• Child Health Nursing Research
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• v.9 no.3
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• pp.323-331
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• 2003

Purpose: The purpose of this study is to analyze the factors affecting Deviant Behaviors of Korean High school Students. Method: Data was collected from October 8 to 31, 2002. The subjects for this study were 697 Korean High school Students(boys 347, girls 350), recruited from two High School located in Seoul. Data collection was conducted through the use of 6 Questionnaire that modified by the investigator. The data was analyzed by the SPSS win 10.0 program using Descriptive statistics, Pearson Correlation coefficient and stepwise multiple regression. Result: 1) The mean of total item score the Deviant Behaviors scales was 1.59, which was slightly low. 2) There was a significant correlation between Deviant Behaviors, Type A Personality, Aggression, Impulsivity, Stress and Social Support(γ= .11 ～ .65, p<.001), but It was no significant correlation Type A Personality and Stress(γ= -.01). 3) Stepwise multiple regression analysis showed that (1) Impulsivity, Social Support and Type A Personality were the predictors of Deviant Behaviors and account for 18.6％ of the variance in Deviant Behaviors. (2) Impulsivity account for 3.6％ of the variance in Deviant Behaviors in Subjects with a lower degree of score the Deviant Behaviors. Impulsivity and Social Support account for 23.2％ of the variance in Deviant Behaviors in subjects with higher degree of score the Deviant Behaviors. (3) Impulsivity account for 18.3％ of the variance in Deviant Behaviors in High school girls (n=350). Impulsivity and Social Support account for 20.1％ of the variance in Deviant Behaviors in High school boys (n=347). Conclusion: Impulsivity and Social Support account for Deviant Behaviors of High school Students. Therefore it is necessary to develop nursing intervention to reduce the level of Impulsivity, to increase the Social Support in order to decrease the Deviant Behaviors.

• Proceedings of the Korea Water Resources Association Conference
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• 2023.05a
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• pp.244-244
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• 2023

Both the frequency and the magnitude of hydrometeorological extreme events such as severe floods and droughts are increasing. In order to prevent a damage from the climatic disaster, hydrological models are often simulated under various meteorological conditions. While performing the simulations, a synthetic data generated through time series models which maintains the key statistical characteristics of the sample data are widely applied. However, the synthetic data can easily maintains both the average and the variance of the sample data, but the quantile is not maintained well. In this study, we proposes a data generation method which maintains the quantile of the sample data well. The equations of the former maintenance of variance extension (MOVE) are expanded to maintain quantile rather than the average or the variance of the sample data. The equations are derived and the coefficients are determined based on the characteristics of the sample data that we aim to preserve. Monte Carlo simulation is utilized to assess the performance of the proposed data generation method. A time series data (data length of 500) is regarded as the sample data and selected randomly from the sample data to create the data set (data length of 30) for simulation. Data length of the selected data set is expanded from 30 to 500 by using the proposed method. Then, the average, the variance, and the quantile difference between the sample data, and the expanded data are evaluated with relative root mean square error for each simulation. As a result of the simulation, each equation which is designed to maintain the characteristic of data performs well. Moreover, expanded data can preserve the quantile of sample data more precisely than that those expanded through the conventional time series model.

• Journal of the Korean Society of Clothing and Textiles
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• v.25 no.3
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• pp.560-568
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• 2001

The aim of the present study was to measure intra-individual consistency in clothing and fabric evaluation and to examine its relation to the ratings. A sample of 93 female and 97 male university students rated clothing of 4 styles of daytime wear and 2 fabrics on 15 pairs of polar adjectives twice in 7-days interval. Correlation coefficients between the two ratings for each subject, intra-individual consistency in the evaluation, ranged from -0.12 to 0.89 and mean coefficient was 0.63 of female and -0.01 to 0.78 and mean coefficient was 0.54 of male. Based on the coefficients, the subjects were classified into three groups: high, medium, and low intra-individual consistency. Analysis of variance of mean ratings by the three groups revealed that significant difference existed in 24% of female and 23% of male in 90 combinations of 6 clothing and 15 semantic differential scales. Female of subjects with high intra-individual consistency were most likely definite to evaluate clothing, whereas the ones with low were least. But male subjects were not definite. Mean correlation coefficients for style evaluation subscales of female was 0.39, but male was 0.44. Among the semantic differential scales, high stability in the two ratings was observed for the synthetic clothing evaluation. Correlation coefficients for each clothing obtained from the mean score of the subjects in each semantics differential scale were around 0.98, including that the mean scores of the subjects in each scale could yield excellent stability in clothing evaluation.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.41 no.1
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• pp.110-117
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• 2018

Machines and facilities are physically or chemically degenerated by continuous usage. One of the results of this degeneration is the process mean shift. By the result of degeneration, non-conforming products and malfunction of machine occur. Therefore a periodic preventive resetting the process is necessary. This type of preventive action is called 'preventive maintenance policy.' Preventive maintenance presupposes that the preventive (resetting the process) cost is smaller than the cost of failure caused by the malfunction of machine. The process mean shift problem is a field of preventive maintenance. This field deals the interrelationship between the quality cost and the process resetting cost before machine breaks down. Quality cost is the sum of the non-conforming item cost and quality loss cost. Quality loss cost is due to the deviation between the quality characteristics from the target value. Under the process mean shift, the quality cost is increasing continuously whereas the process resetting cost is constant value. The objective function is total costs per unit wear, the decision variables are the wear limit (resetting period) and the initial process mean. Comparing the previous studies, we set the process variance as an increasing concave function and set the quality loss function as Cpm+ simultaneously. In the Cpm+, loss function has different cost coefficients according to the direction of the quality characteristics from target value. A numerical example is presented.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.20 no.1
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• pp.100-106
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• 2009

This paper deals with an estimation problem of the gain and phase imbalances between the in-phase and quadrature components in the quadrature receivers which are widely used in wireless communications. It is shown that the estimates derived from the suggested auto-calibration algorithm is asymptotically minimum-variance unbiased as a function of the sampling time. In order to show this characteristic, the probability density functions of the estimates for the gain and phase imbalances are derived first. Then the mean and variance functions are investigated analytically or numerically based on the density functions.

• Journal of Korean Academy of Nursing Administration
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• v.14 no.4
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• pp.458-466
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• 2008

Purpose: The purpose of this study was to identify the effects of perceived organizational support on organizational commitment and career commitment of nurses. Method: The subjects of this study were 336 nurses who were working in the 6 hospitals. The data were collected by structured questionnaire from Oct. 9 to Nov. 7 of 2006. Data were analyzed using descriptive statistics, t-test, ANOVA, Scheffe test, Pearson correlation coefficients, and multiple regression. Results: The mean score of POS was 2.87, organizational commitment was 3.30 and career commitment was 3.08. The POS was positively correlated with organizational commitment and career commitment. The POS and marital status explained 21.3% of the variance for affective commitment, 12.1% of the variance of continuous commitment. The POS and career explained 14.8% of the variance for career commitment. Conclusion: The findings showed that POS was important factor for enhancing organizational commitment and career commitment of clinical nurses. Therefore, the nurse manager must establish the strategies to improve the POS of the nurses in order to promote the organizational commitment and career commitment.

• Nuclear Engineering and Technology
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• v.52 no.2
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• pp.287-295
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• 2020

Group method of data handling (GMDH) is considered one of the earliest deep learning methods. Deep learning gained additional interest in today's applications due to its capability to handle complex and high dimensional problems. In this study, multi-layer GMDH networks are used to perform uncertainty quantification (UQ) and sensitivity analysis (SA) of nuclear reactor simulations. GMDH is utilized as a surrogate/metamodel to replace high fidelity computer models with cheap-to-evaluate surrogate models, which facilitate UQ and SA tasks (e.g. variance decomposition, uncertainty propagation, etc.). GMDH performance is validated through two UQ applications in reactor simulations: (1) low dimensional input space (two-phase flow in a reactor channel), and (2) high dimensional space (8-group homogenized cross-sections). In both applications, GMDH networks show very good performance with small mean absolute and squared errors as well as high accuracy in capturing the target variance. GMDH is utilized afterward to perform UQ tasks such as variance decomposition through Sobol indices, and GMDH-based uncertainty propagation with large number of samples. GMDH performance is also compared to other surrogates including Gaussian processes and polynomial chaos expansions. The comparison shows that GMDH has competitive performance with the other methods for the low dimensional problem, and reliable performance for the high dimensional problem.

• The Mathematical Education
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• v.45 no.2 s.113
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• pp.145-154
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• 2006

The main purpose of this study is to investigate the effect of departures from normality and equal variance on the two-sample test when the variances are unknown. We have found that type I error brought about a little bit change which is ignorable in relation to kurtosis. But the change of type I error was mainly based on the skewness of the parent population. In introductory statistics classes where data analysis includes techniques for detecting skewness of two populations, we recommend the two-sample t-test when maximal skewness of two populations is smalter than the value 4 when the variances seem equal. Furthermore, our simulations reveal that the two-sample t-test appears somewhat more robust than that of z-test if the assumption of equal variance is satisfied. In the case of unequal variance, the two-sample t-test appears somewhat more robust provided the t-statistic using Satterthwaite's approximate degrees of freedom.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.17 no.1
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• pp.57-66
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• 2007

Scanning worms increase network traffic load because they randomly scan network addresses to find hosts that are susceptible to infection. Since propagation speed is faster than human reaction, scanning worms cause severe network congestion. So we need to build an early detection system which can automatically detect and quarantine such attacks. We propose algorithms to detect scanning worms using network traffic characteristics such as variance, variance to mean ratio(VMR) and correlation coefficient. The proposed algorithm have been verified by computer simulation. Compared to existing algorithm, the proposed algorithm not only reduced computational complexity but also improved detection accuracy.

• Educational Technology International
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• v.6 no.2
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• pp.113-130
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• 2005

The purpose of this study was to determine the teaching styles of professors who teach adult students in selected higher institutions. It also identified whether professors' teaching styles were teacher-centered or learner-centered and examined the relationship between instructors' teaching styles and such instructor demographic variables as gender, years of teaching experience, and taught level of courses. This study used The Principles of Adult Learning Scale(PALS) (Conti,1983) to measure instructional preferences. Demographic characteristics were collected through a personal data inventory. The analysis of variance (ANOVA) and multivariate analysis of variance (MANOVA) tests were used to analyze the data. The data were examined for significance at the .05 level of confidence by means of analysis of variance. The dependent variables in this study were teaching styles of full-time professor, as represented by the seven subscores from the standardized instrument on the PALS. The seven subscores were: (1) learner-centered activities, (2) personalizing instruction, (3) relating to experience, (4) assessing student needs, (5) climate building, (6) participation in the learning process, and (7) flexibility for personal development. The study established that there was a significant difference in mean scores on the PALS between participants when examined by the number of years of teaching experiences.