• The Journal of the Korea Contents Association
• /
• v.15 no.7
• /
• pp.618-628
• /
• 2015

The purpose of this study was to seek out the structural relation among Relationship Benefits, Customer Satisfactions, and Customer Preference of members to Personal Training. For the subject of this study, we selected 227 persons among men of more than 20 years old age who used Personal Training Center more than 1 month through the Convenient Sampling Method. For the analysis, we used SPSS 15.0 Statistics Package and AMOS 7.0 program as a research tool and have carried out Frequency Analysis, Confirmatory Factor Analysis, Reliability Analysis, and Structural Equation Model Analysis. The results were as follows: First, the Customization Benefits among Relational Benefits have a positive effect on the Customer Satisfaction. Second, the Psychological Benefits and Social Benefits among Relational Benefits have a positive effect on the Customer Preference. Third, the Customer Satisfaction has a positive effect on the Customer Preference.

• Journal of the Korea Convergence Society
• /
• v.9 no.11
• /
• pp.465-477
• /
• 2018

The purpose of this study is to analyze the effect of three - week high altitude ski training on the myocardial performance of cross - country skiers and to provide basic data for the future improvement of cross - country skiers'. The subjects were 6 cross - country skiing male college athletes. To investigate the effects of periodic and high altitude training on cross - country skiers, a general linear model ANOVA with repeated measure And analyzed using the Paired Samples t-test. In high altitude ski training for 3 weeks, the body composition did not change but the isokinetic muscular function of the shoulder joint, hip joint, knee joint, and ankle joint was decreased. Therefore, further study is needed if it is considered that continuous strength training should be performed during the ski training period such as SP period.

• Journal of the Korean Applied Science and Technology
• /
• v.38 no.3
• /
• pp.860-869
• /
• 2021

This study tried to provide basic data for predicting consumers by applying the structural analysis method (SEM) to the causal relationship model that applied the technology acceptance theory for mobile home training, exercise commitment, and continuous intention. Therefore, in order to identify the strategic tools due to the current COVID-19 pandemic, the survey was conducted using the mobile program "Survey Monkey" according to the sampling plan from February 1, 2021 to May 21, 2021, and a total of 287 valid samples. people were used in the final analysis of consumers. As a result of the study, it was found that the acceptance model had a significant effect(+) on exercise commitment, and the acceptance model had a significant effect(+) on the exercise continuation intention. Finally, it was found that exercise commitment had a significant(+) effect on exercise continuity intention.

• Journal of the Korean Data and Information Science Society
• /
• v.16 no.4
• /
• pp.861-870
• /
• 2005

The Bayesian model selection procedures for the shape parameter of gamma distribution are proposed in order to test that the failure rate of gamma distribution is constant, increasing or decreasing. The encompassing intrinsic Bayes factor by Beger and Pericchi (1996) based on Jeffreys prior for shape parameter is used to investigate the usefulness of the proposed Bayesian model selection procedures via both real data and pseudo data.

• Journal of the Korean Applied Science and Technology
• /
• v.41 no.2
• /
• pp.413-423
• /
• 2024

Sixteen male college students were divided into two groups: a face-to-face group(n=8) and a real-time non-face-to-face exercise group(n=8), engaging in 30minute sessions twice a week for a duration of 8 weeks. Body composition and physical strength were measured as dependent variables before and after the home training period. For data analysis, a two-way ANOVA with repeated measures was conducted to evaluate the effects on body composition and physical strength, considering differences in exercise methods and measurement periods. Post hoc analysis using Bonferroni correction was applied. To compare the mean difference in change between groups, the pre-post difference was calculated, and an independent t-test was performed. The statistical significance level was set at p<.05. The results showed that 8 weeks of home training led to an increase in skeletal muscle mass and improvements in muscle strength, muscular endurance, and cardiorespiratory endurance in male college students, regardless of whether they participated in face-to-face or real-time non-face-to-face exercise. Moreover, there was no significant difference in exercise effectiveness between the face-to-face and real-time non-face-to-face exercise methods. Thus, these findings suggest that real-time non-face-to-face exercise can be as effective as face-to-face exercise in enhancing skeletal muscles and physical strength in male college students. Additionally, if a real-time non-face-to-face exercise program is validated for individuals with mobility issues or the elderly, it could serve as an effective alternative for those who face challenges in participating in face-to-face exercise sessions.

• Journal of the Korean Data and Information Science Society
• /
• v.11 no.2
• /
• pp.269-277
• /
• 2000

We propose the Bayesian testing for the equality of two log-normal population means. Specifically we use the intrinsic Bayes factors suggested by Berger and Perichi (1996, 1998) based on the noninformative priors for the parameters. In order to investigate the usefulness of the proposed Bayesian testing procedures, we compare it with classical tests via both real data analysis and simulation.

• Fire Science and Engineering
• /
• v.34 no.2
• /
• pp.97-102
• /
• 2020

The purpose of this study was to investigate the effectiveness of a Tabata exercise program as an alternative for firefighters whose working environment makes it difficult to manage physical fitness. The Tabata exercise program, in which 16 people participated, led to increased muscle mass and reduced weight and body fat, which not only improved the health of the firefighters but also improved their cardiopulmonary endurance, muscle endurance, and muscle strength, which are essential elements during firefighting emergency relief activities. On the basis of these results, it is believed that developing and providing guidelines for scientific and systematic exercise programs to firefighters will lead to better work capability during fire and disaster situations.

• Journal of the Korea Academia-Industrial cooperation Society
• /
• v.10 no.10
• /
• pp.2837-2845
• /
• 2009

This study certified that the mentality training that utilized ICT learning has been working as an important base having much effect on learner's basic attitude on physical education class, improvement of bodily exercise function, and class satisfaction, and that the exercise ability was improved in the scope of speed, form(posture), accuracy(shooting success rate), and adaptability(performance ability). It means it is a much more step-forwarded educational method that the advantages of ICT learning and mentality training at the existing learning method were applied to the reality. Regarding the object of this study, it is a little bit unreasonable to generalize its study results in that it wasn't intended for national unit sampling. Therefore, in the future study, it is necessary to continue to advance the study that its representative-ness was supplemented through the balanced sampling between area and area, and between grade and grade.

• The Korean Journal of Applied Statistics
• /
• v.11 no.2
• /
• pp.435-449
• /
• 1998

In this article we have introduced a Bayes criterion for the variable selection in multiple discriminant analysis (MDA). The criterion is a default Bayes factor for the comparision of homo/heteroscadasticity of the multivariate normal means. The default Bayes factor is obtained from a development of the imaginary training sample method introduced by Spiegelhalter and Smith (1982). Based an the criterion, we also provided a test for additional discrimination in MDA. The advantage of the criterion is that it is not only applicable for the optimal subset selection method but for the stepwise method. More over, the criterion can be reduced to that for two-group discriminant analysis. Thus the criterion can be regarded as an unified alternative to variable selection criteria suggested by various sampling theory approaches. To illustrate the performance of the criterion, a numerical study has bean done via Monte Carlo experiment.

• Journal of Intelligence and Information Systems
• /
• v.28 no.2
• /
• pp.19-31
• /
• 2022

In Machine learning, we usually divide the entire data into training data and test data, train the model using training data, and use test data to determine the accuracy and generalization performance of the model. In the case of models with low generalization performance, the prediction accuracy of newly data is significantly reduced, and the model is said to be overfit. This study is about a method of generating training data based on central limit theorem and combining it with existed training data to increase normality and using this data to train models and increase generalization performance. To this, data were generated using sample mean and standard deviation for each feature of the data by utilizing the characteristic of central limit theorem, and new training data was constructed by combining them with existed training data. To determine the degree of increase in normality, the Kolmogorov-Smirnov normality test was conducted, and it was confirmed that the new training data showed increased normality compared to the existed data. Generalization performance was measured through differences in prediction accuracy for training data and test data. As a result of measuring the degree of increase in generalization performance by applying this to K-Nearest Neighbors (KNN), Logistic Regression, and Linear Discriminant Analysis (LDA), it was confirmed that generalization performance was improved for KNN, a non-parametric technique, and LDA, which assumes normality between model building.