• The Journal of Korean Philosophical History
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• no.56
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• pp.209-238
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• 2018

The purpose of this research is to analyze characteristics of the method of education or learning reflected in the teacher-student relationship in On Haeng Lok By Toe-gye and explore their relevance to modern education. By writing various works and conversing with his students, Toe-gye devoted himself in the education of the traditional Confucian principles. Specially, he taught his students based on two Confucian educative principles, namely Shim Deuk(心得) and Goong Haeng(躬行). Judging from this, Toe-gye can be seen as someone who tries to fulfill the role of teacher as dictated in the educative principles of the Confucianism. In Confucianism, teacher is responsible for forming a well-rounded view on life in student, rather than simply transmitting knowledge. As such, the teacher was supposed to find a harmonious way to create something new based on what was inherited from the past generation and try to do his best in learning new things himself and teaching his students. These Toe-gye managed to do successfully, earning his students' trust and respect. Being moved and inspired by their teacher, the students continued their intellectual pursuit. This relationship between Toe-gye and his students can be analyzed from the perspective of education method and discussed in terms of cognitive learning and adult learning. In terms of cognitive learning, the education method reflected in the relationship is similar to potential learning, insight learning, and imitation learning. In terms of adult learning, it is similar to self-directed learning and communicative learning.!

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.11 no.5
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• pp.165-174
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• 2016

Recently, global economic recession has a significant influence on promoting launch of start-ups around the world. As is often the case, powerful and bright entrepreneurship is required so that the start-ups may be successful in their target markets. Despite the fact that numerous studies about impact of the entrepreneurship on start-ups exist in literature, there is no study attempting to recognize importance of social learning about entrepreneurship on individual creativity of those who have intentions to become entrepreneurs of start-ups. In this sense, this study proposes a new research model in which social learning about entrepreneurship is assumed to have an influence on individual creativity of start-ups candidates. For the sake of proving the proposed research model more rigorously, we include those constructs such as para-social interaction, imitation of role model, and internal motivation. We garnered 89 valid questionnaires from college students who were invited to the experiments designed for this study. Results proved that para-social interaction and imitation of role model affect internal motivation significantly, which in turn affects individual creativity positively. These results also provide theoretical directions revealing the embedding process of entrepreneurial capital among society.

• 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 Christian Education in Korea
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• v.61
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• pp.81-107
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• 2020

This study reviews Bandura's vicarious reinforcement in observational learning process and implies this concept into Christian education in terms of spiritual role modeling. The first part of this study answers three questions: "what is vicarious reinforcement?" "how does vicarious reinforcement take place in observational learning?" and "how does vicarious reinforcement affect observer's behavior change?" Bandura conceptualizes the learning process with observational learning and imitative or non-imitative performance. Based on this concept, Bandura define the roles of vicarious reinforcement in the four steps of observational learning process: attention, retention, motor reproduction, and motivational process. Also, the three effects of vicarious reinforcements are explained in the following categories: the observational learning effect, inhibitory or disinhibitory effects, and eliciting effect. Adapting the structure of observational learning theory in terms of the effect of vicarious reinforcement and the function of role models, the second part of this study examines the biblical concept of imitation of Christ and the modeling strategy of discipleship. Especially Paul's spiritual role model serves as positive vicarious reinforcement for the Christian believers to perform the desired behaviors. Also, Paul's condemnation serves as explicit negative vicarious reinforcement. Then, the last part of this study covers the implication of these findings from observational learning and empirical studies in terms of spiritual role modeling to Christian education.

• International Journal of Computer Science & Network Security
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• v.23 no.4
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• pp.116-122
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• 2023

Sentiment categorization technique be commonly isolated interested in threes significant classifications name Machine Learning Procedure (ML), Lexicon Based Method (LB) also finally, the Hybrid Method. In Machine Learning Methods (ML) utilizes phonetic highlights with apply notable ML algorithm. In this paper, in classification and identification be complete base under in optimizations technique called sequential minimal optimization with Random Forest algorithm (SMORF) for expanding the exhibition and proficiency of sentiment classification framework. The three existing classification algorithms are compared with proposed SMORF algorithm. Imitation result within experiential structure is Precisions (P), recalls (R), F-measures (F) and accuracy metric. The proposed sequential minimal optimization with Random Forest (SMORF) provides the great accuracy.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.2
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• pp.713-728
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• 2021

This paper proposes an augmented reality technique to generate acrobatic scenes from hitting motion videos. After a user shoots a motion that mimics hitting an object with hands or feet, their pose is analyzed using motion tracking with deep learning to track hand or foot movement while hitting the object. Hitting position and time are then extracted to generate the object's moving trajectory using physics optimization and synchronized with the video. The proposed method can create videos for hitting objects with feet, e.g. soccer ball lifting; fists, e.g. tap ball, etc. and is suitable for augmented reality applications to include virtual objects.

• Proceedings of the Korea Information Processing Society Conference
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• 2020.05a
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• pp.559-562
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• 2020

시각-언어 이동 문제는 시각 이해와 언어 이해 능력을 함께 요구하는 복합 지능 문제이다. 본 논문에서는 시각-언어 이동 에이전트를 위한 새로운 학습 모델을 제안한다. 이 모델은 데모 데이터에 기초한 모방 학습과 행동 보상에 기초한 강화 학습을 함께 결합한 복합 학습을 채택하고 있다. 따라서 이 모델은 데모 데이타에 편향될 수 있는 모방 학습의 문제와 상대적으로 낮은 데이터 효율성을 갖는 강화 학습의 문제를 상호 보완적으로 해소할 수 있다. 또한, 제안 모델은 서로 다른 두 학습 간에 발생 가능한 학습 불균형도 고려하여 손실 정규화를 포함하고 있다. 또, 제안 모델에서는 기존 연구들에서 사용되어온 목적지 기반 보상 함수의 문제점을 발견하고, 이를 해결하기 위해 설계된 새로은 최적 경로 기반 보상 함수를 이용한다. 본 논문에서는 Matterport3D 시뮬레이션 환경과 R2R 벤치마크 데이터 집합을 이용한 다양한 실들을 통해, 제안 모델의 높은 성능을 입증하였다.

• Nuclear Engineering and Technology
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• v.56 no.4
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• pp.1472-1479
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• 2024

Cyber-Physical Energy Systems (CPESs) integrate cyber and hardware components to ensure a reliable and safe physical power production and supply. Renewable Energy Sources (RESs) add uncertainty to energy demand that can be dealt with flexible operation (e.g., load-following) of CPES; at the same time, scenarios that could result in severe consequences due to both component stochastic failures and aging of the cyber system of CPES (commonly overlooked) must be accounted for Operation & Maintenance (O&M) planning. In this paper, we make use of Deep Reinforcement Learning (DRL) to search for the optimal O&M strategy that, not only considers the actual system hardware components health conditions and their Remaining Useful Life (RUL), but also the possible accident scenarios caused by the failures and the aging of the hardware and the cyber components, respectively. The novelty of the work lies in embedding the cyber aging model into the CPES model of production planning and failure process; this model is used to help the RL agent, trained with Proximal Policy Optimization (PPO) and Imitation Learning (IL), finding the proper rejuvenation timing for the cyber system accounting for the uncertainty of the cyber system aging process. An application is provided, with regards to the Advanced Lead-cooled Fast Reactor European Demonstrator (ALFRED).

• Journal of the Korea Institute of Information and Communication Engineering
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• v.26 no.12
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• pp.1832-1837
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• 2022

In the SW education of non-majors who encounter liberal arts education experience difficulties in the SW development environment and understanding they encounter for the first time, relevance to their major, and convergence thinking ability. In order to compensate for the difficulties of non-major learners in liberal arts education, a relatively easily accessible software was used to utilize a demonstration-oriented model that can be applied to beginners in SW education. In order to understand the logical flow of applications and problem solving used in real life, we proposed a convergence SW teaching method that combines repeated implementation through demonstration by the instructor and imitation of the learner, and learning indicators to increase the learning satisfaction and achievement of the learner. In the experiment applying the teaching and learning method proposed in this paper, meaningful results were shown when evaluating the learning effect, academic achievement, learning satisfaction, and teaching and learning method aspects of SW education.

• Proceedings of the Korea Information Processing Society Conference
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• 2018.10a
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• pp.759-762
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• 2018

최근 들어 점차 지능형 서비스 로봇들이 인간의 실생활 속으로 들어옴에 따라, 로봇 스스로 다양한 물체들을 효과적으로 조작할 수 있는 지식을 습득하는 기계 학습 기술들이 매우 주목을 받고 있다. 전통적으로 로봇 행위 학습 분야에는 강화 학습 혹은 심층 강화 학습 기술들이 주로 많이 적용되어 왔으나, 이들은 대부분 물체 조작 작업과 같이 다차원 연속 상태 공간과 행동 공간에서 최적의 행동 정책을 학습하는데 여러가지 한계점을 가지고 있다. 따라서 본 논문에서는 전문가의 데모 데이터를 활용해 보다 효율적으로 물체 조작 행위들을 학습할 수 있는 모방 학습과 강화 학습의 통합 프레임워크를 제안한다. 이 통합 프레임워크는 학습의 효율성을 향상시키기 위해, 기존의 GAIL 학습 체계를 토대로 PPO 기반 강화 학습 단계의 도입, 보상 함수의 확장, 상태 유사도 기반 데모 선택 전략의 채용 등을 새롭게 시도한 것이다. 다양한 성능 비교 실험들을 통해, 본 논문에서 제안한 통합 학습 프레임워크인 PGAIL의 우수성을 확인할 수 있었다.