• Journal of the Korean Applied Science and Technology
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• v.40 no.4
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• pp.685-698
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• 2023

This study aimed to identify binge-eating behavior and food addiction in Korean population and to determine their associations with obesity, eating behaviors, mental health and cognitive characteristics. We collected clinical questionnaire scores related to eating problems (e.g. binge eating, food addiction, food cravings), mental health (e.g. depression), and cognitive functions (e.g. impulsivity, emotion regulation) in 257 Korean adults in the normal and the obese weight ranges. Binge-eating and food addiction were most frequent in obese women (binge-eating: 46.6%, food addiction: 29.3%) when we divided the participants into 4 groups depending on gender and obesity status. The independence test using the data with propensity score matching confirmed that binge-eating and food addiction were more prevalent in obese individuals. Finally, we constructed the logistic regression models using forward selection method to evaluate the influence of various clinical questionnaire scores on binge-eating and food addiction respectively. Binge-eating was significantly associated with the clinical scales of eating disorders, food craving, state anxiety, and emotion regulation (cognitive reappraisal) as well as food addiction. Food addiction demonstrated the significant effect of food craving, binge-eating, the interaction of obesity and age, and years of education. In conclusion, we found that binge-eating and food addiction are much more frequent in females and obese individuals. Both binge-eating and food addiction commonly involved eating problems (e.g. food craving), but there was difference in mental health and cognitive risk factors. Therefore, it is required to distinguish food addiction from binge-eating and investigate intrinsic and environmental risk factors for each pathology.

• Journal of Korean Academy of Nursing
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• v.46 no.4
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• pp.552-561
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• 2016

Purpose: The purpose of this study was to investigate factors influencing lifetime smoking and current smoking among adolescents in South Korea. Methods: Hierarchical logistic regression was conducted based on complex sample analysis using statistics from the 10th (2014) Korea Youth Risk Behavior Web-Based Survey. The study sample comprised 72,060 adolescents aged 12 to 18. Results: The significant factors influencing adolescent lifetime smoking were female gender, older age, higher stress, higher weekly allowance, lower economic status, living apart from parents, parental smoking, sibling smoking, peer smoking, observation of school personnel smoking, and coed school compared to boys' school. The significant factors influencing adolescent current smoking were female gender, older age, higher stress, higher weekly allowance, both higher and lower economic status compared to middle economic status, living apart from parents, parental smoking, sibling smoking, peer smoking, observation of school personnel smoking, and coed school compared to boys' school. Conclusion: Factors identified in this study need to be considered in programs directed at prevention of adolescent smoking and smoking cessation programs, as well as policies.

• The Journal of the Korea Contents Association
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• v.18 no.2
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• pp.616-625
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• 2018

The purpose of this study was to analyze the relationship between the BMI and suicidal ideation using Korean Health Panel data from 2009 to 2013 to identify risk factors for suicide. We conducted a logistic regression analysis using the R statistical package to analyze the relationship between the BMI and the suicidal ideation. The results of this study show that all models with BMI had a statistically significant as a significant variable. It was found that the obese group was more likely to suicide ideation than the other groups. Based on these results, we try to find social implications for suicide prevention and intervention.

• Journal of Intelligence and Information Systems
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• v.6 no.1
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• pp.83-96
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• 2000

본연구는 웹 상에서 원격 협력 학습을 위한 수준별 협력 학습자 진단 및 스케줄링 에이전트의 설계와 구현에 관한 연구이다. 원격 협력 학습은 동일한 학습내용에 흥미를 갖는 아동이 동시에 학습할 수 있는 환경이 필요하며 학습자의 지식 또한 비슷한 수준이어야 효과적인 협력학습을 할 수 있다. 분산 환경의 이질적인 학습자를 모으기 위해서는 좀 더 자율적이고 지능적인 시스템이 필요하며 학습자에 대한 지식을 표현하는 학습자 모델이 요구된다. 이를 위해 에이전트 시스템이 적절하게 사용될수 있으며 학습자의 수준을 판단하기 위한 진단 에이전트와 협력학습이 가능한 여러명의 학습자들을 알맞은 시간과 서버에 연결하는 스케줄링 에이전트를 웹 기반 지능형 교수 시스템에 접목하였다. 학습자 수준을 진단하는 진단 에이전트는 확신도를 높이기 위해 3-모수 로지스틱 확신공식과 시간 가중치 확신인자 공식을 적용하여 신뢰도를 높였다 또한 협력학습의 스케줄링을 위해 다양한 제약조건들의 최적해를 구하기 위해 제약 만족 문제(CSP)로 스케줄링 에이전트를 모델링하였다 본 연구에서 설계 구현한 협력학습자 진단 및 스케줄링 에이전트의 효율성을 살펴보기 위해 여러명의 학습자를 대상으로 실험하였다. 실험을 통해 각 학습자의 지식 수준 진단과 다수의 학습자가 적절한 협력학습을 하기 위한 스케줄링이 매우 효율적으로 이루어짐을 볼 수 있었다.

• The Journal of Korean Association of Computer Education
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• v.6 no.2
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• pp.1-8
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• 2003

In existing the learner testing system, it has a weak point to determine differently the difficulty of each question which has been estimated by teacher's view and the subjective stand point. In this paper, we develops the learner testing system which supports the estimation of the individual ability of learner, provides the questions for suitable to the individual learner level, and able to estimate the question of individual that used by three parameters such as the difficulty parameter, discrimination parameter, and guessing parameter. Also, it is applied to three-parameters logistic model of IRT(Item Response Theory) for using CAT(Computer Adaptive Testing) technique.

• Korean Journal of Social Welfare
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• v.68 no.2
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• pp.5-25
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• 2016

Despite previous literatures suggesting the co-occurrence of substance abuse and behavioral addiction, their relationship has not been systematically explored. Especially, college students are a high risk group for alcohol use and gambling activities and they have various psychosocial problems due to addictive behaviors. This study aimed to empirically examine that drinking problems predict gambling problems among college students. A total of 455 college students who experienced drinking and gambling completed a survey. Logistic regression analysis were performed. After adjusting for demographics and family related variables, drinking problems predicted the occurrence of problem gambling. Implications for social work practice, policy planning and research area on addiction are discussed.

• Journal of the Korean Society of Manufacturing Process Engineers
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• v.19 no.2
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• pp.111-118
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• 2020

Recently, bioenergy research using microalgae, one of the most promising biofuel sources, has attracted much attention. Cell disruption, which can be classified as physical or chemical, is essential to extract functional ingredients from microalgae. In this study, we investigated the cell disruption efficiency of Chlorella sp. using low-frequency non-focused ultrasound (LFNFU). This is a continuously physical method that is superior to chemical methods with respect to environmental friendliness and low processing cost. A flat panel photobioreactor was employed to cultivate Chlorella sp. and its growth curve was fitted both with Logistic and Gompertz models. The temporal change in cell reduction by cell disruption using LFNFU was fitted with a Logistic model. The experimental conditions that were investigated were the initial concentration of microalgal cells, relative amplitude of output ultrasound waves, processing volume of microalgal cells, and initial pH value. The optimal conditions for the most efficient cell disruption were determined through the various tests.

• Transactions of the Korean Society for Noise and Vibration Engineering
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• v.21 no.5
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• pp.400-407
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• 2011

The suggested method of previous Son's study dichotomized subjective response data to modeling noise exposure-response. The method used maximum liklihood estimation instead of least square estimation and the noise exposure-response curve of the study was logistic regression analysis result. The method was originated to modeling community response rate such as %HA or %A. It can be useful when the subjective response was investigated based on predicted noise level. It is difficult to measure the single source emitting noise such as railway because various traffic noise sources combined in our life. The suggested method was adopted to model in this study and railway noise-exposure response curves were modeled because the noise level of this area was predicted data. The data of this study was used by previous Ko's paper but he dealt the area as combined noise area and divided the data by dominant noise source. But this study used all data of this area because the annoyance response to railway noise was higher than other noise according to the result of correlation analysis. The trend of the %HA and %A prediction model to train noise of this study is almost same as the model based on measured noise of previous Lim's study although the investigated areas and methods were different.

• Journal of Biomedical Engineering Research
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• v.22 no.4
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• pp.369-375
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• 2001

The statistical regression model is one of the most frequently used clinical analysis methods. It has basic assumption of linearity, additivity and normal distribution of data. However, most of biological data in medical field are nonlinear and unevenly distributed. To overcome the discrepancy between the basic assumption of statistical model and actual biological data, we propose a new analytical method based on artificial neural network. The newly developed multilayer perceptron(MLP) is trained with 120 data set (60 normal, 60 patient). On applying test data, it shows the discrimination power of 0.76. The diabetic risk factors were also identified from the MLP neural network model and the logistic regression model. The signigicant risk factors identified by MLP model were post prandial glucose level(PP2), sex(male), fasting blood sugar(FBS) level, age, SBP, AC and WHR. Those from the regression model are sex(male), PP2, age and FBS. The combined risk factors can be identified using the MLP model. Those are total cholesterol and body weight, which is consistent with the result of other clinical studies. From this experiment we have learned that MLP can be applied to the combined risk factor analysis of biological data which can not be provided by the conventional statistical method.

• Journal of Advanced Navigation Technology
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• v.17 no.6
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• pp.652-657
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• 2013

In this paper the design methode of a separated composition state machine based on the compositive map with two chaotic maps together and the result of that is proposed. The digital circuits of chaotic composition map for the use of chaotic binary stream generator are designed in this work. The discretized truth table of chaotic composition function which is composed of two chaotic functions - the saw tooth function and skewed logistic function - is made out, and also simplefied Boolean algebras of digital circuits are obtained as a mathematical model. Consequently, the digital circuits of the map for chaotic composition function are presented in this paper.