• 공학교육연구
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• 제25권4호
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• pp.35-41
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• 2022

In order to enhance basic competencies for freshmen at engineering college, whose learning ability is gradually declining, a new course was developed to review basic mathematics and physics through a process of collecting opinions from fellow professors. Tests in six fields of math and physics with the same problems showed the correct answer rate rose from 24.8% at the beginning of the semester to 59.0% at the end of the semester after operating the course developed. According to the survey, the students' self-evaluated confidence on the basic competencies in 16 fields of math and physics showed a significant increase. Students with high confidence in basic competencies also received high actual grades. General high school graduates' confidence point in basic competencies improved from 54.7 at the beginning to 75.3 points at the end of the semester, while specialized high school graduates' enhanced from 38.3 to 64.0 which is higher than that of general high school graduates at the beginning of the semester.

• 지구물리와물리탐사
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• 제26권3호
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• pp.114-125
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• 2023

편미분방정식의 해를 구하기 위한 여러 수치해법들의 한계와 순수 데이터 기반 기계학습의 단점을 극복하기 위해 물리정보신경망(physics-informed neural network, PINN)이 제안되었다. 물리정보신경망은 편미분방정식을 손실함수 구성에 직접 활용하여 기계학습 훈련에 물리적 제약을 주는 기법으로 파동방정식 모델링에도 활용될 수 있다. 그러나 물리정보신경망을 이용하여 파동방정식을 풀기 위해서는 신경망 훈련 시 입력에 대한 2차 미분이 수행되어야 하고, 그 결과로 출력되는 파동장은 복잡한 역학적 현상들을 포함하고 있어 섬세한 전략이 필요하다. 이 해설 논문에서는 물리정보신경망의 기본 개념을 설명하고 파동방정식 모델링에 활용하기 위한 고려사항들에 대해 고찰하였다. 이러한 고려사항에는 공간좌표 정규화, 활성함수 선정, 물리손실 추가 전략이 포함된다. 훈련자료의 공간좌표를 정규화한 후 사용하면 파동방정식 모델링을 위한 신경망 훈련에서 초기 조건이 더 정확하게 반영되는 것을 수치 실험을 통해 보였다. 또한 신경망을 통한 파동장 예측에 가장 적절한 활성함수를 선정하기 위해 여러 함수들의 특성을 비교했다. 특성 비교는 각 활성함수들의 입력자료에 대한 미분과 수렴성을 중심으로 이루어졌다. 마지막으로 신경망 훈련 중 손실함수에 물리손실을 추가하는 두가지 시나리오의 결과를 비교하였다. 수치 실험을 통해 훈련 초기부터 물리손실을 활용하는 전략보다 초기 훈련단계 이후부터 물리손실을 적용하는 커리큘럼 기반 학습전략이 효과적이라는 결과를 도출했다. 추가로 이 결과를 물리손실을 전혀 사용하지 않은 훈련 결과와 비교하여 PINN기법의 효과를 확인하였다.

• 한국과학교육학회지
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• 제8권1호
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• pp.73-79
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• 1988

According to the previous studies, the science education departments in the college of education should develop better curricula to teach future secondary school teachers in a more professional way As one example of such curricula developments. one important topics of modem physics was integrated to teach the future high school physics teachers In the physics education departments. The title is "The Physics of Solar Constant Measurement The surrounding core physics for this measurements were pulled together with these important points in minds(1) clear goal of learning In the teachers college physics(2) Clear explanation of physics and visualization of important technologies for the high school students(3) these teachings should encourage for the students to use the knowledge and technologies learned through the class toward the industrial applications Korea will move toward one of the heavily industrialized countries in the world where the physics education can become key player to manufacture physics based products. Therefore developments of physics curricula which relates teachers college, high school, and industry will become more and more Important.

• 영재교육연구
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• 제22권3호
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• pp.729-755
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• 2012

우리나라의 소외된 영재를 이해하기 위한 노력의 일환으로 질적 사례연구 기법을 적용하여 가난하고 신체적 아픔이 있는 과학영재의 학습 특성과 전술에 대해 알아보았다. 이를 위해 연구 현장인 주말 물리교실을 중심으로 참여관찰을 했고, 참여자와 참여자의 어머니, 초등학교 5학년 때 담임교사와 면담을 진행하였다. 또한 현지문헌과 자기보고서를 추가로 활용하여 참여자를 종합적으로 이해하고자 했다. 그 결과, 참여자의 학습 특성은 내적동기와 열등감에서 기인하는 '능동적인 학습'과 배척된 관계 속에서 드러나는 '배움을 향한 몸부림'으로 정리할 수 있었다. 참여자의 학습 전술은 경제적 여력이 충분하지 않은 상태에서 배움에 대한 욕구를 충족하기 위한 전략적인 수단으로 '다양한 학습 경로'과 '메타인지적 사고', 그리고 '맞장구치기'의 세 전술을 발견하였다.

• Geomechanics and Engineering
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• 제25권1호
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• pp.1-16
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• 2021

Machine learning models have been widely used for landslide susceptibility assessment (LSA) in recent years. The large number of inputs or conditioning factors for these models, however, can reduce the computation efficiency and increase the difficulty in collecting data. Feature selection is a good tool to address this problem by selecting the most important features among all factors to reduce the size of the input variables. However, two important questions need to be solved: (1) how do feature selection methods affect the performance of machine learning models? and (2) which feature selection method is the most suitable for a given machine learning model? This paper aims to address these two questions by comparing the predictive performance of 13 feature selection-based machine learning (FS-ML) models and 5 ordinary machine learning models on LSA. First, five commonly used machine learning models (i.e., logistic regression, support vector machine, artificial neural network, Gaussian process and random forest) and six typical feature selection methods in the literature are adopted to constitute the proposed models. Then, fifteen conditioning factors are chosen as input variables and 1,017 landslides are used as recorded data. Next, feature selection methods are used to obtain the importance of the conditioning factors to create feature subsets, based on which 13 FS-ML models are constructed. For each of the machine learning models, a best optimized FS-ML model is selected according to the area under curve value. Finally, five optimal FS-ML models are obtained and applied to the LSA of the studied area. The predictive abilities of the FS-ML models on LSA are verified and compared through the receive operating characteristic curve and statistical indicators such as sensitivity, specificity and accuracy. The results showed that different feature selection methods have different effects on the performance of LSA machine learning models. FS-ML models generally outperform the ordinary machine learning models. The best FS-ML model is the recursive feature elimination (RFE) optimized RF, and RFE is an optimal method for feature selection.

• 수산해양교육연구
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• 제24권1호
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• pp.25-33
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• 2012

Though science and technology are evolving rapidly in recent years, the traditional science education has limits for students to be satisfied their interests and needs because they couldn't follow these speeds. STEAM as a education integrating science, technology, engineering, arts and mathematics has strengths of increasing interests and understandings in science and technology and improving integrated thinking and problem solving ability for leaners. In this study we analyze the elementary school curriculum and construct physics learning based on STEAM and develop a android application to increase interests in science and improve problem solving ability. In the future, we need to analyze and develop the curriculum and contents for the STEAM education.

• 반도체디스플레이기술학회지
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• 제19권4호
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• pp.88-93
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• 2020

Based on the capture-emission energy (CEE) maps of CMOS devices, a physics-informed machine learning model for the bias temperature instability (BTI)-induced threshold voltage shifts and low frequency noise is presented. In order to incorporate physics theories into the machine learning model, the integration of artificial neural network (IANN) is employed for the computation of the threshold voltage shifts and low frequency noise. The model combines the computational efficiency of IANN with the optimal estimation of Gaussian mixture model (GMM) with soft clustering. It enables full lifetime prediction of BTI under various stress and recovery conditions and provides accurate prediction of the dynamic behavior of the original measured data.

• 공학교육연구
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• 제23권6호
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• pp.68-76
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• 2020

This study presents a teaching model to increase the participation and interest, and to improve their understanding of physical concepts of first-year engineering students taking physics(2) course at a three-year college. In the class, a team task solution based on teamwork and a peer learning method through questions and answers between participants in each team were applied so that learners could actively participate in the class to discuss and present. We examined how the activities of each team affected students' interest in subjects, motivation to learn, and the degree of understanding of physical concepts. In the team activity, students were able to actively participate through emotional sharing between learners and free questions and explanations, and it was confirmed that academic achievement was improved by comparing the final exam evaluation results with the evaluation results of the previous three years.

• 한국과학교육학회지
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• 제22권2호
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• pp.276-285
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• 2002

본 연구는 제 7차 교육과정에 따른 7학년 과학 교과서 에너지 및 지구 영역에 제시된 과학학습 개념을 구체적 개념과 형식적 개념 수준으로 나누어 비교 분석하였다. 분석에 사용된 교과서는 현행 제 7차 교육과정의 검정을 받은 교과서를 대상으로 하였으며, 에너지 영역의 빛, 힘, 파동 단원과 지구 영역의 지구의 구조, 지각의 물질, 해수의 운동과 성분 단원 등 총 6개 단원에 제시된 과학학습 개념을 분석하였다. 출판사별로 제시된 개념의 수가 다소 차이는 있으나 에너지 영역에 제시된 과학학습 개념의 수는 54 - 74개로 나타났으며, 지구 영역에 제시된 개념의 수는 86 - 120개로 나타났다. 또한 에너지영역에서는 형식적 수준의 개념이, 지구 영역에서는 구체적 수준의 개념이 훨씬 많이 제시된 것으로 나타났다. 이는 교과서 개발시 학습자의 인지 수준을 고려한 과학학습 개념의 제시 또는 교과 영역별 비중의 차이를 두어 단원을 구성 배치하는 것이 바람직하다는 것을 시사해주고 있다.

• 한국멀티미디어학회논문지
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• 제21권8호
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• pp.969-981
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• 2018

Recent advances in machine learning approaches such as deep neural network and reinforcement learning offer significant performance improvements in generating detailed and varied motions in physically simulated virtual environments. The optimization methods are highly attractive because it allows for less understanding of underlying physics or mechanisms even for high-dimensional subtle control problems. In this paper, we propose an efficient learning method for stochastic policy represented as deep neural networks so that agent can generate various energetic motions adaptively to the changes of tasks and states without losing interactivity and robustness. This strategy could be realized by our novel trajectory search method motivated by the trust region policy optimization method. Our value-based trajectory smoothing technique finds stably learnable trajectories without consulting neural network responses directly. This policy is set as a trust region of the artificial neural network, so that it can learn the desired motion quickly.