• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.48 no.3
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• pp.175-185
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• 2020

The six-DOF aerodynamic coefficients of a missile entail inherent physical gradient constraints originated from the geometric characteristics of a cylindrical fuselage. To effectively adopt the freely available gradient information in aerodynamic coefficients modeling, this research employed gradient-enhanced (GE) Gaussian process. To investigate the accuracy of aerodynamic coefficients predicted with gradients information, we compared two Gaussian-process-based models: ordinary and GE Gaussian process models with and without gradient information, respectively. As a result, we found that GE Gaussian process models were able to comply with imposed gradient information and more accurate than ordinary Gaussian process models. However, we also found that GE Gaussian process modeling cannot handle gradient information continuously and ends up with more samples due to additional gradient information.

• The KIPS Transactions:PartB
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• v.8B no.5
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• pp.429-440
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• 2001

To achieve a computer tutor framework with high learning effects as well as practicality, the goal of this research has been set to developing an intelligent tutor for problem-solving in mathematics domain. The maine feature of the CyberTutor, a computer tutor developed in this research, is the facilitation of a learning environment interacting in accordance with the learners differing inferential capabilities and needs. The pedagogical information, the driving force of such an interactive learning, comprises of tutoring strategies used commonly in various domains such as phvsics and mathematics, in which the main contents of learning is the comprehension and the application of principles. These tutoring strategies are those of testing learners hypotheses test, providing hints, and generating explanations. We illustrate the feasibility and the behavior of our propose framework with a sample problem-solving learning in geometry. The proposed tutorial framework is an advancement from previous works in several aspects. Firstly, it is more practical since it supports handing of a wide range of problem types, including not only proof types but also finding-unkown tpes. Secondly, it is aimed at facilitating a personal tutor environment by adapting to learners of varying capabilities. Finally, learning effects are maximized by its tutorial dialogues which are derived from real-time problem-solving inference instead of from built-in procedures.

• Proceedings of the Korean Vacuum Society Conference
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• 2010.02a
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• pp.262-262
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• 2010

반도체 제조공정 중 식각공정(Etching)은 웨이퍼표면으로부터 화학적, 물리적으로 불필요한 물질들을 선택적으로 제거하는 방법이다. 식각공정 중 하나인 플라즈마 식각(Plasma etching) 공정에서 오버식각(over-etching) 과언더식각(under-etching) 되는것을피하기위해서통계적인방법을기준으로식각종료점(endpoint)를 결정한다. 본 논문의 목표는 통계적인 분석방법을 이용하지 않고 실시간 식각 데이터(realtime etching data)를 사용해서 식각 종료점을 검출하는 것이다. 식각 데이터는 시계열 데이터(time-series data)이기 때문에 간단한 구조와 적은 계산량으로 빠른 수렴속도와 좋은 안정도를 가진 Radial Basis Function Neural Network's (RBF-NN) 를 이용하여 시계열 모델(time-series model)을 구현 하였다. 광학방사분광기(Optical Emission Spectroscopy: OES)로부터 나온 6개의 데이터 세트중에서 4개의 데이터 세트는 RBF-NN을 학습하는데 사용되고 2개의 데이터 세트는 모델의 성과를 시험해 보기 위하여 사용하였다. 학습을 위한 데이터들은 Matrix화 시켜서 목표값을 설정하여 학습시킨다. 실험한 결과 학습한 RBF-NN 모형이 식각 종료점(endpoint)를 정확하게 검출된다는 것을 보여준다.

• KNOM Review
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• v.21 no.2
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• pp.1-9
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• 2018

Network virtualization refers to a technology creating independent virtual network environment on a physical network. Network virtualization technology can share the physical network resources to reduce the cost of establishing the network for each user and enables the network administrator to dynamically change the network configuration according to the purpose. Although the network management can be handled dynamically, the management is manual, and it does not maximize the profit of network virtualization. In this paper, we propose Machine-Learning technology to allow the network to learn by itself and manage its management dynamically. The proposed approach is to dynamically allocate appropriate resources by predicting resource demand of VNF in service function chaining, which is a core and essential problem in virtual network management. Our goal is to predict the resource demand of the VNF and dynamically allocate the appropriate resources to reduce the cost of network operation while preventing service interruption.

• The Journal of Korean Association of Computer Education
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• v.17 no.5
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• pp.25-34
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• 2014

The object of this study is to improve obstacles related with physical affordance and cognitive affordance by adopting Norman's theory for affordance of mobile application of museum education. As to conduct the study, I have selected the mobile device application of G museum for the need of observation and interviewing, and have applied think aloud protocol to analyse matters of affordance. By this study, learners will be possible to conduct appropriate behavior instantly under the circumstance of unfamiliar museum mobile environment because of sophisticated design to make learners perceive and understand information provided with designed interface based on realistic improvement that will be made by inspecting possible problems of affordance in position of museum education mobile system users. Additionally learners will be able to control contents of museum and to experience interactive learning which motivate exploratory activity to accomplish meaningful learning.

• Journal of The Korean Association of Information Education
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• v.5 no.1
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• pp.17-29
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• 2001

As the result of the rapid development of communication technology makes the circumstance and method of education change, distance education is adapted to new fields of education so that students can be educated what they need in the time and space which they want instead of relying on the existing physical frameworks. In this paper Learner-Analysing System was designed and implemented in the distance education based on the web. For the easy access and the demand of customers, this Learner-Analysing System is composed of the Q&A-processing module which can produce proper results for questions and acquisitions of their own study materials through efficient search, and the student-analyzing module for reinforcement of feedback through correct analysis.

• Journal of The Korean Association For Science Education
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• v.35 no.1
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• pp.27-35
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• 2015

General education is changed by accepting the change in education environment to digital generation, emphasis about student-centered education, and change of teacher's role. E-learning has taken center stage as an effective learning environment but the problems are drawn for the absence of interaction that is important in learning. In this study, on the basis of questionnaire results about learning using website, we operated blended-learning where students come and go in cyberspace and physical space to set up the lesson environment for emphasizing interaction. We selected a control group (N=40) and an experimental group (N=40) from second grade students in a middle school for this research. General instructor-led lessons were implemented in the control group and blended-learning lessons to emphasize interaction between teacher and students were implemented in the experimental group. The experiments were applied to eight class-hours in 'characteristics of matter' unit. We implemented Test of Science Related Attitude (TOSRA) to the students before and after the lessons and administered questionnaire for checking attitude changes and perception in students. The results of the test show that the experimental group students were more encouraged and became more confident and curious about scientific learning than the control group students. The analysis of the interview and results of TOSRA show that blended-learning provided guidance and feedback by the teacher to the experimental group students more than the control group students. Blended-learning is suggested as a learning-method that is helpful in improving scientific attitude in students because it enables them to express their experiences without limit of time-space and promote interaction between teacher and students.

• 한국지구과학회:학술대회논문집
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• 2005.09a
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• pp.199-206
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• 2005

지구과학은 실생활과 직접적으로 관련되거나 학생들이 친숙하게 생각하는 많은 개념들을 포함하고 있으며 이런 특성은 과학캠프나 자연탐사 활동에서 지구과학과 관련한 탐구주제가 학생들의 큰 호응을 얻는 사실을 설명할 수 있다. 하지만 정규 수업에서 이루어지는 지구과학 실험 수업은 교과서에 제시된 자료해석 위주의 실험이 많아 학생들의 흥미를 반감시키는 경우가 많으며 자료 측정과정에서 과학자가 느낄 수 있는 중요하고 귀중한 경험을 놓칠수 있다. 그런데 인터넷에서는 해양과 대기, 천문, 지질 각 분야에서 지속적으로 갱신되는 실시간 관측 자료를 제공해 주는 곳이 많으며 이 실시간 자료들을 고등학교의 지구과학 실험에 이용할 수 있도록 학습 프로그램을 개발하면 기존의 자료해석 실험이 가지는 한계를 극복하고 학생들의 지구과학에 대한 호응도를 높일 수 있으며 보다 다양한 탐구 능력을 향상 시킬 수 있다. 이러한 학습 프로그램의 특징은 탐구 중심 혹은 문제 해결 학습을 강조하는 현재의 교육 목표를 잘 반영할 수 있다. 본 연구의 목적은 인터넷에서 제공되는 지구과학 관련 실시간 자료들을 활용할 수 있는 고등학교 지구과학 학습 프로그램을 개발하고 이를 지원할 수 있는 홈페이지를 구축한 후 실제로 수업에 적용하여 교육적 효과를 확인하는 것이다. 연구 결과 실시간 자료를 이용하는 고등학교 학생들을 위한 지구과학 학습 프로그램을 12개의 주제에 대해서 완성했다. 먼저 지구과학실험 시간에 적용할 수 있는 2차시 분량의 학습 프로그램 6주제를 개발했는데 개발 주제는 '지진이 일어나고 있는 곳은 어디인가?', '진앙으로 판의 경계를 결정하기', '진앙의 위치를 찾아라', '암영대를 찾아라', '태양의 자전 주기를 측정하기', '태양활동' 이다. 각 주제별로 수업소개, 학습목표, 과정, 학습자료(학생 활동지, 기타 자료, 수업 내용과 관련한 실시간 자료 제공 사이트 목록), 평가 자료를 만들었다. 학생들의 활동 내용은 <지진이 일어나고 있는 곳은 어디인가?>의 주제를 예로 들면 학생들이 실시간으로 전 세계의 지진활동을 모니터하는 사이트에 접속하여 최근에 지진이 어디에서 일어났는지 알아보고 지난 30일 동안에 일어났던 규모 4 이상의 지진 자료를 다운 받아 세계 지도에 점으로 표시한다. 이 때 학생들은 손으로 진앙의 위치를 지도에 그리는 대신 엑셀의 그래프 기능을 이용하여 지도에 진앙의 위치를 나타내게 한다. 또 개인 과제 연구에 적용할 수 있는 학습 프로그램을 5가지 개발하여 학생들이 특정한 주제에 대하여 탐구 과제를 수행할 수 있게 했는데 개발된 주제는 '태양 활동으로 오로라 예측하기', '국제 우주 정거장을 찾아내자', '한반도 부근의 해양 기상과 해수의 물리적 상호 관계', '일기도 분석을 통한 태풍의 진로 예측', '우리 나라 조석 예측' 이다. 마지막으로 사이버 학습을 통해서 수업 시간의 제약 없이 과제 형태로 처치 가능한 학습 프로그램 개발 했는데 그 주제는 '태풍이 저위도에서 북상하는 이유?'이다. 개발된 프로그램 중 2차시 학습 프로그램은 과학고 중학생 영재반에게, 개인 탐구 과제는 과학고 1학년 학생들에게, 사이버 수업 형태는 과학고 2학년 학생에게 적용시켰다. 실시간 자료 활용을 지원하는 홈페이지를 만들어 자료 제공 사이트에 대한 메타 자료를 데이터베이스화했으며 이를 통해 학생들이 원하는 실시간 자료를 검색하여 찾을 수 있고 홈페이지를 방분했을 때 이해하기 어려운 그래프나 각 홈페이지가 제공하는 자료들에 대한 처리 방법을 도움말로 제공받을 수 있게 했다. 실시간 자료들을 이용한 학습은 학생들의 학습 의욕과 탐구 능력을 향상시켰으며 컴퓨터 활용 능력과 외국어 자료 활용 능력을 향상 시키는데도 도움을 주었다.

• The KIPS Transactions:PartB
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• v.13B no.1 s.104
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• pp.15-20
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• 2006

This paper describes research on intelligent game character through performance enhancements of physics engine in computer games. The algorithm that recognizes the physics situation uses momentum back-propagation neural networks. Also, we present an experiment and its results, integration methods that display optimum performance based on the physics situation. In this experiment on integration methods, the Euler method was shown to produce the best results in terms of fps in a simulation environment with collision detection. Simulation with collision detection was shown similar fps for all three methods and the Runge-kutta method was shown the greatest accuracy. In the experiment on physics situation recognition, a physics situation recognition algorithm where the number of input layers (number of physical parameters) and output layers (destruction value for the master car) is fixed has shown the best performance when the number of hidden layers is 3 and the learning count number is 30,000. Since we tested with rigid bodies only, we are currently studying efficient physics situation recognition for soft body objects.

• Geophysics and Geophysical Exploration
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• v.25 no.1
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• pp.26-37
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• 2022

Recently, multiple methods using small aircraft for geophysical exploration have been suggested as a result of the development of information and communication technology. In this study, we introduce the hybrid unmanned aircraft electromagnetic system of the Korea Institute of Geosciences and Mineral resources, which is under development. Additionally, data processing and interpretation methods are suggested via the analysis of datasets obtained using the system under development to verify the system. Because the system uses a three-component receiver hanging from a drone, the effects of rotation on the obtained data are significant and were therefore corrected using a rotation matrix. During the survey, the heights of the source and the receiver and their offsets vary in real time and the measured data are contaminated with noise. The noise makes it difficult to interpret the data using the conventional method. Therefore, we developed a recurrent neural network (RNN) model to enable rapid predictions of the apparent resistivity using magnetic field data. Field data noise is included in the training datasets of the RNN model to improve its performance on noise-contaminated field data. Compared with the results of the electrical resistivity survey, the trained RNN model predicted similar apparent resistivities for the test field dataset.