• Journal of the Korean Geotechnical Society
• /
• v.33 no.6
• /
• pp.27-36
• /
• 2017

Gravel compaction pile method has been widely used to improve the soft ground on the land or sea as one of the soft ground improvement technique. The ultimate bearing capacity of the ground reinforced by gravel compaction piles is affected by the soil strength, the replacement ratio of pile, construction conditions, and so on, and various prediction equations have been proposed to predict this. However, the prediction of the ultimate bearing capacity using the existing models has a very large error and variation, and it is not suitable for practical design. In this study, multiple regression analysis was performed using field loading test results to predict the ultimate bearing capacity of ground reinforced by gravel compaction pile, and the most efficient input variables are selected through evaluation of error by leave one out cross validation, and a multiple regression equation for the prediction of ultimate bearing capacity was proposed. In addition, the prediction error was evaluated by applying artificial neural network using the selected input variables, and the results were compared with those of the existing model.

• The Journal of the Convergence on Culture Technology
• /
• v.6 no.1
• /
• pp.111-120
• /
• 2020

As an application of big data and artificial intelligence techniques, this study proposes an atypical language-based sentimental opinion poll methodology, unlike conventional opinion poll methodology. An alternative method for the sentimental classification model based on existing statistical analysis was to collect real-time Twitter data related to parliamentary elections and perform empirical analyses on the Polarity and Intensity of public opinion using attribute-based sensitivity analysis. In order to classify the polarity of words used on individual SNS, the polarity of the new Twitter data was estimated using the learned Lasso and Ridge regression models while extracting independent variables that greatly affect the polarity variables. A social network analysis of the relationships of people with friends on SNS suggested a way to identify peer group sensitivity. Based on what voters expressed on social media, political opinion sensitivity analysis was used to predict party approval rating and measure the accuracy of the predictive model polarity analysis, confirming the applicability of the sensitivity analysis methodology in the political field.

• Safety and Health at Work
• /
• v.9 no.4
• /
• pp.454-461
• /
• 2018

Background: Working conditions and psychosocial work factors have acquired an important role explaining the well-being and performance of professional drivers, including those working in the field of public transport. This study aimed to examine the association between job strain and the operational performance of public transport drivers and to compare the expositions with psychosocial risk at work of three different types of transport workers: taxi drivers, city bus drivers, and interurban bus drivers. Method: A sample of 780 professional drivers was drawn from three transport companies in Bogota (Colombia). The participants answered the Job Content Questionnaire and a set of sociodemographic and driving performance questions, including age, professional driving experience, work schedules, and accidents and penalties suffered in the last 2 years. Results: Analyses showed significant associations between measures of socio-labor variables and key performance indicators such road traffic accidents and penalties. Furthermore, multiple linear regression analysis contributed to explain significantly suffered accidents from key variables of the Job Demand-Control model, essentially from job strain. In addition, throughout post-hoc analyses, significant differences were found in terms of perceived social support, job strain, and job insecurity. Conclusion: Work stress is an issue that compromises the safety of professional drivers. This research provides evidence supporting a significant effect of job strain on the professional driver's performance. Moreover, the statistically significant differences between taxi drivers, city bus drivers, and interurban bus drivers in their expositions to work-related stress suggest the need for tailored occupational safety interventions on each occupational group.

• Journal of Korean society of Dental Hygiene
• /
• v.23 no.5
• /
• pp.387-393
• /
• 2023

Objectives: This study was attempted in order to understand about the satisfaction with major and the dental hygiene professionalism in dental hygiene students and to grasp the factors affecting the career preparation behavior. Methods: The research subjects included 264 juniors and seniors who are attending dental hygiene department across Korea (three locations in Gyeonggi province, one locations in Daejeon, four locations in Jeolla province, and one locations in Daegu). Data were collected using the online questionnaire between March 6 to April 7, 2023. The chosen data analysis method included descriptive statistical analysis, t-test, one-way ANOVA, Pearson's correlation coefficient, and stepwise multiple linear regression. Results: The following average scores were obtained from those surveyed: 3.25 points concerning career preparation behavior, 3.83 points concerning the respondent's satisfaction with their major, and 3.45 points concerning dental hygiene professionalism. As for a difference in career preparation behavior according to general characteristics, a meaningful difference was shown in terms of gender, motivation for entering the field, and first desired employment. Aspects of career preparation behavior, satisfaction with one's major, and dental hygiene professionalism showed a significant correlation and were confirmed to explain the prediction of 29.1% of the variation in the regression model. Conclusions: For the sake of having an integrated understanding about career preparation behavior among dental hygiene students, there is a need to conduct repeated research on diverse variables and to inquire into a causal relationships between such variables.

• Korean Journal of Applied Biomechanics
• /
• v.33 no.4
• /
• pp.175-184
• /
• 2023

Objective: The purpose of this study is to investigate the effects of human motion and javelin kinematics during the energy transfer in javelin throwing on records, and to provide evidence-based training insights for athletes and coaches to enhance records. Method: Three javelin throw athletes (age: 22.67 ± 0.58 years, height: 178.33 ± 7.37 cm, weight: 83.67 ± 1.15 kg) were recruited for this study. Each athlete attempted ten maximum record trials, and the kinematic data from each performance were analyzed to determine their influence on the records. The Theia3d Markerless system was used for motion analysis. Results: Key factors were modeled and identified at each moment. In E1, main variables were COM Y (𝛽 8.162, p<.05) and COM velocity Z (𝛽 -72.489, p<.05); in E2, COM X (𝛽 -17.604, p<.05); in E3, COM X (𝛽 -18.606, p<.05), COM velocity Y (𝛽 38.694, p<.05), and COM velocity X (𝛽 66.323, p<.05). For the javelin throw dynamics in E3, key determinants were Attitude angle and Javelin velocity in the Y-axis. Conclusion: The study reveals that controlled vertical movement, center of mass management during braking, and enhanced pelvic rotation significantly improve javelin throw performance. These kinematic strategies are critical for record enhancement in javelin throwing.

• Journal of Korean Society for Quality Management
• /
• v.52 no.2
• /
• pp.377-394
• /
• 2024

Purpose: To derive the optimal conditions for the Rubber based colloidal suspension manufacturing process, which made using a stirrer, to apply the mixture design method. Methods: We used two process component and one process variable Mixture design to derive the optimal conditions for the process. The response variables were selected for rotational viscometer measures which can represent Rubber based colloidal suspension quality. The input variables were selected as the values of rubber-organic solvent expressed in proportions as process components and stirring amount as a process variable which are controllable factors in the process. Results: Based on the results of the experiment, rubber and organic solvent and the interaction between stirring amount and rubber and the interaction between stirring amount and rubber and organic solvent were significant. Reproducibility of the regression model was confirmed by the observation that the values obtained from the reproducibility experiment fell within the confidence interval. Additionally, the model predictions were found to be in close agreement with the field measurements. Conclusion: In this study, a regression model was developed to predict the viscosity change of colloidal suspensions based on the proportion of rubber based colloidal suspension. The developed regression model can lead to improved product quality.

• Journal of Wetlands Research
• /
• v.13 no.3
• /
• pp.411-426
• /
• 2011

The construction of composite indicators should be normalized and weighted to render them comparable and evaluable variables in the field, which undergoes absence of a distinct methodology and where the application of universally popular method is common. Constructing of indices does not compare and analyze applying various normalizing and weighting, but constructer generally use chosen method and develops indicators and indices in most research. In this study, indices are applied various normalization and weighting methods, thereby analyzing how much impact the index and identifying individual characteristics derive a more reasonable way to help other research in the future. 5 different methods of normalization and 4 different types of weights were compared and analyzed. There are different results depending applied normalized methods and Z-score method best reflects the characteristics of the variables. According to weighting methods, the calculated results show little difference, but the ranking results of indices did not changed significantly. It might be better to provide constructors with a set of normalization and weighting methods to reflect their characteristics in order to build flood indices through the result of this study.

• Journal of the Korean Association of Geographic Information Studies
• /
• v.13 no.4
• /
• pp.170-180
• /
• 2010

With development of the earth's subsurface space, the need for a reliable subsurface spatial model such as a cross-section, boring log is increasing. However, the ground mass was essentially uncertain. To generate model was uncertain because of the shortage of data and the absence of geotechnical interpretation standard(non-statistical uncertainty) as well as field environment variables(statistical uncertainty). Therefore, the current interpretation of the data and the generation of the model were accomplished by a highly trained experts. In this study, a geotechnical ontology model was developed using the current expert experience and knowledge, and the information content was calculated in the ontology hierarchy. After the relative distance between the information contents in the ontology model was combined with the distance between cluster centers, a cluster analysis that considered the geotechnical semantics was performed. In a comparative test of the proposed method, k-means method, and expert's interpretation, the proposed method is most similar to expert's interpretation, and can be 3D-GIS visualization through easily handling massive data. We expect that the proposed method is able to generate the more reasonable subsurface spatial information model without geotechnical experts' help.

• Journal of the Korea Academia-Industrial cooperation Society
• /
• v.17 no.5
• /
• pp.645-652
• /
• 2016

The statistical modeling of input random variables is necessary in reliability analysis, reliability-based design optimization, and statistical validation and calibration of analysis models of mechanical systems. In statistical modeling methods, there are the Akaike Information Criterion (AIC), AIC correction (AICc), Bayesian Information Criterion, Maximum Likelihood Estimation (MLE), and Bayesian method. Those methods basically select the best fitted distribution among candidate models by calculating their likelihood function values from a given data set. The number of data or parameters in some methods are considered to identify the distribution types. On the other hand, the engineers in a real field have difficulties in selecting the statistical modeling method to obtain a statistical model of the experimental data because of a lack of knowledge of those methods. In this study, commonly used statistical modeling methods were compared using statistical simulation tests. Their advantages and disadvantages were then analyzed. In the simulation tests, various types of distribution were assumed as populations and the samples were generated randomly from them with different sample sizes. Real engineering data were used to verify each statistical modeling method.

• Steel and Composite Structures
• /
• v.37 no.6
• /
• pp.663-678
• /
• 2020

This paper proposes a novel topology optimization method generating multiple materials for external linear plane crack structures based on the combination of IsoGeometric Analysis (IGA) and eXtended Finite Element Method (X-FEM). A so-called eXtended IsoGeometric Analysis (X-IGA) is derived for a mechanical description of a strong discontinuity state's continuous boundaries through the inherited special properties of X-FEM. In X-IGA, control points and patches play the same role with nodes and sub-domains in the finite element method. While being similar to X-FEM, enrichment functions are added to finite element approximation without any mesh generation. The geometry of structures based on basic functions of Non-Uniform Rational B-Splines (NURBS) provides accurate and reliable results. Moreover, the basis function to define the geometry becomes a systematic p-refinement to control the field approximation order without altering the geometry or its parameterization. The accuracy of analytical solutions of X-IGA for the crack problem, which is superior to a conventional X-FEM, guarantees the reliability of the optimal multi-material retrofitting against external cracks through using topology optimization. Topology optimization is applied to the minimal compliance design of two-dimensional plane linear cracked structures retrofitted by multiple distinct materials to prevent the propagation of the present crack pattern. The alternating active-phase algorithm with optimality criteria-based algorithms is employed to update design variables of element densities. Numerical results under different lengths, positions, and angles of given cracks verify the proposed method's efficiency and feasibility in using X-IGA compared to a conventional X-FEM.