
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• v.28 no.4
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• pp.397-404
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• 2022

Accurate prediction of the undrained shaft resistance is essential for robust design of bored piles in undrained condition. The undrained shaft resistance is calculated using the undrained adhesion factor multiplied by the undrained cohesion of the soil. However, the available correlations to predict the undrained adhesion factor have been developed using simple regression techniques and the accuracy of these correlations has not been thoroughly assessed in previous studies. The lack of the assessment of these correlations made it difficult for geotechnical engineers to select the most accurate correlation in routine designs. Furthermore, limited attempts have been made in previous studies to use advanced data mining techniques to develop simple and accurate correlation to predict the undrained adhesion factor. This research, therefore, has been conducted to fill these gaps in knowledge by developing novel and robust correlation to predict the undrained adhesion factor. The development of the new correlation has been conducted using the multi-objective evolutionary polynomial regression analysis. The new correlation outperformed the available empirical correlations, where the new correlation scored lower mean absolute error, mean square error, root mean square error and standard deviation of measured to predicted adhesion factor, and higher mean, a20-index and coefficient of correlation. The correlation also successfully showed the influence of the undrained cohesion and the effective stress on the adhesion factor. Hence, the new correlation enhances the design accuracy and can be used by practitioner geotechnical engineers to ensure optimized designs of bored piles in undrained conditions.

• Textile Coloration and Finishing
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• v.18 no.4
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• pp.43-48
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• 2006

A phthalocyanine reactive dye was applied to nylon fibers to study the effects of the temperature and pH on % exhaustion and fixation. In addition, appropriate predictable empirical models, relatively new approaches in dyeing process, were developed incorporating interactions effects of temperature and pH for predicting the both % exhaustion and fixation. The significance of the mathematical model developed was ascertained using Excel regression (solver) analysis module. A very high correlation coefficient was obtained ($R^2=0.9895$ for % exhaustion, $R^2=0.9932$ for fixation) for the model which shows prominent prediction capacity of the model for the unknown conditions. The predictable polynomial equations developed from the Experimental results were thoroughly analyzed by ANOVA (Analysis of Variance) statistical concepts.

• Textile Coloration and Finishing
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• v.20 no.2
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• pp.19-28
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• 2008

The objective of this study was to apply the statistical technique known as design of experiments to optimize the % exhaustion variables for phthalocyanine dyeing of nylon fiber. In this study, a three-factor Central Composite Rotatable Design (CCRD) was used to establish the optimum conditions for the phthalocyanine reactive dyeing of nylon fiber. Temperature, pH and liquor ratio were considered as the variable of interest. Acidic solution with higher temperature and lower liquor ratio were found to be suitable conditions for higher % exhaustion. These three variables were used as independent variables, whose effects on % exhaustion were evaluated. Significant polynomial regression models describing the changes on % exhaustion and % fixation with respect to independent variables were established with coefficient of determination, R2, greater than 0.90. Close agreement between experimental and predicted yields was obtained. Optimum conditions were obtained using surface plots and Monte Carlo simulation techniques where maximum dyeing efficiency is achieved. The significant level of both the main effects and interaction was observed by analysis of variance (ANOVA) approach. Based on the statistical analysis, the results have provided much valuable information on the relationship between response variables and independent variables. This study demonstrates that the CCRD could be efficiently applied for the empirical modeling of % exhaustion and % fixation in dyeing. It also shows that it is an economical way of obtaining the maximum amount of information in a short period of time with least number of experiments.

• Textile Coloration and Finishing
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• v.19 no.2
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• pp.14-23
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• 2007

This research article explores the use of phthalocyanine reactive dye on nylon substrate. The effect of factors such as pH, temperature, liquor ratio and alkali addition on level of dye exhaustion, fixation and total fixation efficiency. Low pH, high temperature and low liquor ratio were found to be suitable conditions for maximum % exhaustion values. The effect of sulphatoethylsulphone(SES) and vinylsulphone(VS) form of the dyes on level of dye fixation was also discussed. The optimized exhaustion (%E), fixation(%F) and total fixation efficiency were determined. Modification of the dyeing process with alkali addition displayed that dye fixation(%) increased by alkali addition. Vinylsulphone(VS) moiety of the dye was found to be superior to. maximum fixation (%F). Appropriate predictable empirical models, relatively a new approach in dyeing processes, were developed incorporating interactions effects of temperature, pH and liquor ratio for predicting % exhaustion, fixation and total fixation efficiency. The significance of the mathematical model developed was ascertained using microsoft excel regression(solver) analysis module. High correlation coefficient was obtained (R2=0.9895 for % exhaustion, R2=0.9932 for fixation, R2=0.9965 for total fixation efficiency) for the model which shows prominent prediction capacity of the model for my conditions. The predictable polynomial equations developed from tile experimental results were thoroughly analyzed by ANOVA (Analysis of Variance) statistical concepts.

• Journal of Korea Entertainment Industry Association
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• v.15 no.3
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• pp.269-278
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• 2021

This study was inteded to include online social relations and ability use information and communication devices to analyze the elderly's participation in volunteer activities and provide basic data to identify the elderly's participation in volunteer activities. The statistical data of the 2017 National survey of Senior Citizen, only 10,073 people aged 65 or older were sampled out of 10,299 people. The participation rate of volunteering was frequently analyzed, and the difference in participation in volunteer according to the factors was Chi-square analysis and One-way variance analysis. A polynomial regression analysis was conducted to identify the effect factors of participation in volunteering. As a results. 3.9% of older adults are volunteering and 11.5% are experienced in the past. Participation in volunteer activity differed significantly depending on age, education level, economic level, subjective health, body function, ability use information and communication devices, social networks, frequency of face-to-face contact and frequency of non face contact. In the regression analysis, utilization of communication and device, social networking, face to face contact frequency were show to be the effect factors. In order to promote elderly's participation in volunteer activities, consideration of related resources reported in prior studies, social relations, frequency of face-to-face contact and ability to use information and communication devices is considered important.

• Asian-Australasian Journal of Animal Sciences
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• v.29 no.5
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• pp.607-614
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• 2016

The objectives of the study were to estimate genetic parameters for milk production traits of Holstein cattle using random regression models (RRMs), and to compare the goodness of fit of various RRMs with homogeneous and heterogeneous residual variances. A total of 126,980 test-day milk production records of the first parity Holstein cows between 2007 and 2014 from the Dairy Cattle Improvement Center of National Agricultural Cooperative Federation in South Korea were used. These records included milk yield (MILK), fat yield (FAT), protein yield (PROT), and solids-not-fat yield (SNF). The statistical models included random effects of genetic and permanent environments using Legendre polynomials (LP) of the third to fifth order (L3-L5), fixed effects of herd-test day, year-season at calving, and a fixed regression for the test-day record (third to fifth order). The residual variances in the models were either homogeneous (HOM) or heterogeneous (15 classes, HET15; 60 classes, HET60). A total of nine models (3 orders of $polynomials{\times}3$ types of residual variance) including L3-HOM, L3-HET15, L3-HET60, L4-HOM, L4-HET15, L4-HET60, L5-HOM, L5-HET15, and L5-HET60 were compared using Akaike information criteria (AIC) and/or Schwarz Bayesian information criteria (BIC) statistics to identify the model(s) of best fit for their respective traits. The lowest BIC value was observed for the models L5-HET15 (MILK; PROT; SNF) and L4-HET15 (FAT), which fit the best. In general, the BIC values of HET15 models for a particular polynomial order was lower than that of the HET60 model in most cases. This implies that the orders of LP and types of residual variances affect the goodness of models. Also, the heterogeneity of residual variances should be considered for the test-day analysis. The heritability estimates of from the best fitted models ranged from 0.08 to 0.15 for MILK, 0.06 to 0.14 for FAT, 0.08 to 0.12 for PROT, and 0.07 to 0.13 for SNF according to days in milk of first lactation. Genetic variances for studied traits tended to decrease during the earlier stages of lactation, which were followed by increases in the middle and decreases further at the end of lactation. With regards to the fitness of the models and the differential genetic parameters across the lactation stages, we could estimate genetic parameters more accurately from RRMs than from lactation models. Therefore, we suggest using RRMs in place of lactation models to make national dairy cattle genetic evaluations for milk production traits in Korea.

• Korean Journal of Plant Resources
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• v.23 no.2
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• pp.172-178
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• 2010

This study was carried out to investigate abiotic and biotic environmental factors affecting homoharringtonine (HHT) contents of Cephalotaxus koreana, whereby, to provide basic information of high value-added industry production of HHT as a promising anti-cancer agent. For correlation between abiotic environmental factors (soil moisture, soil pH, habitat density and temperature) and HHT contents, the contents were highly correlated with soil moisture (0.77) and soil pH (-0.68). For multiple regression analysis of relationship between abiotic environmental factors (soil moisture and soil pH) and HHT contents, soil moisture appeared to be strongly affecting the contents relatively due to being significant at only its regression coefficient ($26.48^{***}$). For the effect of biotic environmental factors (damage index) affecting HHT contents, the contents was quadratic with equation of $H=278.23+1242D-398.87D^2$, also, damage index had strong effect on the contents. Finally, for the result of the most influencing an environmental factor on HHT contents, both damage index and soil moisture were suitable in second polynomial regression, also, damage index ($R^2=0.73^{***}$) was turned out to be more influencing factor than soil moisture ($R^2=0.67^{**}$) on HHT contents relatively. Therefore, we predict that HHT contents in the trees of Cephalotaxus koreana is produced as a chemical defense mechanism triggered by a stress-related damage of fungi or insects.

• Applied Chemistry for Engineering
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• v.30 no.1
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• pp.29-33
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• 2019

Carbon nanotubes, nanofibers and powders were used for acetylene adsorption experiments. A total of 15 different experiments were designed by 3-level of Box-Behnken Design (BBD) with 3 factors including the Pd concentration of 0 to 5%, adsorption temperature of 30 to $80^{\circ}C$ and $C_2H_2/CO_2$ of 3 to 10. Based on those data, a second order polynomial regression analysis was used to derive the adsorption amount prediction equation according to operating conditions. The adsorption temperature showed the greatest influence index while the $C_2H_2/CO_2$ ratio showed the smallest according to the F-value measurement of the ANOVA analysis. However, there was little interaction between major factors. In the adsorption optimization analysis, a 22.0 mmol/g was adsorbed under the conditions of Pd concentration of 3.0%, adsorption temperature of $47^{\circ}C$ and $C_2H_2/CO_2$ of 10 with 95.9% accuracy.

• Smart Structures and Systems
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• v.21 no.5
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• pp.695-703
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• 2018

Based on monitoring data collected from the Nanjing Dashengguan Bridge over the last five years, this paper systematically investigates the effects of temperature field and train loadings on the structural responses of this long-span high-speed railway bridge, and establishes the early warning thresholds for various structural responses. Then, some lessons drawn from the structural health monitoring system of this bridge are summarized. The main context includes: (1) Polynomial regression models are established for monitoring temperature effects on modal frequencies of the main girder and hangers, longitudinal displacements of the bearings, and static strains of the truss members; (2) The correlation between structural vibration accelerations and train speeds is investigated, focusing on the resonance characteristics of the bridge at the specific train speeds; (3) With regard to various static and dynamic responses of the bridge, early warning thresholds are established by using mean control chart analysis and probabilistic analysis; (4) Two lessons are drawn from the experiences in the bridge operation, which involves the lacks of the health monitoring for telescopic devices on the beam-end and bolt fractures in key members of the main truss.

• Proceedings of the KSME Conference
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• 2001.06b
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• pp.501-506
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• 2001

Computer modeling is essential to evaluate possible design of suspension for a railway vehicles. By creating a simulation, the engineers are able to assess the feasibility of a given design and change the design factors to get a better design. But if one wishes to perform complex analysis on the simulation, such as railway vehicle dynamic, the computational time can become overwhelming. Therefore, many researchers have turned to surrogate modeling. A surrogate model is essentially a regression performed on a data sampling of the simulation. In the most general sense, metamodels(surrogate model) take the form $y(x)=f(x)+{\varepsilon}$, where y(x) is the true simulation output, f(x) is the metamodel output, and $\varepsilon$ is the error between the two. In this paper, a second order polynomial equation is partially used as a metamodel to represent the forty-six dynamic performances for high speed train. The number of factors as design variables of the metamodel is twenty-nine, which are composed the dynamic characteristics of suspension. This metamodel is used to search the optimum values of suspension characteristics which minimize the dynamic responses for high speed train. This optimization is a multi-objective problem which have many design variables. This paper shows that the response surface model which is made through the design of analysis of computer experiments method is very efficient to solve this complex optimization problem.