• 한국정보처리학회논문지
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• 제6권5호
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• pp.1303-1311
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• 1999

In order to learn in dynamic environments, reinforcement learning algorithms like Q-learning, TD(0)-learning, TD(λ)-learning have been proposed. however, most of them have a drawback of very slow learning because the reinforcement value is given when they reach their goal state. In this thesis, we have proposed a reinforcement learning method that can approximate fast to the goal state in maze environments. The proposed reinforcement learning method is separated into global learning and local learning, and then it executes learning. Global learning is a learning that uses the replacing eligibility trace method to search the goal state. In local learning, it propagates the goal state value that has been searched through global learning to neighboring sates, and then searches goal state in neighboring states. we can show through experiments that the reinforcement learning method proposed in this thesis can find out an optimal solution faster than other reinforcement learning methods like Q-learning, TD(o)learning and TD(λ)-learning.

• 한국멀티미디어학회논문지
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• 제20권11호
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• pp.1793-1802
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• 2017

In the recent years, IT and Network Technology has rapidly advanced environment in accordance with the needs of the times, the usage of the smart learning service is increasing. Smart learning is extended from e-learning which is limited concept of space and place. This system can be easily exposed to the various security threats due to characteristic of wireless service system. Therefore, this paper proposes the improvement methods of smart learning system security by use of faults analysis methods such as the FTA(Fault Tree Analysis) and FMECA(Failure Mode Effects and Criticality Analysis) utilizing the consolidated analysis method which maximized advantage and minimized disadvantage of each technique.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2000년도 제15차 학술회의논문집
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• pp.489-489
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• 2000

Recently, Reinforcement Learning(RL) methods have been used far teaming problems in Partially Observable Markov Decision Process(POMDP) environments. Conventional RL-methods, however, have limited applicability to POMDP To overcome the partial observability, several algorithms were proposed [5], [7]. The aim of this paper is to extend our previous algorithm for POMDP, called Labeling Q-learning(LQ-learning), which reinforces incomplete information of perception with labeling. Namely, in the LQ-learning, the agent percepts the current states by pair of observation and its label, and the agent can distinguish states, which look as same, more exactly. Labeling is carried out by a hash-like function, which we call Labeling Function(LF). Numerous labeling functions can be considered, but in this paper, we will introduce several labeling functions based on only 2 or 3 immediate past sequential observations. We introduce the basic idea of LQ-learning briefly, apply it to maze problems, simple POMDP environments, and show its availability with empirical results, look better than conventional RL algorithms.

• 한국수학교육학회지시리즈A:수학교육
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• 제43권4호
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• pp.405-417
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• 2004

This study is on the use of mathematics program for School Mathematics Education. According to the ‘technology principle’ by NCTM and teaching-learning methods by the 7th curriculum, we developed mathematics learning activities with mathematics program. This activity is to construct designs with graphs by using mathematics program(GrafEq.). In this study, we practiced these learning activities with pre-service mathematics teachers. The mathematics educational effects of these learning activities in this study are analyzed as follows; active & spontaneous search for mathematical knowledge, the experience of problem solving, affirmative view-point of mathematics, understanding of practical use of mathematics, acquisition an interest & motivation of learning mathematics etc. When students learn graphs of function, the concept of inequality in secondary school mathematics class., mathematics teachers can make a good use of constructing designs by mathematics program(GrafEq.). This will help to practice of teaching-learning methods by the 7th curriculum.

• 한국컴퓨터정보학회논문지
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• 제22권1호
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• pp.153-159
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• 2017

In recent years there has been a rise in the use and interest of the flipped learning as a teaching and learning paradigm. The flipped learning model includes any use of Internet technology to enrich the learning in a classroom, so that a professor can spend more time interacting with students instead of lecturing. In the flipped model, students viewed video lectures online outside of class time. Students then performed two kinds of assignments, a teamwork assignment and an individual work assignment, through the class time. In this paper, we propose a flipped educational model for a college class. This experimental research compares class of college algorithm using the flipped classroom methods and the traditional lecture-homework structure and its effect on student achievement. The result data of mid-term exam and final exam were analyzed and compared with previous year data. The findings of this research show that there was not a significant difference in the scores of student between two lecturing methods. The survey result and lecture evaluation by students show that students are in favor of the flipped learning.

• 한국수학교육학회지시리즈A:수학교육
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• 제42권4호
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• pp.453-463
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• 2003

The main purpose of this paper is to develope elementary mathematics teaching-learning programs for pre-service elementary teachers. The elementary mathematics education program developed in this work is divided into two parts: One is the theory, the other is the practice. The theory deals with the foundations of mathematics, the objectives of mathematics education, the history of mathematics education in Korea, the psychology of mathematics learning, the theories of mathematics teaching and learning, and the methods of assessment. With respect to the practice, this study examines the background knowledge and activities of numbers and their operation, geometry, measurement, statistics and probability, pattern and function.

• 한국의사학회지
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• 제28권2호
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• pp.15-22
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• 2015

Objectives : The each college of Korean medicine in Korea adopts diverse textbooks for the medical history class, resulting in educational contents variations. This proposal aimed for the standardization of educational contents. Methods : The transition of medical history curriculum will be attempted based on the understanding of paradigm change in modern education. The first step is investigation on the course credit and curriculum grade of medical history class presented in education status reports of all Korean medicine schools. The next step is study on the various methods about changes of medical history education base on the learning objectives of colleges of Korean medicine. Results : The researchers of medical history should make an agreement on modification of learning objectives of the curriculum, and then educational standardization must be achieved by publishing a medical history textbook in accordance with the modified learning objectives. Conclusions : The researchers of medical history must collaborate to standardize medical history education by developing and applying internet-based flipped learning model.

• 비교문화연구
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• 제29권
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• pp.413-452
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• 2012

The purpose of this study is to examine and apply Blended Learning to Chinese reading class and give some suggestions for Chinese reading class for realizing the interactive model for reading. For learner's improvement in Chinese reading level, various teaching methods need to be applied to Chinese reading class. Among teaching methods, this article tried to apply Blened Learning in terms of interaction, because Blended Learning can follow the general trend that all of people use laptop, smartphone, etc., and also can be contribution to reading as performance in foreign language learning. As a result, Blended Learning can make learner prepare class for giving online contents, and can make teacher and learner have more chances of interaction in class for improving reading competence.

• 전자통신동향분석
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• 제36권5호
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• pp.82-91
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• 2021

This study reviews application of data-driven anomaly detection techniques to the aviation domain. Recent advances in deep learning have inspired significant anomaly detection research, and numerous methods have been proposed. However, some of these advances have not yet been explored in aviation systems. After briefly introducing aviation safety issues, data-driven anomaly detection models are introduced. Along with traditional statistical and well-established machine learning models, the state-of-the-art deep learning models for anomaly detection are reviewed. In particular, the pros and cons of hybrid techniques that incorporate an existing model and a deep model are reviewed. The characteristics and applications of deep learning models are described, and the possibility of applying deep learning methods in the aviation field is discussed.

• 반도체디스플레이기술학회지
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• 제21권4호
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• pp.156-162
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• 2022

Image compression plays an important role in encoding and improving various forms of images in the digital era. Recent researches have focused on the principle of deep learning as one of the most exciting machine learning methods to show that it is good scheme to analyze, classify and compress images. Various neural networks are able to adapt for image compressions, such as deep neural networks, artificial neural networks, recurrent neural networks and convolution neural networks. In this review paper, we discussed how to apply the rule of deep learning to obtain better image compression with high accuracy, low loss-ness and high visibility of the image. For those results in performance, deep learning methods are required on justified manner with distinct analysis.