• Journal of Science Education
• /
• v.36 no.2
• /
• pp.341-353
• /
• 2012

The purpose of this study is to explore how science teachers' views about physics and biology are different. From a survey of 45 science teachers who majored in physics, this study found that their views about physics were closer to the experts' views than their views about biology. But it seemed that their views about neither physics nor biology were similar to the experts' views. Compared their views about physics with their views about biology in six dimensions, in four dimensions including structure dimension, methodology dimension, validity dimension, and reflective thinking dimension, the scores of the views about physics were higher, in learnability dimension, the scores of the views about biology were higher, and in personal relevance dimension, the scores of the two kinds of views were similar. Specially the their views about physics in learnability dimension were closer to novices'. In addition, science teachers majored in physics seemed to think that compared with biology, physics is coherent, systematic and reasoning, but it is not learnable.

• Proceedings of the Korea Association of Information Systems Conference
• /
• 1997.10a
• /
• pp.75-82
• /
• 1997

본 논문에서는 가상 현실 (VR) 기술을 적용하여 3차원의 가상 공간에서 일체의 실 험 도구 없이 중학교 전과정의 물리 실험을 할 수 있는 시스템의 개발에 대해 기술한다. 이 시스템의 목표는 가정과 학교에서 피교육자가 3차원 그래픽과 실시간 동작 및 상호 작용을 통해 실제와 유사한 실험을 함으로써 그 교육 및 학습 효과를 증대시키는데 있다. 이 시스 템에서 다루는 내용은 중학교 물상 교과목 전반에 걸쳐 엄선한 실험들로 단순히 보여주거나 들려주는 단방향적인 교육이 아닌 피교육자가 직접 참여할 수 있는 실험이 되도록 하였다. 이렇게 함으로써 피교육자의 학습 동기를 유발하고 학습 효과를 최대화 할 수 있다.

• Geophysics and Geophysical Exploration
• /
• v.23 no.2
• /
• pp.72-88
• /
• 2020

Recent rapid advances in computer hardware performance have led to relatively low computational costs, increasing the number of applications of machine-learning techniques to geophysical problems. In particular, deep-learning techniques are gaining in popularity as the number of cases successfully solving complex and nonlinear problems has gradually increased. In this paper, applications of seismic data denoising methods using deep-learning techniques are introduced and investigated. Depending on the type of attenuated noise, these studies are grouped into denoising applications of coherent noise, random noise, and the combination of these two types of noise. Then, we investigate the deep-learning techniques used to remove the corresponding noise. Unlike conventional methods used to attenuate seismic noise, deep neural networks, a typical deep-learning technique, learn the characteristics of the noise independently and then automatically optimize the parameters. Therefore, such methods are less sensitive to generalized problems than conventional methods and can reduce labor costs. Several studies have also demonstrated that deep-learning techniques perform well in terms of computational cost and denoising performance. Based on the results of the applications covered in this paper, the pros and cons of the deep-learning techniques used to remove seismic noise are analyzed and discussed.

• Geophysics and Geophysical Exploration
• /
• v.26 no.4
• /
• pp.199-210
• /
• 2023

The physical properties of rocks in reservoirs change after CO₂ injection, we modeled a reservoir with a transition zone within which the physical properties change linearly. The function of the Wolf reflection coefficient consists of the velocity ratio of the upper and lower layers, the frequency, and the thickness of the transition zone. This function can be used to estimate the thickness of a reservoir or seafloor transition zone. In this study, we propose a method for predicting the thickness of the transition zone using deep learning. To apply deep learning, we modeled the thickness-dependent Wolf reflection coefficient on an artificial transition zone formation model consisting of sandstone reservoir and shale cap rock and generated time-frequency spectral images using the continuous wavelet transform. Although thickness estimation performed by comparing spectral images according to different thicknesses and a spectral image from a trace of the seismic stack did not always provide accurate thicknesses, it can be applied to field data by obtaining training data in various environments and thus improving its accuracy.

• Proceedings of the Korea Water Resources Association Conference
• /
• 2023.05a
• /
• pp.97-97
• /
• 2023

최근 재해분야에서 인공신경망(ANN), 기계학습(ML), 딥러닝(DL) 등 AI 기술이 활용성이 점차 증가하고 있으며, 센싱정보와 연계한 시설물 안전관리, 원격탐사와 연계한 재해감시(녹조, 산사태, 산불 등), 수문시계열(수위, 유량 등) 예측, 레이더·위성강수 자료의 보정과 예측, 상하수도 관망누수예측 등 다양한 분야에서 AI 기술이 적용되고 그 활용성이 검증된 바 있다. 본 연구에서는 ML, DL, 물리기반신경망(Pysics-informed Neural Networks, PINNs)을 이용한 다양한 재해분석 사례를 소개하고, 그 활용성과 한계에 대해서 논의하고자 한다. 주요사례로는 (1) SAR영상과 기계학습을 이용한 재해피해지역(울진 산불) 감지, (2) 국가 디지털 정보를 이용한 산사태 위험지역 판별(인제 산사태) (3) 기계학습 및 딥러닝 기법을 이용한 위성강수 자료의 보정·예측 및 유출해석, (4) 수리해석을 위한 수치해석분야에서의 PINNs의 적용성(1차원 Saint-Venant 식 해석) 평가 연구결과를 공유한다. 특히, 자료의 입·출력 자료만으로 학습된 인공신경망 모형 대신 지배방정식(물리방정식)을 만족하도록 강제한 PINNs의 경우, 인공신경망 모형보다 우수한 모의능력을 보여주었으며, 향후 복잡한 수리모델링 등 수치해석분야에서 그 활용가능성이 매우 높을 것으로 판단된다.

• Journal of The Korean Association For Science Education
• /
• v.24 no.6
• /
• pp.1206-1215
• /
• 2004

In this study, we explored the students' perceptions on the online discussion system for physics investigation as the physics education program. With these, we explored the characteristics of online discussion system. For these, the questions and interviews were executed in order to get informations about user-friendly characteristics of on-line discussion learning system of physics investigation, asynchronicity of on-line investigation discussion, on-line investigation discussion related to writing, visual cues and physical presence of on-line investigation discussion and preference of on-line investigation discussion. The students represented that there were two advantages in the online investigation discussion. One is that they could participate in the on-line investigation discussion without the restriction of time and space, and the other is that they could enter into a dispute with sufficient consideration because of the asynchronicity characteristic of online investigation discussion. Although the online educational activity is mainly achieved by independent work on the part of students, the role of teacher and parents is more important than the technical part of online educational system for the active participation.

• Korean Educational Research Journal
• /
• v.38 no.2
• /
• pp.109-125
• /
• 2017

The purpose of this study was to examine how group argumentations mediated individual arguments by analyzing physics teachers' group argumentation and individual follow-up written arguments. Five in-service physics teachers participated in this study, two middle school and three high school teachers. The topics of argumentation included physics topics and pedagogy of them. Findings showed that the teachers constructed much more elaborated individual written arguments than group argumentation, which seemed to be resulted from different perceptions of teachers' verbal and written argumentations. Also, in their written arguments the teachers selectively utilized their colleagues' ideas shared during group argumentation. Lastly, teachers' argumentation showed different features between topics of physics and physics pedagogy. These differences were related to their orientations toward argumentation about content knowledge and teaching. These findings shed light on a productive physics teacher professional development in argumentation.

• Journal of the Korea Computer Graphics Society
• /
• v.27 no.1
• /
• pp.9-17
• /
• 2021

In this paper, we present a simple and fast supervised learning framework based on model predictive control so as to learn motion controllers for a physic-based character to track given example motions. The proposed framework is composed of two components: training data generation and offline learning. Given an example motion, the former component stochastically controls the character motion with an optimal controller while repeatedly updating the controller for tracking the example motion through model predictive control over a time window from the current state of the character to a near future state. The repeated update of the optimal controller and the stochastic control make it possible to effectively explore various states that the character may have while mimicking the example motion and collect useful training data for supervised learning. Once all the training data is generated, the latter component normalizes the data to remove the disparity for magnitude and units inherent in the data and trains an artificial neural network with a simple architecture for a controller. The experimental results for walking and running motions demonstrate how effectively and fast the proposed framework produces physics-based motion controllers.

• Proceedings of the Korea Contents Association Conference
• /
• 2014.11a
• /
• pp.421-422
• /
• 2014

최근 네트웤의 발달로 웹기반 강의시스템과 문제풀이 학습법이 많이 도입되고 있다. 이 학습법은 물리적 폐쇄성을 극복하고 학습자의 편이성을 제공하고, 학습자와 교수자간 상호작용성을 높일 수 있으므로 학습효과를 최대로 고양시킬 수 있다. 현재 웹 기반용 학습시스템들은 많이 개발되어 있으나 온라인에서 학습할 수 있는 실습교과목에 대한 교수-학습 모형 개발은 미흡한 편이다. 본 논문에서는 사례로서 실습교과목을 위한 실습문제풀이학습시스템을 위한 교수설계모형을 제안한다.

• Journal of the Korean Recycled Construction Resources Institute
• /
• v.9 no.3
• /
• pp.348-355
• /
• 2021

Asphalt, widely used for road pavement, has different required physical properties depending on the environment to which the road is exposed. Therefore, it is essential to maximize the life of asphalt roads by evaluating the physical properties of asphalt according to additives and selecting an appropriate formulation considering road traffic and climatic environment. Dynamic shear rheometer(DSR) test is mainly used to measure resistance to rutting among various physical properties of asphalt. However, the DSR test has limitations in that the results are different depending on the experimental setting and can only be measured within a specific temperature range. Therefore, in this study, to overcome the limitations of the DSR test, the rheological characteristics were predicted by learning the images collected from atomic force microscopy. Images and rheology properties were trained through EfficientNet, one of the deep learning architectures, and transfer learning was used to overcome the limitation of the deep learning model, which require many data. The trained model predicted the rheological properties of the asphalt binder with high accuracy even though different types of additives were used. In particular, it was possible to train faster than when transfer learning was not used.