• 제어로봇시스템학회:학술대회논문집
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• 2005.06a
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• pp.1044-1047
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• 2005

In this paper, we present the strategy of object search for distributed autonomous robotic systems (DARS). The DARS are the systems that consist of multiple autonomous robotic agents to whom required functions are distributed. For instance, the agents should recognize their surrounding at where they are located and generate some rules to act upon by themselves. In this paper, we introduce the strategy for multiple DARS robots to search a hidden object at the unknown area. First, we present an area-based action making process to determine the direction change of the robots during their maneuvers. Second, we also present Q learning adaptation to enhance the area-based action making process. Third, we introduce the coordinate system to represent a robot's current location. In the end of this paper, we show experimental results using hexagon-based Q learning to find the hidden object.

• International Journal of Control, Automation, and Systems
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• v.6 no.6
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• pp.904-914
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• 2008

An action-selection-mechanism(ASM) has been proposed to work as a fully connected finite state machine to deal with sequential behaviors as well as to allow a state in the task program to migrate to any state in the task, in which a primitive node in association with a state and its transitional conditions can be easily inserted/deleted. Also, such a primitive node can be learned by a shortest path-finding-based reinforcement learning technique. Specifically, we define a behavioral motivation as having state-dependent value as a primitive node for action selection, and then sequentially construct a network of behavioral motivations in such a way that the value of a parent node is allowed to flow into a child node by a releasing mechanism. A vertical path in a network represents a behavioral sequence. Here, such a tree for our proposed ASM can be newly generated and/or updated whenever a new behavior sequence is learned. To show the validity of our proposed ASM, experimental results of a mobile robot performing the task of pushing- a- box-in to- a-goal(PBIG) will be illustrated.

• Journal of Korean Academy of Nursing Administration
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• v.20 no.5
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• pp.587-598
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• 2014

Purpose:This study was conducted to identify the effects of an action learning based creative problem-solving (CPS) course on problem solving, creativity and team-member exchange in nursing students. Methods: A quasi-experimental study applying a non-equivalent control group pre-post design was employed. Sophomore nursing students (32 in the experimental group and 33 in the control group) were recruited from a university in G-city, Korea. Problem solving, creativity and team-member exchange were measured for the pretest and posttest using self-report questionnaires. Kolmogorov-Smirnov test, Chi-square, Fisher's exact test, t-test, and ANCOVA with SPSS/Win 20.0 program were used to analyze the data. Results: The scores for problem solving, creativity and team-member exchange in the experimental group were significantly higher than those of the control group. Conclusion: Results of this study indicate that an action learning based CPS course is an effective teaching method to improve nursing students' competencies. In the future longitudinal studies are needed to assess the long term effects of the course.

• Journal of Korean Academy of Fundamentals of Nursing
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• v.25 no.4
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• pp.259-268
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• 2018

Purpose: The purpose of this study was to identify the effects of the action learning approach on the self-confidence and knowledge of health assessment, critical thinking ability and class satisfaction in students taking health assessment courses. Methods: This non-equivalent control group pretest-posttest study enrolled 127 nursing students as participants, with 64 and 63 in the experimental and control group, respectively. These two groups attended 33 hours (2 or 4 hours per week for 11 weeks) of action learning and traditional classes, respectively. Differences in the dependent variables between the two groups were compared before and after the intervention using independent t-test. Results: The action learning group reported significantly greater self-confidence in health assessment (t=5.10, p<.001) and critical thinking ability (t=2.23, p=.027) than the control group. There was no significant difference in knowledge of health assessment or class satisfaction between two groups (p>.05). Conclusion: These findings indicate that action learning is an effective intervention for enhancing self-confidence and critical thinking ability in nursing education.

• Journal of the Korean Institute of Intelligent Systems
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• v.7 no.5
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• pp.60-66
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• 1997

This paper presents a fuzzy inference-based reinforcement learning algorithm of dynamci recurrent neural networks, which is very similar to the psychological learning method of higher animals. By useing the fuzzy inference technique the linguistic and concetional expressions have an effect on the controller's action indirectly, which is shown in human's behavior. The intervlas of fuzzy membership functions are found optimally by genetic algorithms. And using recurrent neural networks composed of dynamic neurons as action-generation networks, past state as well as current state is considered to make an action in dynamical environment. We show the validity of the proposed learning algorithm by applying it to the inverted pendulum control problem.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.3
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• pp.754-774
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• 2023

Wrestling is one of the popular events for modern sports. It is difficult to quantitatively describe a wrestling game between athletes. And deep learning can help wrestling training by human recognition techniques. Based on the characteristics of latest wrestling competition rules and human recognition technologies, a set of wrestling competition video analysis and retrieval system is proposed. This system uses a combination of literature method, observation method, interview method and mathematical statistics to conduct statistics, analysis, research and discussion on the application of technology. Combined the system application in targeted movement technology. A deep learning-based facial recognition psychological feature analysis method for the training and competition of classical wrestling after the implementation of the new rules is proposed. The experimental results of this paper showed that the proportion of natural emotions of male and female wrestlers was about 50%, indicating that the wrestler's mentality was relatively stable before the intense physical confrontation, and the test of the system also proved the stability of the system.

• Journal of Digital Convergence
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• v.19 no.8
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• pp.397-404
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• 2021

The aim of this study is to explore the educational experience of nursing students after designing an action learning class suitable for basic nursing science class and applying it. A total 100 freshmen nursing students taking a basic nursing science class of K university in S city participated in this study. Data was collected from May 2019 to June 2020. The action learning class consisted of 5-6 people per team, a total of 9 teams, reflection diaries were collected and analyzed using the qualitative content analysis method of Krippendorff (2004). The analysis produced 45 significant statements in total, 8 themes and 4 categries for the experience of basic nursing science class based on action learning. The 4 categories were 'confidence in anatomy', 'growing teamwork', 'learned how to study', 'difficulties in the process'. The action learning applied class was found to be effective in problem-solving ability, teamwork, and self-directed learning. Therefore, it is proposed to evaluate the effect of action learning in other nursing subjects as well.

• Journal of the Korean Institute of Intelligent Systems
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• v.14 no.4
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• pp.457-467
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• 2004

In this paper, a robot is considered as an agent, a structure of robot is presented which consisted by multi-subagents and they have diverse capacity such as perception, intelligence, action etc., required for robot. Also, subagents are consisted by micro-agent($\mu$agent) charged for elementary action required. The structure of robot control have two sub-agents, the one is behavior based reactive controller and action selection sub agent, and action selection sub-agent select a action based on the high label action and high performance, and which have a learning mechanism based on the reinforcement learning. For presented robot structure, it is easy to give intelligence to each element of action and a new approach of multi robot control. Presented robot is simulated for two goals: chaotic exploration and obstacle avoidance, and fabricated by using 8bit microcontroller, and experimented.

• Journal of Information Technology Services
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• v.10 no.3
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• pp.21-30
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• 2011

Expectation for improvement of creativity and problem-solving capability has increased the creative robotics classes in the form of after-school activity in more than half of total elementary schools. While Ministry of Education, Science and Technology has promoted 'Green IT Guidelines' as a part of 'Eco-friendly Green School Development Project', the Green issues have not been considered enough in those creative robotics classes. In this paper, we would like to address the Green issues, especially in the r-Learning services based on UCR (User Created Robots). First, trend of green IT education, r-Learning services and UCR are reviewed. And the current status of eco-related operations and teachers' perception in the robotics classes of elementary schools is investigated. Examples of Green UCR are also searched and green programs based on the three kinds of UCR, UC-TR, UC-AR, UC-CR, are explored. Finally, we propose action plans to promote the UCR-based r-Learning service reflecting green issues.

• 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.