• Research in Community and Public Health Nursing
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• v.24 no.2
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• pp.195-204
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• 2013

Purpose: This study was carried out to find factors affecting weight-control behavior intention in female college students based on the theory of planned behavior. Methods: The subjects were 453 female students from everywhere other than the Gangwon Province and Jeju Island. Data were collected by using a questionnaire. Results: The factors affecting weight-control behavior intention in female college students within 2 weeks were attitudes and subjective norms. These two factors accounted for 20.0% of weight-control behavior intention. Also, when body shape satisfaction and BMI were added to variables of the theory of planned behavior like attitudes, subjective norms, and perceived behavior control, these 5 factors accounted for a total of 34.1%. Conclusion: Due to their distorted perception in preferring skinny body shapes, female college students are likely to attempt at inappropriate weight control behavior. Through intervention with such factors as attitudes and body image satisfaction, which have been derived from the results of this study, healthy weight control behavior should be pursued in practice.

• Journal of Institute of Control, Robotics and Systems
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• v.13 no.8
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• pp.760-768
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• 2007

This paper introduces an emotional behavior decision model based on linear system for intelligent service robots. An emotional model should make different behavior decisions according to the purpose of the robots. We propose an emotional behavior decision model which can change the character of intelligent service robots and make different behavior decisions although the situation and environment remain the same. We defined each emotional element such as reactive dynamics, internal dynamics, emotional dynamics, and behavior dynamics by state dynamic equations. The proposed system model is a linear dynamic system. If you want to add one external stimulus or behavior, you need to add just one dimensional vector to the matrix of external stimulus or behavior dynamics. The case of removing is same. The change of reactive dynamics, internal dynamics, emotional dynamics, and behavior dynamics also follows the same procedure. We implemented a cyber robot and an emotional head robot using 3D character for verifying the performance of the proposed emotional behavior decision model.

• Journal of the Korean Society of Clothing and Textiles
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• v.24 no.4
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• pp.560-570
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• 2000

The purposes of this study were to segment the cosmeceuticals market based on consumer usage behavior and to develop a profile of each segment using appearance-related variables, purchase attributes, purchase behavior and demographics. A total of 518 responses collected from a questionnaire survey to female consumers was analyzed. Cluster analysis on usage behavior of cosmeceutical products identified three groups including: Anti-aging/Whitening/Slimming product users (22% ); Pore-control product users(20%); and Minimum users(57%). MANOVA, ANOVA and Chi-square analysis revealed significant differences among the three groups on 2 appearance-related variables, 3 purchase attribute factors, 4 purchase behaviors, and 2 demographic characteristics. Based on the results, the study developed a profile of each segment and provided marketing implications.

• Journal of Institute of Control, Robotics and Systems
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• v.8 no.7
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• pp.607-612
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• 2002

An automatic design method of behaviors in behavior-based robotics is proposed. With this method, a robot can design its behaviors by itself without aids of human designer. Automating design procedure of behaviors can make the human designer free from somewhat tedious endeavor that requires to predict all possible situations in which the robot will work and to design a suitable behavior for each situation. A simple reinforcement learning strategy is the main frame of this method and the key parameter of the learning process is significant change of reward value. A successful application to mobile robot navigation is reported too.

• 제어로봇시스템학회:학술대회논문집
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• 1997.10a
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• pp.124-127
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• 1997

In this paper, we propose a method of cooperative control based on immune system in distributed autonomous robotic system(DARS). Immune system is living body's self-protection and self-maintenance system. Thus these features can be applied to decision making of optimal swarm behavior in dynamically changing environment. For the purpose of applying immune system to DARS, a robot is regarded as a B lymphocyte(B cell), each environmental condition as an antigen, and a behavior strategy as an antibody 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 simulated and suppressed by other robot using communication. Finally much simulated strategy is adopted as a swarm behavior strategy. This control scheme is based on clonal selection and idiotopic network hypothesis. And it is used for decision making of optimal swarm strategy.

• Journal of Korean Academy of Nursing
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• v.32 no.1
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• pp.16-27
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• 2002

This study was conducted to develop and test the hypothetical model which explains smoking cessation behavior was established based on the Triandis' interpersonal behavior theory. Method: The data were collected from the 400 university student smokers enrolled in the universities located in Seoul and Kyung-In province. The study was analyzed by path analysis with LIESREL 8 program. Results: All of the fit statistics, except the Chi-square value, it showed the hypothetical model was well fitted to the data. Benefit, affect, and social influences related to smoking cessation behavior had significant direct effect on intention to smoking cessation as shown in the study of the hypothetical model. Perceived barrier and the physiologic arousal related to smoking cessation had significant direct effects on performing smoking cessation behavior, whereas numbers of previous attempts to quit smoking and intention to smoking cessation did not.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1998.03a
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• pp.127-130
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• 1998

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. Thus these features can be applied to decision making of optimal swarm behavior in dynamically changing environment. For the purpose of 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-call 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 scheme is based of 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.

• Journal of the Korean Home Economics Association
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• v.34 no.4
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• pp.295-308
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• 1996

The purpose of this study was to explore how family types clustered based on the intra system dynamics explained housewive's managerial behavior. The data were collected by means of questionnaire distributed to a stratified sample of 544 housewives in Seoul who lived with husband and children. The questionnaires included FACES Ⅱ and Ⅲ, Communication Scale, Managerial behavior Scale and Life Satisfaction Scale. Frequency, percentile, mean, correlation, factor analysis, cluster analysis, One-way ANOVA with Scheffe test, and multiple regression were used to analyze the data. This study had resulted in three major findings. The first was that families were clustered by four types, named structed-separated family, flexible-connected family, change oriented emashed, and rigid-disengaed family. The second finding was that a difference in managerial behavior was found among four types of family. Housewives whose family were more connected each other and adapted more easily to changing situations showed better managerial behavior. The last one was that the managerial behavior of housewives was better explained by family types than socio-demographic variables. The recommendations for future research and the better ways to lead effective managerial behavior were suggested.

• Journal of Korea Multimedia Society
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• v.25 no.6
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• pp.807-818
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• 2022

In order to further improve the accuracy and time efficiency of behavior recognition in intelligent monitoring scenarios, a human behavior recognition algorithm based on YOLO combined with LSTM and CNN is proposed. Using the real-time nature of YOLO target detection, firstly, the specific behavior in the surveillance video is detected in real time, and the depth feature extraction is performed after obtaining the target size, location and other information; Then, remove noise data from irrelevant areas in the image; Finally, combined with LSTM modeling and processing time series, the final behavior discrimination is made for the behavior action sequence in the surveillance video. Experiments in the MSR and KTH datasets show that the average recognition rate of each behavior reaches 98.42% and 96.6%, and the average recognition speed reaches 210ms and 220ms. The method in this paper has a good effect on the intelligence behavior recognition.

• Journal of Korea Multimedia Society
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• v.25 no.2
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• pp.432-439
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• 2022

This paper introduces deliberative behavior tree using utility theory. The proposed approach combine the strengths of behavior trees and utility theory to implement complex behavior of AI agents in an easier and more concise way. To achieve this goal, we devised and implemented three types of additional behavior tree nodes, which evaluate utility values of its own node or its subtree while traversing and selecting its child nodes based on the evaluated values. In order to validate our approach, we implemented a sample scenario using conventional behavior tree and our proposed deliberative tree respectively. And then we compared and analyzed the simulation results.