• Journal of Korean Elementary Science Education
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• v.34 no.4
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• pp.382-393
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• 2015

The purpose of this study was to examine the effects of elementary science teaching program strengthening aesthetic experiences on science learning motivation and achievement. The subjects of this study were divided into two groups. The experiment group practiced elementary science teaching program strengthening aesthetic experiences, while the control group practiced teacher guided-based instruction. The results of this study were as follows. First, science teaching program strengthening aesthetic experiences gave a significant influence on increasing the science learning motivation. Second, science teaching program strengthening aesthetic experiences gave a significant influence on increasing the science achievement. In conclusion, this study showed that elementary science teaching program strengthening aesthetic experiences gave a positive influence on the science learning motivation and science achievement in elementary school science.

• Proceedings of the KAIS Fall Conference
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• 2009.12a
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• pp.965-967
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• 2009

정보화 사회를 거쳐 유비쿼터스 환경으로 나아가고 있는 정보기술은 이제 우리의 생활 곳곳에서 그 대체를 생각할 수 없을 정도로 중추적인 역할을 하고 있으나 역설적으로 그에 따른 역기능이 큰 사회문제로 대두되고 있는 실정이다. 따라서 현재의 정보기술에 바탕을 둔 정보윤리교육은 최근의 사회문제를 해결하기 위한 필요성뿐 만 아니라 미래의 건전한 유비쿼터스 세상의 구현을 위해서도 그 필요성이 강조되고 있다. 본 논문에서는 정보기술의 건전화와 올바른 이용 문화의 확산을 위해, 역기능 중심의 윤리적인 관점보다는 정보기술의 순기능을 중심으로 하여 역기능에 대해 분석 및 고찰하고, 순기능의 강화방안과 그에 따른 정보기술과 문화에 대해 다루는 새로운 접근 방법으로 문제기반학습을 적용한 정보윤리 교과과정을 제안한다.

• The Journal of Korea Robotics Society
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• v.15 no.4
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• pp.398-403
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• 2020

Robot's throwing control is difficult to accurately calculate because of air resistance and rotational inertia, etc. This complexity can be solved by using machine learning. Reinforcement learning using reward function puts limit on adapting to new environment for robots. Therefore, this paper applied deep reinforcement learning using neural network without reward function. Throwing is evaluated as a success or failure. AI network learns by taking the target position and control policy as input and yielding the evaluation as output. Then, the task is carried out by predicting the success probability according to the target location and control policy and searching the policy with the highest probability. Repeating this task can result in performance improvements as data accumulates. And this model can even predict tasks that were not previously attempted which means it is an universally applicable learning model for any new environment. According to the data results from 520 experiments, this learning model guarantees 75% success rate.

• Annual Conference on Human and Language Technology
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• 2018.10a
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• pp.147-150
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• 2018

목적 지향 대화 시스템은 자연어 이해, 대화 관리자, 자연어 생성과 같은 세분화 모델들의 결합으로 이루어져있어 하위 모델에 대한 오류 전파에 취약하다. 이러한 문제점을 해결하기 위해 자연어 이해 모델과 대화 관리자를 하나의 네트워크로 구성하고 오류에 강건한 심층 Q 네트워크를 제안한다. 본 논문에서는 대화의 전체 흐름을 파악 할 수 있는 순환 신경망인 LSTM에 심층 Q 네트워크 적용한 심층 순환 Q 네트워크 기반 목적 지향 대화 시스템을 제안한다. 실험 결과, 제안한 심층 순환 Q 네트워크는 LSTM, 심층 Q 네트워크보다 각각 정밀도 1.0%p, 6.7%p 높은 성능을 보였다.

• Proceedings of the Korean Society of Computer Information Conference
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• 2015.01a
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• pp.119-122
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• 2015

이러닝은 인터넷과 시스템적 특성을 이용하여 사용자들의 정보나 지식획득에 기여하고 있다. 특히 많은 기업들이 구성원들의 역량 강화를 위해 이러닝을 도입하고 있어, 사용자 중심의 설계가 더욱 중요시되고 있다. 이에 본 연구에서는 사용자들이 이러닝에서 학습을 수용하고 채택하는 과정이 시스템의 실제 사용 정도와 관련이 있는 것으로 보고, 기술의 전유(appropriation)로 사용자의 태도와 행동을 예측해보고자 한다. 그리고 기업의 이러닝 도입 활성화를 위해서 요구되는 방안에 대하여 기술사용 관점에서 논의하고자 한다.

• Proceedings of the Korean Society for Quality Management Conference
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• 2010.04a
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• pp.411-417
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• 2010

캠퍼스 중심의 정규 대학 연구가 급격히 감소될 것으로 예상되면서 대학도 매우 치열한 경쟁 체계를 벗어 날 수 없게 되었다. 이에 다양한 매체를 적용하는 학습방법과 학생의 개념을 다양화하려는 대학들의 노력이 강화됨에 따라 대학 서비스의 핵심인 교육서비스와 행정서비스에 대한 품질 향상은 매우 중요한 과제가 되었다. 본 연구는 대학 서비스의 핵심 중 하나인 행정 서비스를 중심으로 서비스 제공자가 인식한 서비스 품질과 서비스 이용자가 인식한 서비스 품질의 수준이 직무 만족도에 어떠한 영향을 미치는 가를 분석하였다 또한 서비스 제공자가 인식하는 서비스 제공 품질과 서비스 이용자가 인식한 서비스 사용 품질에 관한 두 인식의 차이를 고찰하였다.

• The Korean Journal of Emergency Medical Services
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• v.10 no.2
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• pp.25-34
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• 2006

Purpose: The purpose was to test the effects of cardiopulmonary resuscitation competency program on knowledge of CPR, self-efficacy, and the skills of CPR of nursing college students. Method: A convenience sampling of non-equivalent control group was used. Nursing college students were assigned either to conventional CPR education(N=28) or to intervention that involved a conventional CPR education and CPR competency program(N=28). CPR competency program was focused to enhance the self-directed learning on CPR training. Result: There were no significant differences on the CPR competency of knowledge and self-efficacy between experimental and control groups. However in the skills competency of CPR, there were significant differences between two groups. Conclusion: It is promising that CPR competency program can be effective to increase the skills of CPR of nursing college students.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 2006.05a
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• pp.1235-1239
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• 2006

Fractal manufacturing system (FrMS) distinguishes itself from other manufacturing systems by the fact that there is a fractal repeated at every scale. A fractal is a volatile organization which consists of goal-oriented agents referred to as AIR-units (autonomous and intelligent resource units). AIR-units unrestrictedly reconfigure fractals in accordance with their own goals. Their goals can be dynamically changed along with the environmental status. Since goals of AIR-units are represented as fuzzy models, an AIR-unit itself is a fuzzy logic controller. This paper presents a goal regulation mechanism in the FrMS. In particular, a reinforcement learning method is adopted as a regulating mechanism of the fuzzy goal model, which uses only weak reinforcement signal. Goal regulation is achieved by building a feedforward neural network to estimate compatibility level of current goals, which can then adaptively improve compatibility by using the gradient descent method. Goal-oriented features of AIR-units are also presented.

• IEMEK Journal of Embedded Systems and Applications
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• v.15 no.5
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• pp.261-268
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• 2020

Genetic algorithms find the optimal solution by mimicking the evolution of natural organisms. In this study, the genetic algorithm was used to enable Pac-Man's reinforcement learning, and a simulator to observe the evolutionary process was implemented. The purpose of this paper is to reinforce the learning of the Pacman AI of the simulator, and utilize genetic algorithm and artificial neural network as the method. In particular, by building a low-power artificial neural network and applying it to a genetic algorithm, it was intended to increase the possibility of implementation in a low-power embedded system.

• Journal of Institute of Control, Robotics and Systems
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• v.12 no.7
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• pp.666-670
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• 2006

This paper presents the novel feature selection method for Emotion Recognition, which may include a lot of original features. Specially, the emotion recognition in this paper treated speech signal with emotion. The feature selection has some benefits on the pattern recognition performance and 'the curse of dimension'. Thus, We implemented a simulator called 'IFS' and those result was applied to a emotion recognition system(ERS), which was also implemented for this research. Our novel feature selection method was basically affected by Reinforcement Learning and since it needs responses from human user, it is called 'Interactive feature Selection'. From performing the IFS, we could get 3 best features and applied to ERS. Comparing those results with randomly selected feature set, The 3 best features were better than the randomly selected feature set.