• Journal of the Korean Home Economics Association
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• v.51 no.2
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• pp.253-261
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• 2013

The purpose of this study was to investigate the moderating effects of parental monitoring on the relationship between children's dependency on mobile phones and control of learning behavior. The data came from the 2010 Korean Children and Youth Panel (N = 1,609) conducted by the National Youth Policy Institute. The analysis method used was Structural Equation Modeling by using SPSS 17.0 and AMOS 7.0. To test the significant moderating effects, Ping's two-step technique, which is free from the requirement of nonlinear constraints, was used. Our results demonstrated that children's dependency on mobile phones had negative effects on control of learning behavior, and the interaction effects between such dependency and parental monitoring affected the control of learning behavior. Thus, these results proved the moderating effects of parental monitoring in the control of learning behavior. This study suggests that parental monitoring buffers against having difficulties to control and adjust one's behavior associated with control of learning behavior, which is affected by the dependency on mobile phones among children. We discussed that the risks of children's dependency on mobile phones and parental monitoring should be acknowledge as a significant protective factor.

• 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.

• International journal of advanced smart convergence
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• v.9 no.4
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• pp.115-119
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• 2020

Recently, CCTV installations are rapidly increasing in the public and private sectors to prevent various crimes. In accordance with the increasing number of CCTVs, video-based abnormal behavior detection in control systems is one of the key technologies for safety. This is because it is difficult for the surveillance personnel who control multiple CCTVs to manually monitor all abnormal behaviors in the video. In order to solve this problem, research to recognize abnormal behavior using deep learning is being actively conducted. In this paper, we propose a model for detecting abnormal behavior based on the deep learning model that is currently widely used. Based on the abnormal behavior video data provided by AI Hub, we performed a comparative experiment to detect anomalous behavior through violence learning and fainting in videos using 2D CNN-LSTM, 3D CNN, and I3D models. We hope that the experimental results of this abnormal behavior learning model will be helpful in developing intelligent CCTV.

• International Journal of Control, Automation, and Systems
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• v.1 no.1
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• pp.142-148
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• 2003

Reinforcement learning is considered as an important tool for robotic learning in unknown/uncertain environments. In this paper, we propose an evaluation function expressed in a vector form to realize multi-dimensional reinforcement learning. The novel feature of the proposed method is that learning one behavior induces parallel learning of other behaviors though the objectives of each behavior are different. In brief, all behaviors watch other behaviors from a critical point of view. Therefore, in the proposed method, there is cross-criticism and parallel learning that make the multi-dimensional learning process more efficient. By ap-plying the proposed learning method, we carried out multi-dimensional evaluation (reward) and multi-dimensional learning simultaneously in one trial. A special neural network (Q-net), in which the weights and the output are represented by vectors, is proposed to realize a critic net-work for Q-learning. The proposed learning method is applied for behavior planning of mobile robots.

• Korean Journal of Child Studies
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• v.32 no.4
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• pp.83-97
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• 2011

The purpose of this study was to examine the relationship between socioeconomic status, maternal involvement in learning, parenting behavior and children's self-determination motivation. The participants of this study consisted of 333 fifth- and sixth-grade elementary school children and their mothers living in Seoul. The results of this study indicated that mothers with a higher educational attainment reported greater autonomy support behavior and involvement in their offspring's learning. Conversely, mothers with low incomes were found to use psychological control and were also found to be involved in learning to a lesser degree. Hierarchical multiple regression analysis indicated that children whose mothers were less involved in learning showed higher levels of self-determination motivation. Additionally, maternal support for autonomy and psychological control had a number of moderating effects on the association between maternal involvement in learning and the child's self-determination motivation. Specifically, children tended to exhibit significantly lower levels of self-determination motivation when mothers were more involved in learning among those who received less support in terms of autonomy. Conversely, children had significantly higher levels of self-determination motivation when mothers were less involved in learning when it came to those children who were under less psychological control.

• Journal of Technologic Dentistry
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• v.41 no.3
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• pp.233-244
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• 2019

Purpose: The purpose of the study is to learn the importance of self-directed learning about career-preparation behavior of department of dental technology students. Methods: Using the questionnaire, the department of dental technology in Gyeongnam Province conducted a survey of students of department of dental technology at A and B college for one month from May 15, 2019 through June 15, 2019, and finally 204 students were surveyed for Self-esteem, Self-determination, Self-efficacy, Internal control, College life adaptation, Self-directed learning, and Career-preparation behavior. Results: Self-esteem among students has been shown to improve self-directed learning by increasing the stress of college life, and self-efficacy has only a direct effect on self-directed learning. In addition, self-determination and internal control of department of dental technology students were found to be variables that have a common positive effect on college life adaptation and self-directed learning. In addition, college life adaptation gives direct positive effect to self-directed learning, but indirect effect through self-directed learning was found to be stronger than direct effect on career-preparation behavior, and the career-preparation behavior of students was further strengthened through self-directed learning. Conclusion: The changes in college restructuring and various policies also suggest that students should actively seek ways to instill certainty about their major's vision and career path within the college rather than deciding their future through extreme measures such as academic secession at a time when anxiety and uncertainty about their career is strong.

• Journal of Institute of Control, Robotics and Systems
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• v.9 no.7
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• pp.527-535
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• 2003

A behavior-based control and learning architecture is proposed, where reinforcement learning is applied to learn proper associations between stimulus and response by using two types of memory called as short Term Memory and Long Term Memory. In particular, to solve delayed-reward problem, a knowledge-propagation (KP) method is proposed, where well-designed or well-trained S-R(stimulus-response) associations for low-level sensors are utilized to learn new S-R associations for high-level sensors, in case that those S-R associations require the same objective such as obstacle avoidance. To show the validity of our proposed KP method, comparative experiments are performed for the cases that (ⅰ) only a delayed reward is used, (ⅱ) some of S-R pairs are preprogrammed, (ⅲ) immediate reward is possible, and (ⅳ) the proposed KP method is applied.

• Korean Medical Education Review
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• v.15 no.1
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• pp.46-53
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• 2013

The purpose of this study was to analyze the relationship among medical students' learning motivation, characteristics of multiple intelligence, and academic achievement. The participants were 144 medical students. The data were collected by administering learning motivation tests (self-confidence, self-efficacy, level of task, emotion of learning, learning behavior, failure tolerance, task difficulty, and academic self-efficacy), a multiple intelligence test (linguistic intelligence, logical-mathematical intelligence, musical intelligence, bodily-kinesthetic intelligence, spatial intelligence, interpersonal intelligence, intrapersonal intelligence, and naturalistic intelligence), and two semesters of grades. There is a correlation between multiple intelligences and learning motivation. Among academic self-efficacy of academic motivation, the self-control efficacy (0.28) and behavior (0.18) subscales are significantly positively correlated with academic achievement. However, the emotion subscale (-0.18) was significantly negatively correlated. Learning motivation was correlated with two of the eight multiple intelligence profiles: the intrapersonal intelligence (0.18) and bodily-kinesthetic intelligence (-0.19). The structural equation modeling analysis showed that the behavior and self-control efficacy subscales of intrapersonal intelligence had an impact on academic achievement. An analysis according to the academic achievement group showed significant differences in self-control efficacy and emotion subscales with intrapersonal intelligence. A positive relationship can be observed between learning motivation and some characteristics of multiple intelligence of medical school students. In light of the findings, it is worth examining whether we can control medical students' learning motivation through educational programs targeting self-control efficacy and intrapersonal intelligence.

• Hwankyungkyoyuk
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• v.22 no.2
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• pp.23-42
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• 2009

The purpose of this study is to investigate the influences of water environmental program using PBL on pro-environmental behavior for 4th graders in elementary school. The results of this study are as follows. First, the experimental group is more effective in promoting the pro-environmental attitude compared with the control group. Second, the experimental group is more effective in promoting the pro-environmental behavior compared with the control group. Third, the experimental group is not more effective in promoting the pro-environmental knowledge compared with the control group. Fourth, students are concerned and interested in the environment and environmental problems of the daily-life. And, they presented positive attitude and active participation toward environmental preservation. So, the process of PBL using water environmental program showed that it promotes active learning participation and various action. In conclusion, the environmental education program through PBL program has a positive effect on pro-environmental attitudes and behavior of elementary school students. It is demanded that more intensive research on this study should be done, linking with teaching and learning method, as a follow-up activity.

• Journal of Institute of Control, Robotics and Systems
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• v.18 no.5
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• pp.465-470
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• 2012

This paper proposes a distributed autonomous control method of swarm robot behavior strategy based on artificial immune system and an optimization strategy for artificial immune system. The behavior strategies of swarm robot in the system are depend on the task distribution in environment and we have to consider the dynamics of the system environment. In this paper, the behavior strategies divided into dispersion and aggregation. For applying to artificial immune system, an individual of swarm is regarded as a B-cell, each task distribution in environment 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, the agent selects an appropriate behavior strategy. And its behavior strategy is stimulated and suppressed by other agent using communication. Finally much stimulated strategy is adopted as a swarm behavior strategy. In order to decide more accurately select the behavior strategy, the optimized parameter learning procedure that is represented by stimulus function of antigen to antibody in artificial immune system is required. In this paper, particle swarm optimization algorithm is applied to this learning procedure. The proposed method shows more adaptive and robustness results than the existing system at the viewpoint that the swarm robots learning and adaptation degree associated with the changing of tasks.