• Journal of Navigation and Port Research
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• v.43 no.3
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• pp.160-165
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• 2019

The conduct of seafarer is major cause of marine accidents. This study models the behavior of the seafarer based on the Hidden Markov Model (HMM). Additionally, through the path analysis of the behavior estimated by the model, the kind of situations, procedures and errors that may have caused the marine accidents were interpreted. To successfully implement the model, the seafarer behaviors were observed by means of the summarized verdict reports issued by the Korean Maritime Safety Tribunal, and the observed results converted into behavior data suitable for HMM learning through the behavior classification framework based on the SRKBB (Skill-, Rule-, and Knowledge-Based Behavior). As a result of modeling the seafarer behaviors by the type of vessels, it was established that there was a difference between the models, and the possibility of identifying the preferred path of the seafarer behaviors. Through these results, it is expected that the model implementation technique proposed in this study can be applied to the prediction of the behavior of the seafarer as well as contribute to the prioritization of the behavior correction among seafarers, which is necessary for the prevention of marine accidents.

• Research in Community and Public Health Nursing
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• v.26 no.2
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• pp.128-139
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• 2015

Purpose: The purpose of this study was to identify factors affecting health promotion behavior among workers with high risk of metabolic syndrome. This study was based on the planned behavior theory. Methods: The participants were 167 workers at high risk of metabolic syndrome. Data were collected using a structured questionnaire. Surveyed variables were attitude, subjective norm, perceived behavioral control, intention, and health promotion behavior. Data were analyzed using descriptive statistics, t-test, ANOVA, Pearson's correlation coefficients, and hierarchical regression analysis with SPSS/WIN 22.0. Results: Perceived behavioral control affected the intention of health promotion behavior among the workers with high risk of metabolic syndrome. It explained 62% of variance in the intention of health promotion behavior (F=40.09, p<.001). Perceived behavioral control and occupation affected health promotion behavior among the risk workers with high risk of metabolic syndrome. The two factors explained 16% of variance in health promotion behavior (F=4.95, p<.001). Conclusion: The findings of this study suggest that perceived behavioral control is the only factor affecting health promotion behavior when the theory of planned behavior was applied. Therefore, intervention programs for improving health promotion behavior should be focused on strengthening perceived behavioral control.

• The Journal of Information Systems
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• v.28 no.2
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• pp.129-152
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• 2019

Purpose The purpose of this study is to empirically identify the risky behavior of mobile device users using the Internet of Things on a situational perspective. Design/methodology/approach This study made a design of the research model based on model of the determinants of risk behavior. Data were collected through a survey including hypothetical scenario. SmartPLS 2.0 was used for the structural model analysis and t-test was conducted to compare the between normal and situational behavior. Findings The results were as follows. First, the central roles of risk propriety and risk perception were verified empirically. Second, we identified the role of locus of control as a new factor of impact on risky behavior. Third, mobile risk propensity has been shown to increase risk perception. Fouth, it has been shown that risk perception does not directly affect risky behavior and reduce the relationship between mobile risk propensity and risk behavior. According to the empirical analysis result, Determinants of risk behavior for mobile users were identified based on a theoretical framework. And it raised the need to pay attention to the impact of locus of control on risk behavior in the IS security field. It provided direction to the approach to risky behavior of mobile device users. In addition, this study confirmed that there was a possibility of taking risky behavior in the actual decision-making.

• Journal of the Korean Institute of Intelligent Systems
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• v.11 no.7
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• pp.591-597
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• 2001

In this paper, we propose a method of cooperative control (T-cell modeling) and selection of group behavior strategy (B-cell modeling) based on immune system in distributed autonomous robotic system (DARS). Immune system is living body's self-protection and self-maintenance system. These features can be applied to decision making of optimal swarm behavior in dynamically changing environment. For applying immune system to DARS, a robot is regarded as a B-cell, each environmental condition as an antigen, a behavior strategy as an antibody and control parameter as a T-cell respectively. The executing process of proposed method is as follows. When the environmental condition changes, a robot selects an appropriate behavior strategy. And its behavior strategy is stimulated and suppressed by other robot using communication. Finally much stimulated strategy is adopted as a swarm behavior strategy. This control school is based on clonal selection and idiotopic network hypothesis. And it is used for decision making of optimal swarm strategy. By T-cell modeling, adaptation ability of robot is enhanced in dynamic environments.

• Nutrition Research and Practice
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• v.10 no.3
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• pp.352-358
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• 2016

BACKGROUND/OBJECTIVES: Nutrition is a determinant factor of health in elderly people. Independent living in elderly people can be maintained or enhanced by improvement of nutritional behavior. Hence, the present study was conducted to determine the impact of Health Belief Model (HBM)-based intervention on the nutritional behavior of elderly women. SUBJECTS/METHODS: Cluster-random sampling was used to assess the sample of this clinical trial study. The participants of this study attended a 12-week nutrition education program consisting of two (2) sessions per week. There was also a follow-up for another three (3) months. Smart PLS 3.5 and SPSS 19 were used for structural equation modeling, determination of model fitness, and hypotheses testing. RESULTS: The findings indicate that intervention had a significant effect on knowledge improvement as well as the behavior of elderly women. The model explained 5 to 70% of the variance in nutritional behavior. In addition, nutritional behavior was positively affected by the HBM constructs comprised of perceived susceptibility, self-efficacy, perceived benefits, and barriers after the intervention program. CONCLUSION: The results of this study show that HBM-based educational intervention has a significant effect in improving nutritional knowledge and behavior among elderly women.

• Journal of Korean society of Dental Hygiene
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• v.15 no.6
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• pp.999-1007
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• 2015

Objectives: The objective of the study is to investigate oral health behavior and oral health education experience in Korean adolescents from the ninth(2013) web-based survey of Korean youth risk behavior. Methods: The subjects were 72,435 students through the ninth 2013 web-based survey of Korean youth risk behavior conducted by the Ministry of Education, Science, and Technology, the Ministry of Health and Welfare, and the Korea Centers for Disease Control. The questionnaire consisted of socio-demographical characteristics of the subjects, oral health behavior, and oral health education experience. Data were analyzed by SPSS 18.0 program. Results: Oral health education had much influence on tooth brushing after lunch, oral cavity disease prevention, sealants, fluoride application, scaling experience, and consumption of vegetables, milk, carbonated soft drinks, noodles, and snacks. The oral health education had a great impact on those who took good oral health behavior into action. Conclusions: It is very important and necessary to develop the continuing effective oral health education program for the adolescents and make them tale into action.

• Fashion & Textile Research Journal
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• v.24 no.2
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• pp.195-205
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• 2022

This study examines the image management behavior according to self-monitoring, self-objectification of profile-based SNS users. Questionnaires were administered to 313 SNS users including both men and women in their 20s to 30s. The SPSS 25.0 package was utilized for data analysis, which included frequency analysis, factor analysis, Cronbach's ?, t-test, and regression analysis. The study analyzed self-monitoring in 2 groups (high, low), self-objectification for 2 factors (body surveillance, body shame), and image management behavior for 5 factors (fashion oriented, instrumentality, conformity, ostentation, interpersonal disposition). The results revealed: first, self-monitoring groups exhibited significant differences in self-objectification. The higher self-monitoring group was more influenced by body surveillance and body shame compared to the low self-monitoring group. Second, self-objectification had a positive influence on all the factors of image management behavior. Especially, body surveillance demonstrated a high influence on instrumentality and body shame showed a high influence on ostentation. Third, the self-monitoring groups showed significant differences in all the factors of image management behavior. The higher self-monitoring group demonstrated more influence of image management behavior compared to the low self-monitoring group. These results provide useful information in understanding the influence of social media on users' psychological attitude and consciousness toward their body and image management behavior.

• Journal of Information Technology Services
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• v.13 no.2
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• pp.35-53
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• 2014

Company Social Networks (CSN) has emerged as a commonly used marketing channel. One of the most important advantages in CSN is the user's voluntary behavior as a relational performance. We classified level of voluntary behavior, as 'consumption', 'active participation', and 'creative contribution' to comprehend different relational performance of CSN. Moreover, we proposed research model to compare positive attitude and negative attitude. This study aims to investigate 'the process of user's voluntary behavior in CSN' which is causal relationship between benefits of CSN, user's attitude and voluntary behavior. Empirical results with 175 valid questionnaire data revealed that CSN benefit factors played a significant role in trust and distrust. Based on these effects, trust and distrust have different influences as level of voluntary behavior, just as proposed. For practitioners, it is crucial finding that users are more active behavior based on strong trust.

• Journal of Animal Environmental Science
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• v.3 no.2
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• pp.105-113
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• 1997

The environment for pig production should be controlled according to a criterion based on the pig's thermoregulartory behavior. Quantifying the pig's thermoregulatroy behavior was needed to prepare a criterion based on the pig's thermoregulatory behavior. Therefore, this study was conducted to quantify the pig's thermoregulatory behavior. The raw images were acquired according to the pig's thermoregulatory behavior and they were processed to binary images. The mean deviations of x and y coordinates of pig's images in a binary image were computed and they were multiplied. The values computed in this manner showed very wide differences according to the pig's thermoregulatory behavior. Therefore, the image processing and mean deviation can be certainly used as a method for classifying the pig's thermoregulatory behavior.

• International Journal of Control, Automation, and Systems
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• v.6 no.2
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• pp.282-287
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• 2008

In distributed mobile robot systems, autonomous robots accomplish complicated tasks through intelligent cooperation with each other. This paper presents behavior learning and online distributed evolution for cooperative behavior of a group of autonomous robots. Learning and evolution capabilities are essential for a group of autonomous robots to adapt to unstructured environments. Behavior learning finds an optimal state-action mapping of a robot for a given operating condition. In behavior learning, a Q-learning algorithm is modified to handle delayed rewards in the distributed robot systems. A group of robots implements cooperative behaviors through communication with other robots. Individual robots improve the state-action mapping through online evolution with the crossover operator based on the Q-values and their update frequencies. A cooperative material search problem demonstrated the effectiveness of the proposed behavior learning and online distributed evolution method for implementing cooperative behavior of a group of autonomous mobile robots.