• Proceedings of the KIEE Conference
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• 2005.10b
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• pp.174-176
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• 2005

This treatise studies composing process that renew information mastered by interactive experience between environment and system via network among individuals. In the previous study map information regarding free space is learned by using of reinforced learning algorithm, which enable each individual to construct optimal action policy. Based on those action policy each individuals can obtain optimal path. Moreover decision process to distinguish best optimal path by comparing those in the network composed of each individuals is added. Also information about the finally chosen path is being updated. A self renewing method of each system information by sharing the each individual data via network is proposed Data enrichment by shilling the information of many maps not in the single map is tried Numerical simulation is conducted to confirm the propose concept. In order to prove its suitability experiment using micro-mouse by integrating and comparing the information between individuals is carried out in various types of map to reveal successful result.

• Journal of Korean Association for Spatial Structures
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• v.21 no.2
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• pp.41-48
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• 2021

A smart tuned mass damper (TMD) is widely studied for seismic response reduction of various structures. Control algorithm is the most important factor for control performance of a smart TMD. This study used a Deep Deterministic Policy Gradient (DDPG) among reinforcement learning techniques to develop a control algorithm for a smart TMD. A magnetorheological (MR) damper was used to make the smart TMD. A single mass model with the smart TMD was employed to make a reinforcement learning environment. Time history analysis simulations of the example structure subject to artificial seismic load were performed in the reinforcement learning process. Critic of policy network and actor of value network for DDPG agent were constructed. The action of DDPG agent was selected as the command voltage sent to the MR damper. Reward for the DDPG action was calculated by using displacement and velocity responses of the main mass. Groundhook control algorithm was used as a comparative control algorithm. After 10,000 episode training of the DDPG agent model with proper hyper-parameters, the semi-active control algorithm for control of seismic responses of the example structure with the smart TMD was developed. The simulation results presented that the developed DDPG model can provide effective control algorithms for smart TMD for reduction of seismic responses.

• Journal of Fisheries and Marine Sciences Education
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• v.29 no.3
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• pp.677-688
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• 2017

The purpose of this study is to analyze the action learning lesson about the improvement process of the job support program of P university students. As a research method, we applied the related classes during the semester to the students who took courses in the course of 'Human Resource Development', which is a subject of P university, and analyzed the learner's reflection journal, interview data. As a result of the research, we went through the problem selection stage, the team construction and the team building stage. And then we searched for the root cause of the problem, clarified the problem, derived the possible solution, determined the priority and created the action plan. There are 10 solutions to the practical problems of poor job camps. Through two interviews with field experts it offered final solutions focused on promoting employment and Camp students participate in the management of post-employment into six camps. According to the first rank, job board integration, vendor selection upon student feedback, reflecting improved late questionnaire, public relations utilizing KakaoTalk, recruiting additional selection criteria, the camp provides recorded images in order. The results of this study suggest that the university's employment support program will strengthen the competitiveness of students' employment and become the basic data for the customized employment support program.

• Korean Medical Education Review
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• v.22 no.2
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• pp.93-98
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• 2020

This paper examined the principles of experiential learning theory (ELT) relevant to learning from experience and introduces examples which were applied to medical education from a theoretical perspective. The experiential learning theory encompasses the theories of Dewey, Kolb, and Schon, whose key ideas highlight the continuity of experience, interaction of experience, and reflection. According to ELT, growth is education, and education is a reconstruction of experience. Experiential learning is a way of thinking about the educational process by constructing meaningful learning through reflection-in-action. This paper examines the learning principles of ELT and presents practical examples of the application of ELT and its implications for medical education. An in-depth understanding and application of ELT could benefit the field of medical education.

• The Korean Nurse
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• v.31 no.5
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• pp.65-71
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• 1992

In nursing, a practice discipline, it has been argued whether the mastery of clinical competence depends on types of learning styles, learning experience, and the use of specialized skills. All these problems are not limited to nursing education. Later educators identified the importance of reflective learning which is a vital element in any form of learning and that teachers and students need to consider how they can incorporate some forms of reflection in the courses. The purpose of this study is to review educational articles for understanding better what reflection in learning is, to identify the theme which is of important relevance to professional practice, from the book, Educating the reflective practitioner, and to discuss the theme within nursing education. Reflection in learning was defined by Dewey(1933) as the process which is involved the perception of relationships and connections between the parts of an experience. This experience is passed on when two people becoming involved with each other in a conversation. schon(1987) emphasized that learning conversation, which is a part of the interaction of student and teacher, is an important factor of the process of reflection-om-actopm. In clinical nursing education, good relationships between teacher and student, faculty's role, interpersonal skills are critical in learning conversation. Then Practing nurses who accept the need to choose nursing actionss on the basis of reflection, who accepet the necessity for understanding and being able to communicate the reasons for action are a powerfful force for the development of nursing into an increasingly more effective profession for the benefit of patients.

• International Journal of Computer Science & Network Security
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• v.22 no.7
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• pp.171-180
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• 2022

In the knowledge development of online assessment in learning management systems (LMSs), many assessments are evaluated weekly in the summer training course for undergraduate students in the Faculty of Computing and Information Technology at King Abdul-Aziz University in Saudi Arabia. The number of performance assessments in the summer training course reaches 15 weeks. Many of them, however, are sent or done informally or through unreliable ways and cannot be verified by third parties. Therefore, applying the concept of digital transformation is essential. This research study reported herein used the action design research (ADR) method to build a new information technology system that could assist in the digital transformation. An electronic platform was designed, developed, implemented, and evaluated using the ADR method so that the main people involved in the summer training process (i.e., students, academic supervisors, and administrators) would have a high level of satisfaction with it. The study was conducted on 452 students, 105 academic supervisors, and 15 administrative staff and was conducted during the summer semester of 2020. All the training processes were digitally transformed and automated to control and raise the level and reliability of the training. All involved people were satisfied, thus, shifting the process to be in a digital form assist in achieving the high-level goal.

• Journal of Korean Association for Spatial Structures
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• v.23 no.2
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• pp.29-36
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• 2023

Reinforcement learning (RL) is widely applied to various engineering fields. Especially, RL has shown successful performance for control problems, such as vehicles, robotics, and active structural control system. However, little research on application of RL to optimal structural design has conducted to date. In this study, the possibility of application of RL to structural design of reinforced concrete (RC) beam was investigated. The example of RC beam structural design problem introduced in previous study was used for comparative study. Deep q-network (DQN) is a famous RL algorithm presenting good performance in the discrete action space and thus it was used in this study. The action of DQN agent is required to represent design variables of RC beam. However, the number of design variables of RC beam is too many to represent by the action of conventional DQN. To solve this problem, multi-agent DQN was used in this study. For more effective reinforcement learning process, DDQN (Double Q-Learning) that is an advanced version of a conventional DQN was employed. The multi-agent of DDQN was trained for optimal structural design of RC beam to satisfy American Concrete Institute (318) without any hand-labeled dataset. Five agents of DDQN provides actions for beam with, beam depth, main rebar size, number of main rebar, and shear stirrup size, respectively. Five agents of DDQN were trained for 10,000 episodes and the performance of the multi-agent of DDQN was evaluated with 100 test design cases. This study shows that the multi-agent DDQN algorithm can provide successfully structural design results of RC beam.

• The Journal of the Convergence on Culture Technology
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• v.9 no.6
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• pp.531-538
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• 2023

Recently, many university classes have been changing from instructor-centered classes to learner-centered classes, and universities are trying to establish a new direction for university education, especially to foster talented people suitable for the Fourth Industrial Revolution. To this end, universities are presenting various competencies necessary for students and focusing on research on efficient education plans for each competency. Among them, creativity is considered the most important competency that students should obtain in universities. Developing a creative problem-solving-based subject where various majors gather to produce results while conducting creative team activities away from desk classes is considered a meaningful subject to cultivate capacities suitable for the requirements of the times. Therefore, this study purpose to develop creative problem-solving-based subjects and analyze the results of class progress. This creative problem-solving-based class is an Action Learning class for step-by-step idea development, which starts with a theoretical lecture for creative idea development and then consists of five stages of Action Learning. The tasks of action learning used in this class consisted of ceramic expression to increase the intimacy of the formed group and the group's collective expression, ideas in life to combine and compress individual ideas into one, environmental improvement programs around schools, and finally UCC on various topics. In the theoretical lecture conducted throughout the class, a class was conducted on Scientific Thinking for creative problem solving, and then a group-type action learning class was conducted sequentially. This Action Learnin process gradually increased the difficulty level and led to in-depth learning by increasing the level of difficulty step by step.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 2005.05a
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• pp.960-968
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• 2005

The high cost in maintaining complex manufacturing process makes it necessary to enhance an efficient maintenance system. For the effective maintenance of manufacturing process, precise fault diagnosis should be performed and an appropriate maintenance action should be executed. This paper suggests an intelligent fault diagnosis system using hybrid data mining. In this system, the rules for the fault diagnosis are generated by hybrid decision tree/genetic algorithm and the most effective maintenance action is selected by decision network and AHP. To verify the proposed intelligent fault diagnosis system, we compared the accuracy of the hybrid decision tree/genetic algorithm with one of the general decision tree learning algorithm(C4.5) by data collected from a coil-spring manufacturing process.

• Journal of the Korean earth science society
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• v.25 no.3
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• pp.129-141
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• 2004

A peer assessment method was implemented as part of an action research project where the primary goal was to create constructivist science classrooms. Student reflective writings, which had been created in the process of action research, were analyzed to examine the perceptions of the students concerning the peer assessment method employed in their earth science classrooms. Five perception categories and thirteen statements were developed from the student writings. These indicated that the students appreciated the positive nature of peer assessment and the effects of it as an alternative method for promoting learning. It was also revealed, however, that some students displayed inappropriate behaviors toward peer assessment which led to negative perceptions of the new assessment method. Implications for future use of peer assessment in science classrooms are discussed. Limitations founded in the present study as well as possible solutions are provided.