• Journal of Korean Home Economics Education Association
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• v.34 no.2
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• pp.77-93
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• 2022

The purpose of this study was to identify the latent profiles according to the core competencies of middle school students and to examine whether the parent-child relationship predicts the latent profiles based on the core competencies for students defined by the Korean Ministry of Education. To achieve the purpose of the study, a total of 6,065 second-year middle school students were analyzed from the fourth year of the Korean Educational Longitudinal Study (KELS 2013), which was conducted by the Korean Educational Development Institute in 2016. The results of latent profile analysis and multinomial logistic regression are as follows. First, three latent profiles (higher competency group (19.54%), middle competency group (49.03%), and lower competency group (31.43%)) were derived according to the core competencies. For all groups, the highest competency was communication ability, and the lowest was creative ability. Second, the variables of the parent-child relationship (i.e., parental academic support, emotional support, and attachment level) were significant in predicting differences among the core competency latent profiles. In particular, the emotional support of parents was found to have the greatest influence on the core competencies of middle school students. This study identified the influence of the parent-child relationship on the development of core competencies. Further, it showed that a good family relationship is important for the holistic development of adolescents.

• Journal of Intelligence and Information Systems
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• v.18 no.1
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• pp.125-141
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• 2012

These days, the malicious attacks and hacks on the networked systems are dramatically increasing, and the patterns of them are changing rapidly. Consequently, it becomes more important to appropriately handle these malicious attacks and hacks, and there exist sufficient interests and demand in effective network security systems just like intrusion detection systems. Intrusion detection systems are the network security systems for detecting, identifying and responding to unauthorized or abnormal activities appropriately. Conventional intrusion detection systems have generally been designed using the experts' implicit knowledge on the network intrusions or the hackers' abnormal behaviors. However, they cannot handle new or unknown patterns of the network attacks, although they perform very well under the normal situation. As a result, recent studies on intrusion detection systems use artificial intelligence techniques, which can proactively respond to the unknown threats. For a long time, researchers have adopted and tested various kinds of artificial intelligence techniques such as artificial neural networks, decision trees, and support vector machines to detect intrusions on the network. However, most of them have just applied these techniques singularly, even though combining the techniques may lead to better detection. With this reason, we propose a new integrated model for intrusion detection. Our model is designed to combine prediction results of four different binary classification models-logistic regression (LOGIT), decision trees (DT), artificial neural networks (ANN), and support vector machines (SVM), which may be complementary to each other. As a tool for finding optimal combining weights, genetic algorithms (GA) are used. Our proposed model is designed to be built in two steps. At the first step, the optimal integration model whose prediction error (i.e. erroneous classification rate) is the least is generated. After that, in the second step, it explores the optimal classification threshold for determining intrusions, which minimizes the total misclassification cost. To calculate the total misclassification cost of intrusion detection system, we need to understand its asymmetric error cost scheme. Generally, there are two common forms of errors in intrusion detection. The first error type is the False-Positive Error (FPE). In the case of FPE, the wrong judgment on it may result in the unnecessary fixation. The second error type is the False-Negative Error (FNE) that mainly misjudges the malware of the program as normal. Compared to FPE, FNE is more fatal. Thus, total misclassification cost is more affected by FNE rather than FPE. To validate the practical applicability of our model, we applied it to the real-world dataset for network intrusion detection. The experimental dataset was collected from the IDS sensor of an official institution in Korea from January to June 2010. We collected 15,000 log data in total, and selected 10,000 samples from them by using random sampling method. Also, we compared the results from our model with the results from single techniques to confirm the superiority of the proposed model. LOGIT and DT was experimented using PASW Statistics v18.0, and ANN was experimented using Neuroshell R4.0. For SVM, LIBSVM v2.90-a freeware for training SVM classifier-was used. Empirical results showed that our proposed model based on GA outperformed all the other comparative models in detecting network intrusions from the accuracy perspective. They also showed that the proposed model outperformed all the other comparative models in the total misclassification cost perspective. Consequently, it is expected that our study may contribute to build cost-effective intelligent intrusion detection systems.

• Journal of Digital Convergence
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• v.15 no.11
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• pp.245-250
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• 2017

Flash floods is defined as the flooding of intense rainfall over a relatively small area that flows through river and valley rapidly in short time with no advance warning. So that it can cause damage property and casuality. This study is to establish the flash-flood warning system using 38 accident data, reported from the National Disaster Information Center and Land Surface Model(TOPLATS) between 2009 and 2012. Three variables were used in the Land Surface Model: precipitation, soil moisture, and surface runoff. The three variables of 6 hours preceding flash flood were reduced to 3 factors through factor analysis. Decision tree, random forest, Naive Bayes, Support Vector Machine, and logistic regression model are considered as big data methods. The prediction performance was evaluated by comparison of Accuracy, Kappa, TP Rate, FP Rate and F-Measure. The best method was suggested based on reproducibility evaluation at the each points of flash flood occurrence and predicted count versus actual count using 4 years data.

• Korean Journal of Health Education and Promotion
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• v.24 no.5
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• pp.55-68
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• 2007

The purpose of this study was to investigate differences in longevity factors for 3 age groups: centenarians, octogenarians, and sexagenarians. There were 389 elderly Korean subjects who participated in this study. This included 109 centenarians who participated in the 1-wave study that examined the longevity factors using the health behaviors and psychological reactions. The study that examined the longevity factors by using odds ratio of the logistic regression analysis. The major findings indicate that female centenarians and octogenarians scored the highest in abstinence from alcoholic drinking than males from these same 2 age groups (OR=9.04, CI=4.04, 20.24). Centenarians scored higher in non-visiting hospitals (OR=11.48, CI=5.56, 23.70) and showed little emotion when watching sad scenes, upon comparison with the younger 2 age groups. Also, centenarians scored higher in laughter (OR=11.59, CI=5.04, 26.63), showing no anxiety (OR=12.49, CI=6.42, 24.32), and displaying indifference towards games, when compared with the younger 2 age groups. The findings imply that Korean centenarian leads to a life of longevity is the result not only of psychological reactions but also of health behaviors. Also, these results support future studies of improvement of longevity factors.

• Physical Therapy Korea
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• v.4 no.2
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• pp.18-35
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• 1997

The purpose of this study were to determine causes of low back pain in bus drivers who usually work in prolonged sitting position and to find the relationship of back pain with general characteristics and work environmental characteristics, stress symptoms, to com맹re lordosis angle of habitual driving posture and resting posture in low back pain group and non-low back pain group, and to use this result as a basis for improvement of work environment and comprehensive rehabilitative management of low back pain. Ninety-eight bus drivers were selected from the membership of an urban transit union in Seoul. These informations were collected from May 1, 1997 to May 25, 1997 by means of structured questionnaires and X-ray findings. These data were analyzed by $x^2$ test, t-test, logistic regression using SAS. The major results were as follows: 1. Of the respondents, 66.3% of bus drivers were found to be experiencing back pain. 2. Of the respondents of low back pain groups, 78.5% reported that major cause of low back pain was due to prolonged sitting. 3. The group with experiences of frequent or continuous vibration had more low back pain (p<0.05). There were no significant differences among other work environmental factors. 4. Average stress score was significantly higher in the group with low back pain than in the group without low back pain (p<0.05). 5. The results of the logistic regression analyses were statistically significant vibration from seat and stress score among the risk factors (p<0.05). 6. Averaged lordosis angle of habitual driving posture was $7.6{\pm}14.9$ degrees in low back pain group, $16.5{\pm}8.7$ degrees in non-low back pain and averaged lordosis angle of resting sitting posture was $10.8{\pm}13.7$ degrees in low back pain, $18.9{\pm}9.6$ degrees in non-low back pain group but the difference in mean lordosis angles of two groups was not statistically significant. The results of this study indicate that professional drivers developed chronicity of low back pain due to unfit seat, poor habitual posture and stress from vibration or other risk factors. Therefore, there is need to improve work environment, i.e. enough resting, to set a seat to support lumbar spine properly and to provide comprehensive rehabilitation program including early diagnosis, proper treatment and education for self help management.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.18 no.11
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• pp.653-662
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• 2017

This study was conducted to provide basic data for the prevention and management of precocious puberty by investigating the relevance of physical parents characteristics and behavioral characteristics of girls with precocious puberty. The research period was June 1, 2016 to February 25, 2017. The subjects were 312 girls matched depending on gender and age which were composed of 104 female patients of precocious puberty visiting H oriental medicine clinic and S one in Seoul, 104 girls of A control group and 104 girls of B control group attending elementary school in gun. A total of 312 of each group were compared though t-test, $X^2$-test, and ANOVA to examine the physical parent characteristics and behavioral characteristics between girls with precocious puberty and normal girls, after which influence factors for precocious puberty were analyzed through multinomial logistic regression. The main influencing factor was found to be the height of their own and mothers' menarcheal age in terms of body and parent characteristics. Other factors influencing behavioral characteristics included the frequency of meat intake and eating-out in terms of eating habits. Additionally, watching TV, hours of using smart phone and number of private institute girls attending were also found to be influencing factors. Thus, interest in school and society in increasing precocious puberty in girls is important and extensive investigation of the causes and frequency of precocious puberty is necessary.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.34 no.3
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• pp.1023-1031
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• 2014

This study is conducted to evaluate the supply effectiveness of the Long-term Secured Public Rental Housing (LSPRH) program implemented by the City of Seoul since March, 2012. To estimate the residential intention of potential applicants for the housing program, two logistic regression models of residential intention are estimated based on the residential characteristics of real residents. The purpose of this model estimation is to find out factors influencing the supply of LSPRH and thus to suggest the ideas for improving the effectiveness of the rental housing program operation. The analysis result shows that financial condition, income level, and public support are the major determinants for selecting LSPRH. It is also revealed that those who are currently living in rental housing or living in a house more expensive than those in nearby areas have higher residential intention for the LSPRH program. The result also presents that some problems associated with the traditional rental housing programs - such as visually recognizable low-quality exterior building design or the concentration of rental housing on limited spots - could be solved by this new type of public housing program. Lastly, one interesting finding different from previous research is that people with the intention to move into either traditional rental housing or LSPRH do not necessarily prefer a more spacious house than their current rental house.

• Journal of Korean Society of Transportation
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• v.27 no.6
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• pp.97-106
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• 2009

The purpose of this study is to analyze factors for resolving problems of Jejusi Residential Parking Permit Program and to draw up a plan by examining Jejusi case. This program was started by way of showing an example in October, 2005, and then has been put in practice in the heart of Jejusi since October, 2007. Jejusi government introduced this system to prevent all kinds of accidents by the indiscreet parking near the residental street and to guarantee the rights to live comfortably by encouraging a pleasant parking environment. As the result of the survey which is about residents' participation from the process of decision-making, the answers such as "Should Participate" and "Surely should participate" are at the high rate of 87.5 percent. A matter of the utmost importance is that this system should be reformed to make the visitors pay parking if they want to park near there. Almost 36 percent of the respondents have a negative opinion, and this is because they consider their visitors. Especially the important factors from the Logistic Regression Analysis are that the government should exercise stricter control over illegal parking, that the system should be improved for the visitors to pay parking, and that the government should support the declining sales of the shops near shopping streets. The suggestions are as follows. First, the government should enforce on reducing the illegal parking. Second, they should secure the parking lots for visitors. Third, they should minimize the impact on the declining sales of the shops near shopping streets.

• Journal of Korean Society of Forest Science
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• v.86 no.1
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• pp.87-97
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• 1997

In this research, photo images of uncultivated marginal lands were simulated to visualize alternative land use patterns using image capture technology. Based on an original photos, 3 simulated images were created ; barren condition, aforested condition and shrub-covered condition. The simulated images were then used to evaluate respondents' visual preference(SBE value) and willingness to pay for the agricultural development tax as a hypothetical payment vehicle. The SBE values for barren condition are the lowest, as expected. When original condition is changed to forested or shrubbed, the SBE values are increased significantly. The logistic models for the willingness to pay for the various alternative land uses performed significantly, ${\rho}$ statistics for 6 models ranges from 0.3 to 0.4 and correct percentage for predicted probability are about 75%. Among independent variables, the amount of tax offered is the most influencing factor to predict the probability. Income also shows some relationship with no statistical significance. Other variables behave inconsistently in the model. When SBE and WTP are correlated, rather consistent trends can be observed. With the increase of SBE, WTP predicted by the model increases accordingly. It can be concluded that enhancement of scenic quality of the agricultural lands leads to increase of people's willingness to pay to support the rural environmental conservation.

• Journal of Digital Convergence
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• v.15 no.7
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• pp.97-109
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• 2017

Domestic vehicle penetration rate is growing at 3% per year, but consumers are increasingly buying used cars due to steady price hikes Nevertheless, the used car export market is expected to decline due to import regulations of major countries and the low grade environment of Used car export complex. Therefore, this study using Fuzzy-AHP was aimed to find operational factors of Used car logistics complex and establish a practical management plan of Used car logistic complex in incheon port. Fuzzy-AHP is the method that can be calculated weight of multi-level criteria and change linguistic ambiguity of human to Fuzzy Number. So it's able to propose the realistic decision making alternatives. As a result of the literacture reviews, present study focused on the analysis of the present situation of the logistics of the used car and the activation of the complex, suggested the activation plan and activation of the logistics complex. In the analysis of operational factors, logistic complex cost factors were found to be the most important factors by recording the weighted value of 0.306 in the above factors. The detailed factors were as follows: rent, accessibility, and logistics site size. It is necessary to compute competitive rent for the highly-advanced used car logistics complex, and to realize the rental support policy and to consider designating the free trade zone. In addition, it is necessary to expand the access infrastructure and secure the scale of the company for overseas buyers, and it is necessary to improve the overall government laws and introduce IT system for the future.