• Journal of KIISE:Software and Applications
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• v.37 no.5
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• pp.363-369
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• 2010

Demonstration-based learning has the advantage that a user can easily teach his/her robot new task knowledge just by demonstrating directly how to perform the task. However, many previous demonstration-based learning techniques used a kind of attribute-value vector model to represent their state spaces and policies. Due to the limitation of this model, they suffered from both low efficiency of the learning process and low reusability of the learned policy. In this paper, we present a new demonstration-based learning method, in which the relational model is adopted in place of the attribute-value model. Applying the relational instance-based learning to the training examples extracted from the records of the user demonstrations, the method derives a relational instance-based policy which can be easily utilized for other similar tasks in the same domain. A relational policy maps a context, represented as a pair of (state, goal), to a corresponding action to be executed. In this paper, we give a detail explanation of our demonstration-based relational policy learning method, and then analyze the effectiveness of our learning method through some experiments using a robot simulator.

• The Journal of Korea Robotics Society
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• v.17 no.1
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• pp.40-47
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• 2022

A reward function suitable for a task is required to manipulate objects through reinforcement learning. However, it is difficult to design the reward function if the ample information of the objects cannot be obtained. In this study, a demonstration-based object manipulation algorithm called stochastic exploration guided by demonstration (SEGD) is proposed to solve the design problem of the reward function. SEGD is a reinforcement learning algorithm in which a sparse reward explorer (SRE) and an interpolated policy using demonstration (IPD) are added to soft actor-critic (SAC). SRE ensures the training of the critic of SAC by collecting prior data and IPD limits the exploration space by making SEGD's action similar to the expert's action. Through these two algorithms, the SEGD can learn only with the sparse reward of the task without designing the reward function. In order to verify the SEGD, experiments were conducted for three tasks. SEGD showed its effectiveness by showing success rates of more than 96.5% in these experiments.

• Journal of Fashion Business
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• v.26 no.1
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• pp.105-118
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• 2022

This study investigated the effective pedagogical strategies for sewing by examining the efficacy of sewing videos as supplemental learning materials and demonstration tools. Sewing videos were created for face-to-face apparel construction courses, and students' opinions on sewing videos as an educational tool were collected. Videos with subtitles were offered to Apparel Construction Course 1, whereas videos with narration and subtitles were offered to Apparel Construction Course 2. As "supplemental learning materials," students rated videos as more effective for learning and satisfying than "documents with text and images." The effectiveness and satisfaction scores for Apparel Construction Course 2 were significantly higher than those for Apparel Construction Course 1. Furthermore, videos were utilized significantly more than documents, and most students preferred videos over documents. The main benefits of videos as supplemental learning materials were repetitive learning at the learner's convenience and the detailed presentation of the sewing process. Students regarded narration as more effective and satisfying than subtitles. Narrations were expected to be offered along with subtitles. As "demonstration tools," students rated videos as more effective for learning and satisfying than traditional "sewing samples." Students preferred "demonstration with videos" to "demonstration with sewing samples." The main benefits of video demonstration were a close-up view, presentation of the entire sewing process, and shorter wait time without the need for group teaching. Students wanted more sewing videos and narrations to be offered, and various sewing machine feet to be used in the videos. Educational methods for sewing were suggested based on student opinions.

• Proceedings of the Korea Information Processing Society Conference
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• 2019.05a
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• pp.485-486
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• 2019

Simulated driving behavior is an important aspect of realistic simulation systems. To simulate natural driving behavior, this paper proposes an imitation learning method based on active learning that combines demonstration and experience. Driving demonstrations are collected from human drivers in a driving simulator. A driving behavior policy is learned from these demonstrations. The driving demonstration dataset is augmented with new demonstrations that the original demonstrations did not contain, in the form of behaviors from another driving behavior policy learned from experience. The final driving behavior policy is learned from an augmented demonstration dataset.

• Journal of Information Processing Systems
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• v.8 no.3
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• pp.409-420
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• 2012

The decision-making by agents in games is commonly based on reinforcement learning. To improve the quality of agents, it is necessary to solve the problems of the time and state space that are required for learning. Such problems can be solved by Macro-Actions, which are defined and executed by a sequence of primitive actions. In this line of research, the learning time is reduced by cutting down the number of policy decisions by agents. Macro-Actions were originally defined as combinations of the same primitive actions. Based on studies that showed the generation of Macro-Actions by learning, Macro-Actions are now thought to consist of diverse kinds of primitive actions. However an enormous amount of learning time and state space are required to generate Macro-Actions. To resolve these issues, we can apply insights from studies on the learning of tasks through Programming by Demonstration (PbD) to generate Macro-Actions that reduce the learning time and state space. In this paper, we propose a method to define and execute Macro-Actions. Macro-Actions are learned from a human subject via PbD and a policy is learned by reinforcement learning. In an experiment, the proposed method was applied to a car simulation to verify the scalability of the proposed method. Data was collected from the driving control of a human subject, and then the Macro-Actions that are required for running a car were generated. Furthermore, the policy that is necessary for driving on a track was learned. The acquisition of Macro-Actions by PbD reduced the driving time by about 16% compared to the case in which Macro-Actions were directly defined by a human subject. In addition, the learning time was also reduced by a faster convergence of the optimum policies.

• Journal of the Architectural Institute of Korea Planning & Design
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• v.36 no.3
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• pp.49-58
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• 2020

Based on Merrill's instructional theory, this study pursued to develop a demonstration-based architectural design class operation model for the 3^rd year undergraduate students taking a Spring semester design studio class. The model was designed and used particularly to improve architectural thinking abilities of under-motivated learners. Learning effects of the model were examined based on the preliminary data obtained for 3 consecutive years, 2017 through 2019. A total of 52 students were participated in the class and observed by the instructor. Once developed, the model has been continually updated and improved based on results of each class operation. Five types of demo. were used in the model. First, direct contacts of the instructor with under-motivated learners were turned out to be the most preferred demo(demo. 4), while watching and listening of the demo(demo.3) between the instructor and motivated learners taking place in class was ranked at the second place. Belief of under-motivated learners on the instructor as a professional should be highly valued for improving their architectural thinking abilities. Second, motivated peers' direct help for under-motivated ones was placed in the third rank. Social attitudes of under-motivated learners towards accepting motivated ones' helps were determined the particular demo's appropriateness. Third, a set of guidelines for operating the model in undergraduate design studio classes were developed and suggested.

• Journal of Fashion Business
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• v.24 no.6
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• pp.135-147
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• 2020

This study explored students' online fashion studio class experiences, and investigated the factors affecting their class satisfaction. An online survey of college students who were enrolled in online studio classes within apparel and fashion-related departments during the spring of 2020 was conducted in June 2020. Responses from a total of 213 participants were included in the final data. Respondents rated lecture clips as the most useful, followed by teacher demonstration and feedback, PowerPoint (PPT) supplements, and Q&As. Frequently mentioned areas of improvement were online platform stability and video quality. Many respondents also stated that more streamlined teacher-student communication channels, immediate and meticulous teacher feedback, the adoption of course contents developed specifically for an online environment, and provisions for equipment usage would be desirable. Student satisfaction of an online fashion design studio class was significantly affected by teaching presence, social presence, online learning system stability, perceived usefulness of teacher's demonstration, and affective response toward COVID-19. Students satisfaction of an online garment construction studio class was significantly affected by teaching and social presence, online learning system stability, and perceived usefulness of teacher's demonstration. Based on these findings, we recommend developing teaching contents and methods that allow students to feel included in class and establish an online system with various functions to enhance the sense of social connection that can enable two-way communication.

• Journal of the Korean Society for Precision Engineering
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• v.14 no.4
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• pp.46-55
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• 1997

This paper presents the development of intelligent performance management of computer communication networks for larger-scale integrated systems and the demonstration of its efficacy using computer simula- tion. The innermost core of the performance management is based on fuzzy set theory. This fuzzy perfor- mance manager has learning ability by using principles of neuro-fuzzy model, neuralnetwork, genetic algo- rithm(GA). Two types of performance managers are described in this paper. One is the Neuro-Fuzzy Per- formance Manager(NFPM) of which learning ability is based on the conventional gradient method, and the other is GA-based Neuro-Fuzzy Performance Manager(GNFPM)with its learning ability based on a genetic algorithm. These performance managers have been evaluated via discrete event simulation of a computer network.

• The Journal of the Korea Contents Association
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• v.19 no.3
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• pp.466-474
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• 2019

The purpose of this study was to investigate the enhancing creative competency and changes in academic challenge for the pre-service teachers. For this purpose, 94 pre-service teachers participated in project learning through the preparation of the convergence instruction and the class demonstration during one semester. The pre and post questionnaire survey was conducted the measurement of creative leader competence and K-NSSE for academic challenge. Analysis of data was performed using the IBM SPSS 18.0 program for the corresponding sample t test. The creative competency included 'higher mental thinking', 'problem solving', 'curiosity', 'sensitivity' 'task commitment', 'the pursuit of social value', and 'co-operations and considerations'. This results was significant(p< .05). Academic challenge, high-order learning domain and learning strategies domain were significant(p< .05). Based on this, in order to generalize the convergence education and convergence lesson, it is necessary to design various convergence lessons and practice study to make a plan and practice it. In addition, the implications for the necessity of correcting and supplementing the effects after repeated convergence lessons were discussed.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2018.05a
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• pp.327-331
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• 2018

The purpose of this study was to design a competence teaching-learning model that could help college students improve their computational thinking among core competences in SW education. A competence teaching-learning model, UDDPAAP (Unplugged-Demonstration-Decomposition-Pattern Recognition-Abstraction-Algorithm-Programming), was designed by analyzing competences of learners with no experience in software coding, by reconstructing DMM, DDD, and DPAA among the five existing SW-based teaching-learning models, and by analyzing unplugged activity and the Bebras challenge computational thinking scale carefully. The unplugged activity partially adapted to instruction for college students and some items chosen from the Bebras challenge computational thinking scale were applied to the existing teaching-learning model. To determine the effects of the study, pretest was conducted in freshmen for computational thinking and self-confidence on the basis of the experience in SW and computer information literacy education, and posttest following instruction applying the UDDPAAP teaching-learning model. The students provided with SW education based on the UDDPAAP teaching-learning model saw their computational thinking competence improved.