• Communications of Mathematical Education
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• v.15
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• pp.181-187
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• 2003

새로이 열리고 있는 정보화시대에 부응하기 위해 교육계에서는 컴퓨터를 교육매체를 활용한 교수 ${\cdot}$ 학습방법에 대한 연구를 활발히 전개해 나가고 있다. 최근에는 인터넷을 활용하여 좀더 효과적인 교수 ${\cdot}$ 학습 방법의 개선에 대해 연구하는 움직임들이 활발히 진행되고 있다. 이에 중학교 수학과를 중심으로 하여 인터넷 상의 학습자료들을 검색하고 이를 먼저 홈페이지별로 분류하여 그에 따른 내용들을 분석하고, 반대로 항목별로 홈페이지를 중학교 교과서 내적인 내용과 외적인 내용들로 분류하고 그에 대한 분석을 하여 봄으로써 현재 인터넷 상의 학습자료들을 알아보아 향후 인터넷을 활용하여 중학교 수학의 교수 ${\cdot}$ 학습 방법을 개선하는 연구 및 인터넷 상에 새로운 자료를 올리는 사람들에게 도움이 되도록 하고자 한다. 또한 간단하나마 인터넷을 활용하는 수업에 대해서도 기술하여 인터넷 상의 학습자료를 활용하는 방안을 모색하여 보고자 한다.

• Journal of Intelligence and Information Systems
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• v.4 no.2
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• pp.1-21
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• 1998

귀납적 학습방법들의 분류성능을 비교 평가하기 위하여 대표적 분류문제의 하나인 신용평가 문제를 사용하였다. 분류기로서 사용된 귀납적 학습방법론들은 통계학의 다변량 판별분석(MDA), 기계학습 분야의 C4.5, 신경망의 다계층 퍼셉트론(MLP) 및 Cascade Correlation Network(CCN)의 4 가지이며, 학습자료로는 국내 3개 신용평가기관이 발표한 신용등급 및 공포된 재무제표를 사용하였다. 신용등급 예측의 정확도에 의한 분류성능을 평가하였는데 연도별 평가와 시계열 평가의 두 가지를 실시하였다. Cascade Correlation Network이 가장 좋은 분류성능을 보였지만 4가지 분류기들 사이에 통계적으로 유의한 차이는 발견되지 않았다. 이는 사용된 학습자료가 갖는 한계로 인한 것으로 추정되지만, 성능평가 과정에 있어 학습자료의 전처리 과정이 분류성과의 제고에 매우 유효함이 입증되었다.

• Journal of KIISE:Software and Applications
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• v.37 no.7
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• pp.491-502
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• 2010

When human beings acquire and develop knowledge through education, their prior knowledge influences the next learning process. As this is a fact that should be considered in machine learning, we need to examine the effects of controlling the order of training sequence on machine learning. In this research, the role of the supervisor is extended to control the order of training samples, in addition to just instructing the target values for classification problems. The supervisor sequences the training examples categorized by SOM to the learning model which in this case is MLP. The proposed method is distinguished in that it selects the most instructive example from categories formed by SOM to assist the learning progress, while others use SOM only as a preprocessing method for training samples. The result shows that the method is effective in terms of the number of samples used and time taken in training.

• Geophysics and Geophysical Exploration
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• v.25 no.4
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• pp.177-188
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• 2022

Recently, the application of distributed acoustic sensors (DAS), which can replace geophones and seismometers, has significantly increased along with interest in micro-seismic monitoring technique, which is one of the CO₂ storage monitoring techniques. A significant amount of temporally and spatially continuous data is recorded in a DAS monitoring system, thereby necessitating fast and accurate data processing techniques. Because event detection and seismic phase picking are the most basic data processing techniques, they should be performed on all data. In this study, a machine learning-based P, S wave phase picking algorithm was developed to compensate for the limitations of conventional phase picking algorithms, and it was modified using a transfer learning technique for the application of DAS data consisting of a single component with a low signal-to-noise ratio. Our model was constructed by modifying the convolution-based EQTransformer, which performs well in phase picking, to the ResUNet structure. Not only the global earthquake dataset, STEAD but also the augmented dataset was used as training datasets to enhance the prediction performance on the unseen characteristics of the target dataset. The performance of the developed algorithm was verified using K-net and KiK-net data with characteristics different from the training data. Additionally, after modifying the trained model to suit DAS data using the transfer learning technique, the performance was verified by applying it to the DAS field data measured in the Pohang Janggi basin.

• Proceedings of the Korea Water Resources Association Conference
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• 2019.05a
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• pp.140-140
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• 2019

본 연구에서는 Terra MODIS 위성자료와 Tensorflow를 활용해 1 km 공간 해상도의 토양수분을 산정하는 알고리즘을 개발하고, 국내 관측 자료를 활용해 검증하고자 한다. 토양수분 모의를 위한 입력 자료는 Terra MODIS NDVI(Normalized Difference Vegetation Index)와 LST(Land Surface Temperature), GPM(Global Precipitation Measurement) 강우 자료를 구축하고, 농촌진흥청에서 제공하는 1:25,000 정밀토양도를 기반으로 모의하였다. 여기서, LST와 GPM의 자료는 기상청의 종관기상관측지점의 LST, 강우 자료와 조건부합성(Conditional Merging, CM) 기법을 적용해 결측치를 보간하였고, 모든 위성 자료의 공간해상도를 1 km로 resampling하여 활용하였다. 토양수분 산정 기술은 인공 신경망(Artificial Neural Network) 모형의 딥 러닝(Deep Learning)을 적용, 기계 학습기반의 패턴학습을 사용하였다. 패턴학습에는 Python 라이브러리인 TensorFlow를 사용하였고 학습 자료로는 농촌진흥청 농업기상정보서비스에서 101개 지점의 토양수분 자료(2014 ~ 2016년)를 활용하고, 모의 결과는 2017 ~ 2018년까지의 자료로 검증하고자 한다.

• The Journal of Korean Association of Computer Education
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• v.11 no.1
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• pp.75-83
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• 2008

The prior data structure coursewares have been limited to using a simple screen structure with text, pictures or plain animation so that they have been failed to promote favorable interaction between learners and instructors, and unnecessarily charges the screen. In order to overcome these problems, this paper provides an applet-based simulation environment which enables learner to operate and control the data structure operation with their own data, therefore, the learners can actively and positively participate in a study with this courseware. The instructors can easily deliver the education contents to the learners by using web simulation suitable for IT education media. Also, the courseware can offer a class feedback and required data for students estimation by recording a log.

• Proceedings of the Korea Database Society Conference
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• 2000.11a
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• pp.350-357
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• 2000

본 논문에서는 자기 주도적 학습 환경 구현에 매우 적합한 특성을 지니고 있는 웹을 통해 보다 효과적인 수학학습 시스템을 설계할 수 있는 방안을 제시한다. 기존의 Web에서 제공되는 수학학습을 살펴보면 텍스트 위주의 설명, 단순한 문제를 제공하고 정, 오답의 결과만을 보여 주는 형태가 많았다. 그러나 본 논문에서는 학생들의 흥미와 학습동기를 유발시키기 위해 동영상, 음성, 애니메이션 등의 멀티미디어 자료를 이용하여 학습내용을 전개하도록 구성하였다. 다양한 멀티미디어 자료의 제공으로 학습자의 학습동기와 흥미를 유발시키고 자기 주도적 학습을 가능하게 하여 학습 성취감을 증가시킬 수 있다.

• Geophysics and Geophysical Exploration
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• v.25 no.2
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• pp.71-84
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• 2022

When acquiring seismic data, various types of simultaneously recorded seismic noise hinder accurate interpretation. Therefore, it is essential to attenuate this noise during the processing of seismic data and research on seismic noise attenuation. For this purpose, machine learning is extensively used. This study attempts to attenuate noise in prestack seismic data using unsupervised machine learning. Three unsupervised machine learning models, N2NUNET, PATCHUNET, and DDUL, are trained and applied to synthetic and field prestack seismic data to attenuate the noise and leave clean seismic data. The results are qualitatively and quantitatively analyzed and demonstrated that all three unsupervised learning models succeeded in removing seismic noise from both synthetic and field data. Of the three, the N2NUNET model performed the worst, and the PATCHUNET and DDUL models produced almost identical results, although the DDUL model performed slightly better.

• Journal of Educational Research in Mathematics
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• v.23 no.2
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• pp.117-133
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• 2013

Mathematical misconception is one of the big obstacles of the underachieving students to learn mathematics correctly. This study aims to develop the instruction materials for secondary school students who are underachieving in mathematics to reduce the occurrence of the misconception and to help them to build the correct concept in the mathematical learning. Before developing the material, we tried to collect the misconception cases occurring in common mathematics lesson. This materials tries to provide key educational contents for mathematics teachers who is responsible for teaching underachieving student and help them to creative interesting ideas for lessons. The materials could be used not only as an teaching materials for underachieving students or students with the misconceptions, but also could be used as training materials for mathematics teachers.

• Journal of Science Education
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• v.39 no.2
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• pp.151-164
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• 2015

This study's purpose is to investigate elementary school teachers' perception on infographics learning materials. For this, this study made a questionnaire about infographics cognition or not, the need for development of infographics learning materials, science class applied infographics. And then, this study conducted a survey of 300 elementary school teachers. Through this process, this study had the results that most elementary school teachers had not some experience infographics and had never applied infographics during the class. On the other hand, elementary school teachers who had some experience said that they had used infographics in society subject and science subject. And they said that infographics was used in the development stage of class. In the need for development of infographics learning materials, this study had the results that elementary school teachers recognized the quantities of infographics which could apply in learning materials of elementary school inadequate. And elementary school teachers said that they needed infographics learning materials which could actually apply in class. Also, elementary school teachers said that the subject which could be most applied infographics learning materials was society subject and science subject. And they said that infographics learning materials was a good method which can be applied to third grade and fourth grade in elementary school. In science class applied infographics learning materials, elementary school teachers said that the best class stage to presenting infographics learning materials was full-scale lesson of each chapter in curriculum and they recognized that infographics learning materials must be used for understanding scientific concepts. Add to this, elementary school teachers recognized that the development of learning materials with the application of infographics learning materials must take precedence in order for education applied infographics learning materials to carry out successfully.